Sample records for brazil predictive risk

  1. Shallow landslide prediction and analysis with risk assessment using a spatial model in the coastal region in the state of São Paulo, Brazil

    NASA Astrophysics Data System (ADS)

    Camarinha, P. I. M.; Canavesi, V.; Alvalá, R. C. S.

    2013-10-01

    In Brazil, most of the disasters involving landslide occur in coastal regions, with population density concentrated on steep slopes. Thus, different approaches have been used to evaluate the landslide risk, although the greatest difficulty is related to the scarcity of spatial data with good quality. In this context, four cities located on the southeast coast of Brazil - Santos, Cubatão, Caraguatatuba and Ubatuba - in a region with the rough reliefs of the Serra do Mar and with a history of natural disasters were evaluated. Spatial prediction by fuzzy gamma technique was used for the landslide susceptibility mapping, considering environmental variables from data and software in the public domain. To validate the susceptibility mapping results, it was overlapped with risk sectors provided by the Geological Survey of Brazil (CPRM). A positive correlation was observed between the classes most susceptible and the location of these sectors. The results were also analyzed from the categorization of risk levels provided by CPRM. To compare the approach with other studies using landslide-scar maps, correlated indexes were evaluated, which also showed satisfactory results, thus indicating that the methodology presented is appropriate for risk assessment in urban areas and can be replicated to municipalities that do not have risk areas mapped.

  2. Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil.

    PubMed

    Lowe, Rachel; Coelho, Caio As; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

    2016-02-24

    Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics.

  3. Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil

    PubMed Central

    Lowe, Rachel; Coelho, Caio AS; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

    2016-01-01

    Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics. DOI: http://dx.doi.org/10.7554/eLife.11285.001 PMID:26910315

  4. Risk analysis and prediction of visceral leishmaniasis dispersion in São Paulo State, Brazil.

    PubMed

    Sevá, Anaiá da Paixão; Mao, Liang; Galvis-Ovallos, Fredy; Tucker Lima, Joanna Marie; Valle, Denis

    2017-02-01

    Visceral leishmaniasis (VL) is an important neglected disease caused by a protozoan parasite, and represents a serious public health problem in many parts of the world. It is zoonotic in Europe and Latin America, where infected dogs constitute the main domestic reservoir for the parasite and play a key role in VL transmission to humans. In Brazil this disease is caused by the protozoan Leishmania infantum chagasi, and is transmitted by the sand fly Lutzomyia longipalpis. Despite programs aimed at eliminating infection sources, the disease continues to spread throughout the Country. VL in São Paulo State, Brazil, first appeared in the northwestern region, spreading in a southeasterly direction over time. We integrate data on the VL vector, infected dogs and infected human dispersion from 1999 to 2013 through an innovative spatial temporal Bayesian model in conjunction with geographic information system. This model is used to infer the drivers of the invasion process and predict the future progression of VL through the State. We found that vector dispersion was influenced by vector presence in nearby municipalities at the previous time step, proximity to the Bolívia-Brazil gas pipeline, and high temperatures (i.e., annual average between 20 and 23°C). Key factors affecting infected dog dispersion included proximity to the Marechal Rondon Highway, high temperatures, and presence of the competent vector within the same municipality. Finally, vector presence, presence of infected dogs, and rainfall (approx. 270 to 540mm/year) drove the dispersion of human VL cases. Surprisingly, economic factors exhibited no noticeable influence on disease dispersion. Based on these drivers and stochastic simulations, we identified which municipalities are most likely to be invaded by vectors and infected hosts in the future. Prioritizing prevention and control strategies within the identified municipalities may help halt the spread of VL while reducing monitoring costs. Our results

  5. Risk analysis and prediction of visceral leishmaniasis dispersion in São Paulo State, Brazil

    PubMed Central

    Mao, Liang; Galvis-Ovallos, Fredy; Tucker Lima, Joanna Marie; Valle, Denis

    2017-01-01

    Visceral leishmaniasis (VL) is an important neglected disease caused by a protozoan parasite, and represents a serious public health problem in many parts of the world. It is zoonotic in Europe and Latin America, where infected dogs constitute the main domestic reservoir for the parasite and play a key role in VL transmission to humans. In Brazil this disease is caused by the protozoan Leishmania infantum chagasi, and is transmitted by the sand fly Lutzomyia longipalpis. Despite programs aimed at eliminating infection sources, the disease continues to spread throughout the Country. VL in São Paulo State, Brazil, first appeared in the northwestern region, spreading in a southeasterly direction over time. We integrate data on the VL vector, infected dogs and infected human dispersion from 1999 to 2013 through an innovative spatial temporal Bayesian model in conjunction with geographic information system. This model is used to infer the drivers of the invasion process and predict the future progression of VL through the State. We found that vector dispersion was influenced by vector presence in nearby municipalities at the previous time step, proximity to the Bolívia-Brazil gas pipeline, and high temperatures (i.e., annual average between 20 and 23°C). Key factors affecting infected dog dispersion included proximity to the Marechal Rondon Highway, high temperatures, and presence of the competent vector within the same municipality. Finally, vector presence, presence of infected dogs, and rainfall (approx. 270 to 540mm/year) drove the dispersion of human VL cases. Surprisingly, economic factors exhibited no noticeable influence on disease dispersion. Based on these drivers and stochastic simulations, we identified which municipalities are most likely to be invaded by vectors and infected hosts in the future. Prioritizing prevention and control strategies within the identified municipalities may help halt the spread of VL while reducing monitoring costs. Our results

  6. Risk factors for myocardial infarction in Brazil.

    PubMed

    Piegas, Leopoldo S; Avezum, Alvaro; Pereira, Júlio César R; Neto, João Manoel Rossi; Hoepfner, Clóvis; Farran, Jorge A; Ramos, Rui F; Timerman, Ari; Esteves, José Péricles

    2003-08-01

    Approximately three-quarters of cardiovascular disease deaths in the world come from developing countries, and acute myocardial infarction (AMI) is an important cause of death. Brazil is one of the largest countries in Latin America and the contemporary evaluation of risk factors for AMI is crucial for a more efficacious disease management. The Acute Myocardial Infarction Risk Factor Assessment in Brazil (AFIRMAR) study is a case-control, hospital-based study involving 104 hospitals in 51 cities in Brazil, designed to evaluate risk factors for a first ST-segment elevation AMI. A total of 1279 pairs, matched by age (+/- 5 years) and sex, were enrolled. The conditional multivariable analysis of 33 variables showed the following independent risk factors for AMI: > or =5 cigarettes per day (odds ratio [OR] 4.90, P <.00001); glucose > or =126 mg/dL (OR 2.82, P <.00001); waist/hip ratio > or =0.94 (OR 2.45, P <.00001); family history of CAD (OR 2.29, P <.00001), low-density lipoprotein-cholesterol 100 to 120 mg/dL (OR 2.10, P <.00001); reported hypertension (OR 2.09, P <.00001); <5 cigarettes per day (OR 2.07, P =.0171); low-density lipoprotein-cholesterol >120 mg/dL (OR 1.75, P <.00001); reported diabetes mellitus (OR 1.70, P =.0069); waist/hip ratio 0.90 to 0.93 (OR 1.52, P =.0212); alcohol intake (up to 2 days/week) (OR 0.75, P <.0309); alcohol intake (3-7 days/week) (OR 0.60, P =.0085); family income R$600 to R$1200 and college education (OR 2.92, P =.0499); family income >R$1200 and college education (OR 0.68, P = 0.0239) The independent risk factors for AMI in Brazil showed a conventional distribution pattern (smoking, diabetes mellitus and central obesity among others) with different strengths of association; most of them being preventable by implementation of adequate policies.

  7. Can the six-minute walk distance predict the occurrence of acute exacerbations of COPD in patients in Brazil?

    PubMed Central

    Morakami, Fernanda Kazmierski; Morita, Andrea Akemi; Bisca, Gianna Waldrich; Felcar, Josiane Marques; Ribeiro, Marcos; Furlanetto, Karina Couto; Hernandes, Nidia Aparecida; Pitta, Fabio

    2017-01-01

    ABSTRACT Objective: To evaluate whether a six-minute walk distance (6MWD) of < 80% of the predicted value can predict the occurrence of acute exacerbations of COPD in patients in Brazil over a 2-year period. Methods: This was a retrospective cross-sectional study involving 50 COPD patients in Brazil. At enrollment, anthropometric data were collected and patients were assessed for pulmonary function (by spirometry) and functional exercise capacity (by the 6MWD). The patients were subsequently divided into two groups: 6MWD ≤ 80% of predicted and 6MWD > 80% of predicted. The occurrence of acute exacerbations of COPD over 2 years was identified by analyzing medical records and contacting patients by telephone. Results: In the sample as a whole, there was moderate-to-severe airflow obstruction (mean FEV1 = 41 ± 12% of predicted) and the mean 6MWD was 469 ± 60 m (86 ± 10% of predicted). Over the 2-year follow-up period, 25 patients (50%) experienced acute exacerbations of COPD. The Kaplan-Meier method showed that the patients in whom the 6MWD was ≤ 80% of predicted were more likely to have exacerbations than were those in whom the 6MWD was > 80% of predicted (p = 0.01), whereas the Cox regression model showed that the former were 2.6 times as likely to have an exacerbation over a 2-year period as were the latter (p = 0.02). Conclusions: In Brazil, the 6MWD can predict acute exacerbations of COPD over a 2-year period. The risk of experiencing an acute exacerbation of COPD within 2 years is more than twice as high in patients in whom the 6MWD is ≤ 80% of predicted. PMID:29365003

  8. Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models.

    PubMed

    Scholte, Ronaldo G C; Schur, Nadine; Bavia, Maria E; Carvalho, Edgar M; Chammartin, Frédérique; Utzinger, Jürg; Vounatsou, Penelope

    2013-11-01

    Soil-transmitted helminths (Ascaris lumbricoides, Trichuris trichiura and hookworm) negatively impact the health and wellbeing of hundreds of millions of people, particularly in tropical and subtropical countries, including Brazil. Reliable maps of the spatial distribution and estimates of the number of infected people are required for the control and eventual elimination of soil-transmitted helminthiasis. We used advanced Bayesian geostatistical modelling, coupled with geographical information systems and remote sensing to visualize the distribution of the three soil-transmitted helminth species in Brazil. Remotely sensed climatic and environmental data, along with socioeconomic variables from readily available databases were employed as predictors. Our models provided mean prevalence estimates for A. lumbricoides, T. trichiura and hookworm of 15.6%, 10.1% and 2.5%, respectively. By considering infection risk and population numbers at the unit of the municipality, we estimate that 29.7 million Brazilians are infected with A. lumbricoides, 19.2 million with T. trichiura and 4.7 million with hookworm. Our model-based maps identified important risk factors related to the transmission of soiltransmitted helminths and confirm that environmental variables are closely associated with indices of poverty. Our smoothed risk maps, including uncertainty, highlight areas where soil-transmitted helminthiasis control interventions are most urgently required, namely in the North and along most of the coastal areas of Brazil. We believe that our predictive risk maps are useful for disease control managers for prioritising control interventions and for providing a tool for more efficient surveillance-response mechanisms.

  9. Determination of aflatoxin risk components for in-shell Brazil nuts.

    PubMed

    Vargas, E A; dos Santos, E A; Whitaker, T B; Slate, A B

    2011-09-01

    A study was conducted on the risk from aflatoxins associated with the kernels and shells of Brazil nuts. Samples were collected from processing plants in Amazonia, Brazil. A total of 54 test samples (40 kg) were taken from 13 in-shell Brazil nut lots ready for market. Each in-shell sample was shelled and the kernels and shells were sorted in five fractions: good kernels, rotten kernels, good shells with kernel residue, good shells without kernel residue, and rotten shells, and analysed for aflatoxins. The kernel:shell ratio mass (w/w) was 50.2/49.8%. The Brazil nut shell was found to be contaminated with aflatoxin. Rotten nuts were found to be a high-risk fraction for aflatoxin in in-shell Brazil nut lots. Rotten nuts contributed only 4.2% of the sample mass (kg), but contributed 76.6% of the total aflatoxin mass (µg) in the in-shell test sample. The highest correlations were found between the aflatoxin concentration in in-shell Brazil nuts samples and the aflatoxin concentration in all defective fractions (R(2)=0.97). The aflatoxin mass of all defective fractions (R(2)=0.90) as well as that of the rotten nut (R(2)=0.88) were also strongly correlated with the aflatoxin concentration of the in-shell test samples. Process factors of 0.17, 0.16 and 0.24 were respectively calculated to estimate the aflatoxin concentration in the good kernels (edible) and good nuts by measuring the aflatoxin concentration in the in-shell test sample and in all kernels, respectively. © 2011 Taylor & Francis

  10. Unrestrained outsourcing in Brazil: more precarization and health risks for workers.

    PubMed

    Druck, Graça

    2016-06-20

    This article discusses the current status of outsourcing in Brazil, with new regulation underway featuring a bill of law under review by the National Congress, aimed at allowing outsourcing for all activities. The authors argue that outsourcing and precarization of work are inseparable phenomena, based on the results of 20 years of research in Brazil that reveals the more precarious working conditions of outsourced workers in different occupational categories. They focus particularly on workers' health: outsourcing of risks has led to more fatal work accidents, invariably at higher rates in outsourced workers. Finally, the article contends that to remove restraints on outsourcing in Brazil amounts to legalizing and legitimizing predatory workforce exploitation, disregarding workers' physical limits, exposing them to risk of fatal accidents, and reverting to forms of work that violate the human condition.

  11. Risk analysis for occurrences of schistosomiasis in the coastal area of Porto de Galinhas, Pernambuco, Brazil

    PubMed Central

    2014-01-01

    Background Manson’s schistosomiasis continues to be a severe public health problem in Brazil, where thousands of people live under the risk of contracting this parasitosis. In the Northeast of Brazil, schistosomiasis has expanded from rural areas to the coast of Pernambuco State, where the intermediate host is Biomphalaria glabrata snails. This study aims at presenting situational analyses on schistosomiasis at the coastal locality of Porto de Galinhas, Pernambuco, Brazil, by determining the risk factors relating to its occurrence from the epidemiological and spatial perspectives. Methods In order to gather prevalence data, a parasitological census surveys were conducted in 2010 in the light of the Kato-Katz technique. Furthermore, malacological surveys were also conducted in the same years so as to define the density and infection rates of the intermediate host. Lastly, socioeconomic-behavioral survey was also conducted to determine the odds ratio for infection by Schistosoma mansoni. Based on these data, spatial analyses were done, resulting in maps of the risk of disease transmission. To predict the risk of schistosomiasis occurrence, a multivariate logistic regression was performed using R 2.13 software. Results Based on prevalence, malacological and socioeconomic-behavioural surveys, it was identified a prevalence of 15.7% in the investigated population (2,757 individuals). Due to the malacological survey, 36 breeding sites were identified, of which 11 were classified as foci of schistosomiasis transmission since they pointed out snails which were infected by Schistosoma mansoni. Overall, 11,012 snails (Biomphalaria glabrata) were collected. The multivariate regression model identified six explanatory variables of environmental, socioeconomic and demographic nature. Spatial sweep analysis by means of the Bernoulli method identified one statistically significant cluster in Salinas (RR = 2.2; p-value < 0.000), the district with the highest occurrence

  12. Using Remote Sensing, Weather, and Demographic Data to Create Risk Maps for Zika, Dengue, and Chikungunya in Brazil

    NASA Astrophysics Data System (ADS)

    Manore, C.; Conrad, J.; Del Valle, S.; Ziemann, A.; Fairchild, G.; Generous, E. N.

    2017-12-01

    Mosquito-borne diseases such as Zika, dengue, and chikungunya viruses have dynamics coupled to weather, ecology, human infrastructure, socio-economic demographics, and behavior. We use time-varying remote sensing and weather data, along with demographics and ecozones to predict risk through time for Zika, dengue, and chikungunya outbreaks in Brazil. We use distributed lag methods to quantify the lag between outbreaks and weather. Our statistical model indicates that the relationships between the variables are complex, but that quantifying risk is possible with the right data at appropriate spatio-temporal scales.

  13. The Zika Virus Outbreak in Brazil: Knowledge Gaps and Challenges for Risk Reduction.

    PubMed

    Garcia Serpa Osorio-de-Castro, Claudia; Silva Miranda, Elaine; Machado de Freitas, Carlos; Rochel de Camargo, Kenneth; Cranmer, Hilarie Hartel

    2017-06-01

    We analyzed uncertainties and complexities of the Zika virus outbreak in Brazil, and we discuss risk reduction for future emergencies. We present the public health situation in Brazil and concurrent determinants of the epidemic and the knowledge gaps that persist despite building evidence from research, making public health decisions difficult. Brazil has adopted active measures, but producing desired outcomes may be uncertain because of partial or unavailable information. Reducing population group vulnerabilities and acting on environmental issues are medium- to long-term measures. Simultaneously dealing with information gaps, uncontrolled disease spread, and vulnerabilities is a new risk scenario and must be approached decisively to face emerging biothreats.

  14. Youth Perspectives on Risk and Resiliency: A Case Study from Juiz De Fora, Brazil

    ERIC Educational Resources Information Center

    Morrison, Penelope; Nikolajski, Cara; Borrero, Sonya; Zickmund, Susan

    2014-01-01

    The present work seeks to contribute to studies of cross-cultural risk and resiliency by presenting results from qualitative research with adolescents attending programs for at-risk youth in Juiz de Fora, Brazil. In 1990, Brazil introduced the Child and Adolescent Act (ECA), a significant piece of legislation that has had a direct impact on how…

  15. Pet snakes illegally marketed in Brazil: Climatic viability and establishment risk.

    PubMed

    Fonseca, Érica; Solé, Mirco; Rödder, Dennis; de Marco, Paulo

    2017-01-01

    Invasive species are one among many threats to biodiversity. Brazil has been spared, generically, of several destructive invasive species. Reports of invasive snakes' populations are nonexistent, but the illegal pet trade might change this scenario. Despite the Brazilian laws forbid to import most animals, illegal trade is frequently observed and propagules are found in the wild. The high species richness within Brazilian biomes and accelerated fragmentation of natural reserves are a critical factors facilitating successful invasion. An efficient way to ease damages caused by invasive species is identifying potential invaders and consequent prevention of introductions. For the identification of potential invaders many factors need to be considered, including estimates of climate matching between areas (native vs. invaded). Ecological niche modelling has been widely used to predict potential areas for invasion and is an important tool for conservation biology. This study evaluates the potential geographical distribution and establishment risk of Lampropeltis getula (Linnaeus, 1766), Lampropeltis triangulum (Lacépède, 1789), Pantherophis guttatus (Linnaeus, 1766), Python bivittatus Kuhl, 1820 and Python regius (Shaw, 1802) through the Maximum Entropy modelling approach to estimate the potential distribution of the species within Brazil and qualitative evaluation of specific biological attributes. Our results suggest that the North and Midwest regions harbor major suitable areas. Furthermore, P. bivittatus and P. guttatus were suggested to have the highest invasive potential among the analyzed species. Potentially suitable areas for these species were predicted within areas which are highly relevant for Brazilian biodiversity, including several conservation units. Therefore, these areas require special attention and preventive measures should be adopted.

  16. Pet snakes illegally marketed in Brazil: Climatic viability and establishment risk

    PubMed Central

    Rödder, Dennis; de Marco, Paulo

    2017-01-01

    Invasive species are one among many threats to biodiversity. Brazil has been spared, generically, of several destructive invasive species. Reports of invasive snakes’ populations are nonexistent, but the illegal pet trade might change this scenario. Despite the Brazilian laws forbid to import most animals, illegal trade is frequently observed and propagules are found in the wild. The high species richness within Brazilian biomes and accelerated fragmentation of natural reserves are a critical factors facilitating successful invasion. An efficient way to ease damages caused by invasive species is identifying potential invaders and consequent prevention of introductions. For the identification of potential invaders many factors need to be considered, including estimates of climate matching between areas (native vs. invaded). Ecological niche modelling has been widely used to predict potential areas for invasion and is an important tool for conservation biology. This study evaluates the potential geographical distribution and establishment risk of Lampropeltis getula (Linnaeus, 1766), Lampropeltis triangulum (Lacépède, 1789), Pantherophis guttatus (Linnaeus, 1766), Python bivittatus Kuhl, 1820 and Python regius (Shaw, 1802) through the Maximum Entropy modelling approach to estimate the potential distribution of the species within Brazil and qualitative evaluation of specific biological attributes. Our results suggest that the North and Midwest regions harbor major suitable areas. Furthermore, P. bivittatus and P. guttatus were suggested to have the highest invasive potential among the analyzed species. Potentially suitable areas for these species were predicted within areas which are highly relevant for Brazilian biodiversity, including several conservation units. Therefore, these areas require special attention and preventive measures should be adopted. PMID:28817630

  17. Trends in fire risk and burned area in Brazil in the 20th century

    NASA Astrophysics Data System (ADS)

    Silva, P.; Bastos, A.; DaCamara, C.; Libonati, R.

    2016-12-01

    Fire has a significant contribution to the global greenhouse gas emissions and vast ecological and climatic impacts. Worldwide, Brazil is one of the areas most affected by fire, which highly influences the state of the vegetation cover, the ecological diversity of the region and has significant consequences to the global CO2 balance [1]. Hence, with the increasing evidence of human induced climate change, it becomes essential to understand the present and future trends of fire risk in Brazil. Although a large number of fires in Brazil are anthropogenic, it has been shown that the burned area is mainly controlled by meteorological conditions [2], therefore being partially determined by fire risk. In this study we use a fire danger index specifically tailored for the Brazilian climate and biome characteristics, the MFDI developed by INPE, to assess the patterns and trends of fire risk in Brazil. The index relies on values of maximum temperature, accumulated precipitation over different periods, minimum relative humidity and vegetation cover to estimate the likelihood of fire occurrence. We test the sensitivity of the index to different climate reanalyses and evaluate the trends in fire risk in Brazil during the past four decades for different biomes. We further assess the link between the calculated fire risk and observed fire occurrence and burned area. Finally, we compare the results with fire risk simulated by a regional climate model (RCA4 forced by EC-Earth from CORDEX) in order to evaluate its suitability for future projections of fire risk and burned area. [1] Bowman, D. M. et al. Fire in the earth system. Science, v. 324, p. 481-484, 24 apr. 2009. [2] Libonati, R. et al. An Algorithm for Burned Area Detection in the Brazilian Cerrado Using 4 μm MODIS Imagery. Remote Sensing, v. 7, p. 15782-15803, 2015.

  18. Crime and violence in Brazil: Systematic review of time trends, prevalence rates and risk factors☆

    PubMed Central

    Murray, Joseph; Cerqueira, Daniel Ricardo de Castro; Kahn, Tulio

    2013-01-01

    Between 1980 and 2010 there were 1 million homicides in Brazil. Dramatic increases in homicide rates followed rises in inequality, more young men in the population, greater availability of firearms, and increased drug use. Nevertheless, disarmament legislation may have helped reduce homicide rates in recent years. Despite its very high rate of lethal violence, Brazil appears to have similar levels of general criminal victimization as several other Latin American and North American countries. Brazil has lower rates of drug use compared to other countries such as the United States, but the prevalence of youth drug use in Brazil has increased substantially in recent years. Since 1990, the growth of the Brazilian prison population has been enormous, resulting in the fourth largest prison population in the world. Through a systematic review of the literature, we identified 10 studies assessing the prevalence of self-reported offending in Brazil and 9 studies examining risk factors. Levels of self-reported offending seem quite high among school students in Brazil. Individual and family-level risk factors identified in Brazil are very similar to those found in high-income countries. PMID:24027422

  19. Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.

    PubMed

    Lewnard, Joseph A; Jirmanus, Lara; Júnior, Nivison Nery; Machado, Paulo R; Glesby, Marshall J; Ko, Albert I; Carvalho, Edgar M; Schriefer, Albert; Weinberger, Daniel M

    2014-10-01

    Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets.

  20. University of North Carolina Caries Risk Assessment Study: comparisons of high risk prediction, any risk prediction, and any risk etiologic models.

    PubMed

    Beck, J D; Weintraub, J A; Disney, J A; Graves, R C; Stamm, J W; Kaste, L M; Bohannan, H M

    1992-12-01

    The purpose of this analysis is to compare three different statistical models for predicting children likely to be at risk of developing dental caries over a 3-yr period. Data are based on 4117 children who participated in the University of North Carolina Caries Risk Assessment Study, a longitudinal study conducted in the Aiken, South Carolina, and Portland, Maine areas. The three models differed with respect to either the types of variables included or the definition of disease outcome. The two "Prediction" models included both risk factor variables thought to cause dental caries and indicator variables that are associated with dental caries, but are not thought to be causal for the disease. The "Etiologic" model included only etiologic factors as variables. A dichotomous outcome measure--none or any 3-yr increment, was used in the "Any Risk Etiologic model" and the "Any Risk Prediction Model". Another outcome, based on a gradient measure of disease, was used in the "High Risk Prediction Model". The variables that are significant in these models vary across grades and sites, but are more consistent among the Etiologic model than the Predictor models. However, among the three sets of models, the Any Risk Prediction Models have the highest sensitivity and positive predictive values, whereas the High Risk Prediction Models have the highest specificity and negative predictive values. Considerations in determining model preference are discussed.

  1. Risk of Dengue for Tourists and Teams during the World Cup 2014 in Brazil

    PubMed Central

    van Panhuis, Willem G.; Hyun, Sangwon; Blaney, Kayleigh; Marques, Ernesto T. A.; Coelho, Giovanini E.; Siqueira, João Bosco; Tibshirani, Ryan; da Silva, Jarbas B.; Rosenfeld, Roni

    2014-01-01

    Abstract Background This year, Brazil will host about 600,000 foreign visitors during the 2014 FIFA World Cup. The concern of possible dengue transmission during this event has been raised given the high transmission rates reported in the past by this country. Methodology/Principal Findings We used dengue incidence rates reported by each host city during previous years (2001–2013) to estimate the risk of dengue during the World Cup for tourists and teams. Two statistical models were used: a percentile rank (PR) and an Empirical Bayes (EB) model. Expected IR's during the games were generally low (<10/100,000) but predictions varied across locations and between models. Based on current ticket allocations, the mean number of expected symptomatic dengue cases ranged from 26 (PR, 10th–100th percentile: 5–334 cases) to 59 (EB, 95% credible interval: 30–77 cases) among foreign tourists but none are expected among teams. These numbers will highly depend on actual travel schedules and dengue immunity among visitors. Sensitivity analysis for both models indicated that the expected number of cases could be as low as 4 or 5 with 100,000 visitors and as high as 38 or 70 with 800,000 visitors (PR and EB, respectively). Conclusion/Significance The risk of dengue among tourists during the World Cup is expected to be small due to immunity among the Brazil host population provided by last year's epidemic with the same DENV serotypes. Quantitative risk estimates by different groups and methodologies should be made routinely for mass gathering events. PMID:25079960

  2. General outcomes and risk factors for minor and major amputations in Brazil.

    PubMed

    Leite, Jose O; Costa, Leandro O; Fonseca, Walter M; Souza, Debora U; Goncalves, Barbara C; Gomes, Gabriela B; Cruz, Lucas A; Nister, Nilder; Navarro, Tulio P; Bath, Jonathan; Dardik, Alan

    2018-06-01

    Objectives Major and minor amputations are associated with significant rates of mortality. However, little is known about the impact of unplanned redo-amputation during the same hospitalization on outcomes. The objectives of this study were to identify the risk factors associated with in-hospital mortality after both major and minor amputations as well as the results of unplanned redo-amputation on outcome. Methods Retrospective study of 342 consecutive patients who were treated with lower extremity amputation in Brazil between January 2013 and October 2014. Results The in-hospital mortality rate was higher in major compared to minor amputation (25.6% vs. 4.1%; p < 0.0001). Whereas chronic kidney disease, chronic obstructive pulmonary disease, and planned staged amputation predicted in-hospital mortality after major amputation, age, and congestive heart failure predicted mortality after minor amputation. The white blood cell count predicted in-hospital mortality following both major and minor amputation. However, postoperative infection predicted in-hospital mortality only following major amputation. Conclusions In-hospital mortality was high after major amputations. Unplanned redo-amputation was not a predictor of in-hospital mortality after major or minor amputation. Planned staged amputation was associated with reduced survival after major but not minor amputation. Postoperative infection predicted mortality after major amputation. Systemic diseases and postoperative white blood cell were associated with in-hospital mortality. This study suggests a possible link between a pro-inflammatory state and increased in-hospital mortality following amputation.

  3. Breast cancer risks and risk prediction models.

    PubMed

    Engel, Christoph; Fischer, Christine

    2015-02-01

    BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

  4. Forecasting Temporal Dynamics of Cutaneous Leishmaniasis in Northeast Brazil

    PubMed Central

    Lewnard, Joseph A.; Jirmanus, Lara; Júnior, Nivison Nery; Machado, Paulo R.; Glesby, Marshall J.; Ko, Albert I.; Carvalho, Edgar M.; Schriefer, Albert; Weinberger, Daniel M.

    2014-01-01

    Introduction Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. Methodology/Principal Findings We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. Significance These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets. PMID:25356734

  5. Mammographic density, breast cancer risk and risk prediction

    PubMed Central

    Vachon, Celine M; van Gils, Carla H; Sellers, Thomas A; Ghosh, Karthik; Pruthi, Sandhya; Brandt, Kathleen R; Pankratz, V Shane

    2007-01-01

    In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models. PMID:18190724

  6. Using open source data for flood risk mapping and management in Brazil

    NASA Astrophysics Data System (ADS)

    Whitley, Alison; Malloy, James; Chirouze, Manuel

    2013-04-01

    Whitley, A., Malloy, J. and Chirouze, M. Worldwide the frequency and severity of major natural disasters, particularly flooding, has increased. Concurrently, countries such as Brazil are experiencing rapid socio-economic development with growing and increasingly concentrated populations, particularly in urban areas. Hence, it is unsurprising that Brazil has experienced a number of major floods in the past 30 years such as the January 2011 floods which killed 900 people and resulted in significant economic losses of approximately 1 billion US dollars. Understanding, mitigating against and even preventing flood risk is high priority. There is a demand for flood models in many developing economies worldwide for a range of uses including risk management, emergency planning and provision of insurance solutions. However, developing them can be expensive. With an increasing supply of freely-available, open source data, the costs can be significantly reduced, making the tools required for natural hazard risk assessment more accessible. By presenting a flood model developed for eight urban areas of Brazil as part of a collaboration between JBA Risk Management and Guy Carpenter, we explore the value of open source data and demonstrate its usability in a business context within the insurance industry. We begin by detailing the open source data available and compare its suitability to commercially-available equivalents for datasets including digital terrain models and river gauge records. We present flood simulation outputs in order to demonstrate the impact of the choice of dataset on the results obtained and its use in a business context. Via use of the 2D hydraulic model JFlow+, our examples also show how advanced modelling techniques can be used on relatively crude datasets to obtain robust and good quality results. In combination with accessible, standard specification GPU technology and open source data, use of JFlow+ has enabled us to produce large-scale hazard maps

  7. Risk of symptomatic dengue for foreign visitors to the 2014 FIFA World Cup in Brazil

    PubMed Central

    Massad, Eduardo; Wilder-Smith, Annelies; Ximenes, Raphael; Amaku, Marcos; Lopez, Luis Fernandez; Coutinho, Francisco Antonio Bezerra; Coelho, Giovanini Evelim; da Silva, Jarbas Barbosa; Struchiner, Claudio José; Burattini, Marcelo Nascimento

    2014-01-01

    Brazil will host the FIFA World Cup™, the biggest single-event competition in the world, from June 12-July 13 2014 in 12 cities. This event will draw an estimated 600,000 international visitors. Brazil is endemic for dengue. Hence, attendees of the 2014 event are theoretically at risk for dengue. We calculated the risk of dengue acquisition to non-immune international travellers to Brazil, depending on the football match schedules, considering locations and dates of such matches for June and July 2014. We estimated the average per-capita risk and expected number of dengue cases for each host-city and each game schedule chosen based on reported dengue cases to the Brazilian Ministry of Health for the period between 2010-2013. On the average, the expected number of cases among the 600,000 foreigner tourists during the World Cup is 33, varying from 3-59. Such risk estimates will not only benefit individual travellers for adequate pre-travel preparations, but also provide valuable information for public health professionals and policy makers worldwide. Furthermore, estimates of dengue cases in international travellers during the World Cup can help to anticipate the theoretical risk for exportation of dengue into currently non-infected areas. PMID:24863976

  8. Risk of symptomatic dengue for foreign visitors to the 2014 FIFA World Cup in Brazil.

    PubMed

    Massad, Eduardo; Wilder-Smith, Annelies; Ximenes, Raphael; Amaku, Marcos; Lopez, Luis Fernandez; Coutinho, Francisco Antonio Bezerra; Coelho, Giovanini Evelim; Silva, Jarbas Barbosa da; Struchiner, Claudio José; Burattini, Marcelo Nascimento

    2014-06-01

    Brazil will host the FIFA World Cup™, the biggest single-event competition in the world, from June 12-July 13 2014 in 12 cities. This event will draw an estimated 600,000 international visitors. Brazil is endemic for dengue. Hence, attendees of the 2014 event are theoretically at risk for dengue. We calculated the risk of dengue acquisition to non-immune international travellers to Brazil, depending on the football match schedules, considering locations and dates of such matches for June and July 2014. We estimated the average per-capita risk and expected number of dengue cases for each host-city and each game schedule chosen based on reported dengue cases to the Brazilian Ministry of Health for the period between 2010-2013. On the average, the expected number of cases among the 600,000 foreigner tourists during the World Cup is 33, varying from 3-59. Such risk estimates will not only benefit individual travellers for adequate pre-travel preparations, but also provide valuable information for public health professionals and policy makers worldwide. Furthermore, estimates of dengue cases in international travellers during the World Cup can help to anticipate the theoretical risk for exportation of dengue into currently non-infected areas.

  9. [Risk factors of stillbirths in Fortaleza-Brazil: a case-control study].

    PubMed

    Rouquayrol, M Z; Correia, L L; Barbosa, L M; Xavier, L G; Oliveira, J W; Fonseca, W

    1996-01-01

    Stillbirths are a common event in areas where reproductive health care is poorly delivered, such as the Northeast region of Brazil. This case-control study aimed to identify risk factors associated to foetal deaths occurred in a major obstetric facility of Fortaleza, 1.7 million inhabitants, Northeastern Brazil. 125 stillborn foetus over 20 weeks of gestation (cases) were compared to 250 healthy newborns (controls), in relation to socioeconomic, reproductive, behavioral and morbidity characteristics of their mothers. Crude and adjusted Odds Ratios were then calculated. After adjustment for confounders, the following characteristics of the mother remained as risk factors for stillbirths, with OR statistically significant at the 95% level: attending <5 antenatal consultations during pregnancy (OR=3.30; CI=1.92 - 5.07 ), illiterate mother (OR=3.30; CI=1.84 - 5.92 ), mother's age above 19 (OR=2.73; CI=1.42 - 5.24 ), monthly family income of 1 minimum wage or less (OR=2.12; CI=1.03 - 4.35 ) and severe illnesses or complication during pregnancy (OR=1.75; CI=1.01 - 3.03 ). Inadequate attendance to antenatal care consultations was the risk factor most strongly associated to stillbirths. Similarly, it was the condition most amenable to change in a short term, among those identified as risk factors.

  10. Evaluation of clinical risk factors for osteoporosis and applicability of the FRAX tool in Joinville City, Southern Brazil.

    PubMed

    Silva, Dalisbor Marcelo Weber; Borba, Victoria Zeghbi Cochenski; Kanis, John A

    2017-12-09

    Clinical risk factors for fracture in Southern Brazil are similar to those used in Fracture Risk Assessment Tool (FRAX®). Age-dependent intervention thresholds had higher accuracy than a fixed cut-off point. Access to bone mineral density testing is wanted for a large part of the Brazilian population. The FRAX® has an option to calculate the risk of fracture without this costly evaluation but relies on the clinical risk factors (CRFs) identified in the source cohorts used to generate FRAX. The aims of this study were to determine whether the CRFs used in FRAX are also risk indicators for individuals in Southern Brazil and to evaluate possible intervention thresholds for treatment in Brazil. We determined the CRFs for hip fractures in women and men aged 50 years and more with a hip fracture and controls in Joinville, Southern Brazil (April 1, 2010, and March 31, 2012). For intervention thresholds, we determined the accuracy of using the fixed thresholds of National Osteoporosis Foundation (NOF), USA, compared with the age-dependent thresholds of the National Osteoporosis Guideline Group (NOGG), UK. CRFs that were significant for hip fracture were very similar to FRAX, apart from chronic obstructive pulmonary disease and malabsorptive intestinal disease. FRAX based on the NOGG and NOF models had an accuracy of 64.2 and 58.7%, respectively. CRFs used in FRAX® were similar to those in the Southern Brazil. The NOGG model seems to be more accurate to discriminate patients with increased fracture risk in this population compared to the NOF model, but not significantly.

  11. The Impact of Dietary and Metabolic Risk Factors on Cardiovascular Diseases and Type 2 Diabetes Mortality in Brazil.

    PubMed

    Otto, Marcia C de Oliveira; Afshin, Ashkan; Micha, Renata; Khatibzadeh, Shahab; Fahimi, Saman; Singh, Gitanjali; Danaei, Goodarz; Sichieri, Rosely; Monteiro, Carlos A; Louzada, Maria L C; Ezzati, Majid; Mozaffarian, Dariush

    2016-01-01

    Trends in food availability and metabolic risk factors in Brazil suggest a shift toward unhealthy dietary patterns and increased cardiometabolic disease risk, yet little is known about the impact of dietary and metabolic risk factors on cardiometabolic mortality in Brazil. Based on data from Global Burden of Disease (GBD) Study, we used comparative risk assessment to estimate the burden of 11 dietary and 4 metabolic risk factors on mortality due to cardiovascular diseases and diabetes in Brazil in 2010. Information on national diets and metabolic risks were obtained from the Brazilian Household Budget Survey, the Food and Agriculture Organization database, and large observational studies including Brazilian adults. Relative risks for each risk factor were obtained from meta-analyses of randomized trials or prospective cohort studies; and disease-specific mortality from the GBD 2010 database. We quantified uncertainty using probabilistic simulation analyses, incorporating uncertainty in dietary and metabolic data and relative risks by age and sex. Robustness of findings was evaluated by sensitivity to varying feasible optimal levels of each risk factor. In 2010, high systolic blood pressure (SBP) and suboptimal diet were the largest contributors to cardiometabolic deaths in Brazil, responsible for 214,263 deaths (95% uncertainty interval [UI]: 195,073 to 233,936) and 202,949 deaths (95% UI: 194,322 to 211,747), respectively. Among individual dietary factors, low intakes of fruits and whole grains and high intakes of sodium were the largest contributors to cardiometabolic deaths. For premature cardiometabolic deaths (before age 70 years, representing 40% of cardiometabolic deaths), the leading risk factors were suboptimal diet (104,169 deaths; 95% UI: 99,964 to 108,002), high SBP (98,923 deaths; 95%UI: 92,912 to 104,609) and high body-mass index (BMI) (42,643 deaths; 95%UI: 40,161 to 45,111). suboptimal diet, high SBP, and high BMI are major causes of cardiometabolic

  12. The Impact of Dietary and Metabolic Risk Factors on Cardiovascular Diseases and Type 2 Diabetes Mortality in Brazil

    PubMed Central

    de Oliveira Otto, Marcia C.; Afshin, Ashkan; Micha, Renata; Khatibzadeh, Shahab; Fahimi, Saman; Singh, Gitanjali; Danaei, Goodarz; Sichieri, Rosely; Monteiro, Carlos A; Louzada, Maria L. C.; Ezzati, Majid; Mozaffarian, Dariush

    2016-01-01

    Background Trends in food availability and metabolic risk factors in Brazil suggest a shift toward unhealthy dietary patterns and increased cardiometabolic disease risk, yet little is known about the impact of dietary and metabolic risk factors on cardiometabolic mortality in Brazil. Methods Based on data from Global Burden of Disease (GBD) Study, we used comparative risk assessment to estimate the burden of 11 dietary and 4 metabolic risk factors on mortality due to cardiovascular diseases and diabetes in Brazil in 2010. Information on national diets and metabolic risks were obtained from the Brazilian Household Budget Survey, the Food and Agriculture Organization database, and large observational studies including Brazilian adults. Relative risks for each risk factor were obtained from meta-analyses of randomized trials or prospective cohort studies; and disease-specific mortality from the GBD 2010 database. We quantified uncertainty using probabilistic simulation analyses, incorporating uncertainty in dietary and metabolic data and relative risks by age and sex. Robustness of findings was evaluated by sensitivity to varying feasible optimal levels of each risk factor. Results In 2010, high systolic blood pressure (SBP) and suboptimal diet were the largest contributors to cardiometabolic deaths in Brazil, responsible for 214,263 deaths (95% uncertainty interval [UI]: 195,073 to 233,936) and 202,949 deaths (95% UI: 194,322 to 211,747), respectively. Among individual dietary factors, low intakes of fruits and whole grains and high intakes of sodium were the largest contributors to cardiometabolic deaths. For premature cardiometabolic deaths (before age 70 years, representing 40% of cardiometabolic deaths), the leading risk factors were suboptimal diet (104,169 deaths; 95% UI: 99,964 to 108,002), high SBP (98,923 deaths; 95%UI: 92,912 to 104,609) and high body-mass index (BMI) (42,643 deaths; 95%UI: 40,161 to 45,111). Conclusion suboptimal diet, high SBP, and high

  13. Global Potential Distribution of Bactrocera carambolae and the Risks for Fruit Production in Brazil

    PubMed Central

    Marchioro, Cesar A.

    2016-01-01

    The carambola fruit fly, Bactrocera carambolae, is a tephritid native to Asia that has invaded South America through small-scale trade of fruits from Indonesia. The economic losses associated with biological invasions of other fruit flies around the world and the polyphagous behaviour of B. carambolae have prompted much concern among government agencies and farmers with the potential spread of this pest. Here, ecological niche models were employed to identify suitable environments available to B. carambolae in a global scale and assess the extent of the fruit acreage that may be at risk of attack in Brazil. Overall, 30 MaxEnt models built with different combinations of environmental predictors and settings were evaluated for predicting the potential distribution of the carambola fruit fly. The best model was selected based on threshold-independent and threshold-dependent metrics. Climatically suitable areas were identified in tropical and subtropical regions of Central and South America, Sub-Saharan Africa, west and east coast of India and northern Australia. The suitability map of B. carambola was intersected against maps of fruit acreage in Brazil. The acreage under potential risk of attack varied widely among fruit species, which is expected because the production areas are concentrated in different regions of the country. The production of cashew is the one that is at higher risk, with almost 90% of its acreage within the suitable range of B. carambolae, followed by papaya (78%), tangerine (51%), guava (38%), lemon (30%), orange (29%), mango (24%) and avocado (20%). This study provides an important contribution to the knowledge of the ecology of B. carambolae, and the information generated here can be used by government agencies as a decision-making tool to prevent the carambola fruit fly spread across the world. PMID:27832144

  14. Global Potential Distribution of Bactrocera carambolae and the Risks for Fruit Production in Brazil.

    PubMed

    Marchioro, Cesar A

    2016-01-01

    The carambola fruit fly, Bactrocera carambolae, is a tephritid native to Asia that has invaded South America through small-scale trade of fruits from Indonesia. The economic losses associated with biological invasions of other fruit flies around the world and the polyphagous behaviour of B. carambolae have prompted much concern among government agencies and farmers with the potential spread of this pest. Here, ecological niche models were employed to identify suitable environments available to B. carambolae in a global scale and assess the extent of the fruit acreage that may be at risk of attack in Brazil. Overall, 30 MaxEnt models built with different combinations of environmental predictors and settings were evaluated for predicting the potential distribution of the carambola fruit fly. The best model was selected based on threshold-independent and threshold-dependent metrics. Climatically suitable areas were identified in tropical and subtropical regions of Central and South America, Sub-Saharan Africa, west and east coast of India and northern Australia. The suitability map of B. carambola was intersected against maps of fruit acreage in Brazil. The acreage under potential risk of attack varied widely among fruit species, which is expected because the production areas are concentrated in different regions of the country. The production of cashew is the one that is at higher risk, with almost 90% of its acreage within the suitable range of B. carambolae, followed by papaya (78%), tangerine (51%), guava (38%), lemon (30%), orange (29%), mango (24%) and avocado (20%). This study provides an important contribution to the knowledge of the ecology of B. carambolae, and the information generated here can be used by government agencies as a decision-making tool to prevent the carambola fruit fly spread across the world.

  15. The Prevalence of Strabismus and Associated Risk Factors in a Southeastern Region of Brazil.

    PubMed

    Schaal, Luisa Fioravanti; Schellini, Silvana Artioli; Pesci, Leonardo Toledo; Galindo, Alicia; Padovani, Carlos Roberto; Corrente, Jose Eduardo

    2018-01-01

    To determine the prevalence of strabismus and associated risk factors in southeastern Brazil. A cross-sectional, population-based study using a systematic sample from nine municipalities in a southeastern region of Brazil composed of 1852 individuals aged ≥1 and ≤12 years old was done. Visual acuity (VA), ocular alignment, and refractive error (RE) were evaluated. Strabismic individuals (strabismus group) were compared to orthotropic individuals (orthotropic group) to analyze risk factors linked to heterotropias. Prevalence of strabismus was 0.81% in this population. In the strabismus group, we found 40% with hyperopia, 6.67% with astigmatism, 3.33% with myopia, 6.67% with amblyopia, and 8.33% with moderate anisometropia. In the orthotropic group, 6.85% had hyperopia, 18.12% astigmatism, 14.82% myopia, 0.19% amblyopia, and 4.37% moderate anisometropia. The prevalence of strabismus in southeastern Brazil was 0.81%. Strabismic individuals had more hyperopia. Amblyopia and moderate anisometropia were associated with strabismus.

  16. Maternal risk factors for HIV infection in infants in northeastern Brazil

    PubMed Central

    de Lemos, Lígia M.D.; Lippi, Joseph; Rutherford, George W.; Duarte, Gabriella S.; Martins, Nágyla G.R.; Santos, Victor S.; Gurgel, Ricardo Q.

    2017-01-01

    SUMMARY Introduction While the rate of vertically transmitted HIV infection has fallen in most regions of Brazil, there have been no similar decreases in northern and northeastern Brazil. Objective The objective of this study was to evaluate the risk factors associated with vertical transmission in the state of Sergipe in northeastern Brazil. Methods This was a retrospective cohort study. We recorded clinic and registry data for all HIV-infected pregnant women and exposed children diagnosed in Sergipe from 1990 to 2011. Results We identified 538 deliveries and 561 HIV-exposed infants (23 sets of twins). One hundred one (18.9%) infants were HIV-infected. In the multivariate analysis, infant antiretroviral prophylaxis was a significant protective factor (adjusted odds ratio (aOR) 0.07, 95% confidence interval (CI) 0.01–0.41, p=0.003). Breastfeeding was marginally associated with an increased odds of perinatal transmission (aOR 4.52, 95% CI 0.78–26.17, p = 0.092). The attributable risk percentage for breastfeeding over the study period was 91.0%. Transmission decreased from 91 per 100 live births before 1997 to 2 per 100 in 2011 following the adoption of the prevention protocol. Conclusion Transmission declined over the study period. The screening of pregnant women and timely initiation of prophylaxis and therapy are issues that require further attention. PMID:23791426

  17. Spatio-temporal distribution of soil-transmitted helminth infections in Brazil.

    PubMed

    Chammartin, Frédérique; Guimarães, Luiz H; Scholte, Ronaldo Gc; Bavia, Mara E; Utzinger, Jürg; Vounatsou, Penelope

    2014-09-18

    In Brazil, preventive chemotherapy targeting soil-transmitted helminthiasis is being scaled-up. Hence, spatially explicit estimates of infection risks providing information about the current situation are needed to guide interventions. Available high-resolution national model-based estimates either rely on analyses of data restricted to a given period of time, or on historical data collected over a longer period. While efforts have been made to take into account the spatial structure of the data in the modelling approach, little emphasis has been placed on the temporal dimension. We extracted georeferenced survey data on the prevalence of infection with soil-transmitted helminths (i.e. Ascaris lumbricoides, hookworm and Trichuris trichiura) in Brazil from the Global Neglected Tropical Diseases (GNTD) database. Selection of the most important predictors of infection risk was carried out using a Bayesian geostatistical approach and temporal models that address non-linearity and correlation of the explanatory variables. The spatial process was estimated through a predictive process approximation. Spatio-temporal models were built on the selected predictors with integrated nested Laplace approximation using stochastic partial differential equations. Our models revealed that, over the past 20 years, the risk of soil-transmitted helminth infection has decreased in Brazil, mainly because of the reduction of A. lumbricoides and hookworm infections. From 2010 onwards, we estimate that the infection prevalences with A. lumbricoides, hookworm and T. trichiura are 3.6%, 1.7% and 1.4%, respectively. We also provide a map highlighting municipalities in need of preventive chemotherapy, based on a predicted soil-transmitted helminth infection risk in excess of 20%. The need for treatments in the school-aged population at the municipality level was estimated at 1.8 million doses of anthelminthic tablets per year. The analysis of the spatio-temporal aspect of the risk of infection

  18. Assessing participation in community-based physical activity programs in Brazil.

    PubMed

    Reis, Rodrigo S; Yan, Yan; Parra, Diana C; Brownson, Ross C

    2014-01-01

    This study aimed to develop and validate a risk prediction model to examine the characteristics that are associated with participation in community-based physical activity programs in Brazil. We used pooled data from three surveys conducted from 2007 to 2009 in state capitals of Brazil with 6166 adults. A risk prediction model was built considering program participation as an outcome. The predictive accuracy of the model was quantified through discrimination (C statistic) and calibration (Brier score) properties. Bootstrapping methods were used to validate the predictive accuracy of the final model. The final model showed sex (women: odds ratio [OR] = 3.18, 95% confidence interval [CI] = 2.14-4.71), having less than high school degree (OR = 1.71, 95% CI = 1.16-2.53), reporting a good health (OR = 1.58, 95% CI = 1.02-2.24) or very good/excellent health (OR = 1.62, 95% CI = 1.05-2.51), having any comorbidity (OR = 1.74, 95% CI = 1.26-2.39), and perceiving the environment as safe to walk at night (OR = 1.59, 95% CI = 1.18-2.15) as predictors of participation in physical activity programs. Accuracy indices were adequate (C index = 0.778, Brier score = 0.031) and similar to those obtained from bootstrapping (C index = 0.792, Brier score = 0.030). Sociodemographic and health characteristics as well as perceptions of the environment are strong predictors of participation in community-based programs in selected cities of Brazil.

  19. Quantifying prognosis with risk predictions.

    PubMed

    Pace, Nathan L; Eberhart, Leopold H J; Kranke, Peter R

    2012-01-01

    Prognosis is a forecast, based on present observations in a patient, of their probable outcome from disease, surgery and so on. Research methods for the development of risk probabilities may not be familiar to some anaesthesiologists. We briefly describe methods for identifying risk factors and risk scores. A probability prediction rule assigns a risk probability to a patient for the occurrence of a specific event. Probability reflects the continuum between absolute certainty (Pi = 1) and certified impossibility (Pi = 0). Biomarkers and clinical covariates that modify risk are known as risk factors. The Pi as modified by risk factors can be estimated by identifying the risk factors and their weighting; these are usually obtained by stepwise logistic regression. The accuracy of probabilistic predictors can be separated into the concepts of 'overall performance', 'discrimination' and 'calibration'. Overall performance is the mathematical distance between predictions and outcomes. Discrimination is the ability of the predictor to rank order observations with different outcomes. Calibration is the correctness of prediction probabilities on an absolute scale. Statistical methods include the Brier score, coefficient of determination (Nagelkerke R2), C-statistic and regression calibration. External validation is the comparison of the actual outcomes to the predicted outcomes in a new and independent patient sample. External validation uses the statistical methods of overall performance, discrimination and calibration and is uniformly recommended before acceptance of the prediction model. Evidence from randomised controlled clinical trials should be obtained to show the effectiveness of risk scores for altering patient management and patient outcomes.

  20. Risk prediction and impaired tactile sensory perception among cancer patients during chemotherapy.

    PubMed

    Cardoso, Ana Carolina Lima Ramos; Araújo, Diego Dias de; Chianca, Tânia Couto Machado

    2018-01-08

    to estimate the prevalence of impaired tactile sensory perception, identify risk factors, and establish a risk prediction model among adult patients receiving antineoplastic chemotherapy. historical cohort study based on information obtained from the medical files of 127 patients cared for in the cancer unit of a private hospital in a city in Minas Gerais, Brazil. Data were analyzed using descriptive and bivariate statistics, with survival and multivariate analysis by Cox regression. 57% of the 127 patients included in the study developed impaired tactile sensory perception. The independent variables that caused significant impact, together with time elapsed from the beginning of treatment up to the onset of the condition, were: bone, hepatic and regional lymph node metastases; alcoholism; palliative chemotherapy; and discomfort in lower limbs. impaired tactile sensory perception was common among adult patients during chemotherapy, indicating the need to implement interventions designed for early identification and treatment of this condition.

  1. Risk-taking behavior for HIV acquisition during pregnancy in Porto Alegre, Brazil.

    PubMed

    Yeganeh, Nava; Varella, Ivana; Santos, Breno Riegel; Gonçalves de Melo, Marineide; Simon, Mariana; Melo, Taui; Nielsen-Saines, Karin

    2012-01-01

    Recent studies suggest that acquisition of HIV-1 infection during pregnancy and breastfeeding is associated with a high risk of HIV mother-to-child transmission. This study evaluates risk factors associated with HIV acquisition during pregnancy in women delivering at a large metropolitan medical facility located in the south of Brazil. From February to August 2009, our group conducted a cross-sectional study assessing women's risk for HIV acquisition by administering an oral survey to peripartum women. Of 2465 participants, 42% (n = 1046) knew that partner had been tested for HIV. During pregnancy, 82% (n = 2022) of participants never used condoms; yet 97% (n = 2399) practiced vaginal sex. Multivariate logistic regression analysis showed that patients with more years of education, in a relationship for more than 1 year, and who knew their own HIV status were more likely to know their partners' HIV status (P < 0.05). Those who were in relationship for more than 1 year and were married/living together were more likely to be comfortable discussing HIV testing with partners (P < 0.05). In conclusion, women in Brazil are at risk of HIV-infection during pregnancy as they remain sexually active, often do not know their sexual partner's HIV status, and have minimal condom use.

  2. Landscape, Environmental and Social Predictors of Hantavirus Risk in São Paulo, Brazil

    PubMed Central

    Uriarte, Maria; Tambosi, Leandro Reverberi; Prado, Amanda; Pardini, Renata; D´Andrea, Paulo Sérgio; Metzger, Jean Paul

    2016-01-01

    Hantavirus Pulmonary Syndrome (HPS) is a disease caused by Hantavirus, which are negative-sense RNA viruses in the family Bunyaviridae that are highly virulent to humans. Numerous factors modify risk of Hantavirus transmission and consequent HPS risk. Human-driven landscape change can foster transmission risk by increasing numbers of habitat generalist rodent species that serve as the principal reservoir host. Climate can also affect rodent population dynamics and Hantavirus survival, and a number of social factors can influence probability of HPS transmission to humans. Evaluating contributions of these factors to HPS risk may enable predictions of future outbreaks, and is critical to development of effective public health strategies. Here we rely on a Bayesian model to quantify associations between annual HPS incidence across the state of São Paulo, Brazil (1993–2012) and climate variables (annual precipitation, annual mean temperature), landscape structure metrics (proportion of native habitat cover, number of forest fragments, proportion of area planted with sugarcane), and social factors (number of men older than 14 years and Human Development Index). We built separate models for the main two biomes of the state (cerrado and Atlantic forest). In both biomes Hantavirus risk increased with proportion of land cultivated for sugarcane and HDI, but proportion of forest cover, annual mean temperature, and population at risk also showed positive relationships in the Atlantic forest. Our analysis provides the first evidence that social, landscape, and climate factors are associated with HPS incidence in the Neotropics. Our risk map can be used to support the adoption of preventive measures and optimize the allocation of resources to avoid disease propagation, especially in municipalities that show medium to high HPS risk (> 5% of risk), and aimed at sugarcane workers, minimizing the risk of future HPS outbreaks. PMID:27780250

  3. Assessing Participation in Community-Based Physical Activity Programs in Brazil

    PubMed Central

    REIS, RODRIGO S.; YAN, YAN; PARRA, DIANA C.; BROWNSON, ROSS C.

    2015-01-01

    Purpose This study aimed to develop and validate a risk prediction model to examine the characteristics that are associated with participation in community-based physical activity programs in Brazil. Methods We used pooled data from three surveys conducted from 2007 to 2009 in state capitals of Brazil with 6166 adults. A risk prediction model was built considering program participation as an outcome. The predictive accuracy of the model was quantified through discrimination (C statistic) and calibration (Brier score) properties. Bootstrapping methods were used to validate the predictive accuracy of the final model. Results The final model showed sex (women: odds ratio [OR] = 3.18, 95% confidence interval [CI] = 2.14–4.71), having less than high school degree (OR = 1.71, 95% CI = 1.16–2.53), reporting a good health (OR = 1.58, 95% CI = 1.02–2.24) or very good/excellent health (OR = 1.62, 95% CI = 1.05–2.51), having any comorbidity (OR = 1.74, 95% CI = 1.26–2.39), and perceiving the environment as safe to walk at night (OR = 1.59, 95% CI = 1.18–2.15) as predictors of participation in physical activity programs. Accuracy indices were adequate (C index = 0.778, Brier score = 0.031) and similar to those obtained from bootstrapping (C index = 0.792, Brier score = 0.030). Conclusions Sociodemographic and health characteristics as well as perceptions of the environment are strong predictors of participation in community-based programs in selected cities of Brazil. PMID:23846162

  4. Hepatitis B virus infection in Haemodialysis Centres from Santa Catarina State, Southern Brazil. Predictive risk factors for infection and molecular epidemiology

    PubMed Central

    Carrilho, Flair J; Moraes, Cleusa R; Pinho, João RR; Mello, Isabel MVGC; Bertolini, Dennis A; Lemos, Marcílio F; Moreira, Regina C; Bassit, Leda C; Cardoso, Rita A; Ribeiro-dos-Santos, Gabriela; Da Silva, Luiz C

    2004-01-01

    Background Patients under haemodialysis are considered at high risk to acquire hepatitis B virus (HBV) infection. Since few data are reported from Brazil, our aim was to assess the frequency and risk factors for HBV infection in haemodialysis patients from 22 Dialysis Centres from Santa Catarina State, south of Brazil. Methods This study includes 813 patients, 149 haemodialysis workers and 772 healthy controls matched by sex and age. Serum samples were assayed for HBV markers and viraemia was detected by nested PCR. HBV was genotyped by partial S gene sequencing. Univariate and multivariate statistical analyses with stepwise logistic regression analysis were carried out to analyse the relationship between HBV infection and the characteristics of patients and their Dialysis Units. Results Frequency of HBV infection was 10.0%, 2.7% and 2.7% among patients, haemodialysis workers and controls, respectively. Amidst patients, the most frequent HBV genotypes were A (30.6%), D (57.1%) and F (12.2%). Univariate analysis showed association between HBV infection and total time in haemodialysis, type of dialysis equipment, hygiene and sterilization of equipment, number of times reusing the dialysis lines and filters, number of patients per care-worker and current HCV infection. The logistic regression model showed that total time in haemodialysis, number of times of reusing the dialysis lines and filters, and number of patients per worker were significantly related to HBV infection. Conclusions Frequency of HBV infection among haemodialysis patients at Santa Catarina state is very high. The most frequent HBV genotypes were A, D and F. The risk for a patient to become HBV positive increase 1.47 times each month of haemodialysis; 1.96 times if the dialysis unit reuses the lines and filters ≥ 10 times compared with haemodialysis units which reuse < 10 times; 3.42 times if the number of patients per worker is more than five. Sequence similarity among the HBV S gene from isolates

  5. PredictABEL: an R package for the assessment of risk prediction models.

    PubMed

    Kundu, Suman; Aulchenko, Yurii S; van Duijn, Cornelia M; Janssens, A Cecile J W

    2011-04-01

    The rapid identification of genetic markers for multifactorial diseases from genome-wide association studies is fuelling interest in investigating the predictive ability and health care utility of genetic risk models. Various measures are available for the assessment of risk prediction models, each addressing a different aspect of performance and utility. We developed PredictABEL, a package in R that covers descriptive tables, measures and figures that are used in the analysis of risk prediction studies such as measures of model fit, predictive ability and clinical utility, and risk distributions, calibration plot and the receiver operating characteristic plot. Tables and figures are saved as separate files in a user-specified format, which include publication-quality EPS and TIFF formats. All figures are available in a ready-made layout, but they can be customized to the preferences of the user. The package has been developed for the analysis of genetic risk prediction studies, but can also be used for studies that only include non-genetic risk factors. PredictABEL is freely available at the websites of GenABEL ( http://www.genabel.org ) and CRAN ( http://cran.r-project.org/).

  6. Multifactorial disease risk calculator: Risk prediction for multifactorial disease pedigrees.

    PubMed

    Campbell, Desmond D; Li, Yiming; Sham, Pak C

    2018-03-01

    Construction of multifactorial disease models from epidemiological findings and their application to disease pedigrees for risk prediction is nontrivial for all but the simplest of cases. Multifactorial Disease Risk Calculator is a web tool facilitating this. It provides a user-friendly interface, extending a reported methodology based on a liability-threshold model. Multifactorial disease models incorporating all the following features in combination are handled: quantitative risk factors (including polygenic scores), categorical risk factors (including major genetic risk loci), stratified age of onset curves, and the partition of the population variance in disease liability into genetic, shared, and unique environment effects. It allows the application of such models to disease pedigrees. Pedigree-related outputs are (i) individual disease risk for pedigree members, (ii) n year risk for unaffected pedigree members, and (iii) the disease pedigree's joint liability distribution. Risk prediction for each pedigree member is based on using the constructed disease model to appropriately weigh evidence on disease risk available from personal attributes and family history. Evidence is used to construct the disease pedigree's joint liability distribution. From this, lifetime and n year risk can be predicted. Example disease models and pedigrees are provided at the website and are used in accompanying tutorials to illustrate the features available. The website is built on an R package which provides the functionality for pedigree validation, disease model construction, and risk prediction. Website: http://grass.cgs.hku.hk:3838/mdrc/current. © 2017 WILEY PERIODICALS, INC.

  7. Brief Report: Young People at Risk for Eating Disorders in Southeast Brazil

    ERIC Educational Resources Information Center

    Moya, Tatiana; Fleitlich-Bilyk, Bacy; Goodman, Robert

    2006-01-01

    A representative sample of 7-14-year-old young people in southeast Brazil (N=1251) was assessed using standardized parent and youth interviews, thereby identifying an "at-risk" group of young people who met one or more DSM-IV criteria for anorexia and/or bulimia nervosa. These young people were compared with an age and gender matched…

  8. Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil

    NASA Astrophysics Data System (ADS)

    Lowe, Rachel; Bailey, Trevor C.; Stephenson, David B.; Graham, Richard J.; Coelho, Caio A. S.; Sá Carvalho, Marilia; Barcellos, Christovam

    2011-03-01

    This paper considers the potential for using seasonal climate forecasts in developing an early warning system for dengue fever epidemics in Brazil. In the first instance, a generalised linear model (GLM) is used to select climate and other covariates which are both readily available and prove significant in prediction of confirmed monthly dengue cases based on data collected across the whole of Brazil for the period January 2001 to December 2008 at the microregion level (typically consisting of one large city and several smaller municipalities). The covariates explored include temperature and precipitation data on a 2.5°×2.5° longitude-latitude grid with time lags relevant to dengue transmission, an El Niño Southern Oscillation index and other relevant socio-economic and environmental variables. A negative binomial model formulation is adopted in this model selection to allow for extra-Poisson variation (overdispersion) in the observed dengue counts caused by unknown/unobserved confounding factors and possible correlations in these effects in both time and space. Subsequently, the selected global model is refined in the context of the South East region of Brazil, where dengue predominates, by reverting to a Poisson framework and explicitly modelling the overdispersion through a combination of unstructured and spatio-temporal structured random effects. The resulting spatio-temporal hierarchical model (or GLMM—generalised linear mixed model) is implemented via a Bayesian framework using Markov Chain Monte Carlo (MCMC). Dengue predictions are found to be enhanced both spatially and temporally when using the GLMM and the Bayesian framework allows posterior predictive distributions for dengue cases to be derived, which can be useful for developing a dengue alert system. Using this model, we conclude that seasonal climate forecasts could have potential value in helping to predict dengue incidence months in advance of an epidemic in South East Brazil.

  9. Cancer Risk Prediction and Assessment

    Cancer.gov

    Cancer prediction models provide an important approach to assessing risk and prognosis by identifying individuals at high risk, facilitating the design and planning of clinical cancer trials, fostering the development of benefit-risk indices, and enabling estimates of the population burden and cost of cancer.

  10. Perinatal and sociodemographic factors at birth predicting conduct problems and violence to age 18 years: comparison of Brazilian and British birth cohorts.

    PubMed

    Murray, Joseph; Maughan, Barbara; Menezes, Ana M B; Hickman, Matthew; MacLeod, John; Matijasevich, Alicia; Gonçalves, Helen; Anselmi, Luciana; Gallo, Erika A G; Barros, Fernando C

    2015-08-01

    Many low- and middle-income countries have high levels of violence. Research in high-income countries shows that risk factors in the perinatal period are significant precursors of conduct problems which can develop into violence. It is not known whether the same early influences are important in lower income settings with higher rates of violence. This study compared perinatal and sociodemographic risk factors between Brazil and Britain, and their role in explaining higher rates of conduct problems and violence in Brazil. Prospective population-based birth cohort studies were conducted in Pelotas, Brazil (N = 3,618) and Avon, Britain (N = 4,103). Eleven perinatal and sociodemographic risk factors were measured in questionnaires completed by mothers during the perinatal period. Conduct problems were measured in questionnaires completed by mothers at age 11, and violence in self-report questionnaires completed by adolescents at age 18. Conduct problems were predicted by similar risk factors in Brazil and Britain. Female violence was predicted by several of the same risk factors in both countries. However, male violence in Brazil was associated with only one risk factor, and several risk factor associations were weaker in Brazil than in Britain for both females and males. Almost 20% of the higher risk for conduct problems in Brazil compared to Britain was explained by differential exposure to risk factors. The percentage of the cross-national difference in violence explained by early risk factors was 15% for females and 8% for males. A nontrivial proportion of cross-national differences in antisocial behaviour are related to perinatal and sociodemographic conditions at the start of life. However, risk factor associations are weaker in Brazil than in Britain, and influences in other developmental periods are probably of particular importance for understanding male youth violence in Brazil. © 2014 The Authors. Journal of Child Psychology and Psychiatry published by

  11. Neuroanatomy Predicts Individual Risk Attitudes

    PubMed Central

    Gilaie-Dotan, Sharon; Tymula, Agnieszka; Cooper, Nicole; Kable, Joseph W.; Glimcher, Paul W.

    2014-01-01

    Over the course of the last decade a multitude of studies have investigated the relationship between neural activations and individual human decision-making. Here we asked whether the anatomical features of individual human brains could be used to predict the fundamental preferences of human choosers. To that end, we quantified the risk attitudes of human decision-makers using standard economic tools and quantified the gray matter cortical volume in all brain areas using standard neurobiological tools. Our whole-brain analysis revealed that the gray matter volume of a region in the right posterior parietal cortex was significantly predictive of individual risk attitudes. Participants with higher gray matter volume in this region exhibited less risk aversion. To test the robustness of this finding we examined a second group of participants and used econometric tools to test the ex ante hypothesis that gray matter volume in this area predicts individual risk attitudes. Our finding was confirmed in this second group. Our results, while being silent about causal relationships, identify what might be considered the first stable biomarker for financial risk-attitude. If these results, gathered in a population of midlife northeast American adults, hold in the general population, they will provide constraints on the possible neural mechanisms underlying risk attitudes. The results will also provide a simple measurement of risk attitudes that could be easily extracted from abundance of existing medical brain scans, and could potentially provide a characteristic distribution of these attitudes for policy makers. PMID:25209279

  12. Risk terrain modeling predicts child maltreatment.

    PubMed

    Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

    2016-12-01

    As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  13. Developmental dyslexia: predicting individual risk

    PubMed Central

    Thompson, Paul A; Hulme, Charles; Nash, Hannah M; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J

    2015-01-01

    Background Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. Methods The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited to three groups: children at family risk of dyslexia, children with concerns regarding speech, and language development at 3;06 years and controls considered to be typically developing. At 8 years, children were classified as ‘dyslexic’ or not. Logistic regression models were used to predict the individual risk of dyslexia and to investigate how risk factors accumulate to predict poor literacy outcomes. Results Family-risk status was a stronger predictor of dyslexia at 8 years than low language in preschool. Additional predictors in the preschool years include letter knowledge, phonological awareness, rapid automatized naming, and executive skills. At the time of school entry, language skills become significant predictors, and motor skills add a small but significant increase to the prediction probability. We present classification accuracy using different probability cutoffs for logistic regression models and ROC curves to highlight the accumulation of risk factors at the individual level. Conclusions Dyslexia is the outcome of multiple risk factors and children with language difficulties at school entry are at high risk. Family history of dyslexia is a predictor of literacy outcome from the preschool years. However, screening does not reach an acceptable clinical level until close to school entry when letter knowledge, phonological awareness, and RAN, rather than family risk, together provide good sensitivity and specificity as a screening battery. PMID:25832320

  14. Calibration plots for risk prediction models in the presence of competing risks.

    PubMed

    Gerds, Thomas A; Andersen, Per K; Kattan, Michael W

    2014-08-15

    A predicted risk of 17% can be called reliable if it can be expected that the event will occur to about 17 of 100 patients who all received a predicted risk of 17%. Statistical models can predict the absolute risk of an event such as cardiovascular death in the presence of competing risks such as death due to other causes. For personalized medicine and patient counseling, it is necessary to check that the model is calibrated in the sense that it provides reliable predictions for all subjects. There are three often encountered practical problems when the aim is to display or test if a risk prediction model is well calibrated. The first is lack of independent validation data, the second is right censoring, and the third is that when the risk scale is continuous, the estimation problem is as difficult as density estimation. To deal with these problems, we propose to estimate calibration curves for competing risks models based on jackknife pseudo-values that are combined with a nearest neighborhood smoother and a cross-validation approach to deal with all three problems. Copyright © 2014 John Wiley & Sons, Ltd.

  15. Risk and the physics of clinical prediction.

    PubMed

    McEvoy, John W; Diamond, George A; Detrano, Robert C; Kaul, Sanjay; Blaha, Michael J; Blumenthal, Roger S; Jones, Steven R

    2014-04-15

    The current paradigm of primary prevention in cardiology uses traditional risk factors to estimate future cardiovascular risk. These risk estimates are based on prediction models derived from prospective cohort studies and are incorporated into guideline-based initiation algorithms for commonly used preventive pharmacologic treatments, such as aspirin and statins. However, risk estimates are more accurate for populations of similar patients than they are for any individual patient. It may be hazardous to presume that the point estimate of risk derived from a population model represents the most accurate estimate for a given patient. In this review, we exploit principles derived from physics as a metaphor for the distinction between predictions regarding populations versus patients. We identify the following: (1) predictions of risk are accurate at the level of populations but do not translate directly to patients, (2) perfect accuracy of individual risk estimation is unobtainable even with the addition of multiple novel risk factors, and (3) direct measurement of subclinical disease (screening) affords far greater certainty regarding the personalized treatment of patients, whereas risk estimates often remain uncertain for patients. In conclusion, shifting our focus from prediction of events to detection of disease could improve personalized decision-making and outcomes. We also discuss innovative future strategies for risk estimation and treatment allocation in preventive cardiology. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. Spatial and temporal analysis of Aids cases in Brazil, 1996-2011: increased risk areas over time.

    PubMed

    Sousa, Artur Iuri Alves de; Pinto, Vitor Laerte

    2016-01-01

    to identify areas with greater risk of AIDS transmission in Brazil. this is an ecological study involving georeference of AIDS cases incidence, prevalence and density in Brazilian municipalities using the Kernel method for the periods 1996-1999, 2000-2003, 2004-2007 and 2008-2011. 633,512 AIDS cases were reported between 1996-2011; between 2008-2011, there was increased risk of AIDS transmission in Recife-João Pessoa region, the emergence of areas with average density in the regions of Belém, São Luís, Maceió, Aracaju and Salvador, and a decline in the intensity of risk in São Paulo, Campinas and Ribeirão Preto; prevalence rates were most concentrated in the Southeast, South and Midwest regions of the country. overall, AIDS incidence in Brazil showed successive increases in the periods analyzed; case prevalence indicates spatial clusters, with high concentrations in the Southeast, South and Midwest regions.

  17. Predicting child maltreatment: A meta-analysis of the predictive validity of risk assessment instruments.

    PubMed

    van der Put, Claudia E; Assink, Mark; Boekhout van Solinge, Noëlle F

    2017-11-01

    Risk assessment is crucial in preventing child maltreatment since it can identify high-risk cases in need of child protection intervention. Despite widespread use of risk assessment instruments in child welfare, it is unknown how well these instruments predict maltreatment and what instrument characteristics are associated with higher levels of predictive validity. Therefore, a multilevel meta-analysis was conducted to examine the predictive accuracy of (characteristics of) risk assessment instruments. A literature search yielded 30 independent studies (N=87,329) examining the predictive validity of 27 different risk assessment instruments. From these studies, 67 effect sizes could be extracted. Overall, a medium significant effect was found (AUC=0.681), indicating a moderate predictive accuracy. Moderator analyses revealed that onset of maltreatment can be better predicted than recurrence of maltreatment, which is a promising finding for early detection and prevention of child maltreatment. In addition, actuarial instruments were found to outperform clinical instruments. To bring risk and needs assessment in child welfare to a higher level, actuarial instruments should be further developed and strengthened by distinguishing risk assessment from needs assessment and by integrating risk assessment with case management. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Predicting readmission risk with institution-specific prediction models.

    PubMed

    Yu, Shipeng; Farooq, Faisal; van Esbroeck, Alexander; Fung, Glenn; Anand, Vikram; Krishnapuram, Balaji

    2015-10-01

    The ability to predict patient readmission risk is extremely valuable for hospitals, especially under the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services which went into effect starting October 1, 2012. There is a plethora of work in the literature that deals with developing readmission risk prediction models, but most of them do not have sufficient prediction accuracy to be deployed in a clinical setting, partly because different hospitals may have different characteristics in their patient populations. We propose a generic framework for institution-specific readmission risk prediction, which takes patient data from a single institution and produces a statistical risk prediction model optimized for that particular institution and, optionally, for a specific condition. This provides great flexibility in model building, and is also able to provide institution-specific insights in its readmitted patient population. We have experimented with classification methods such as support vector machines, and prognosis methods such as the Cox regression. We compared our methods with industry-standard methods such as the LACE model, and showed the proposed framework is not only more flexible but also more effective. We applied our framework to patient data from three hospitals, and obtained some initial results for heart failure (HF), acute myocardial infarction (AMI), pneumonia (PN) patients as well as patients with all conditions. On Hospital 2, the LACE model yielded AUC 0.57, 0.56, 0.53 and 0.55 for AMI, HF, PN and All Cause readmission prediction, respectively, while the proposed model yielded 0.66, 0.65, 0.63, 0.74 for the corresponding conditions, all significantly better than the LACE counterpart. The proposed models that leverage all features at discharge time is more accurate than the models that only leverage features at admission time (0.66 vs. 0.61 for AMI, 0.65 vs. 0.61 for HF, 0.63 vs. 0.56 for PN, 0.74 vs. 0.60 for All

  19. Developmental dyslexia: predicting individual risk.

    PubMed

    Thompson, Paul A; Hulme, Charles; Nash, Hannah M; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J

    2015-09-01

    Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited to three groups: children at family risk of dyslexia, children with concerns regarding speech, and language development at 3;06 years and controls considered to be typically developing. At 8 years, children were classified as 'dyslexic' or not. Logistic regression models were used to predict the individual risk of dyslexia and to investigate how risk factors accumulate to predict poor literacy outcomes. Family-risk status was a stronger predictor of dyslexia at 8 years than low language in preschool. Additional predictors in the preschool years include letter knowledge, phonological awareness, rapid automatized naming, and executive skills. At the time of school entry, language skills become significant predictors, and motor skills add a small but significant increase to the prediction probability. We present classification accuracy using different probability cutoffs for logistic regression models and ROC curves to highlight the accumulation of risk factors at the individual level. Dyslexia is the outcome of multiple risk factors and children with language difficulties at school entry are at high risk. Family history of dyslexia is a predictor of literacy outcome from the preschool years. However, screening does not reach an acceptable clinical level until close to school entry when letter knowledge, phonological awareness, and RAN, rather than family risk, together provide good sensitivity and specificity as a screening battery. © 2015 The Authors. Journal of Child Psychology and Psychiatry published by

  20. Climate Prediction for Brazil's Nordeste: Performance of Empirical and Numerical Modeling Methods.

    NASA Astrophysics Data System (ADS)

    Moura, Antonio Divino; Hastenrath, Stefan

    2004-07-01

    Comparisons of performance of climate forecast methods require consistency in the predictand and a long common reference period. For Brazil's Nordeste, empirical methods developed at the University of Wisconsin use preseason (October January) rainfall and January indices of the fields of meridional wind component and sea surface temperature (SST) in the tropical Atlantic and the equatorial Pacific as input to stepwise multiple regression and neural networking. These are used to predict the March June rainfall at a network of 27 stations. An experiment at the International Research Institute for Climate Prediction, Columbia University, with a numerical model (ECHAM4.5) used global SST information through February to predict the March June rainfall at three grid points in the Nordeste. The predictands for the empirical and numerical model forecasts are correlated at +0.96, and the period common to the independent portion of record of the empirical prediction and the numerical modeling is 1968 99. Over this period, predicted versus observed rainfall are evaluated in terms of correlation, root-mean-square error, absolute error, and bias. Performance is high for both approaches. Numerical modeling produces a correlation of +0.68, moderate errors, and strong negative bias. For the empirical methods, errors and bias are small, and correlations of +0.73 and +0.82 are reached between predicted and observed rainfall.


  1. Toxoplasmosis screening and risk factors amongst pregnant females in Natal, northeastern Brazil.

    PubMed

    Barbosa, Isabelle Ribeiro; de Carvalho Xavier Holanda, Cecília Maria; de Andrade-Neto, Valter Ferreira

    2009-04-01

    Toxoplasmosis results in systemic disease, and if the mother is infected for the first time during gestation, the fetus may suffer substantial damage. Relatively little is known about the epidemiology of toxoplasmosis in pregnancy in most states of northeastern Brazil. Seroprevalence of toxoplasmosis among pregnant woman was studied in Natal, capital of Rio Grande do Norte State, in northeastern Brazil, from March to December 2007. The sera were tested for IgM and avidity of IgG antibodies to Toxoplasma by a microparticle enzyme immunoassay. The overall seroprevalence was high [126/190 (66.3%)]; prevalence increased with age indicating that in this setting most infections occur in adulthood (third decade of life). Only one pregnant woman was IgM positive and had high-avidity antibodies. The high percentage of pregnant women who are vulnerable to this parasite (33.1%) favors primary infection during pregnancy. Our studies show that direct contact with cats or dogs was highly associated with toxoplasmosis (odds ratio 2.72, P<0.001, 95% CI 1.46-5.02). The number of years in school (P<0.001), precarious socioeconomic status and limited knowledge about the disease (Prisk factors for infection corroborate other studies in Brazil.

  2. Trends and predictions for gastric cancer mortality in Brazil.

    PubMed

    de Souza Giusti, Angela Carolina Brandão; de Oliveira Salvador, Pétala Tuani Candido; Dos Santos, Juliano; Meira, Karina Cardoso; Camacho, Amanda Rodrigues; Guimarães, Raphael Mendonça; Souza, Dyego L B

    2016-07-28

    To analyze the effect of age-period and birth cohort on gastric cancer mortality, in Brazil and across its five geographic regions, by sex, in the population over 20 years of age, as well as make projections for the period 2010-2029. An ecological study is presented herein, which distributed gastric cancer-related deaths in Brazil and its geographic regions. The effects of age-period and birth cohort were calculated by the Poisson regression model and projections were made with the age-period-cohort model in the statistical program R. Progressive reduction of mortality rates was observed in the 1980's, and then higher and lower mortality rates were verified in the 2000's, for both sexes, in Brazil and for the South, Southeast and Midwest regions. A progressive decrease in mortality rates was observed for the Northeast (both sexes) and North (men only) regions within the period 1995-1999, followed by rising rates. Regional differences were demonstrated in the mortality rates for gastric cancer in Brazil, and the least developed regions of the country will present increases in projected mortality rates.

  3. Gastrointestinal parasites of cats in Brazil: frequency and zoonotic risk.

    PubMed

    Monteiro, Maria Fernanda Melo; Ramos, Rafael Antonio Nascimento; Calado, Andréa Maria Campos; Lima, Victor Fernando Santana; Ramos, Ingrid Carla do Nascimento; Tenório, Rodrigo Ferreira Lima; Faustino, Maria Aparecida da Glória; Alves, Leucio Câmara

    2016-04-12

    Gastrointestinal helminths are considered to be the most common parasites affecting cats worldwide. Correct diagnosis of these parasites in animals living in urban areas is pivotal, especially considering the zoonotic potential of some species (e.g. Ancylostoma sp. and Toxocara sp.). In this study, a copromicroscopic survey was conducted using fecal samples (n = 173) from domestic cats living in the northeastern region of Brazil. Samples were examined through the FLOTAC technique and the overall results showed positivity of 65.31% (113/173) among the samples analyzed. Coinfections were observed in 46.01% (52/113) of the positive samples. The most common parasites detected were Ancylostoma sp., Toxocara cati, Strongyloides stercoralis, Trichuris sp., Dipylidium caninum and Cystoisospora sp. From an epidemiological point of view, these findings are important, especially considering that zoonotic parasites (e.g. Ancylostoma sp. and Toxocara sp.) were the nematodes most frequently diagnosed in this study. Therefore, the human population living in close contact with cats is at risk of infection caused by the zoonotic helminths of these animals. In addition, for the first time the FLOTAC has been used to diagnosing gastrointestinal parasites of cats in Brazil.

  4. Clustering of Risk Factors for Non-Communicable Diseases among Adolescents from Southern Brazil.

    PubMed

    Nunes, Heloyse Elaine Gimenes; Gonçalves, Eliane Cristina de Andrade; Vieira, Jéssika Aparecida Jesus; Silva, Diego Augusto Santos

    2016-01-01

    The aim of this study was to investigate the simultaneous presence of risk factors for non-communicable diseases and the association of these risk factors with demographic and economic factors among adolescents from southern Brazil. The study included 916 students (14-19 years old) enrolled in the 2014 school year at state schools in São José, Santa Catarina, Brazil. Risk factors related to lifestyle (i.e., physical inactivity, excessive alcohol consumption, smoking, sedentary behaviour and unhealthy diet), demographic variables (sex, age and skin colour) and economic variables (school shift and economic level) were assessed through a questionnaire. Simultaneous behaviours were assessed by the ratio between observed and expected prevalences of risk factors for non-communicable diseases. The clustering of risk factors was analysed by multinomial logistic regression. The clusters of risk factors that showed a higher prevalence were analysed by binary logistic regression. The clustering of two, three, four, and five risk factors were found in 22.2%, 49.3%, 21.7% and 3.1% of adolescents, respectively. Subgroups that were more likely to have both behaviours of physical inactivity and unhealthy diet simultaneously were mostly composed of girls (OR = 3.03, 95% CI = 1.57-5.85) and those with lower socioeconomic status (OR = 1.83, 95% CI = 1.05-3.21); simultaneous physical inactivity, excessive alcohol consumption, sedentary behaviour and unhealthy diet were mainly observed among older adolescents (OR = 1.49, 95% CI = 1.05-2.12). Subgroups less likely to have both behaviours of sedentary behaviour and unhealthy diet were mostly composed of girls (OR = 0.58, 95% CI = 0.38-0.89); simultaneous physical inactivity, sedentary behaviour and unhealthy diet were mainly observed among older individuals (OR = 0.66, 95% CI = 0.49-0.87) and those of the night shift (OR = 0.59, 95% CI = 0.43-0.82). Adolescents had a high prevalence of simultaneous risk factors for NCDs. Demographic

  5. Risk prediction and aversion by anterior cingulate cortex.

    PubMed

    Brown, Joshua W; Braver, Todd S

    2007-12-01

    The recently proposed error-likelihood hypothesis suggests that anterior cingulate cortex (ACC) and surrounding areas will become active in proportion to the perceived likelihood of an error. The hypothesis was originally derived from a computational model prediction. The same computational model now makes a further prediction that ACC will be sensitive not only to predicted error likelihood, but also to the predicted magnitude of the consequences, should an error occur. The product of error likelihood and predicted error consequence magnitude collectively defines the general "expected risk" of a given behavior in a manner analogous but orthogonal to subjective expected utility theory. New fMRI results from an incentivechange signal task now replicate the error-likelihood effect, validate the further predictions of the computational model, and suggest why some segments of the population may fail to show an error-likelihood effect. In particular, error-likelihood effects and expected risk effects in general indicate greater sensitivity to earlier predictors of errors and are seen in risk-averse but not risk-tolerant individuals. Taken together, the results are consistent with an expected risk model of ACC and suggest that ACC may generally contribute to cognitive control by recruiting brain activity to avoid risk.

  6. Trends and predictions for gastric cancer mortality in Brazil

    PubMed Central

    de Souza Giusti, Angela Carolina Brandão; de Oliveira Salvador, Pétala Tuani Candido; dos Santos, Juliano; Meira, Karina Cardoso; Camacho, Amanda Rodrigues; Guimarães, Raphael Mendonça; Souza, Dyego L B

    2016-01-01

    AIM: To analyze the effect of age-period and birth cohort on gastric cancer mortality, in Brazil and across its five geographic regions, by sex, in the population over 20 years of age, as well as make projections for the period 2010-2029. METHODS: An ecological study is presented herein, which distributed gastric cancer-related deaths in Brazil and its geographic regions. The effects of age-period and birth cohort were calculated by the Poisson regression model and projections were made with the age-period-cohort model in the statistical program R. RESULTS: Progressive reduction of mortality rates was observed in the 1980’s, and then higher and lower mortality rates were verified in the 2000’s, for both sexes, in Brazil and for the South, Southeast and Midwest regions. A progressive decrease in mortality rates was observed for the Northeast (both sexes) and North (men only) regions within the period 1995-1999, followed by rising rates. CONCLUSION: Regional differences were demonstrated in the mortality rates for gastric cancer in Brazil, and the least developed regions of the country will present increases in projected mortality rates. PMID:27605887

  7. Risk prediction model: Statistical and artificial neural network approach

    NASA Astrophysics Data System (ADS)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  8. A scoping review of prevalence, incidence and risk factors for HIV infection amongst young people in Brazil.

    PubMed

    Saffier, Igor Pedrosa; Kawa, Hélia; Harling, Guy

    2017-10-11

    Despite young people being a key population for HIV prevention, the HIV epidemic amongst young Brazilians is perceived to be growing. We therefore reviewed all published literature on HIV prevalence and risk factors for HIV infection amongst 10-25 year olds in Brazil. We searched Embase, LILACS, Proquest, PsycINFO, PubMed, Scopus and Web of Science for studies published up to March 2017 and analyzed reference lists of relevant studies. We included published studies from any time in the HIV epidemic which provided estimates specific to ages 10-25 (or some subset of this age range) for Brazilians on either: (a) HIV prevalence or incidence; or (b) the association between HIV and socio-demographic or behavioral risk factors. Forty eight publications met the inclusion criteria: 44 cross-sectional, two case-control, two cohort. Four studies analysed national data. Forty seven studies provided HIV prevalence estimates, largely for six population subgroups: Counselling and Testing Center attendees; blood donors; pregnant women; institutional individuals; men-who-have-sex-with-men (MSM) and female sex workers (FSW); four provided HIV incidence estimates. Twelve studies showed HIV status to be associated with a wide range of risk factors, including age, sexual and reproductive history, infection history, substance use, geography, marital status, mental health and socioeconomic status. Few published studies have examined HIV amongst young people in Brazil, and those published have been largely cross-sectional and focused on traditional risk groups and the south of the country. Despite these limitations, the literature shows raised HIV prevalence amongst MSM and FSW, as well as amongst those using drugs. Time trends are harder to identify, although rates appear to be falling for pregnant women, possibly reversing an earlier de-masculinization of the epidemic. Improved surveillance of HIV incidence, prevalence and risk factors is a key component of efforts to eliminate HIV in

  9. A New Method to Predict the Epidemiology of Fungal Keratitis by Monitoring the Sales Distribution of Antifungal Eye Drops in Brazil

    PubMed Central

    Ibrahim, Marlon Moraes; de Angelis, Rafael; Lima, Acacio Souza; Viana de Carvalho, Glauco Dreyer; Ibrahim, Fuad Moraes; Malki, Leonardo Tannus; de Paula Bichuete, Marina; de Paula Martins, Wellington; Rocha, Eduardo Melani

    2012-01-01

    Purpose Fungi are a major cause of keratitis, although few medications are licensed for their treatment. The aim of this study is to observe the variation in commercialisation of antifungal eye drops, and to predict the seasonal distribution of fungal keratitis in Brazil. Methods Data from a retrospective study of antifungal eye drops sales from the only pharmaceutical ophthalmologic laboratory, authorized to dispense them in Brazil (Opthalmos) were gathered. These data were correlated with geographic and seasonal distribution of fungal keratitis in Brazil between July 2002 and June 2008. Results A total of 26,087 antifungal eye drop units were sold, with a mean of 2.3 per patient. There was significant variation in antifungal sales during the year (p<0.01). A linear regression model displayed a significant association between reduced relative humidity and antifungal drug sales (R2 = 0.17,p<0.01). Conclusions Antifungal eye drops sales suggest that there is a seasonal distribution of fungal keratitis. A possible interpretation is that the third quarter of the year (a period when the climate is drier), when agricultural activity is more intense in Brazil, suggests a correlation with a higher incidence of fungal keratitis. A similar model could be applied to other diseases, that are managed with unique, or few, and monitorable medications to predict epidemiological aspects. PMID:22457787

  10. Design of risk communication strategies based on risk perception among farmers exposed to pesticides in Rio de Janeiro State, Brazil.

    PubMed

    Peres, Frederico; Rodrigues, Karla Meneses; da Silva Peixoto Belo, Mariana Soares; Moreira, Josino Costa; Claudio, Luz

    2013-01-01

    This study aims to assess pesticide exposure risk perception among farmers from three rural areas of Nova Friburgo, Rio de Janeiro State, Brazil. Data were collected through semi-structured interviews with 66 adults and participatory workshops with 27 teenagers and analyzed through content analysis techniques. Systematized results were discussed at local meetings, and two risk communication initiatives were devised. Study results demonstrated the use of defensive strategies by men and a diminished risk perception among women. Teenagers relied on parents to develop their own work practices. These findings supported the importance of cultural and social determinants of farmers' understandings of risk and of the relevance of different pesticide exposure pathways. Risk perceptions and work practices are strongly influenced by local cultural patterns and, therefore, must be taken into account when developing effective intervention strategies, including risk communication initiatives. Copyright © 2012 Wiley Periodicals, Inc.

  11. Risk Factors for Death from Visceral Leishmaniasis in an Urban Area of Brazil

    PubMed Central

    Druzian, Angelita F.; de Souza, Albert S.; de Campos, Diogo N.; Croda, Julio; Higa, Minoru G.; Dorval, Maria Elizabeth C.; Pompilio, Mauricio A.; de Oliveira, Polliana A.; Paniago, Anamaria M. M.

    2015-01-01

    Background Over the last three decades, the epidemiological profile of visceral leishmaniasis (VL) has changed with epidemics occurring in large urban centers of Brazil, an increase in HIV/AIDS co-infection, and a significant increase in mortality. The objective of this study was to identify the risk factors associated with death among adult patients with VL from an urban endemic area of Brazil. Methodology A prospective cohort study included 134 adult patients with VL admitted to the University Hospital of the Federal University of Mato Grosso do Sul between August 2011 and August 2013. Principal Findings Patients ranged from 18 to 93 years old, with a mean age of 43.6 (±15.7%). Of these patients, 36.6% were co-infected with HIV/AIDS, and the mortality rate was 21.6%. In a multivariate analysis, the risk factors associated with death were secondary bacterial infection (42.86, 5.05–363.85), relapse (12.17, 2.06–71.99), edema (7.74, 1.33–45.05) and HIV/AIDS co-infection (7.33, 1.22–43.98). Conclusions/Significance VL has a high mortality rate in adults from endemic urban areas, especially when coinciding with high rates of HIV/AIDS co-infection. PMID:26274916

  12. [Brazil: street children in the risk zone for HIV and AIDS].

    PubMed

    Ommundsen, C

    1993-08-26

    In the fall of 1992 the Foundation ARCA (Association for Prevention and Assistance of Street Children with AIDS) was established in Sao Paulo. Eventually cooperation developed between Sao Paulo state officials, Noah's Ark, the Swedish Red Cross, and ARCA with a view to establishing a home for street children with AIDS. The lifestyle of these children exposes them to a high risk of infection with AIDS because of narcotic use, sex behavior, and prostitution. Unofficial data from Mexico, the Dominican Republic, and Brazil suggest that 2-10% of street children may be infected with HIV. In February 1989 there 145 million children in the world who worked in the streets, 24 million of them in Brazil (7 million lived permanently in the streets). The state of Sao Paulo had 64% of the 22,545 AIDS cases reported up to March 1993. Approximately 100,000 HIV-positive people are treated at other health facilities in other states of Brazil. The shelter for AIDS-afflicted street children intends to treat the infections or give the children the opportunity to die with dignity. These children are 8-17 years old. The initial 15 beds are envisioned to increase to 60 beds. Preventive promotional campaigns are also planned to reduce the spread of HIV among street kids. In the summer of 1992 the mass media in Sao Paulo and ARCA hosted a cultural fundraiser to which influential persons were invited, and the profits were donated to ARCA. An abandoned motel has also ben donated to ARCA for carrying out the desired activities.

  13. HIV seroprevalence and high-risk sexual behavior among female sex workers in Central Brazil.

    PubMed

    Fernandes, Fernanda R P; Mousquer, Gina J; Castro, Lisie S; Puga, Marco A; Tanaka, Tayana S O; Rezende, Grazielli R; Pinto, Clarice S; Bandeira, Larissa M; Martins, Regina M B; Francisco, Roberta B L; Teles, Sheila A; Motta-Castro, Ana R C

    2014-01-01

    Female sex workers (FSWs) are considered a high-risk group for human immunodeficiency virus (HIV) infection due to their social vulnerability and factors associated with their work. We estimated the prevalence of HIV, and identified viral subtypes and risk factors among FSWs. A cross-sectional study using respondent-driven sampling (RDS) method was conducted among 402 FSWs in Campo Grande city, Brazil, from 2009 to 2011. Participants were interviewed using a standardized questionnaire about sociodemograpic characteristics and risk behavior. Blood samples were collected for serological testing of HIV. Of the 402 FSWs, median age and age of initiating sex work were 25 years (Interquartile range [IQR]: 9) and 20 years (IQR: 6), respectively. The majority reported use of alcohol (88.5%), had 5-9 years (median: 9; IQR: 3) of schooling (54.5%), 68.6% had tattoos/body piercings, and 45.1% had more than seven clients per week (median: 7; IQR: 10). Only 32.9% of FSW reported using a condom with nonpaying partners in the last sexual contact. Prevalence of HIV infection was 1.0% (95% CI: 0.1-2.6%). Genotyping for HIV-1 performed on three samples detected subtypes B, C, and F1. Sex work in the Midwestern region of Brazil is characterized by reduced education, large numbers of clients per week, and inconsistent condom use, mainly with nonpaying partners. Although prevalence of HIV infection is currently low, elevated levels of high-risk sexual behavior confirm a need to implement prevention measures. Specific interventions targeting FSWs must emphasize the risk associated with both clients and nonpaying partners while providing knowledge about HIV prevention.

  14. Nonstandard Lumbar Region in Predicting Fracture Risk.

    PubMed

    Alajlouni, Dima; Bliuc, Dana; Tran, Thach; Pocock, Nicholas; Nguyen, Tuan V; Eisman, John A; Center, Jacqueline R

    Femoral neck (FN) bone mineral density (BMD) is the most commonly used skeletal site to estimate fracture risk. The role of lumbar spine (LS) BMD in fracture risk prediction is less clear due to osteophytes that spuriously increase LS BMD, particularly at lower levels. The aim of this study was to compare fracture predictive ability of upper L1-L2 BMD with standard L2-L4 BMD and assess whether the addition of either LS site could improve fracture prediction over FN BMD. This study comprised a prospective cohort of 3016 women and men over 60 yr from the Dubbo Osteoporosis Epidemiology Study followed up for occurrence of minimal trauma fractures from 1989 to 2014. Dual-energy X-ray absorptiometry was used to measure BMD at L1-L2, L2-L4, and FN at baseline. Fracture risks were estimated using Cox proportional hazards models separately for each site. Predictive performances were compared using receiver operating characteristic curve analyses. There were 565 women and 179 men with a minimal trauma fracture during a mean of 11 ± 7 yr. L1-L2 BMD T-score was significantly lower than L2-L4 T-score in both genders (p < 0.0001). L1-L2 and L2-L4 BMD models had a similar fracture predictive ability. LS BMD was better than FN BMD in predicting vertebral fracture risk in women [area under the curve 0.73 (95% confidence interval, 0.68-0.79) vs 0.68 (95% confidence interval, 0.62-0.74), but FN was superior for hip fractures prediction in both women and men. The addition of L1-L2 or L2-L4 to FN BMD in women increased overall and vertebral predictive power compared with FN BMD alone by 1% and 4%, respectively (p < 0.05). In an elderly population, L1-L2 is as good as but not better than L2-L4 site in predicting fracture risk. The addition of LS BMD to FN BMD provided a modest additional benefit in overall fracture risk. Further studies in individuals with spinal degenerative disease are needed. Copyright © 2017 The International Society for Clinical Densitometry

  15. Evaluation of a Genetic Risk Score to Improve Risk Prediction for Alzheimer's Disease.

    PubMed

    Chouraki, Vincent; Reitz, Christiane; Maury, Fleur; Bis, Joshua C; Bellenguez, Celine; Yu, Lei; Jakobsdottir, Johanna; Mukherjee, Shubhabrata; Adams, Hieab H; Choi, Seung Hoan; Larson, Eric B; Fitzpatrick, Annette; Uitterlinden, Andre G; de Jager, Philip L; Hofman, Albert; Gudnason, Vilmundur; Vardarajan, Badri; Ibrahim-Verbaas, Carla; van der Lee, Sven J; Lopez, Oscar; Dartigues, Jean-François; Berr, Claudine; Amouyel, Philippe; Bennett, David A; van Duijn, Cornelia; DeStefano, Anita L; Launer, Lenore J; Ikram, M Arfan; Crane, Paul K; Lambert, Jean-Charles; Mayeux, Richard; Seshadri, Sudha

    2016-06-18

    Effective prevention of Alzheimer's disease (AD) requires the development of risk prediction tools permitting preclinical intervention. We constructed a genetic risk score (GRS) comprising common genetic variants associated with AD, evaluated its association with incident AD and assessed its capacity to improve risk prediction over traditional models based on age, sex, education, and APOEɛ4. In eight prospective cohorts included in the International Genomics of Alzheimer's Project (IGAP), we derived weighted sum of risk alleles from the 19 top SNPs reported by the IGAP GWAS in participants aged 65 and older without prevalent dementia. Hazard ratios (HR) of incident AD were estimated in Cox models. Improvement in risk prediction was measured by the difference in C-index (Δ-C), the integrated discrimination improvement (IDI) and continuous net reclassification improvement (NRI>0). Overall, 19,687 participants at risk were included, of whom 2,782 developed AD. The GRS was associated with a 17% increase in AD risk (pooled HR = 1.17; 95% CI =   [1.13-1.21] per standard deviation increase in GRS; p-value =  2.86×10-16). This association was stronger among persons with at least one APOEɛ4 allele (HRGRS = 1.24; 95% CI =   [1.15-1.34]) than in others (HRGRS = 1.13; 95% CI =   [1.08-1.18]; pinteraction = 3.45×10-2). Risk prediction after seven years of follow-up showed a small improvement when adding the GRS to age, sex, APOEɛ4, and education (Δ-Cindex =  0.0043 [0.0019-0.0067]). Similar patterns were observed for IDI and NRI>0. In conclusion, a risk score incorporating common genetic variation outside the APOEɛ4 locus improved AD risk prediction and may facilitate risk stratification for prevention trials.

  16. Clustering of Risk Factors for Non-Communicable Diseases among Adolescents from Southern Brazil

    PubMed Central

    2016-01-01

    Introduction The aim of this study was to investigate the simultaneous presence of risk factors for non-communicable diseases and the association of these risk factors with demographic and economic factors among adolescents from southern Brazil. Methods The study included 916 students (14–19 years old) enrolled in the 2014 school year at state schools in São José, Santa Catarina, Brazil. Risk factors related to lifestyle (i.e., physical inactivity, excessive alcohol consumption, smoking, sedentary behaviour and unhealthy diet), demographic variables (sex, age and skin colour) and economic variables (school shift and economic level) were assessed through a questionnaire. Simultaneous behaviours were assessed by the ratio between observed and expected prevalences of risk factors for non-communicable diseases. The clustering of risk factors was analysed by multinomial logistic regression. The clusters of risk factors that showed a higher prevalence were analysed by binary logistic regression. Results The clustering of two, three, four, and five risk factors were found in 22.2%, 49.3%, 21.7% and 3.1% of adolescents, respectively. Subgroups that were more likely to have both behaviours of physical inactivity and unhealthy diet simultaneously were mostly composed of girls (OR = 3.03, 95% CI = 1.57–5.85) and those with lower socioeconomic status (OR = 1.83, 95% CI = 1.05–3.21); simultaneous physical inactivity, excessive alcohol consumption, sedentary behaviour and unhealthy diet were mainly observed among older adolescents (OR = 1.49, 95% CI = 1.05–2.12). Subgroups less likely to have both behaviours of sedentary behaviour and unhealthy diet were mostly composed of girls (OR = 0.58, 95% CI = 0.38–0.89); simultaneous physical inactivity, sedentary behaviour and unhealthy diet were mainly observed among older individuals (OR = 0.66, 95% CI = 0.49–0.87) and those of the night shift (OR = 0.59, 95% CI = 0.43–0.82). Conclusion Adolescents had a high prevalence

  17. Risk factors for ocular toxoplasmosis in Brazil.

    PubMed

    Ferreira, A I C; De Mattos, C C Brandão; Frederico, F B; Meira, C S; Almeida, G C; Nakashima, F; Bernardo, C R; Pereira-Chioccola, V L; De Mattos, L C

    2014-01-01

    The aim of this study was to investigate risk factors for ocular toxoplasmosis (OT) in patients who received medical attention at a public health service. Three hundred and forty-nine consecutive patients, treated in the Outpatient Eye Clinic of Hospital de Base, São José do Rio Preto, São Paulo state, Brazil, were enrolled in this study. After an eye examination, enzyme-linked immunosorbent assay (ELISA) was used to determine anti-Toxoplasma gondii antibodies. The results showed that 25.5% of the patients were seronegative and 74.5% were seropositive for IgG anti-T. gondii antibodies; of these 27.3% had OT and 72.7% had other ocular diseases (OOD). The presence of cats or dogs [odds ratio (OR) 2.22, 95% confidence interval (CI) 1.24-3.98, P = 0.009] and consumption of raw or undercooked meat (OR 1.77, 95% CI 1.05-2.98, P = 0.03) were associated with infection but not with the development of OT. Age (OT 48.2 ± 21.2 years vs. OOD: 69.5 ± 14.7 years, P < 0.0001) and the low level of schooling/literacy (OT vs. OOD: OR 0.414, 95% CI 0.2231-0.7692, P = 0.007) were associated with OT. The presence of dogs and cats as well as eating raw/undercooked meat increases the risk of infection, but is not associated with the development of OT.

  18. Meta-Prediction of MTHFR Gene Polymorphism Mutations and Associated Risk for Colorectal Cancer

    PubMed Central

    Yu, C. H.

    2016-01-01

    The methylenetetrahydrofolate reductase (MTHFR) gene is one of the most investigated of the genes associated with chronic human diseases because of its associations with hyperhomocysteinemia and toxicity. It has been proposed as a prototype gene for the prevention of colorectal cancer (CRC). The major objectives of this meta-analysis were to examine the polymorphism-mutation patterns of MTHFR and their associations with risk for CRC as well as potential contributing factors for mutations and disease risks. This analysis included 33,626 CRC cases and 48,688 controls across 92 studies for MTHFR 677 and 16,367 cases and 24,874 controls across 54 studies for MTHFR 1298, comprising data for various racial and ethnic groups, both genders, and multiple cancer sites. MTHFR 677 homozygous TT genotype was protective (p < .05) for CRC for all included populations; however, with heterogeneity across various racial–ethnic groups and opposing findings, it was a risk genotype for the subgroup of Hispanics (p < .01). Additional countries for which subgroup analyses resulted in 677 TT as a risk genotype included Turkey, Romania, Croatia, Hungary, Portugal, Mexico, Brazil, U.S. Hawai’i, Taiwan, India, and Egypt. Countries with the highest mutation rates and risks for both MTHFR 677 and 1298 genotypes are presented using global maps to visualize the grouping patterns. Meta-predictive analyses revealed that air pollution levels were associated with gene polymorphisms for both genotypes. Future nursing research should be conducted to develop proactive measures to protect populations in cities where air pollution causes more deaths. PMID:26858257

  19. Modelling the spatial distribution of Fasciola hepatica in bovines using decision tree, logistic regression and GIS query approaches for Brazil.

    PubMed

    Bennema, S C; Molento, M B; Scholte, R G; Carvalho, O S; Pritsch, I

    2017-11-01

    Fascioliasis is a condition caused by the trematode Fasciola hepatica. In this paper, the spatial distribution of F. hepatica in bovines in Brazil was modelled using a decision tree approach and a logistic regression, combined with a geographic information system (GIS) query. In the decision tree and the logistic model, isothermality had the strongest influence on disease prevalence. Also, the 50-year average precipitation in the warmest quarter of the year was included as a risk factor, having a negative influence on the parasite prevalence. The risk maps developed using both techniques, showed a predicted higher prevalence mainly in the South of Brazil. The prediction performance seemed to be high, but both techniques failed to reach a high accuracy in predicting the medium and high prevalence classes to the entire country. The GIS query map, based on the range of isothermality, minimum temperature of coldest month, precipitation of warmest quarter of the year, altitude and the average dailyland surface temperature, showed a possibility of presence of F. hepatica in a very large area. The risk maps produced using these methods can be used to focus activities of animal and public health programmes, even on non-evaluated F. hepatica areas.

  20. ["Hormone bomb": risks of emergency contraception from the perspective of pharmacy attendants in Rio de Janeiro, Brazil].

    PubMed

    Brandão, Elaine Reis; Cabral, Cristiane da Silva; Ventura, Miriam; Paiva, Sabrina Pereira; Bastos, Luiza Lena; Oliveira, Naira Villas Boas Vidal de; Szabo, Iolanda

    2016-09-19

    This study focused on views towards emergency contraception among pharmacy attendants in Greater Metropolitan Rio de Janeiro, Brazil. The empirical material came from a socio-anthropological study with 20 semi-structured interviews of pharmacy attendants of both sexes (8 females and 12 males). The interviews showed negative views of emergency contraception, emphasizing its potential health risks. Interviews considered emergency contraception a "hormone bomb" that can harm the female reproductive organs and other organ systems. The pharmacy attendants highlighted the risks of "uncontrolled" or "indiscriminate" use, especially by adolescents and young women. Since they considered it "dangerous" to women's bodies, they assigned the responsibility for orientation and counseling on use of the method to gynecologists rather than to pharmacists. The article discusses the need to expand the public debate on emergency contraception in Brazil to include pharmacists and pharmacy attendants, in addition to health professionals in general and teachers.

  1. Clustering of risk factors for chronic diseases among adolescents from Southern Brazil

    PubMed Central

    Dumith, Samuel C.; Muniz, Ludmila C.; Tassitano, Rafael M.; Hallal, Pedro C.; Menezes, Ana M.B.

    2012-01-01

    Objective To investigate the clustering of risk behaviors for chronic non-communicable diseases and their associated factors among adolescents from Southern Brazil. Methods In 2008, a survey was conducted with 3990 adolescents aged 14–15 years (mean: 14.3; SD: 0.6) from the 1993 Pelotas Birth Cohort Study. Clustering was determined by comparing observed (O) and expected (E) prevalence of all possible combinations of the four risk factors investigated (smoking, alcohol intake, low fruit intake, and physical inactivity). We carried out Poisson regression to evaluate the effect of individual characteristics on the presence of at least three risk behaviors. Results All risk factors tended to cluster together (O/E prevalence = 3.0), especially smoking and alcohol intake (odds ratio to present on behavior in the presence of other > 5.0). Approximately 15% of adolescents displayed three or more risk behaviors. Females (adjusted OR = 1.55), people 15 years and older (OR = 1.47), with black skin color (OR = 1.23), and of low socioeconomic level (OR = 1.29) were more likely to display three or more risk factors. Conclusion These findings suggest that lifestyle-related risk factors tend to cluster among adolescents. Identifying subgroups at greater risk of simultaneously engaging in multiple risk behaviors may aid in the planning of preventive strategies. PMID:22484392

  2. Analysis of working conditions focusing on biological risk: firefighters in Campo Grande, MS, Brazil.

    PubMed

    Contrera-Moreno, Luciana; de Andrade, Sonia Maria Oliveira; Motta-Castro, Ana Rita Coimbra; Pinto, Alexandra Maria Almeida Carvalho; Salas, Frederico Reis Pouso; Stief, Alcione Cavalheiros Faro

    2012-01-01

    Firefighters are exposed to a wide range of risks, among them, biological risk. The objective was to analyze working conditions of firefighters in the city of Campo Grande, MS, Brazil, focusing on risk conditions of exposure to biological material. Three hundred and seven (307) firefighters were interviewed for data collection and observed for ergonomic job analysis (AET). 63.5% of the firefighters suffered some kind of job related accident with blood or body fluids. Statistically significant association was found between having suffered accidents at work and incomplete use of personal protective equipment (PPE). About AET regarding the biological risks, 57.1% of all patients had blood or secretions, which corresponds in average to 16.0% of the total work time, based on a working day of 24 h. Besides biological risks, other stressing factors were identified: emergency and complexity of decision, high responsibility regarding patients and environment, and conflicts. Health promotion and accident prevention actions must be emphasized as measures to minimize these risks.

  3. Predicting stroke through genetic risk functions: the CHARGE Risk Score Project.

    PubMed

    Ibrahim-Verbaas, Carla A; Fornage, Myriam; Bis, Joshua C; Choi, Seung Hoan; Psaty, Bruce M; Meigs, James B; Rao, Madhu; Nalls, Mike; Fontes, Joao D; O'Donnell, Christopher J; Kathiresan, Sekar; Ehret, Georg B; Fox, Caroline S; Malik, Rainer; Dichgans, Martin; Schmidt, Helena; Lahti, Jari; Heckbert, Susan R; Lumley, Thomas; Rice, Kenneth; Rotter, Jerome I; Taylor, Kent D; Folsom, Aaron R; Boerwinkle, Eric; Rosamond, Wayne D; Shahar, Eyal; Gottesman, Rebecca F; Koudstaal, Peter J; Amin, Najaf; Wieberdink, Renske G; Dehghan, Abbas; Hofman, Albert; Uitterlinden, André G; Destefano, Anita L; Debette, Stephanie; Xue, Luting; Beiser, Alexa; Wolf, Philip A; Decarli, Charles; Ikram, M Arfan; Seshadri, Sudha; Mosley, Thomas H; Longstreth, W T; van Duijn, Cornelia M; Launer, Lenore J

    2014-02-01

    Beyond the Framingham Stroke Risk Score, prediction of future stroke may improve with a genetic risk score (GRS) based on single-nucleotide polymorphisms associated with stroke and its risk factors. The study includes 4 population-based cohorts with 2047 first incident strokes from 22,720 initially stroke-free European origin participants aged ≥55 years, who were followed for up to 20 years. GRSs were constructed with 324 single-nucleotide polymorphisms implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with area under the curve statistics comparing the GRS with age and sex, Framingham Stroke Risk Score models, and reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke. In the meta-analysis, adding the GRS to the Framingham Stroke Risk Score, age and sex model resulted in a significant improvement in discrimination (all stroke: Δjoint area under the curve=0.016, P=2.3×10(-6); ischemic stroke: Δjoint area under the curve=0.021, P=3.7×10(-7)), although the overall area under the curve remained low. In all the studies, there was a highly significantly improved net reclassification index (P<10(-4)). The single-nucleotide polymorphisms associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared with the classical epidemiological risk factors for stroke.

  4. A New Scoring System to Predict the Risk for High-risk Adenoma and Comparison of Existing Risk Calculators.

    PubMed

    Murchie, Brent; Tandon, Kanwarpreet; Hakim, Seifeldin; Shah, Kinchit; O'Rourke, Colin; Castro, Fernando J

    2017-04-01

    Colorectal cancer (CRC) screening guidelines likely over-generalizes CRC risk, 35% of Americans are not up to date with screening, and there is growing incidence of CRC in younger patients. We developed a practical prediction model for high-risk colon adenomas in an average-risk population, including an expanded definition of high-risk polyps (≥3 nonadvanced adenomas), exposing higher than average-risk patients. We also compared results with previously created calculators. Patients aged 40 to 59 years, undergoing first-time average-risk screening or diagnostic colonoscopies were evaluated. Risk calculators for advanced adenomas and high-risk adenomas were created based on age, body mass index, sex, race, and smoking history. Previously established calculators with similar risk factors were selected for comparison of concordance statistic (c-statistic) and external validation. A total of 5063 patients were included. Advanced adenomas, and high-risk adenomas were seen in 5.7% and 7.4% of the patient population, respectively. The c-statistic for our calculator was 0.639 for the prediction of advanced adenomas, and 0.650 for high-risk adenomas. When applied to our population, all previous models had lower c-statistic results although one performed similarly. Our model compares favorably to previously established prediction models. Age and body mass index were used as continuous variables, likely improving the c-statistic. It also reports absolute predictive probabilities of advanced and high-risk polyps, allowing for more individualized risk assessment of CRC.

  5. A principal component approach for predicting the stem volume in Eucalyptus plantations in Brazil using airborne LiDAR data

    Treesearch

    Carlos Alberto Silva; Carine Klauberg; Andrew T. Hudak; Lee A. Vierling; Veraldo Liesenberg; Samuel P. C. e Carvalho; Luiz C. E. Rodriguez

    2016-01-01

    Improving management practices in industrial forest plantations may increase production efficiencies, thereby reducing pressures on native tropical forests for meeting global pulp needs. This study aims to predict stem volume (V) in plantations of fast-growing Eucalyptus hybrid clones located in southeast Brazil using field plot and airborne Light Detection...

  6. Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement.

    PubMed

    Iglesias, Adriana I; Mihaescu, Raluca; Ioannidis, John P A; Khoury, Muin J; Little, Julian; van Duijn, Cornelia M; Janssens, A Cecile J W

    2014-05-01

    Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic RIsk Prediction Studies (GRIPS) statement. We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted. Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model. Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models. Copyright © 2014 Elsevier Inc. All rights reserved.

  7. Identification of the high risk emergency surgical patient: Which risk prediction model should be used?

    PubMed

    Stonelake, Stephen; Thomson, Peter; Suggett, Nigel

    2015-09-01

    National guidance states that all patients having emergency surgery should have a mortality risk assessment calculated on admission so that the 'high risk' patient can receive the appropriate seniority and level of care. We aimed to assess if peri-operative risk scoring tools could accurately calculate mortality and morbidity risk. Mortality risk scores for 86 consecutive emergency laparotomies, were calculated using pre-operative (ASA, Lee index) and post-operative (POSSUM, P-POSSUM and CR-POSSUM) risk calculation tools. Morbidity risk scores were calculated using the POSSUM predicted morbidity and compared against actual morbidity according to the Clavien-Dindo classification. The actual mortality was 10.5%. The average predicted risk scores for all laparotomies were: ASA 26.5%, Lee Index 2.5%, POSSUM 29.5%, P-POSSUM 18.5%, CR-POSSUM 10.5%. Complications occurred following 67 laparotomies (78%). The majority (51%) of complications were classified as Clavien-Dindo grade 2-3 (non-life-threatening). Patients having a POSSUM morbidity risk of greater than 50% developed significantly more life-threatening complications (CD 4-5) compared with those who predicted less than or equal to 50% morbidity risk (P = 0.01). Pre-operative risk stratification remains a challenge because the Lee Index under-predicts and ASA over-predicts mortality risk. Post-operative risk scoring using the CR-POSSUM is more accurate and we suggest can be used to identify patients who require intensive care post-operatively. In the absence of accurate risk scoring tools that can be used on admission to hospital it is not possible to reliably audit the achievement of national standards of care for the 'high-risk' patient.

  8. A utility/cost analysis of breast cancer risk prediction algorithms

    NASA Astrophysics Data System (ADS)

    Abbey, Craig K.; Wu, Yirong; Burnside, Elizabeth S.; Wunderlich, Adam; Samuelson, Frank W.; Boone, John M.

    2016-03-01

    Breast cancer risk prediction algorithms are used to identify subpopulations that are at increased risk for developing breast cancer. They can be based on many different sources of data such as demographics, relatives with cancer, gene expression, and various phenotypic features such as breast density. Women who are identified as high risk may undergo a more extensive (and expensive) screening process that includes MRI or ultrasound imaging in addition to the standard full-field digital mammography (FFDM) exam. Given that there are many ways that risk prediction may be accomplished, it is of interest to evaluate them in terms of expected cost, which includes the costs of diagnostic outcomes. In this work we perform an expected-cost analysis of risk prediction algorithms that is based on a published model that includes the costs associated with diagnostic outcomes (true-positive, false-positive, etc.). We assume the existence of a standard screening method and an enhanced screening method with higher scan cost, higher sensitivity, and lower specificity. We then assess expected cost of using a risk prediction algorithm to determine who gets the enhanced screening method under the strong assumption that risk and diagnostic performance are independent. We find that if risk prediction leads to a high enough positive predictive value, it will be cost-effective regardless of the size of the subpopulation. Furthermore, in terms of the hit-rate and false-alarm rate of the of the risk prediction algorithm, iso-cost contours are lines with slope determined by properties of the available diagnostic systems for screening.

  9. Intensification of cattle ranching production systems: socioeconomic and environmental synergies and risks in Brazil.

    PubMed

    Latawiec, A E; Strassburg, B B N; Valentim, J F; Ramos, F; Alves-Pinto, H N

    2014-08-01

    Intensification of Brazilian cattle ranching systems has attracted both national and international attention due to its direct relation with Amazon deforestation on the one hand and increasing demand of the global population for meat on the other. Since Brazilian cattle ranching is predominantly pasture-based, we particularly focus on pasture management. We summarize the most recurrent opportunities and risks associated with pasture intensification that are brought up within scientific and political dialogues, and discuss them within the Brazilian context. We argue that sustainable intensification of pasturelands in Brazil is a viable way to increase agricultural output while simultaneously sparing land for nature. Since environmental degradation is often associated with low-yield extensive systems in Brazil, it is possible to obtain higher yields, while reversing degradation, by adopting practices like rotational grazing, incorporation of legumes and integrated crop-livestock-forestry systems. Technical assistance is however essential, particularly for small- and medium-scale farmers. Sound complementary policies and good governance must accompany these measures so that a 'rebound effect' does not lead to increased deforestation and other adverse social and environmental impacts. It is also important that animal welfare is not compromised. Although the discussion is presented with respect to Brazil, some aspects are relevant to other developing countries.

  10. Development of a Risk Prediction Model and Clinical Risk Score for Isolated Tricuspid Valve Surgery.

    PubMed

    LaPar, Damien J; Likosky, Donald S; Zhang, Min; Theurer, Patty; Fonner, C Edwin; Kern, John A; Bolling, Stephen F; Drake, Daniel H; Speir, Alan M; Rich, Jeffrey B; Kron, Irving L; Prager, Richard L; Ailawadi, Gorav

    2018-02-01

    While tricuspid valve (TV) operations remain associated with high mortality (∼8-10%), no robust prediction models exist to support clinical decision-making. We developed a preoperative clinical risk model with an easily calculable clinical risk score (CRS) to predict mortality and major morbidity after isolated TV surgery. Multi-state Society of Thoracic Surgeons database records were evaluated for 2,050 isolated TV repair and replacement operations for any etiology performed at 50 hospitals (2002-2014). Parsimonious preoperative risk prediction models were developed using multi-level mixed effects regression to estimate mortality and composite major morbidity risk. Model results were utilized to establish a novel CRS for patients undergoing TV operations. Models were evaluated for discrimination and calibration. Operative mortality and composite major morbidity rates were 9% and 42%, respectively. Final regression models performed well (both P<0.001, AUC = 0.74 and 0.76) and included preoperative factors: age, gender, stroke, hemodialysis, ejection fraction, lung disease, NYHA class, reoperation and urgent or emergency status (all P<0.05). A simple CRS from 0-10+ was highly associated (P<0.001) with incremental increases in predicted mortality and major morbidity. Predicted mortality risk ranged from 2%-34% across CRS categories, while predicted major morbidity risk ranged from 13%-71%. Mortality and major morbidity after isolated TV surgery can be predicted using preoperative patient data from the STS Adult Cardiac Database. A simple clinical risk score predicts mortality and major morbidity after isolated TV surgery. This score may facilitate perioperative counseling and identification of suitable patients for TV surgery. Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

  11. Comparing predictions of extinction risk using models and subjective judgement

    NASA Astrophysics Data System (ADS)

    McCarthy, Michael A.; Keith, David; Tietjen, Justine; Burgman, Mark A.; Maunder, Mark; Master, Larry; Brook, Barry W.; Mace, Georgina; Possingham, Hugh P.; Medellin, Rodrigo; Andelman, Sandy; Regan, Helen; Regan, Tracey; Ruckelshaus, Mary

    2004-10-01

    Models of population dynamics are commonly used to predict risks in ecology, particularly risks of population decline. There is often considerable uncertainty associated with these predictions. However, alternatives to predictions based on population models have not been assessed. We used simulation models of hypothetical species to generate the kinds of data that might typically be available to ecologists and then invited other researchers to predict risks of population declines using these data. The accuracy of the predictions was assessed by comparison with the forecasts of the original model. The researchers used either population models or subjective judgement to make their predictions. Predictions made using models were only slightly more accurate than subjective judgements of risk. However, predictions using models tended to be unbiased, while subjective judgements were biased towards over-estimation. Psychology literature suggests that the bias of subjective judgements is likely to vary somewhat unpredictably among people, depending on their stake in the outcome. This will make subjective predictions more uncertain and less transparent than those based on models.

  12. [Rural work and health risks: a review into de "safe use" of pesticides in Brazil].

    PubMed

    de Abreu, Pedro Henrique Barbosa; Alonzo, Herling Gregorio Aguilar

    2014-10-01

    The paradigm of the "safe use" of pesticides is based on measures to control risks in the handling of these products. However, studies carried out in various regions of Brazil reveal a situation of widespread exposure and health damages among rural workers, revealing the ineffectiveness of this paradigm. This work presents a critical review of the "safe use" approach for pesticides in scientific papers published in Brazil in the past 15 years. Results indicate that these studies do not address, simultaneously, all the work activities that involve exposure and risk of intoxication (acquisition, transportation, storage, preparation and application, final disposal of empty containers and sanitization of contaminated clothes/ PPEs), nor do they comprehensively address the "safe use" measures recommended in safety manuals, which are mandatory for each activity. A total of 25 studies were selected and analyzed, revealing a high number of results and analyses regarding activities of preparation and application and final disposal of empty containers. The range of the approaches was seen to be timely in the six work activities. For future studies, a broader approach of the "safe use" of pesticides is recommended, seeking to reveal the complete infeasibility of this safety paradigm.

  13. Development and External Validation of a Melanoma Risk Prediction Model Based on Self-assessed Risk Factors.

    PubMed

    Vuong, Kylie; Armstrong, Bruce K; Weiderpass, Elisabete; Lund, Eiliv; Adami, Hans-Olov; Veierod, Marit B; Barrett, Jennifer H; Davies, John R; Bishop, D Timothy; Whiteman, David C; Olsen, Catherine M; Hopper, John L; Mann, Graham J; Cust, Anne E; McGeechan, Kevin

    2016-08-01

    Identifying individuals at high risk of melanoma can optimize primary and secondary prevention strategies. To develop and externally validate a risk prediction model for incident first-primary cutaneous melanoma using self-assessed risk factors. We used unconditional logistic regression to develop a multivariable risk prediction model. Relative risk estimates from the model were combined with Australian melanoma incidence and competing mortality rates to obtain absolute risk estimates. A risk prediction model was developed using the Australian Melanoma Family Study (629 cases and 535 controls) and externally validated using 4 independent population-based studies: the Western Australia Melanoma Study (511 case-control pairs), Leeds Melanoma Case-Control Study (960 cases and 513 controls), Epigene-QSkin Study (44 544, of which 766 with melanoma), and Swedish Women's Lifestyle and Health Cohort Study (49 259 women, of which 273 had melanoma). We validated model performance internally and externally by assessing discrimination using the area under the receiver operating curve (AUC). Additionally, using the Swedish Women's Lifestyle and Health Cohort Study, we assessed model calibration and clinical usefulness. The risk prediction model included hair color, nevus density, first-degree family history of melanoma, previous nonmelanoma skin cancer, and lifetime sunbed use. On internal validation, the AUC was 0.70 (95% CI, 0.67-0.73). On external validation, the AUC was 0.66 (95% CI, 0.63-0.69) in the Western Australia Melanoma Study, 0.67 (95% CI, 0.65-0.70) in the Leeds Melanoma Case-Control Study, 0.64 (95% CI, 0.62-0.66) in the Epigene-QSkin Study, and 0.63 (95% CI, 0.60-0.67) in the Swedish Women's Lifestyle and Health Cohort Study. Model calibration showed close agreement between predicted and observed numbers of incident melanomas across all deciles of predicted risk. In the external validation setting, there was higher net benefit when using the risk prediction

  14. Evaluation of the health risks to garment workers in the city of Xambrê-PR, Brazil.

    PubMed

    Sant'Ana, Marco Antônio; Kovalechen, Fabrício

    2012-01-01

    This study evaluated the risks for cardiovascular disease and the life habits of garment industry workers in northwestern Paraná state, Brazil. The following parameters were assessed: body composition, cardiorespiratory fitness, eating habits and physical activities by garment industry workers. Cardiovascular risk was observed in some of the studied subjects, in the form of high BMI and reduced maximal oxygen uptake. The development of a workplace quality-of-life program is suggested, aiming to stimulate the development of physical activities to improve the cardiovascular conditioning of workers.

  15. Family Factors Predicting Categories of Suicide Risk

    ERIC Educational Resources Information Center

    Randell, Brooke P.; Wang, Wen-Ling; Herting, Jerald R.; Eggert, Leona L.

    2006-01-01

    We compared family risk and protective factors among potential high school dropouts with and without suicide-risk behaviors (SRB) and examined the extent to which these factors predict categories of SRB. Subjects were randomly selected from among potential dropouts in 14 high schools. Based upon suicide-risk status, 1,083 potential high school…

  16. [Risk indicators associated with the consumption of illicit drugs by schoolchildren in a community in the south of Brazil].

    PubMed

    Backes, Dirce Stein; Zanatta, Fabrício Batistin; Costenaro, Regina Santini; Rangel, Rosiane Filipin; Vidal, Janice; Kruel, Cristina Saling; de Mattos, Karen Mallo

    2014-03-01

    This study sought to identify the risk indicators associated with the consumption of illicit drugs by schoolchildren in public schools in a community in the south of Brazil. This is a non-experimental cross-sectional study conducted with 535 students of primary schoolchildren from six public schools. Data were collected using a questionnaire between October 2011 and March 2012. The results were presented by simple and relative distribution of frequency and odds ratio (OR) and the 95% reliability intervals were calculated to verify the association between the dependent and independent variables. Multivariate analysis was also performed using the question "have you ever used illicit drugs?" Univariate analysis revealed an association between family income, color, period in which the child studied, failure to pass annual tests, use of methods of prevention, smoking habit and knowing someone who uses drugs with the fact of having experimented with the use of illicit drugs. After multivariate analysis, the smoking habit was the only indicator significantly associated with the question of having made use of illicit drugs. The results indicate that the smoking habit is an important indicator of the predictive risk for the use of illicit drugs.

  17. Patterns of Migration and Risks Associated with Leprosy among Migrants in Maranhão, Brazil

    PubMed Central

    Murto, Christine; Chammartin, Frédérique; Schwarz, Karolin; da Costa, Lea Marcia Melo; Kaplan, Charles; Heukelbach, Jorg

    2013-01-01

    Leprosy remains a public health problem in Brazil with new case incidence exceeding World Health Organization (WHO) goals in endemic clusters throughout the country. Migration can facilitate movement of disease between endemic and non-endemic areas, and has been considered a possible factor in continued leprosy incidence in Brazil. A study was conducted to investigate migration as a risk factor for leprosy. The study had three aims: (1) examine past five year migration as a risk factor for leprosy, (2) describe and compare geographic and temporal patterns of migration among past 5-year migrants with leprosy and a control group, and (3) examine social determinants of health associated with leprosy among past 5-year migrants. The study implemented a matched case-control design and analysis comparing individuals newly diagnosed with leprosy (n = 340) and a clinically unapparent control group (n = 340) without clinical signs of leprosy, matched for age, sex and location in four endemic municipalities in the state of Maranhão, northeastern Brazil. Fishers exact test was used to conduct bivariate analyses. A multivariate logistic regression analysis was employed to control for possible confounding variables. Eighty cases (23.5%) migrated 5-years prior to diagnosis, and 55 controls (16.2%) migrated 5-years prior to the corresponding case diagnosis. Past 5 year migration was found to be associated with leprosy (OR: 1.59; 95% CI 1.07–2.38; p = 0.02), and remained significantly associated with leprosy after controlling for leprosy contact in the family, household, and family/household contact. Poverty, as well as leprosy contact in the family, household and other leprosy contact, was associated with leprosy among past 5-year migrants in the bivariate analysis. Alcohol consumption was also associated with leprosy, a relevant risk factor in susceptibility to infection that should be explored in future research. Our findings provide insight into patterns of

  18. Risk factors associated with taeniasis-cysticercosis in Lagamar, Minas Gerais State, Brazil.

    PubMed

    Silva-Vergara, M L; Prata, A; Netto, H V; Vieira, C de O; Castro, J H; Micheletti, L G; Otaño, A S; Franquini Júnior, J

    1998-01-01

    An epidemiological survey was carried out in 3,344 people of an urban town in Lagamar, Minas Gerais, Brazil--during 1992-1993, to evaluate the main risk factors related to taeniasis and cysticercosis. A total number of 875 (78.9%) houses were visited and 1080 (32.3%) subjects were clinically examined. Poor sanitary conditions were positively associated with former history of taeniasis or seizures in households (p < 0.05). It was remarkable the positive relationship between taeniasis and seizures when households were questioned and subjects were clinically evaluated (p < 0.05). The relative risk of seizures was 2.3 between households and 1.7 for individuals clinically examined respectively. The breeding of swine nearby and the chronic carriers of taeniasis are determinant factors in the maintenance of the epidemiological link between taeniasis and cysticercosis in endemic areas.

  19. Predictive risk models for proximal aortic surgery

    PubMed Central

    Díaz, Rocío; Pascual, Isaac; Álvarez, Rubén; Alperi, Alberto; Rozado, Jose; Morales, Carlos; Silva, Jacobo; Morís, César

    2017-01-01

    Predictive risk models help improve decision making, information to our patients and quality control comparing results between surgeons and between institutions. The use of these models promotes competitiveness and led to increasingly better results. All these virtues are of utmost importance when the surgical operation entails high-risk. Although proximal aortic surgery is less frequent than other cardiac surgery operations, this procedure itself is more challenging and technically demanding than other common cardiac surgery techniques. The aim of this study is to review the current status of predictive risk models for patients who undergo proximal aortic surgery, which means aortic root replacement, supracoronary ascending aortic replacement or aortic arch surgery. PMID:28616348

  20. Short-range ensemble predictions based on convection perturbations in the Eta Model for the Serra do Mar region in Brazil

    NASA Astrophysics Data System (ADS)

    Bustamante, J. F. F.; Chou, S. C.; Gomes, J. L.

    2009-04-01

    The Southeast Brazil, in the coastal and mountain region called Serra do Mar, between Sao Paulo and Rio de Janeiro, is subject to frequent events of landslides and floods. The Eta Model has been producing good quality forecasts over South America at about 40-km horizontal resolution. For that type of hazards, however, more detailed and probabilistic information on the risks should be provided with the forecasts. Thus, a short-range ensemble prediction system (SREPS) based on the Eta Model is being constructed. Ensemble members derived from perturbed initial and lateral boundary conditions did not provide enough spread for the forecasts. Members with model physics perturbation are being included and tested. The objective of this work is to construct more members for the Eta SREPS by adding physics perturbed members. The Eta Model is configured at 10-km resolution and 38 layers in the vertical. The domain covered is most of Southeast Brazil, centered over the Serra do Mar region. The constructed members comprise variations of the cumulus parameterization Betts-Miller-Janjic (BMJ) and Kain-Fritsch (KF) schemes. Three members were constructed from the BMJ scheme by varying the deficit of saturation pressure profile over land and sea, and 2 members of the KF scheme were included using the standard KF and a momentum flux added to KF scheme version. One of the runs with BMJ scheme is the control run as it was used for the initial condition perturbation SREPS. The forecasts were tested for 6 cases of South America Convergence Zone (SACZ) events. The SACZ is a common summer season feature of Southern Hemisphere that causes persistent rain for a few days over the Southeast Brazil and it frequently organizes over Serra do Mar region. These events are particularly interesting because of the persistent rains that can accumulate large amounts and cause generalized landslides and death. With respect to precipitation, the KF scheme versions have shown to be able to reach the

  1. Predictive Factors for Fatal Tick-Borne Spotted Fever in Brazil.

    PubMed

    de Oliveira, S V; Willemann, M C A; Gazeta, G S; Angerami, R N; Gurgel-Gonçalves, R

    2017-11-01

    In Brazil, two pathogenic Rickettsia species have been identified causing tick-borne spotted fever (SF). The aetiological agent Rickettsia rickettsii causes serious illness, particularly in the south-eastern region of the country. Moreover, the Rickettsia sp. strain Atlantic Rainforest cause milder clinical manifestations in south-eastern, south and north-east regions. This study has sought to analyse predictive factors for fatal SF. A case-control study was performed using disease notification records in Brazil. The cases included were individuals with laboratory confirmation and fatal progression of SF, while the controls included individuals with SF who were cured. A total of 386 cases and 415 controls were identified (1 : 1.1), and the cases and controls were similar in age. The factors identified as being protective against death were reported presence of ticks (odds ratio [OR], 0.60; 95% confidence interval [CI], 0.41-0.88), residing in urban areas (OR, 0.47, 95% CI, 0.31-0.74) and presenting lymphadenopathy (OR, 0.43; 95% CI, 0.23-0.82). Males exhibited a greater chance of death (OR, 1.57; 95% CI, 1.13-2.18), as did patients who were hospitalized (OR, 10.82; 95% CI, 6.38-18.35) and who presented hypotension or shock (OR, 10.80; 95% CI, 7.33-15.93), seizures (OR, 11.24; 95% CI, 6.49-19.45) and coma (OR of 15.16; 95% CI, 8.51-27.02). The study demonstrates the severity profile of the SF cases, defined either as the frequency of hospitalization (even in cases that were cured) or as the increased frequency of the clinical complications typically found in critical patients. Opportune clinical diagnosis, a careful evaluation of the epidemiological aspects of the disease and adequate care for patients are determining factors for reducing SF fatality rates. © 2017 Blackwell Verlag GmbH.

  2. CHARCOAL-PRODUCING INDUSTRIES IN NORTHEASTERN BRAZIL

    EPA Science Inventory

    Charcoal workers in northeastern Brazil: Occupational risks and effects of exposure to wood smoke
    ABSTRACT
    Brazil has the largest production of charcoal in the world, which is used mostly in the iron and steel industries. In most of the production sites, the process is ba...

  3. Identification of the high risk emergency surgical patient: Which risk prediction model should be used?

    PubMed Central

    Stonelake, Stephen; Thomson, Peter; Suggett, Nigel

    2015-01-01

    Introduction National guidance states that all patients having emergency surgery should have a mortality risk assessment calculated on admission so that the ‘high risk’ patient can receive the appropriate seniority and level of care. We aimed to assess if peri-operative risk scoring tools could accurately calculate mortality and morbidity risk. Methods Mortality risk scores for 86 consecutive emergency laparotomies, were calculated using pre-operative (ASA, Lee index) and post-operative (POSSUM, P-POSSUM and CR-POSSUM) risk calculation tools. Morbidity risk scores were calculated using the POSSUM predicted morbidity and compared against actual morbidity according to the Clavien–Dindo classification. Results The actual mortality was 10.5%. The average predicted risk scores for all laparotomies were: ASA 26.5%, Lee Index 2.5%, POSSUM 29.5%, P-POSSUM 18.5%, CR-POSSUM 10.5%. Complications occurred following 67 laparotomies (78%). The majority (51%) of complications were classified as Clavien–Dindo grade 2–3 (non-life-threatening). Patients having a POSSUM morbidity risk of greater than 50% developed significantly more life-threatening complications (CD 4–5) compared with those who predicted less than or equal to 50% morbidity risk (P = 0.01). Discussion Pre-operative risk stratification remains a challenge because the Lee Index under-predicts and ASA over-predicts mortality risk. Post-operative risk scoring using the CR-POSSUM is more accurate and we suggest can be used to identify patients who require intensive care post-operatively. Conclusions In the absence of accurate risk scoring tools that can be used on admission to hospital it is not possible to reliably audit the achievement of national standards of care for the ‘high-risk’ patient. PMID:26468369

  4. Predicting stroke through genetic risk functions: The CHARGE risk score project

    PubMed Central

    Ibrahim-Verbaas, Carla A; Fornage, Myriam; Bis, Joshua C; Choi, Seung Hoan; Psaty, Bruce M; Meigs, James B; Rao, Madhu; Nalls, Mike; Fontes, Joao D; O’Donnell, Christopher J.; Kathiresan, Sekar; Ehret, Georg B.; Fox, Caroline S; Malik, Rainer; Dichgans, Martin; Schmidt, Helena; Lahti, Jari; Heckbert, Susan R; Lumley, Thomas; Rice, Kenneth; Rotter, Jerome I; Taylor, Kent D; Folsom, Aaron R; Boerwinkle, Eric; Rosamond, Wayne D; Shahar, Eyal; Gottesman, Rebecca F.; Koudstaal, Peter J; Amin, Najaf; Wieberdink, Renske G.; Dehghan, Abbas; Hofman, Albert; Uitterlinden, André G; DeStefano, Anita L.; Debette, Stephanie; Xue, Luting; Beiser, Alexa; Wolf, Philip A.; DeCarli, Charles; Ikram, M. Arfan; Seshadri, Sudha; Mosley, Thomas H; Longstreth, WT; van Duijn, Cornelia M; Launer, Lenore J

    2014-01-01

    Background and Purpose Beyond the Framingham Stroke Risk Score (FSRS), prediction of future stroke may improve with a genetic risk score (GRS) based on Single nucleotide polymorphisms (SNPs) associated with stroke and its risk factors. Methods The study includes four population-based cohorts with 2,047 first incident strokes from 22,720 initially stroke-free European origin participants aged 55 years and older, who were followed for up to 20 years. GRS were constructed with 324 SNPs implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with Area under the curve (AUC) statistics comparing the GRS to age sex, and FSRS models, and with reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke (IS). Results In the meta-analysis, adding the GRS to the FSRS, age and sex model resulted in a significant improvement in discrimination (All stroke: Δjoint AUC =0.016, p-value=2.3*10-6; IS: Δ joint AUC =0.021, p-value=3.7*10−7), although the overall AUC remained low. In all studies there was a highly significantly improved net reclassification index (p-values <10−4). Conclusions The SNPs associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared to the classical epidemiological risk factors for stroke. PMID:24436238

  5. Development and Validation of a Risk Model for Prediction of Hazardous Alcohol Consumption in General Practice Attendees: The PredictAL Study

    PubMed Central

    King, Michael; Marston, Louise; Švab, Igor; Maaroos, Heidi-Ingrid; Geerlings, Mirjam I.; Xavier, Miguel; Benjamin, Vicente; Torres-Gonzalez, Francisco; Bellon-Saameno, Juan Angel; Rotar, Danica; Aluoja, Anu; Saldivia, Sandra; Correa, Bernardo; Nazareth, Irwin

    2011-01-01

    Background Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. Methods A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women. Results 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). Conclusions The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse. PMID:21853028

  6. Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

    PubMed

    King, Michael; Marston, Louise; Švab, Igor; Maaroos, Heidi-Ingrid; Geerlings, Mirjam I; Xavier, Miguel; Benjamin, Vicente; Torres-Gonzalez, Francisco; Bellon-Saameno, Juan Angel; Rotar, Danica; Aluoja, Anu; Saldivia, Sandra; Correa, Bernardo; Nazareth, Irwin

    2011-01-01

    Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women. 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

  7. Attendance at day care centers increases the risk of childhood pneumonia among the urban poor in Fortaleza, Brazil.

    PubMed

    Fonseca; Kirkwood; Barros; Misago; Correia; Flores; Fuchs; Victora

    1996-04-01

    We carried out a case-control study to investigate risk factors for childhood pneumonia in two groups of 650 children aged under two years in the city of Fortaleza, Ceará, Brazil. The cases were children recruited at the main pediatric hospital with a radiological diagnosis of pneumonia, and controls were children of the same age group recruited from the neighbourhood of the cases. In this paper we focus on variables related to childcare practices. Working mothers, proportion of time the mother had worked since the child was born, and use of day care centers emerged as important risk factors with estimated relative risks of 1.58, 1.76 and 5.22, respectively. Also important were the number of children living in the house and presence of grandparents. However, the presence of siblings under two years and the birth order were not associated with pneumonia. All analysis included adjustment for confounding by income, parents' education, and other risk factors as appropriate. This is the first study from a developing country to identify attendance at day care centers as a risk factor for increased childhood morbidity, in this case pneumonia. This finding is of significant public health importance for countries such as Brazil with growing urban populations and an increasing need by mothers to find work outside the home.

  8. Carnival or football, is there a real risk for acquiring dengue fever in Brazil during holidays seasons?

    NASA Astrophysics Data System (ADS)

    Aguiar, Maíra; Rocha, Filipe; Pessanha, José Eduardo Marques; Mateus, Luis; Stollenwerk, Nico

    2015-02-01

    More than 600,000 football fans, coming from all over the world, were expected to visit Brazil during the FIFA World Cup 2014. International travel can become a public health problem when the visitors start to become sick, needing medical intervention and eventually hospitalization. The occurrence of dengue fever infections in Brazil is persistent and has been increasing since the 1980s, and the health authorities were expected to take preventive measures and to warn the visitors about the risks during the tournament period. Before the World Cup started, studies have been published stating that dengue could be a significant problem in some of the Brazilian cities hosting the games. These conclusions were taken after a brief observation of the available data, analyzing its mean and standard deviation only, or based on seasonal climate forecasts, causing alarm for the world cup in Brazil. Here, with a more careful data analysis, we show that the seasonality of the disease plays a major role in dengue transmission. The density of dengue cases in Brazil is residual during winter in the Southern hemisphere (mid June to mid September) and the fans of football were not likely to get dengue during the tournament period.

  9. Carnival or football, is there a real risk for acquiring dengue fever in Brazil during holidays seasons?

    PubMed

    Aguiar, Maíra; Rocha, Filipe; Pessanha, José Eduardo Marques; Mateus, Luis; Stollenwerk, Nico

    2015-02-16

    More than 600,000 football fans, coming from all over the world, were expected to visit Brazil during the FIFA World Cup 2014. International travel can become a public health problem when the visitors start to become sick, needing medical intervention and eventually hospitalization. The occurrence of dengue fever infections in Brazil is persistent and has been increasing since the 1980s, and the health authorities were expected to take preventive measures and to warn the visitors about the risks during the tournament period. Before the World Cup started, studies have been published stating that dengue could be a significant problem in some of the Brazilian cities hosting the games. These conclusions were taken after a brief observation of the available data, analyzing its mean and standard deviation only, or based on seasonal climate forecasts, causing alarm for the world cup in Brazil. Here, with a more careful data analysis, we show that the seasonality of the disease plays a major role in dengue transmission. The density of dengue cases in Brazil is residual during winter in the Southern hemisphere (mid June to mid September) and the fans of football were not likely to get dengue during the tournament period.

  10. International risk of yellow fever spread from the ongoing outbreak in Brazil, December 2016 to May 2017

    PubMed Central

    Dorigatti, Ilaria; Hamlet, Arran; Aguas, Ricardo; Cattarino, Lorenzo; Cori, Anne; Donnelly, Christl A; Garske, Tini; Imai, Natsuko; Ferguson, Neil M

    2017-01-01

    States in south-eastern Brazil were recently affected by the largest Yellow Fever (YF) outbreak seen in a decade in Latin America. Here we provide a quantitative assessment of the risk of travel-related international spread of YF indicating that the United States, Argentina, Uruguay, Spain, Italy and Germany may have received at least one travel-related YF case capable of seeding local transmission. Mitigating the risk of imported YF cases seeding local transmission requires heightened surveillance globally. PMID:28749337

  11. Enhanced clinical pharmacy service targeting tools: risk-predictive algorithms.

    PubMed

    El Hajji, Feras W D; Scullin, Claire; Scott, Michael G; McElnay, James C

    2015-04-01

    This study aimed to determine the value of using a mix of clinical pharmacy data and routine hospital admission spell data in the development of predictive algorithms. Exploration of risk factors in hospitalized patients, together with the targeting strategies devised, will enable the prioritization of clinical pharmacy services to optimize patient outcomes. Predictive algorithms were developed using a number of detailed steps using a 75% sample of integrated medicines management (IMM) patients, and validated using the remaining 25%. IMM patients receive targeted clinical pharmacy input throughout their hospital stay. The algorithms were applied to the validation sample, and predicted risk probability was generated for each patient from the coefficients. Risk threshold for the algorithms were determined by identifying the cut-off points of risk scores at which the algorithm would have the highest discriminative performance. Clinical pharmacy staffing levels were obtained from the pharmacy department staffing database. Numbers of previous emergency admissions and admission medicines together with age-adjusted co-morbidity and diuretic receipt formed a 12-month post-discharge and/or readmission risk algorithm. Age-adjusted co-morbidity proved to be the best index to predict mortality. Increased numbers of clinical pharmacy staff at ward level was correlated with a reduction in risk-adjusted mortality index (RAMI). Algorithms created were valid in predicting risk of in-hospital and post-discharge mortality and risk of hospital readmission 3, 6 and 12 months post-discharge. The provision of ward-based clinical pharmacy services is a key component to reducing RAMI and enabling the full benefits of pharmacy input to patient care to be realized. © 2014 John Wiley & Sons, Ltd.

  12. CSF 5-HIAA Predicts Suicide Risk after Attempted Suicide.

    ERIC Educational Resources Information Center

    Nordstrom, Peter; And Others

    1994-01-01

    Studied suicide risk after attempted suicide, as predicted by cerebrospinal fluid (CSF) monoamine metabolite concentrations, in 92 psychiatric mood disorder inpatients admitted shortly after attempting suicide. Results revealed that low CSF 5-hydroxyindoleacetic acid (5-HIAA) predicted short-range suicide risk after attempted suicide in mood…

  13. Use of comorbidity measures to predict the risk of death in Brazilian in-patients.

    PubMed

    Martins, Monica

    2010-06-01

    To assess the use of comorbidity measures to predict the risk of death in Brazilian in-patients. Data from the Sistema de Informações Hospitalares do Sistema Unico de Saúde (Unified Health System Hospital Information System) were used, which enables only one secondary diagnosis to be recorded. A total of 1,607,697 hospitalizations were selected, all of which occurred in Brazil, between 2003 and 2004, and whose main diagnoses were: ischemic heart disease, congestive cardiac failure, stroke and pneumonia. Charlson Index and Elixhauser comorbidities were the comorbidity measures used. In addition, the simple record of a certain secondary diagnosis was also used. Logistic regression was applied to assess the impact of comorbidity measures on the estimate of risk of death. The baseline model included the following variables: age, sex and main diagnosis. Models to predict death were assessed, based on C-statistic and Hosmer-Lemeshow test. Hospital mortality rate was 10.4% and mean length of stay was 5.7 days. The majority (52%) of hospitalizations occurred among men and mean age was 62.6 years. Of all hospitalizations, 5.4% included a recorded secondary diagnosis, although the odds ratio between death and presence of comorbidity was 1.93. The baseline model showed a discriminatory capacity (C-statistic) of 0.685. The improvement in the models, attributed to the introduction of comorbidity indices, was poor, equivalent to zero when C-statistic with only two digits was considered. Although the introduction of three comorbidity measures in distinct models to predict death improved the predictive capacity of the baseline model, the values obtained are still considered insufficient. The accuracy of this type of measure is influenced by the completeness of the source of information. In this sense, high underreporting of secondary diagnosis, in addition to the well-known lack of space to note down this type of information in the Sistema de Informações Hospitalares, are the

  14. Suicidality among pregnant women in Brazil: prevalence and risk factors.

    PubMed

    Castro e Couto, Tiago; Brancaglion, Mayra Yara Martins; Cardoso, Mauro Nogueira; Faria, Gustavo Coutinho; Garcia, Frederico Duarte; Nicolato, Rodrigo; Aguiar, Regina Amélia Lopes P; Leite, Henrique Vitor; Corrêa, Humberto

    2016-04-01

    Suicide is one of the major causes of preventable death. We evaluated suicidality among pregnant women who participated in prenatal care in Brazil. A total of 255 patients were assessed using semi-structured interviews as well as the Edinburgh Postnatal Depression Scale (EPDS), Beck Depression Inventory (BDI), and Mini-International Neuropsychiatric Interview (MINI) Plus. Thereafter, Stata 12 was used to identify the significant predictors of current suicide risk (CSR) among participants using univariate and multivariate analyses (p < 0.05). According to MINI Plus module C, the lifetime suicide attempt rate was 12.55%. The overall CSR was 23.53%, distributed across risk levels of low (12.55%), moderate (1.18%), and high (9.80%). Our rates approximate those found in another Brazilian study (18.4%). Antenatal depression (AD), lifetime bipolar disorder, and any current anxiety disorder (as measured using the MINI) as well as BDI scores ≥15 and EPDS scores ≥11 were identified as positive risk factors in a univariate analysis (p < 0.001). These factors changed after a multivariate analysis was employed, and only years of education [odds ratio (OR) = 0.45; 95% confidence intervals (CIs) = 0.21-0.99], AD (OR = 3.42; 95% CIs = 1.37-8.53), and EPDS scores ≥11 (OR = 4.44; 95% CIs = 1.97-9.97) remained independent risk factors. AD and other psychiatric disorders were the primary risk factors for suicidality, although only the former remained an independent factor after a multivariate analysis. More than 10 years of education and EPDS scores ≥11 were also independent factors; the latter can be used as a screening tool for suicide risk.

  15. Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability

    PubMed Central

    2011-01-01

    Background Genetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models. Methods We assessed sensitivity, specificity, and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population, the frequency of the high-risk group, and the discriminative accuracy of the risk model, as assessed by the area under the receiver-operating characteristic curve (AUC). In a simulation study, we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies, and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants. Results We show that when the frequency of the high-risk group was lower than the disease frequency, positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency, sensitivity was high but positive predictive value remained low. When both frequencies were equal, both positive predictive value and sensitivity increased with increasing AUC, but higher AUC was needed to maximize both measures. Conclusions The performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in the population. The identification of high-risk groups with appreciable combinations of sensitivity and positive predictive value requires higher AUC. PMID:21797996

  16. Risk avoidance in sympatric large carnivores: reactive or predictive?

    PubMed

    Broekhuis, Femke; Cozzi, Gabriele; Valeix, Marion; McNutt, John W; Macdonald, David W

    2013-09-01

    1. Risks of predation or interference competition are major factors shaping the distribution of species. An animal's response to risk can either be reactive, to an immediate risk, or predictive, based on preceding risk or past experiences. The manner in which animals respond to risk is key in understanding avoidance, and hence coexistence, between interacting species. 2. We investigated whether cheetahs (Acinonyx jubatus), known to be affected by predation and competition by lions (Panthera leo) and spotted hyaenas (Crocuta crocuta), respond reactively or predictively to the risks posed by these larger carnivores. 3. We used simultaneous spatial data from Global Positioning System (GPS) radiocollars deployed on all known social groups of cheetahs, lions and spotted hyaenas within a 2700 km(2) study area on the periphery of the Okavango Delta in northern Botswana. The response to risk of encountering lions and spotted hyaenas was explored on three levels: short-term or immediate risk, calculated as the distance to the nearest (contemporaneous) lion or spotted hyaena, long-term risk, calculated as the likelihood of encountering lions and spotted hyaenas based on their cumulative distributions over a 6-month period and habitat-associated risk, quantified by the habitat used by each of the three species. 4. We showed that space and habitat use by cheetahs was similar to that of lions and, to a lesser extent, spotted hyaenas. However, cheetahs avoided immediate risks by positioning themselves further from lions and spotted hyaenas than predicted by a random distribution. 5. Our results suggest that cheetah spatial distribution is a hierarchical process, first driven by resource acquisition and thereafter fine-tuned by predator avoidance; thus suggesting a reactive, rather than a predictive, response to risk. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

  17. Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool.

    PubMed

    Veeravagu, Anand; Li, Amy; Swinney, Christian; Tian, Lu; Moraff, Adrienne; Azad, Tej D; Cheng, Ivan; Alamin, Todd; Hu, Serena S; Anderson, Robert L; Shuer, Lawrence; Desai, Atman; Park, Jon; Olshen, Richard A; Ratliff, John K

    2017-07-01

    OBJECTIVE The ability to assess the risk of adverse events based on known patient factors and comorbidities would provide more effective preoperative risk stratification. Present risk assessment in spine surgery is limited. An adverse event prediction tool was developed to predict the risk of complications after spine surgery and tested on a prospective patient cohort. METHODS The spinal Risk Assessment Tool (RAT), a novel instrument for the assessment of risk for patients undergoing spine surgery that was developed based on an administrative claims database, was prospectively applied to 246 patients undergoing 257 spinal procedures over a 3-month period. Prospectively collected data were used to compare the RAT to the Charlson Comorbidity Index (CCI) and the American College of Surgeons National Surgery Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator. Study end point was occurrence and type of complication after spine surgery. RESULTS The authors identified 69 patients (73 procedures) who experienced a complication over the prospective study period. Cardiac complications were most common (10.2%). Receiver operating characteristic (ROC) curves were calculated to compare complication outcomes using the different assessment tools. Area under the curve (AUC) analysis showed comparable predictive accuracy between the RAT and the ACS NSQIP calculator (0.670 [95% CI 0.60-0.74] in RAT, 0.669 [95% CI 0.60-0.74] in NSQIP). The CCI was not accurate in predicting complication occurrence (0.55 [95% CI 0.48-0.62]). The RAT produced mean probabilities of 34.6% for patients who had a complication and 24% for patients who did not (p = 0.0003). The generated predicted values were stratified into low, medium, and high rates. For the RAT, the predicted complication rate was 10.1% in the low-risk group (observed rate 12.8%), 21.9% in the medium-risk group (observed 31.8%), and 49.7% in the high-risk group (observed 41.2%). The ACS NSQIP calculator consistently

  18. Improving Disease Prediction by Incorporating Family Disease History in Risk Prediction Models with Large-Scale Genetic Data.

    PubMed

    Gim, Jungsoo; Kim, Wonji; Kwak, Soo Heon; Choi, Hosik; Park, Changyi; Park, Kyong Soo; Kwon, Sunghoon; Park, Taesung; Won, Sungho

    2017-11-01

    Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance. Copyright © 2017 by the Genetics Society of America.

  19. Risk Perception and Occupational Accidents: A Study of Gas Station Workers in Southern Brazil

    PubMed Central

    Cezar-Vaz, Marta Regina; Rocha, Laurelize Pereira; Bonow, Clarice Alves; da Silva, Mara Regina Santos; Vaz, Joana Cezar; Cardoso, Letícia Silveira

    2012-01-01

    The present study aimed to identify the perceptions of gas station workers about physical, chemical, biological and physiological risk factors to which they are exposed in their work environment; identify types of occupational accidents involving gas station workers and; report the development of a socioenvironmental intervention as a tool for risk communication to gas station workers. A quantitative study was performed with 221 gas station workers in southern Brazil between October and December 2010. Data collection was performed between October to December 2010 via structured interviews. The data were analyzed using SPSS 19.0. The participants identified the following risk types: chemical (93.7%), physical (88.2%), physiological (64.3%) and biological (62.4%). In this sample, 94.1% of gas station workers reported occupational accidents, and 74.2% reported fuel contact with the eyes (p < 0.05). It is concluded that workers perceive risks, and that they tend to relate risks with the occurrence of occupational accidents as an indicator of the dangerous nature of their work environment. PMID:22851948

  20. International risk of yellow fever spread from the ongoing outbreak in Brazil, December 2016 to May 2017.

    PubMed

    Dorigatti, Ilaria; Hamlet, Arran; Aguas, Ricardo; Cattarino, Lorenzo; Cori, Anne; Donnelly, Christl A; Garske, Tini; Imai, Natsuko; Ferguson, Neil M

    2017-07-13

    States in south-eastern Brazil were recently affected by the largest Yellow Fever (YF) outbreak seen in a decade in Latin America. Here we provide a quantitative assessment of the risk of travel-related international spread of YF indicating that the United States, Argentina, Uruguay, Spain, Italy and Germany may have received at least one travel-related YF case capable of seeding local transmission. Mitigating the risk of imported YF cases seeding local transmission requires heightened surveillance globally. This article is copyright of The Authors, 2017.

  1. Cardiovascular risk prediction tools for populations in Asia.

    PubMed

    Barzi, F; Patel, A; Gu, D; Sritara, P; Lam, T H; Rodgers, A; Woodward, M

    2007-02-01

    Cardiovascular risk equations are traditionally derived from the Framingham Study. The accuracy of this approach in Asian populations, where resources for risk factor measurement may be limited, is unclear. To compare "low-information" equations (derived using only age, systolic blood pressure, total cholesterol and smoking status) derived from the Framingham Study with those derived from the Asian cohorts, on the accuracy of cardiovascular risk prediction. Separate equations to predict the 8-year risk of a cardiovascular event were derived from Asian and Framingham cohorts. The performance of these equations, and a subsequently "recalibrated" Framingham equation, were evaluated among participants from independent Chinese cohorts. Six cohort studies from Japan, Korea and Singapore (Asian cohorts); six cohort studies from China; the Framingham Study from the US. 172,077 participants from the Asian cohorts; 25,682 participants from Chinese cohorts and 6053 participants from the Framingham Study. In the Chinese cohorts, 542 cardiovascular events occurred during 8 years of follow-up. Both the Asian cohorts and the Framingham equations discriminated cardiovascular risk well in the Chinese cohorts; the area under the receiver-operator characteristic curve was at least 0.75 for men and women. However, the Framingham risk equation systematically overestimated risk in the Chinese cohorts by an average of 276% among men and 102% among women. The corresponding average overestimation using the Asian cohorts equation was 11% and 10%, respectively. Recalibrating the Framingham risk equation using cardiovascular disease incidence from the non-Chinese Asian cohorts led to an overestimation of risk by an average of 4% in women and underestimation of risk by an average of 2% in men. A low-information Framingham cardiovascular risk prediction tool, which, when recalibrated with contemporary data, is likely to estimate future cardiovascular risk with similar accuracy in Asian

  2. Anxiety sensitivity cognitive concerns predict suicide risk.

    PubMed

    Oglesby, Mary Elizabeth; Capron, Daniel William; Raines, Amanda Medley; Schmidt, Norman Bradley

    2015-03-30

    Anxiety sensitivity (AS) cognitive concerns, which reflects fears of mental incapacitation, have been previously associated with suicidal ideation and behavior. The first study aim was to replicate and extend upon previous research by investigating whether AS cognitive concerns can discriminate between those at low risk versus high risk for suicidal behavior. Secondly, we aimed to test the incremental predictive power of AS cognitive concerns above and beyond known suicide risk factors (i.e., thwarted belongingness and insomnia). The sample consisted of 106 individuals (75% meeting current criteria for an Axis I disorder) recruited from the community. Results revealed that AS cognitive concerns were a robust predictor of elevated suicide risk after covarying for negative affect, whereas AS social and physical concerns were not. Those with high, relative to low, AS cognitive scores were 3.67 times more likely to be in the high suicide risk group. Moreover, AS cognitive concerns significantly predicted elevated suicide risk above and beyond relevant suicide risk factors. Results of this study add to a growing body of the literature demonstrating a relationship between AS cognitive concerns and increased suicidality. Incorporating AS cognitive concerns amelioration protocols into existing interventions for suicidal behavior may be beneficial. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  3. Understanding the predictability of seasonal precipitation over northeast Brazil

    NASA Astrophysics Data System (ADS)

    Misra, Vasubandhu

    2006-05-01

    Using multiple long-term simulations of the Center for Ocean-Land-Atmosphere Studies (COLA) atmospheric general circulation model (AGCM) forced with observed sea surface temperature (SST), it is shown that the model has high skill in simulating the February-March-April (FMA) rainy season over northeast Brazil (Nordeste). Separate sensitivity experiments conducted with the same model that entails suppression of all variability except for the climatological annual cycle in SST over the Pacific and Atlantic Oceans reveal that this skill over Nordeste is sensitive to SST anomalies in the tropical Atlantic Ocean. However, the spatial pattern of SST anomalies in the tropical Atlantic Ocean that correlate with FMA Nordeste rainfall are in fact a manifestation of El Niño Southern Oscillation (ENSO) phenomenon in the Pacific Ocean. This study also analyzes the failure of the COLA AGCM in capturing the correct FMA precipitation anomalies over Nordeste in several years of the simulation. It is found that this failure occurs when the SST anomalies over the northern tropical Atlantic Ocean are large and not significantly correlated with contemporaneous SST anomalies over the eastern Pacific Ocean. In two of the relatively large ENSO years when the model failed to capture the correct signal of the interannual variability of precipitation over Nordeste, it was found that the meridional gradient of SST anomalies over the tropical Atlantic Ocean was inconsistent with the canonical development of ENSO. The analysis of the probabilistic skill of the model revealed that it has more skill in predicting flood years than drought. Furthermore, the model has no skill in predicting normal seasons. These model features are consistent with the model systematic errors.

  4. Updating Risk Prediction Tools: A Case Study in Prostate Cancer

    PubMed Central

    Ankerst, Donna P.; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J.; Feng, Ziding; Sanda, Martin G.; Partin, Alan W.; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M.

    2013-01-01

    Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [−2]proPSA measured on an external case control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network. PMID:22095849

  5. Updating risk prediction tools: a case study in prostate cancer.

    PubMed

    Ankerst, Donna P; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J; Feng, Ziding; Sanda, Martin G; Partin, Alan W; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M

    2012-01-01

    Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. Cancer mortality in Brazil

    PubMed Central

    Barbosa, Isabelle R.; de Souza, Dyego L.B.; Bernal, María M.; Costa, Íris do C.C.

    2015-01-01

    Abstract Cancer is currently in the spotlight due to their heavy responsibility as main cause of death in both developed and developing countries. Analysis of the epidemiological situation is required as a support tool for the planning of public health measures for the most vulnerable groups. We analyzed cancer mortality trends in Brazil and geographic regions in the period 1996 to 2010 and calculate mortality predictions for the period 2011 to 2030. This is an epidemiological, demographic-based study that utilized information from the Mortality Information System on all deaths due to cancer in Brazil. Mortality trends were analyzed by the Joinpoint regression, and Nordpred was utilized for the calculation of predictions. Stability was verified for the female (annual percentage change [APC] = 0.4%) and male (APC = 0.5%) sexes. The North and Northeast regions present significant increasing trends for mortality in both sexes. Until 2030, female mortality trends will not present considerable variations, but there will be a decrease in mortality trends for the male sex. There will be increases in mortality rates until 2030 for the North and Northeast regions, whereas reductions will be verified for the remaining geographic regions. This variation will be explained by the demographic structure of regions until 2030. There are pronounced regional and sex differences in cancer mortality in Brazil, and these discrepancies will continue to increase until the year 2030, when the Northeast region will present the highest cancer mortality rates in Brazil. PMID:25906105

  7. Can machine-learning improve cardiovascular risk prediction using routine clinical data?

    PubMed Central

    Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

    2017-01-01

    Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others

  8. Can machine-learning improve cardiovascular risk prediction using routine clinical data?

    PubMed

    Weng, Stephen F; Reps, Jenna; Kai, Joe; Garibaldi, Jonathan M; Qureshi, Nadeem

    2017-01-01

    Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the 'receiver operating curve' (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723-0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739-0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755-0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755-0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759-0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.

  9. Prevalence and risk factors related to preterm birth in Brazil.

    PubMed

    Leal, Maria do Carmo; Esteves-Pereira, Ana Paula; Nakamura-Pereira, Marcos; Torres, Jacqueline Alves; Theme-Filha, Mariza; Domingues, Rosa Maria Soares Madeira; Dias, Marcos Augusto Bastos; Moreira, Maria Elizabeth; Gama, Silvana Granado

    2016-10-17

    The rate of preterm birth has been increasing worldwide, including in Brazil. This constitutes a significant public health challenge because of the higher levels of morbidity and mortality and long-term health effects associated with preterm birth. This study describes and quantifies factors affecting spontaneous and provider-initiated preterm birth in Brazil. Data are from the 2011-2012 "Birth in Brazil" study, which used a national population-based sample of 23,940 women. We analyzed the variables following a three-level hierarchical methodology. For each level, we performed non-conditional multiple logistic regression for both spontaneous and provider-initiated preterm birth. The rate of preterm birth was 11.5 %, (95 % confidence 10.3 % to 12.9 %) 60.7 % spontaneous - with spontaneous onset of labor or premature preterm rupture of membranes - and 39.3 % provider-initiated, with more than 90 % of the last group being pre-labor cesarean deliveries. Socio-demographic factors associated with spontaneous preterm birth were adolescent pregnancy, low total years of schooling, and inadequate prenatal care. Other risk factors were previous preterm birth (OR 3.74; 95 % CI 2.92-4.79), multiple pregnancy (OR 16.42; 95 % CI 10.56-25.53), abruptio placentae (OR 2.38; 95 % CI 1.27-4.47) and infections (OR 4.89; 95 % CI 1.72-13.88). In contrast, provider-initiated preterm birth was associated with private childbirth healthcare (OR 1.47; 95 % CI 1.09-1.97), advanced-age pregnancy (OR 1.27; 95 % CI 1.01-1.59), two or more prior cesarean deliveries (OR 1.64; 95 % CI 1.19-2.26), multiple pregnancy (OR 20.29; 95 % CI 12.58-32.72) and any maternal or fetal pathology (OR 6.84; 95 % CI 5.56-8.42). The high proportion of provider-initiated preterm birth and its association with prior cesarean deliveries and all of the studied maternal/fetal pathologies suggest that a reduction of this type of prematurity may be possible. The association of spontaneous preterm birth with

  10. Strategies to predict rheumatoid arthritis development in at-risk populations

    PubMed Central

    van der Helm-van Mil, Annette H.

    2016-01-01

    The development of RA is conceived as a multiple hit process and the more hits that are acquired, the greater the risk of developing clinically apparent RA. Several at-risk phases have been described, including the presence of genetic and environmental factors, RA-related autoantibodies and biomarkers and symptoms. Intervention in these preclinical phases may be more effective compared with intervention in the clinical phase. One prerequisite for preventive strategies is the ability to estimate an individual’s risk adequately. This review evaluates the ability to predict the risk of RA in the various preclinical stages. Present data suggest that a combination of genetic and environmental factors is helpful to identify persons at high risk of RA among first-degree relatives. Furthermore, a combination of symptoms, antibody characteristics and environmental factors has been shown to be relevant for risk prediction in seropositive arthralgia patients. Large prospective studies are needed to validate and improve risk prediction in preclinical disease stages. PMID:25096602

  11. Spirometry: predicting risk and outcome.

    PubMed

    Brunelli, Alessandro; Rocco, Gaetano

    2008-02-01

    Predicted postoperative FEV1 is certainly the most widely used parameter in preoperative risk stratification [54] and the measure recommend by BTS and ACCP functional guidelines as a first step in the screening of patients for lung resection surgery. Nevertheless, recent evidences have demonstrated that ppoFEV1 is not a reliable predictor of postoperative cardiopulmonary complications in patients with preoperative impaired pulmonary function. This may be because of the fact that the resection of a portion of lung in patients with obstructive disease determines only a minimal loss, or even an improvement, in overall respiratory function and exercise tolerance. This lung volume reduction effect takes place very early, since the first postoperative days, balancing what ever negative physiologic effects a thoracotomy and lung resection may entail. In addition to its poor predictive role in COPD patients, ppoFEV1 largely underestimate the actual loss in the very first days after operation, when most of the complications develop. The rationale to use a parameter which is poorly correlated with the pulmonary function at the moment the complications occur seems unwarranted. At the very best, ppoFEV1 appears a weak surrogate of the immediate postoperative FEV1. The FEV1 measured on the first postoperative day may be 30% less than predicted. Corrective equations have been published to correct this discrepancy with the aim to improve risk stratification.

  12. Predicting relapse risk in childhood acute lymphoblastic leukaemia.

    PubMed

    Teachey, David T; Hunger, Stephen P

    2013-09-01

    Intensive multi-agent chemotherapy regimens and the introduction of risk-stratified therapy have substantially improved cure rates for children with acute lymphoblastic leukaemia (ALL). Current risk allocation schemas are imperfect, as some children are classified as lower-risk and treated with less intensive therapy relapse, while others deemed higher-risk are probably over-treated. Most cooperative groups previously used morphological clearance of blasts in blood and marrow during the initial phases of chemotherapy as a primary factor for risk group allocation; however, this has largely been replaced by the detection of minimal residual disease (MRD). Other than age and white blood cell count (WBC) at presentation, many clinical variables previously used for risk group allocation are no longer prognostic, as MRD and the presence of sentinel genetic lesions are more reliable at predicting outcome. Currently, a number of sentinel genetic lesions are used by most cooperative groups for risk stratification; however, in the near future patients will probably be risk-stratified using genomic signatures and clustering algorithms, rather than individual genetic alterations. This review will describe the clinical, biological, and response-based features known to predict relapse risk in childhood ALL, including those currently used and those likely to be used in the near future to risk-stratify therapy. © 2013 John Wiley & Sons Ltd.

  13. Risk determination after an acute myocardial infarction: review of 3 clinical risk prediction tools.

    PubMed

    Scruth, Elizabeth Ann; Page, Karen; Cheng, Eugene; Campbell, Michelle; Worrall-Carter, Linda

    2012-01-01

    The objective of the study was to provide comprehensive information for the clinical nurse specialist (CNS) on commonly used clinical prediction (risk assessment) tools used to estimate risk of a secondary cardiac or noncardiac event and mortality in patients undergoing primary percutaneous coronary intervention (PCI) for ST-elevation myocardial infarction (STEMI). The evolution and widespread adoption of primary PCI represent major advances in the treatment of acute myocardial infarction, specifically STEMI. The American College of Cardiology and the American Heart Association have recommended early risk stratification for patients presenting with acute coronary syndromes using several clinical risk scores to identify patients' mortality and secondary event risk after PCI. Clinical nurse specialists are integral to any performance improvement strategy. Their knowledge and understandings of clinical prediction tools will be essential in carrying out important assessment, identifying and managing risk in patients who have sustained a STEMI, and enhancing discharge education including counseling on medications and lifestyle changes. Over the past 2 decades, risk scores have been developed from clinical trials to facilitate risk assessment. There are several risk scores that can be used to determine in-hospital and short-term survival. This article critiques the most common tools: the Thrombolytic in Myocardial Infarction risk score, the Global Registry of Acute Coronary Events risk score, and the Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications risk score. The importance of incorporating risk screening assessment tools (that are important for clinical prediction models) to guide therapeutic management of patients cannot be underestimated. The ability to forecast secondary risk after a STEMI will assist in determining which patients would require the most aggressive level of treatment and monitoring postintervention including

  14. Prevalence and risk behaviour for human immunodeficiency virus 1 infection in Marajó Island, Northern Brazil.

    PubMed

    Vallinoto, Antonio C R; Aguiar, Samantha; Sá, Keyla G; Freitas, Felipe Bonfim; Ferreira, Glenda; Lima, Sandra Souza; Hermes, Renata Bezerra; Machado, Luiz Fernando Almeida; Cayres-Vallinoto, Izaura; Ishak, Marluísa; Ishak, Ricardo

    2016-07-01

    Human immunodeficiency virus 1 (HIV-1) infection is a global public health problem, but, so far, there is no published information regarding the epidemiology of HIV-1 in Marajó Archipelago (Pará, Brazil). The present study reports the occurrence of infection by HIV-1 in four municipalities of the Marajó Island, Pará, Brazil. A total of 1877 samples were collected from volunteer blood donors (1296 women and 551 men) living in the municipalities of Anajás, Chaves, Portel and São Sebastião da Boa Vista. Information about risk behaviour assessment was obtained from a questionnaire. Plasma samples were tested for the presence of anti-HIV antibodies using serological tests. The infection was confirmed by nucleic acid amplification assays. Twelve samples were seropositive for HIV by ELISA. Western blot analysis showed four positive samples, eight indeterminate patterns and one found to be negative. Molecular analysis revealed three positive samples. Risk factors for HIV-1 infection included absence of condoms during sexual intercourse (41.3%, São Sebastião da Boa Vista), use of illicit drugs (5.8%, Anajás) and early initiation of sexual activities, from 10-15 years (30.7%). Although the study indicates a low HIV-1 prevalence in Marajó Island, some factors may increase the risk for HIV-1 and these include early sexual initiation, unprotected sexual intercourse and the use of illicit drugs.

  15. Decreasing flood risk perception in Porto Alegre - Brazil and its influence on water resource management decisions

    NASA Astrophysics Data System (ADS)

    Allasia, D. G.; Tassi, R.; Bemfica, D.; Goldenfum, J. A.

    2015-06-01

    Porto Alegre is the capital and largest city in the Brazilian state of Rio Grande do Sul in Southern Brazil with approximately 1.5 million inhabitants. The city lies on the eastern bank of the Guaiba Lake, formed by the convergence of five rivers and leading to the Lagoa dos Patos, a giant freshwater lagoon navigable by even the largest of ships. This river junction has become an important alluvial port as well as a chief industrial and commercial centre. However, this strategic location resulted in severe damage because of its exposure to flooding from the river system, affecting the city in the years 1873, 1928, 1936, 1941 and 1967. In order to reduce flood risk, a complex system of levees and pump stations was implemented during 1960s and 1970s. Since its construction, not a single large flood event occurred. However, in recent years, the levees in the downtown region of Porto Alegre were severally criticized by city planners and population. Several projects have been proposed to demolish the Mauá Wall due to the false perception of lack of flood risk. Similar opinions and reactions against flood infrastructure have been observed in other cities in Brazil, such as Itajaí and Blumenau, with disastrous consequences. This paper illustrates how the perception of flood risk in Porto Alegre has changed over recent years as a result of flood infrastructure, and how such changes in perceptions can influence water management decisions.

  16. Risk factors for Trypanosoma cruzi infection among blood donors in central Brazil.

    PubMed

    Martelli, C M; Andrade, A L; Silva, S A; Zicker, F

    1992-01-01

    Characteristics and possible risk factors associated with Trypanosoma cruzi infection among blood donors were assessed within a routine screening programme in blood banks in an endemic area of Chagas disease. 6,172 voluntary blood donors were interviewed and tested for anti-T. cruzi antibodies by Haemagglutination and Complement Fixation tests in six blood banks in Goiânia-Central Brazil from October 1988 to April 1989. An overall prevalence of 2.3% for T. cruzi infection was obtained, being 3.3% for first-time blood donors and 1.9% for regular ones (p < 0.01). Considering this seropositivity among regular blood donors, selection of candidates relying only on the history of previous donation was found to be inadequate. The risk of infection increased inversely with the degrees of education and monthly income. There was a 9.2 risk of infection (95% CI 3.8-22.6) for those who had lived more than 21 years in an endemic area compared to subjects who had never lived in rural settings, after multivariate analysis. These informations may help to review the criteria of selection of donors in order to improve quality of blood products in endemic areas.

  17. Prediction and Informative Risk Factor Selection of Bone Diseases.

    PubMed

    Li, Hui; Li, Xiaoyi; Ramanathan, Murali; Zhang, Aidong

    2015-01-01

    With the booming of healthcare industry and the overwhelming amount of electronic health records (EHRs) shared by healthcare institutions and practitioners, we take advantage of EHR data to develop an effective disease risk management model that not only models the progression of the disease, but also predicts the risk of the disease for early disease control or prevention. Existing models for answering these questions usually fall into two categories: the expert knowledge based model or the handcrafted feature set based model. To fully utilize the whole EHR data, we will build a framework to construct an integrated representation of features from all available risk factors in the EHR data and use these integrated features to effectively predict osteoporosis and bone fractures. We will also develop a framework for informative risk factor selection of bone diseases. A pair of models for two contrast cohorts (e.g., diseased patients versus non-diseased patients) will be established to discriminate their characteristics and find the most informative risk factors. Several empirical results on a real bone disease data set show that the proposed framework can successfully predict bone diseases and select informative risk factors that are beneficial and useful to guide clinical decisions.

  18. Child and environmental risk factors predicting readiness for learning in children at high risk of dyslexia.

    PubMed

    Dilnot, Julia; Hamilton, Lorna; Maughan, Barbara; Snowling, Margaret J

    2017-02-01

    We investigate the role of distal, proximal, and child risk factors as predictors of reading readiness and attention and behavior in children at risk of dyslexia. The parents of a longitudinal sample of 251 preschool children, including children at family risk of dyslexia and children with preschool language difficulties, provided measures of socioeconomic status, home literacy environment, family stresses, and child health via interviews and questionnaires. Assessments of children's reading-related skills, behavior, and attention were used to define their readiness for learning at school entry. Children at family risk of dyslexia and children with preschool language difficulties experienced more environmental adversities and health risks than controls. The risks associated with family risk of dyslexia and with language status were additive. Both home literacy environment and child health predicted reading readiness while home literacy environment and family stresses predicted attention and behavior. Family risk of dyslexia did not predict readiness to learn once other risks were controlled and so seems likely to be best conceptualized as representing gene-environment correlations. Pooling across risks defined a cumulative risk index, which was a significant predictor of reading readiness and, together with nonverbal ability, accounted for 31% of the variance between children.

  19. Predictive accuracy of combined genetic and environmental risk scores.

    PubMed

    Dudbridge, Frank; Pashayan, Nora; Yang, Jian

    2018-02-01

    The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores. © 2017 WILEY PERIODICALS, INC.

  20. Predictive accuracy of combined genetic and environmental risk scores

    PubMed Central

    Pashayan, Nora; Yang, Jian

    2017-01-01

    ABSTRACT The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores. PMID:29178508

  1. Long-term cortisol measures predict Alzheimer disease risk.

    PubMed

    Ennis, Gilda E; An, Yang; Resnick, Susan M; Ferrucci, Luigi; O'Brien, Richard J; Moffat, Scott D

    2017-01-24

    To examine whether long-term measures of cortisol predict Alzheimer disease (AD) risk. We used a prospective longitudinal design to examine whether cortisol dysregulation was related to AD risk. Participants were from the Baltimore Longitudinal Study of Aging (BLSA) and submitted multiple 24-hour urine samples over an average interval of 10.56 years. Urinary free cortisol (UFC) and creatinine (Cr) were measured, and a UFC/Cr ratio was calculated to standardize UFC. To measure cortisol regulation, we used within-person UFC/Cr level (i.e., within-person mean), change in UFC/Cr over time (i.e., within-person slope), and UFC/Cr variability (i.e., within-person coefficient of variation). Cox regression was used to assess whether UFC/Cr measures predicted AD risk. UFC/Cr level and UFC/Cr variability, but not UFC/Cr slope, were significant predictors of AD risk an average of 2.9 years before AD onset. Elevated UFC/Cr level and elevated UFC/Cr variability were related to a 1.31- and 1.38-times increase in AD risk, respectively. In a sensitivity analysis, increased UFC/Cr level and increased UFC/Cr variability predicted increased AD risk an average of 6 years before AD onset. Cortisol dysregulation as manifested by high UFC/Cr level and high UFC/Cr variability may modulate the downstream clinical expression of AD pathology or be a preclinical marker of AD. © 2016 American Academy of Neurology.

  2. Testing the Predictive Validity of the Hendrich II Fall Risk Model.

    PubMed

    Jung, Hyesil; Park, Hyeoun-Ae

    2018-03-01

    Cumulative data on patient fall risk have been compiled in electronic medical records systems, and it is possible to test the validity of fall-risk assessment tools using these data between the times of admission and occurrence of a fall. The Hendrich II Fall Risk Model scores assessed during three time points of hospital stays were extracted and used for testing the predictive validity: (a) upon admission, (b) when the maximum fall-risk score from admission to falling or discharge, and (c) immediately before falling or discharge. Predictive validity was examined using seven predictive indicators. In addition, logistic regression analysis was used to identify factors that significantly affect the occurrence of a fall. Among the different time points, the maximum fall-risk score assessed between admission and falling or discharge showed the best predictive performance. Confusion or disorientation and having a poor ability to rise from a sitting position were significant risk factors for a fall.

  3. Different type 2 diabetes risk assessments predict dissimilar numbers at 'high risk': a retrospective analysis of diabetes risk-assessment tools.

    PubMed

    Gray, Benjamin J; Bracken, Richard M; Turner, Daniel; Morgan, Kerry; Thomas, Michael; Williams, Sally P; Williams, Meurig; Rice, Sam; Stephens, Jeffrey W

    2015-12-01

    Use of a validated risk-assessment tool to identify individuals at high risk of developing type 2 diabetes is currently recommended. It is under-reported, however, whether a different risk tool alters the predicted risk of an individual. This study explored any differences between commonly used validated risk-assessment tools for type 2 diabetes. Cross-sectional analysis of individuals who participated in a workplace-based risk assessment in Carmarthenshire, South Wales. Retrospective analysis of 676 individuals (389 females and 287 males) who participated in a workplace-based diabetes risk-assessment initiative. Ten-year risk of type 2 diabetes was predicted using the validated QDiabetes(®), Leicester Risk Assessment (LRA), FINDRISC, and Cambridge Risk Score (CRS) algorithms. Differences between the risk-assessment tools were apparent following retrospective analysis of individuals. CRS categorised the highest proportion (13.6%) of individuals at 'high risk' followed by FINDRISC (6.6%), QDiabetes (6.1%), and, finally, the LRA was the most conservative risk tool (3.1%). Following further analysis by sex, over one-quarter of males were categorised at high risk using CRS (25.4%), whereas a greater percentage of females were categorised as high risk using FINDRISC (7.8%). The adoption of a different valid risk-assessment tool can alter the predicted risk of an individual and caution should be used to identify those individuals who really are at high risk of type 2 diabetes. © British Journal of General Practice 2015.

  4. Predictive Modeling of Risk Factors and Complications of Cataract Surgery

    PubMed Central

    Gaskin, Gregory L; Pershing, Suzann; Cole, Tyler S; Shah, Nigam H

    2016-01-01

    Purpose To quantify the relationship between aggregated preoperative risk factors and cataract surgery complications, as well as to build a model predicting outcomes on an individual-level—given a constellation of demographic, baseline, preoperative, and intraoperative patient characteristics. Setting Stanford Hospital and Clinics between 1994 and 2013. Design Retrospective cohort study Methods Patients age 40 or older who received cataract surgery between 1994 and 2013. Risk factors, complications, and demographic information were extracted from the Electronic Health Record (EHR), based on International Classification of Diseases, 9th edition (ICD-9) codes, Current Procedural Terminology (CPT) codes, drug prescription information, and text data mining using natural language processing. We used a bootstrapped least absolute shrinkage and selection operator (LASSO) model to identify highly-predictive variables. We built random forest classifiers for each complication to create predictive models. Results Our data corroborated existing literature on postoperative complications—including the association of intraoperative complications, complex cataract surgery, black race, and/or prior eye surgery with an increased risk of any postoperative complications. We also found a number of other, less well-described risk factors, including systemic diabetes mellitus, young age (<60 years old), and hyperopia as risk factors for complex cataract surgery and intra- and post-operative complications. Our predictive models based on aggregated outperformed existing published models. Conclusions The constellations of risk factors and complications described here can guide new avenues of research and provide specific, personalized risk assessment for a patient considering cataract surgery. The predictive capacity of our models can enable risk stratification of patients, which has utility as a teaching tool as well as informing quality/value-based reimbursements. PMID:26692059

  5. Supplementation with Brazil nuts and green tea extract regulates targeted biomarkers related to colorectal cancer risk in humans.

    PubMed

    Hu, Ying; McIntosh, Graeme H; Le Leu, Richard K; Somashekar, Roshini; Meng, Xing Q; Gopalsamy, Geetha; Bambaca, Libby; McKinnon, Ross A; Young, Graeme P

    2016-12-01

    Se and green tea have been shown in epidemiological, observational and preclinical studies to be inversely related to the risk of developing colorectal cancer (CRC). However, there are limited studies to evaluate their regulatory effects on genes/proteins that relate to CRC oncogenesis in human subjects, such as selenoproteins, WNT signalling pathway, inflammation and methylation. This study examined the effects of supplementation of Se using Brazil nuts and green tea extract (GTE) capsules, alone and in combination, on targeted biomarkers. In total, thirty-two volunteers (>50 years of age) with plasma Se≤1·36 µmol/l were randomised to one of three treatment groups: nine to Se (approximately 48 µg/d) as six Brazil nuts, eleven to four GTE capsules (800 mg (-)-epigallocatechin-3-gallate) and twelve to a combination of Brazil nuts and GTE. Blood and rectal biopsies were obtained before and after each intervention. Plasma Se levels, rectal selenoprotein P (SePP) and β-catenin mRNA increased significantly in subjects consuming Brazil nuts alone or in combination, whereas rectal DNA methyltransferase (DNMT1) and NF-κB mRNA were reduced significantly in subjects consuming GTE alone or in combination. None of the interventions significantly affected rectal acetylated histone H3 or Ki-67 expression at the protein level or plasma C-reactive protein. Effects of the combination of Brazil nuts and GTE did not differ from what would be expected from either agent alone. In conclusion, supplementation of Brazil nuts and/or GTE regulates targeted biomarkers related to CRC oncogenesis, specifically genes associated with selenoproteins (SePP), WNT signalling (β-catenin), inflammation (NF-κB) and methylation (DNMT1). Their combination does not appear to provide additional effects compared with either agent alone.

  6. Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status.

    PubMed

    Hüsing, Anika; Canzian, Federico; Beckmann, Lars; Garcia-Closas, Montserrat; Diver, W Ryan; Thun, Michael J; Berg, Christine D; Hoover, Robert N; Ziegler, Regina G; Figueroa, Jonine D; Isaacs, Claudine; Olsen, Anja; Viallon, Vivian; Boeing, Heiner; Masala, Giovanna; Trichopoulos, Dimitrios; Peeters, Petra H M; Lund, Eiliv; Ardanaz, Eva; Khaw, Kay-Tee; Lenner, Per; Kolonel, Laurence N; Stram, Daniel O; Le Marchand, Loïc; McCarty, Catherine A; Buring, Julie E; Lee, I-Min; Zhang, Shumin; Lindström, Sara; Hankinson, Susan E; Riboli, Elio; Hunter, David J; Henderson, Brian E; Chanock, Stephen J; Haiman, Christopher A; Kraft, Peter; Kaaks, Rudolf

    2012-09-01

    There is increasing interest in adding common genetic variants identified through genome wide association studies (GWAS) to breast cancer risk prediction models. First results from such models showed modest benefits in terms of risk discrimination. Heterogeneity of breast cancer as defined by hormone-receptor status has not been considered in this context. In this study we investigated the predictive capacity of 32 GWAS-detected common variants for breast cancer risk, alone and in combination with classical risk factors, and for tumours with different hormone receptor status. Within the Breast and Prostate Cancer Cohort Consortium, we analysed 6009 invasive breast cancer cases and 7827 matched controls of European ancestry, with data on classical breast cancer risk factors and 32 common gene variants identified through GWAS. Discriminatory ability with respect to breast cancer of specific hormone receptor-status was assessed with the age adjusted and cohort-adjusted concordance statistic (AUROC(a)). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement was used to measure improvements in risk prediction. We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROC(a) going from 2.7% to 4%). Discriminatory ability for all models varied strongly by hormone receptor status. Adding information on common polymorphisms provides small but statistically significant improvements in the quality of breast cancer risk prediction models. We consistently observed better performance for receptor-positive cases, but the gain in discriminatory quality is not sufficient for clinical application.

  7. Violence risk prediction. Clinical and actuarial measures and the role of the Psychopathy Checklist.

    PubMed

    Dolan, M; Doyle, M

    2000-10-01

    Violence risk prediction is a priority issue for clinicians working with mentally disordered offenders. To review the current status of violence risk prediction research. Literature search (Medline). Key words: violence, risk prediction, mental disorder. Systematic/structured risk assessment approaches may enhance the accuracy of clinical prediction of violent outcomes. Data on the predictive validity of available clinical risk assessment tools are based largely on American and North American studies and further validation is required in British samples. The Psychopathy Checklist appears to be a key predictor of violent recidivism in a variety of settings. Violence risk prediction is an inexact science and as such will continue to provoke debate. Clinicians clearly need to be able to demonstrate the rationale behind their decisions on violence risk and much can be learned from recent developments in research on violence risk prediction.

  8. Evaluation of polygenic risk scores for predicting breast and prostate cancer risk.

    PubMed

    Machiela, Mitchell J; Chen, Chia-Yen; Chen, Constance; Chanock, Stephen J; Hunter, David J; Kraft, Peter

    2011-09-01

    Recently, polygenic risk scores (PRS) have been shown to be associated with certain complex diseases. The approach has been based on the contribution of counting multiple alleles associated with disease across independent loci, without requiring compelling evidence that every locus had already achieved definitive genome-wide statistical significance. Whether PRS assist in the prediction of risk of common cancers is unknown. We built PRS from lists of genetic markers prioritized by their association with breast cancer (BCa) or prostate cancer (PCa) in a training data set and evaluated whether these scores could improve current genetic prediction of these specific cancers in independent test samples. We used genome-wide association data on 1,145 BCa cases and 1,142 controls from the Nurses' Health Study and 1,164 PCa cases and 1,113 controls from the Prostate Lung Colorectal and Ovarian Cancer Screening Trial. Ten-fold cross validation was used to build and evaluate PRS with 10 to 60,000 independent single nucleotide polymorphisms (SNPs). For both BCa and PCa, the models that included only published risk alleles maximized the cross-validation estimate of the area under the ROC curve (0.53 for breast and 0.57 for prostate). We found no significant evidence that PRS using common variants improved risk prediction for BCa and PCa over replicated SNP scores. © 2011 Wiley-Liss, Inc.

  9. Variation of surface ozone in Campo Grande, Brazil: meteorological effect analysis and prediction.

    PubMed

    Pires, J C M; Souza, A; Pavão, H G; Martins, F G

    2014-09-01

    The effect of meteorological variables on surface ozone (O3) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O3 concentration, relative humidity and solar radiation. The threshold model that considers two O3 regimes was the one that correctly describes the effect of important meteorological variables in O3 behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O3 regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O3 concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O3 behaviour, being useful to define policy strategies for human health protection regarding air pollution.

  10. New methods for fall risk prediction.

    PubMed

    Ejupi, Andreas; Lord, Stephen R; Delbaere, Kim

    2014-09-01

    Accidental falls are the leading cause of injury-related death and hospitalization in old age, with over one-third of the older adults experiencing at least one fall or more each year. Because of limited healthcare resources, regular objective fall risk assessments are not possible in the community on a large scale. New methods for fall prediction are necessary to identify and monitor those older people at high risk of falling who would benefit from participating in falls prevention programmes. Technological advances have enabled less expensive ways to quantify physical fall risk in clinical practice and in the homes of older people. Recently, several studies have demonstrated that sensor-based fall risk assessments of postural sway, functional mobility, stepping and walking can discriminate between fallers and nonfallers. Recent research has used low-cost, portable and objective measuring instruments to assess fall risk in older people. Future use of these technologies holds promise for assessing fall risk accurately in an unobtrusive manner in clinical and daily life settings.

  11. Prevalence and risk factors associated with Tritrichomonas foetus infection in cattle in the state of Paraíba, Brazil.

    PubMed

    Filho, Ruy Brayner de Oliveira; Malta, Karla Campos; Borges, Jonas de Melo; Oliveira, Pollyanne Raysa Fernandes de; Filho, Givanildo Jacinto Dos Santos; Nascimento, Glaucia Grazielle; Mota, Rinaldo Aparecido; Júnior, José Wilton Pinheiro

    2018-06-26

    The objective of this study was to determine the prevalence of Tritrichomonas foetus infection and to evaluate risk factors associated with this infection among cattle in the state of Paraíba in northeastern Brazil. Samples of cervicovaginal mucus from 290 females and smegma from 59 males [beef, 31; mixed aptitude (beef and dairy), 10; and dairy, 18] from 31 farms were collected. Modified Diamond's medium and polymerase chain reaction (PCR) were used for the laboratory diagnosis of T. foetus infection. Univariate analysis and logistic regression were performed to test for potential risk factors in addition to prevalence mapping. No sample was positive for T. foetus in culture, and the prevalence of T. foetus infection using PCR was 3.7% (13/349) [confidence interval (CI) 95%, 2.1%-6.4%]. In total, 19.3% (6/31) of the farms had at least one animal positive for T. foetus. The contact of females with males from other farms [Odds ratio, 5.9; 95% CI, 1.5-22.4; p = 0.009] was identified as a risk factor for T. foetus infection. This study demonstrates that T. foetus infection is prevalent among dairy cows in the state of Paraíba, Brazil. Sexual resting, removal of positive females, and avoiding contact of females with males from other farms are recommended to reduce the risk of infection.

  12. Comparing HIV risk-related behaviors between 2 RDS national samples of MSM in Brazil, 2009 and 2016.

    PubMed

    Guimarães, Mark Drew Crosland; Kendall, Carl; Magno, Laio; Rocha, Gustavo Machado; Knauth, Daniela Riva; Leal, Andrea Fachel; Dourado, Ines; Veras, Maria Amélia; Brito, Ana Maria de; Kerr, Ligia Regina Franco Sansigolo

    2018-05-01

    Periodic monitoring of sociobehavior characteristics at a national level is an essential component of understanding the dynamics the human immunodeficiency virus (HIV) epidemic worldwide, including Brazil. This paper compares descriptive sociobehavior characteristics in 2 national cross-sectional HIV biological behavioral surveillance surveys (BBSS) conducted in 2009 and 2016 among men who have sex with men (MSM) in Brazil. Respondent driven sampling (RDS) was used for recruitment in both years. Overall proportions were weighted according to Gile's estimator using RDS Analyst Software and 95% confidence intervals were calculated for comparisons between the 2 periods. Further comparisons were stratified by age groups (<25 and 25+ years old). Overall, 3749 and 4176 MSM were recruited in 2009 and 2016, respectively. In 2016, participants were younger than 25 years old (58.3%), with 12 or more years of education (70.4%), with higher socioeconomic status (40.7%), and had a higher proportion of whites (31.8%), as compared to 2009. Also, participants in 2016 reported less alcohol use and binge drinking, but used illicit drugs more frequently. There was an increase among MSM who self-reported their HIV risk as low and had low HIV knowledge while the proportion of those who were never tested for HIV dropped from 49.8% in 2009 to 33.8% in 2016. Although more than three-quarters received free condoms in both years, STD counseling remained low (32% and 38% for 2009 and 2016, respectively). Sexual risk behavior remained at high levels, especially unprotected anal receptive sex and sex with multiple partners. Younger MSM (<25 years old) showed riskier sexual practices than those 25+ years old, when comparing 2016 to 2009. Our results indicate a worrisome risk behavior trend among Brazilian MSM, especially among younger ones. These results can contribute for a better understanding of the HIV epidemics in Brazil, with timely shift in strategies so improved effectiveness in public

  13. Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population: The China-PAR Project (Prediction for ASCVD Risk in China).

    PubMed

    Yang, Xueli; Li, Jianxin; Hu, Dongsheng; Chen, Jichun; Li, Ying; Huang, Jianfeng; Liu, Xiaoqing; Liu, Fangchao; Cao, Jie; Shen, Chong; Yu, Ling; Lu, Fanghong; Wu, Xianping; Zhao, Liancheng; Wu, Xigui; Gu, Dongfeng

    2016-11-08

    The accurate assessment of individual risk can be of great value to guiding and facilitating the prevention of atherosclerotic cardiovascular disease (ASCVD). However, prediction models in common use were formulated primarily in white populations. The China-PAR project (Prediction for ASCVD Risk in China) is aimed at developing and validating 10-year risk prediction equations for ASCVD from 4 contemporary Chinese cohorts. Two prospective studies followed up together with a unified protocol were used as the derivation cohort to develop 10-year ASCVD risk equations in 21 320 Chinese participants. The external validation was evaluated in 2 independent Chinese cohorts with 14 123 and 70 838 participants. Furthermore, model performance was compared with the Pooled Cohort Equations reported in the American College of Cardiology/American Heart Association guideline. Over 12 years of follow-up in the derivation cohort with 21 320 Chinese participants, 1048 subjects developed a first ASCVD event. Sex-specific equations had C statistics of 0.794 (95% confidence interval, 0.775-0.814) for men and 0.811 (95% confidence interval, 0.787-0.835) for women. The predicted rates were similar to the observed rates, as indicated by a calibration χ 2 of 13.1 for men (P=0.16) and 12.8 for women (P=0.17). Good internal and external validations of our equations were achieved in subsequent analyses. Compared with the Chinese equations, the Pooled Cohort Equations had lower C statistics and much higher calibration χ 2 values in men. Our project developed effective tools with good performance for 10-year ASCVD risk prediction among a Chinese population that will help to improve the primary prevention and management of cardiovascular disease. © 2016 American Heart Association, Inc.

  14. Risk prediction of hepatotoxicity in paracetamol poisoning.

    PubMed

    Wong, Anselm; Graudins, Andis

    2017-09-01

    Paracetamol (acetaminophen) poisoning is the most common cause of acute liver failure in the developed world. A paracetamol treatment nomogram has been used for over four decades to help determine whether patients will develop hepatotoxicity without acetylcysteine treatment, and thus indicates those needing treatment. Despite this, a small proportion of patients still develop hepatotoxicity. More accurate risk predictors would be useful to increase the early detection of patients with the potential to develop hepatotoxicity despite acetylcysteine treatment. Similarly, there would be benefit in early identification of those with a low likelihood of developing hepatotoxicity, as this group may be safely treated with an abbreviated acetylcysteine regimen. To review the current literature related to risk prediction tools that can be used to identify patients at increased risk of hepatotoxicity. A systematic literature review was conducted using the search terms: "paracetamol" OR "acetaminophen" AND "overdose" OR "toxicity" OR "risk prediction rules" OR "hepatotoxicity" OR "psi parameter" OR "multiplication product" OR "half-life" OR "prothrombin time" OR "AST/ALT (aspartate transaminase/alanine transaminase)" OR "dose" OR "biomarkers" OR "nomogram". The search was limited to human studies without language restrictions, of Medline (1946 to May 2016), PubMed and EMBASE. Original articles pertaining to the theme were identified from January 1974 to May 2016. Of the 13,975 articles identified, 60 were relevant to the review. Paracetamol treatment nomograms: Paracetamol treatment nomograms have been used for decades to help decide the need for acetylcysteine, but rarely used to determine the risk of hepatotoxicity with treatment. Reported paracetamol dose and concentration: A dose ingestion >12 g or serum paracetamol concentration above the treatment thresholds on the paracetamol nomogram are associated with a greater risk of hepatotoxicity. Paracetamol elimination half

  15. Clinical application of the Melbourne risk prediction tool in a high-risk upper abdominal surgical population: an observational cohort study.

    PubMed

    Parry, S; Denehy, L; Berney, S; Browning, L

    2014-03-01

    (1) To determine the ability of the Melbourne risk prediction tool to predict a pulmonary complication as defined by the Melbourne Group Scale in a medically defined high-risk upper abdominal surgery population during the postoperative period; (2) to identify the incidence of postoperative pulmonary complications; and (3) to examine the risk factors for postoperative pulmonary complications in this high-risk population. Observational cohort study. Tertiary Australian referral centre. 50 individuals who underwent medically defined high-risk upper abdominal surgery. Presence of postoperative pulmonary complications was screened daily for seven days using the Melbourne Group Scale (Version 2). Postoperative pulmonary risk prediction was calculated according to the Melbourne risk prediction tool. (1) Melbourne risk prediction tool; and (2) the incidence of postoperative pulmonary complications. Sixty-six percent (33/50) underwent hepatobiliary or upper gastrointestinal surgery. Mean (SD) anaesthetic duration was 377.8 (165.5) minutes. The risk prediction tool classified 84% (42/50) as high risk. Overall postoperative pulmonary complication incidence was 42% (21/50). The tool was 91% sensitive and 21% specific with a 50% chance of correct classification. This is the first study to externally validate the Melbourne risk prediction tool in an independent medically defined high-risk population. There was a higher incidence of pulmonary complications postoperatively observed compared to that previously reported. Results demonstrated poor validity of the tool in a population already defined medically as high risk and when applied postoperatively. This observational study has identified several important points to consider in future trials. Copyright © 2013 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

  16. The "polyenviromic risk score": Aggregating environmental risk factors predicts conversion to psychosis in familial high-risk subjects.

    PubMed

    Padmanabhan, Jaya L; Shah, Jai L; Tandon, Neeraj; Keshavan, Matcheri S

    2017-03-01

    Young relatives of individuals with schizophrenia (i.e. youth at familial high-risk, FHR) are at increased risk of developing psychotic disorders, and show higher rates of psychiatric symptoms, cognitive and neurobiological abnormalities than non-relatives. It is not known whether overall exposure to environmental risk factors increases risk of conversion to psychosis in FHR subjects. Subjects consisted of a pilot longitudinal sample of 83 young FHR subjects. As a proof of principle, we examined whether an aggregate score of exposure to environmental risk factors, which we term a 'polyenviromic risk score' (PERS), could predict conversion to psychosis. The PERS combines known environmental risk factors including cannabis use, urbanicity, season of birth, paternal age, obstetric and perinatal complications, and various types of childhood adversity, each weighted by its odds ratio for association with psychosis in the literature. A higher PERS was significantly associated with conversion to psychosis in young, familial high-risk subjects (OR=1.97, p=0.009). A model combining the PERS and clinical predictors had a sensitivity of 27% and specificity of 96%. An aggregate index of environmental risk may help predict conversion to psychosis in FHR subjects. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Risk factors for failure to complete a course of latent tuberculosis infection treatment in Salvador, Brazil.

    PubMed

    Machado, Almério; Finkmoore, B; Emodi, K; Takenami, I; Barbosa, T; Tavares, M; Reis, M G; Arruda, S; Riley, L W

    2009-06-01

    Although treatment of latent tuberculosis infection (LTBI) is an essential component of tuberculosis (TB) control in countries such as the United States, it is not widely practiced in most TB-endemic countries. To examine the practice of and adherence to LTBI treatment in a high-risk population in Brazil. We followed household contacts (HHCs) of patients hospitalized with pulmonary TB in Salvador, Brazil, for 6 months after they initiated LTBI treatment with isoniazid (INH). HHCs were asked to return to the hospital once a month for 6 months for follow-up visits and INH refills. Of 101 HHCs who initiated LTBI treatment, 54 (53.5%) completed the 6-month regimen. The risk of treatment non-completion was significantly higher in HHCs who reported side effects to INH (RR 2.69, 95%CI 1.3-5.8, P = 0.01), and in those who had to take two buses for a one-way trip to the hospital (RR 1.8, 95%CI 1.01-3.3, P = 0.04). Of the 101 HHCs, 29 (28.7%) did not return for any follow-up visits; these HHCs were significantly more likely to have a 2-bus commute to the hospital compared to HHCs who completed treatment (OR 20.69, 95%CI 2.1-208.4, P = 0.01). Nearly 50% of HHCs at high risk for developing TB completed a 6-month course of LTBI treatment. Completion of LTBI treatment was most affected by medication intolerance and commuting difficulties for follow-up visits.

  18. Predicting Epidemic Risk from Past Temporal Contact Data

    PubMed Central

    Valdano, Eugenio; Poletto, Chiara; Giovannini, Armando; Palma, Diana; Savini, Lara; Colizza, Vittoria

    2015-01-01

    Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system’s functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission contacts among the elements, however lack of data or delays in providing updated records may hinder its use, especially for time-varying patterns. Here we explore to what extent it is possible to use past temporal data of a system’s pattern of contacts to predict the risk of infection of its elements during an emerging outbreak, in absence of updated data. We focus on two real-world temporal systems; a livestock displacements trade network among animal holdings, and a network of sexual encounters in high-end prostitution. We define the node’s loyalty as a local measure of its tendency to maintain contacts with the same elements over time, and uncover important non-trivial correlations with the node’s epidemic risk. We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies. PMID:25763816

  19. [Prevalence and risk factors for anemia in Southern Brazil].

    PubMed

    Neuman, N A; Tanaka, O Y; Szarfarc, S C; Guimarães, P R; Victora, C G

    2000-02-01

    To measure the prevalence and evaluate the risk factors of anemia. Cross sectional populational based study of the urban area of Criciuma town, in the state of Santa Catarina, Southern Brazil. The study population was a probabilistic sample of 476 children aged under three years. The prevalence of anemia found in the sample was 60.4% for children aged 0 to 35.9 months according to the Brault-Dubuc criteria and 54% for children aged 6 to 35.9 months according to the OMS criteria. The prevalence of anemia increases with age up to 18 months-old and then decreases. It is less prevalent in families where the father has a higher education level and where there is a higher total family income. Nevertheless, even within the 25% higher income group 40% of the children are anemic. The prevalence of anemia is higher among children living in unfinished and overcrowded houses, where the toilet is not equipped with flush, and among children who have two or more older brothers. It is also higher among teenager mothers (<20 years), and 35 years old or older mothers. The prevalence of anemia is lower among women who had 5 to 9 prenatal visits during pregnancy. Low weight at birth was associated with iron deficiency. The nutritional condition was associated with anemia only according to weight/age criteria. Hospitalizations in the last 12 months were not associated with the disease. In the hierarchical multivariate analysis children age, family income, and crowded house were the only significant variables. Reproductive health history, health service visits, birth weight, breast-feeding, anthropometry, and morbidity did not characterize a risk factor of anemia in the multivariate analysis. The study makes it evident that social inequality is a strong determinant of anemia. The risk imposed by anemia to children in regard to their health and intellectual development requires immediate action.

  20. [Human rabies transmitted by dogs: risk areas in Minas Gerais, Brazil, 1991-1999].

    PubMed

    de Miranda, Cristiana Ferreira Jardim; da Silva, José Ailton; Moreira, Elvio Carlos

    2003-01-01

    A retrospective study based on observation with the objective of identifying and characterizing the different risk areas for rabies transmission by dogs took place in Minas Gerais State, Brazil, from 1991 to 1999. Indicators confirmed occurrences of canine and feline rabies, notification of human rabies, and administration of appropriate vaccination. The Minas Gerais State Health System is divided into 25 Regional Health Centers, which are linked to the State Health Department (SES-MG). These Health Centers were utilized in the study. The results of 2,845 records of laboratory diagnosis for canine, feline, and human rabies were analyzed. Consolidated SES-MG reports from 1997 to 1999 for rabies vaccination and notification records for cases of human rabies from the National Health Foundation (FUNASA) were also used. In order to verify the local reality, a semi-structured interview with each regional program director was conducted. Minas Gerais presents four different risk modalities, classified as zero, low, medium, and high.

  1. Risk assessment and remedial policy evaluation using predictive modeling

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Linkov, L.; Schell, W.R.

    1996-06-01

    As a result of nuclear industry operation and accidents, large areas of natural ecosystems have been contaminated by radionuclides and toxic metals. Extensive societal pressure has been exerted to decrease the radiation dose to the population and to the environment. Thus, in making abatement and remediation policy decisions, not only economic costs but also human and environmental risk assessments are desired. This paper introduces a general framework for risk assessment and remedial policy evaluation using predictive modeling. Ecological risk assessment requires evaluation of the radionuclide distribution in ecosystems. The FORESTPATH model is used for predicting the radionuclide fate in forestmore » compartments after deposition as well as for evaluating the efficiency of remedial policies. Time of intervention and radionuclide deposition profile was predicted as being crucial for the remediation efficiency. Risk assessment conducted for a critical group of forest users in Belarus shows that consumption of forest products (berries and mushrooms) leads to about 0.004% risk of a fatal cancer annually. Cost-benefit analysis for forest cleanup suggests that complete removal of organic layer is too expensive for application in Belarus and a better methodology is required. In conclusion, FORESTPATH modeling framework could have wide applications in environmental remediation of radionuclides and toxic metals as well as in dose reconstruction and, risk-assessment.« less

  2. The potential of large studies for building genetic risk prediction models

    Cancer.gov

    NCI scientists have developed a new paradigm to assess hereditary risk prediction in common diseases, such as prostate cancer. This genetic risk prediction concept is based on polygenic analysis—the study of a group of common DNA sequences, known as singl

  3. Multimethod prediction of child abuse risk in an at-risk sample of male intimate partner violence offenders.

    PubMed

    Rodriguez, Christina M; Gracia, Enrique; Lila, Marisol

    2016-10-01

    The vast majority of research on child abuse potential has concentrated on women demonstrating varying levels of risk of perpetrating physical child abuse. In contrast, the current study considered factors predictive of physical child abuse potential in a group of 70 male intimate partner violence offenders, a group that would represent a likely high risk group. Elements of Social Information Processing theory were evaluated, including pre-existing schemas of empathy, anger, and attitudes approving of parent-child aggression considered as potential moderators of negative attributions of child behavior. To lend methodological rigor, the study also utilized multiple measures and multiple methods, including analog tasks, to predict child abuse risk. Contrary to expectations, findings did not support the role of anger independently predicting child abuse risk in this sample of men. However, preexisting beliefs approving of parent-child aggression, lower empathy, and more negative child behavior attributions independently predicted abuse potential; in addition, greater anger, poorer empathy, and more favorable attitudes toward parent-child aggression also exacerbated men's negative child attributions to further elevate their child abuse risk. Future work is encouraged to consider how factors commonly considered in women parallel or diverge from those observed to elevate child abuse risk in men of varying levels of risk. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Limits of Risk Predictability in a Cascading Alternating Renewal Process Model.

    PubMed

    Lin, Xin; Moussawi, Alaa; Korniss, Gyorgy; Bakdash, Jonathan Z; Szymanski, Boleslaw K

    2017-07-27

    Most risk analysis models systematically underestimate the probability and impact of catastrophic events (e.g., economic crises, natural disasters, and terrorism) by not taking into account interconnectivity and interdependence of risks. To address this weakness, we propose the Cascading Alternating Renewal Process (CARP) to forecast interconnected global risks. However, assessments of the model's prediction precision are limited by lack of sufficient ground truth data. Here, we establish prediction precision as a function of input data size by using alternative long ground truth data generated by simulations of the CARP model with known parameters. We illustrate the approach on a model of fires in artificial cities assembled from basic city blocks with diverse housing. The results confirm that parameter recovery variance exhibits power law decay as a function of the length of available ground truth data. Using CARP, we also demonstrate estimation using a disparate dataset that also has dependencies: real-world prediction precision for the global risk model based on the World Economic Forum Global Risk Report. We conclude that the CARP model is an efficient method for predicting catastrophic cascading events with potential applications to emerging local and global interconnected risks.

  5. Prediction of near-term breast cancer risk using a Bayesian belief network

    NASA Astrophysics Data System (ADS)

    Zheng, Bin; Ramalingam, Pandiyarajan; Hariharan, Harishwaran; Leader, Joseph K.; Gur, David

    2013-03-01

    Accurately predicting near-term breast cancer risk is an important prerequisite for establishing an optimal personalized breast cancer screening paradigm. In previous studies, we investigated and tested the feasibility of developing a unique near-term breast cancer risk prediction model based on a new risk factor associated with bilateral mammographic density asymmetry between the left and right breasts of a woman using a single feature. In this study we developed a multi-feature based Bayesian belief network (BBN) that combines bilateral mammographic density asymmetry with three other popular risk factors, namely (1) age, (2) family history, and (3) average breast density, to further increase the discriminatory power of our cancer risk model. A dataset involving "prior" negative mammography examinations of 348 women was used in the study. Among these women, 174 had breast cancer detected and verified in the next sequential screening examinations, and 174 remained negative (cancer-free). A BBN was applied to predict the risk of each woman having cancer detected six to 18 months later following the negative screening mammography. The prediction results were compared with those using single features. The prediction accuracy was significantly increased when using the BBN. The area under the ROC curve increased from an AUC=0.70 to 0.84 (p<0.01), while the positive predictive value (PPV) and negative predictive value (NPV) also increased from a PPV=0.61 to 0.78 and an NPV=0.65 to 0.75, respectively. This study demonstrates that a multi-feature based BBN can more accurately predict the near-term breast cancer risk than with a single feature.

  6. Machine learning derived risk prediction of anorexia nervosa.

    PubMed

    Guo, Yiran; Wei, Zhi; Keating, Brendan J; Hakonarson, Hakon

    2016-01-20

    Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role. In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children's Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects. Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC's of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values. To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting.

  7. On the estimation of risk associated with an attenuation prediction

    NASA Technical Reports Server (NTRS)

    Crane, R. K.

    1992-01-01

    Viewgraphs from a presentation on the estimation of risk associated with an attenuation prediction is presented. Topics covered include: link failure - attenuation exceeding a specified threshold for a specified time interval or intervals; risk - the probability of one or more failures during the lifetime of the link or during a specified accounting interval; the problem - modeling the probability of attenuation by rainfall to provide a prediction of the attenuation threshold for a specified risk; and an accounting for the inadequacy of a model or models.

  8. Breast Cancer Risk Prediction and Mammography Biopsy Decisions

    PubMed Central

    Armstrong, Katrina; Handorf, Elizabeth A.; Chen, Jinbo; Demeter, Mirar N. Bristol

    2012-01-01

    Background Controversy continues about screening mammography, in part because of the risk of false-negative and false-positive mammograms. Pre-test breast cancer risk factors may improve the positive and negative predictive value of screening. Purpose To create a model that estimates the potential impact of pre-test risk prediction using clinical and genomic information on the reclassification of women with abnormal mammograms (BI-RADS3 and BI-RADS4 [Breast Imaging-Reporting and Data System]) above and below the threshold for breast biopsy. Methods The current study modeled 1-year breast cancer risk in women with abnormal screening mammograms using existing data on breast cancer risk factors, 12 validated breast cancer single nucleotide polymorphisms (SNPs), and probability of cancer given the BI-RADS category. Examination was made of reclassification of women above and below biopsy thresholds of 1%, 2%, and 3% risk. The Breast Cancer Surveillance Consortium data were collected from 1996 to 2002. Data analysis was conducted in 2010 and 2011. Results Using a biopsy risk threshold of 2% and the standard risk factor model, 5% of women with a BI-RADS3 mammogram had a risk above the threshold, and 3% of women with BIRADS4A mammograms had a risk below the threshold. The addition of 12 SNPs in the model resulted in 8% of women with a BI-RADS3 mammogram above the threshold for biopsy and 7% of women with BI-RADS4A mammograms below the threshold. Conclusions The incorporation of pre-test breast cancer risk factors could change biopsy decisions for a small proportion of women with abnormal mammograms. The greatest impact comes from standard breast cancer risk factors. PMID:23253645

  9. Predicted cancer risks induced by computed tomography examinations during childhood, by a quantitative risk assessment approach.

    PubMed

    Journy, Neige; Ancelet, Sophie; Rehel, Jean-Luc; Mezzarobba, Myriam; Aubert, Bernard; Laurier, Dominique; Bernier, Marie-Odile

    2014-03-01

    The potential adverse effects associated with exposure to ionizing radiation from computed tomography (CT) in pediatrics must be characterized in relation to their expected clinical benefits. Additional epidemiological data are, however, still awaited for providing a lifelong overview of potential cancer risks. This paper gives predictions of potential lifetime risks of cancer incidence that would be induced by CT examinations during childhood in French routine practices in pediatrics. Organ doses were estimated from standard radiological protocols in 15 hospitals. Excess risks of leukemia, brain/central nervous system, breast and thyroid cancers were predicted from dose-response models estimated in the Japanese atomic bomb survivors' dataset and studies of medical exposures. Uncertainty in predictions was quantified using Monte Carlo simulations. This approach predicts that 100,000 skull/brain scans in 5-year-old children would result in eight (90 % uncertainty interval (UI) 1-55) brain/CNS cancers and four (90 % UI 1-14) cases of leukemia and that 100,000 chest scans would lead to 31 (90 % UI 9-101) thyroid cancers, 55 (90 % UI 20-158) breast cancers, and one (90 % UI <0.1-4) leukemia case (all in excess of risks without exposure). Compared to background risks, radiation-induced risks would be low for individuals throughout life, but relative risks would be highest in the first decades of life. Heterogeneity in the radiological protocols across the hospitals implies that 5-10 % of CT examinations would be related to risks 1.4-3.6 times higher than those for the median doses. Overall excess relative risks in exposed populations would be 1-10 % depending on the site of cancer and the duration of follow-up. The results emphasize the potential risks of cancer specifically from standard CT examinations in pediatrics and underline the necessity of optimization of radiological protocols.

  10. Predicting Risk Sensitivity in Humans and Lower Animals: Risk as Variance or Coefficient of Variation

    ERIC Educational Resources Information Center

    Weber, Elke U.; Shafir, Sharoni; Blais, Ann-Renee

    2004-01-01

    This article examines the statistical determinants of risk preference. In a meta-analysis of animal risk preference (foraging birds and insects), the coefficient of variation (CV), a measure of risk per unit of return, predicts choices far better than outcome variance, the risk measure of normative models. In a meta-analysis of human risk…

  11. Pedophilia: an evaluation of diagnostic and risk prediction methods.

    PubMed

    Wilson, Robin J; Abracen, Jeffrey; Looman, Jan; Picheca, Janice E; Ferguson, Meaghan

    2011-06-01

    One hundred thirty child sexual abusers were diagnosed using each of following four methods: (a) phallometric testing, (b) strict application of Diagnostic and Statistical Manual of Mental Disorders (4th ed., text revision [DSM-IV-TR]) criteria, (c) Rapid Risk Assessment of Sex Offender Recidivism (RRASOR) scores, and (d) "expert" diagnoses rendered by a seasoned clinician. Comparative utility and intermethod consistency of these methods are reported, along with recidivism data indicating predictive validity for risk management. Results suggest that inconsistency exists in diagnosing pedophilia, leading to diminished accuracy in risk assessment. Although the RRASOR and DSM-IV-TR methods were significantly correlated with expert ratings, RRASOR and DSM-IV-TR were unrelated to each other. Deviant arousal was not associated with any of the other methods. Only the expert ratings and RRASOR scores were predictive of sexual recidivism. Logistic regression analyses showed that expert diagnosis did not add to prediction of sexual offence recidivism over and above RRASOR alone. Findings are discussed within a context of encouragement of clinical consistency and evidence-based practice regarding treatment and risk management of those who sexually abuse children.

  12. Applying a new mammographic imaging marker to predict breast cancer risk

    NASA Astrophysics Data System (ADS)

    Aghaei, Faranak; Danala, Gopichandh; Hollingsworth, Alan B.; Stoug, Rebecca G.; Pearce, Melanie; Liu, Hong; Zheng, Bin

    2018-02-01

    Identifying and developing new mammographic imaging markers to assist prediction of breast cancer risk has been attracting extensive research interest recently. Although mammographic density is considered an important breast cancer risk, its discriminatory power is lower for predicting short-term breast cancer risk, which is a prerequisite to establish a more effective personalized breast cancer screening paradigm. In this study, we presented a new interactive computer-aided detection (CAD) scheme to generate a new quantitative mammographic imaging marker based on the bilateral mammographic tissue density asymmetry to predict risk of cancer detection in the next subsequent mammography screening. An image database involving 1,397 women was retrospectively assembled and tested. Each woman had two digital mammography screenings namely, the "current" and "prior" screenings with a time interval from 365 to 600 days. All "prior" images were originally interpreted negative. In "current" screenings, these cases were divided into 3 groups, which include 402 positive, 643 negative, and 352 biopsy-proved benign cases, respectively. There is no significant difference of BIRADS based mammographic density ratings between 3 case groups (p < 0.6). When applying the CAD-generated imaging marker or risk model to classify between 402 positive and 643 negative cases using "prior" negative mammograms, the area under a ROC curve is 0.70+/-0.02 and the adjusted odds ratios show an increasing trend from 1.0 to 8.13 to predict the risk of cancer detection in the "current" screening. Study demonstrated that this new imaging marker had potential to yield significantly higher discriminatory power to predict short-term breast cancer risk.

  13. Shoulder dystocia: risk factors, predictability, and preventability.

    PubMed

    Mehta, Shobha H; Sokol, Robert J

    2014-06-01

    Shoulder dystocia remains an unpredictable obstetric emergency, striking fear in the hearts of obstetricians both novice and experienced. While outcomes that lead to permanent injury are rare, almost all obstetricians with enough years of practice have participated in a birth with a severe shoulder dystocia and are at least aware of cases that have resulted in significant neurologic injury or even neonatal death. This is despite many years of research trying to understand the risk factors associated with it, all in an attempt primarily to characterize when the risk is high enough to avoid vaginal delivery altogether and prevent a shoulder dystocia, whose attendant morbidities are estimated to be at a rate as high as 16-48%. The study of shoulder dystocia remains challenging due to its generally retrospective nature, as well as dependence on proper identification and documentation. As a result, the prediction of shoulder dystocia remains elusive, and the cost of trying to prevent one by performing a cesarean delivery remains high. While ultimately it is the injury that is the key concern, rather than the shoulder dystocia itself, it is in the presence of an identified shoulder dystocia that occurrence of injury is most common. The majority of shoulder dystocia cases occur without major risk factors. Moreover, even the best antenatal predictors have a low positive predictive value. Shoulder dystocia therefore cannot be reliably predicted, and the only preventative measure is cesarean delivery. Copyright © 2014 Elsevier Inc. All rights reserved.

  14. Fetal biometric parameters: Reference charts for a non-selected risk population from Uberaba, Brazil.

    PubMed

    Peixoto, Alberto Borges; da Cunha Caldas, Taciana Mara Rodrigues; Dulgheroff, Fernando Felix; Martins, Wellington P; Araujo Júnior, Edward

    2017-03-01

    To establish reference charts for fetal biometric parameters in a non-selected risk population from Uberaba, Southeast of Brazil. A retrospective cross-sectional study was performed among 5656 non-selected risk singleton pregnant women between 14 and 41 weeks of gestation. The ultrasound exams were performed during routine visits of second and third trimesters. Biparietal diameter (BPD) was measured at the level of the thalami and cavum septi pellucidi. Head circumference (HC) was calculated by the following formula: HC = 1.62*(BPD + occipital frontal diameter, OFD). Abdominal circumference (AC) was measured using the following formula: AC = (anteroposterior diameter + transverse abdominal diameter) × 1.57. Femur diaphysis length (FDL) was obtained in the longest axis of femur without including the distal femoral epiphysis. The estimated fetal weight (EFW) was obtained by the Hadlock formula. Polynomial regressions were performed to obtain the best-fit model for each fetal biometric parameter as the function of gestational age (GA). The mean, standard deviations ( SD ), minimum and maximum of BPD (cm), HC (cm), AC (cm), FDL (cm) and EFW (g) were 6.9 ± 1.9 (2.3 - 10.5), 24.51 ± 6.61 (9.1 - 36.4), 22.8 ± 7.3 (7.5 - 41.1), 4.9 ± 1.6 (1.2 - 8.1) and 1365 ± 1019 (103 - 4777), respectively. Second-degree polynomial regressions between the evaluated parameters and GA resulted in the following formulas: BPD = -4.044 + 0.540 × GA - 0.0049 × GA 2 ( R 2 = 0.97); HC= -15.420 + 2.024 GA - 0.0199 × GA 2 ( R 2 = 0.98); AC = -9.579 + 1.329 × GA - 0.0055 × GA 2 ( R 2 = 0.97); FDL = -3.778 + 0.416 × GA - 0.0035 × GA 2 ( R 2 = 0.98) and EFW = 916 - 123 × GA + 4.70 × GA 2 ( R 2 = 0.96); respectively. Reference charts for the fetal biometric parameters in a non-selected risk population from Uberaba, Southeast of Brazil, were established.

  15. EVALUATING RISK-PREDICTION MODELS USING DATA FROM ELECTRONIC HEALTH RECORDS.

    PubMed

    Wang, L E; Shaw, Pamela A; Mathelier, Hansie M; Kimmel, Stephen E; French, Benjamin

    2016-03-01

    The availability of data from electronic health records facilitates the development and evaluation of risk-prediction models, but estimation of prediction accuracy could be limited by outcome misclassification, which can arise if events are not captured. We evaluate the robustness of prediction accuracy summaries, obtained from receiver operating characteristic curves and risk-reclassification methods, if events are not captured (i.e., "false negatives"). We derive estimators for sensitivity and specificity if misclassification is independent of marker values. In simulation studies, we quantify the potential for bias in prediction accuracy summaries if misclassification depends on marker values. We compare the accuracy of alternative prognostic models for 30-day all-cause hospital readmission among 4548 patients discharged from the University of Pennsylvania Health System with a primary diagnosis of heart failure. Simulation studies indicate that if misclassification depends on marker values, then the estimated accuracy improvement is also biased, but the direction of the bias depends on the direction of the association between markers and the probability of misclassification. In our application, 29% of the 1143 readmitted patients were readmitted to a hospital elsewhere in Pennsylvania, which reduced prediction accuracy. Outcome misclassification can result in erroneous conclusions regarding the accuracy of risk-prediction models.

  16. Recent ecological responses to climate change support predictions of high extinction risk

    PubMed Central

    Maclean, Ilya M. D.; Wilson, Robert J.

    2011-01-01

    Predicted effects of climate change include high extinction risk for many species, but confidence in these predictions is undermined by a perceived lack of empirical support. Many studies have now documented ecological responses to recent climate change, providing the opportunity to test whether the magnitude and nature of recent responses match predictions. Here, we perform a global and multitaxon metaanalysis to show that empirical evidence for the realized effects of climate change supports predictions of future extinction risk. We use International Union for Conservation of Nature (IUCN) Red List criteria as a common scale to estimate extinction risks from a wide range of climate impacts, ecological responses, and methods of analysis, and we compare predictions with observations. Mean extinction probability across studies making predictions of the future effects of climate change was 7% by 2100 compared with 15% based on observed responses. After taking account of possible bias in the type of climate change impact analyzed and the parts of the world and taxa studied, there was less discrepancy between the two approaches: predictions suggested a mean extinction probability of 10% across taxa and regions, whereas empirical evidence gave a mean probability of 14%. As well as mean overall extinction probability, observations also supported predictions in terms of variability in extinction risk and the relative risk associated with broad taxonomic groups and geographic regions. These results suggest that predictions are robust to methodological assumptions and provide strong empirical support for the assertion that anthropogenic climate change is now a major threat to global biodiversity. PMID:21746924

  17. Recent ecological responses to climate change support predictions of high extinction risk.

    PubMed

    Maclean, Ilya M D; Wilson, Robert J

    2011-07-26

    Predicted effects of climate change include high extinction risk for many species, but confidence in these predictions is undermined by a perceived lack of empirical support. Many studies have now documented ecological responses to recent climate change, providing the opportunity to test whether the magnitude and nature of recent responses match predictions. Here, we perform a global and multitaxon metaanalysis to show that empirical evidence for the realized effects of climate change supports predictions of future extinction risk. We use International Union for Conservation of Nature (IUCN) Red List criteria as a common scale to estimate extinction risks from a wide range of climate impacts, ecological responses, and methods of analysis, and we compare predictions with observations. Mean extinction probability across studies making predictions of the future effects of climate change was 7% by 2100 compared with 15% based on observed responses. After taking account of possible bias in the type of climate change impact analyzed and the parts of the world and taxa studied, there was less discrepancy between the two approaches: predictions suggested a mean extinction probability of 10% across taxa and regions, whereas empirical evidence gave a mean probability of 14%. As well as mean overall extinction probability, observations also supported predictions in terms of variability in extinction risk and the relative risk associated with broad taxonomic groups and geographic regions. These results suggest that predictions are robust to methodological assumptions and provide strong empirical support for the assertion that anthropogenic climate change is now a major threat to global biodiversity.

  18. [Drug consumption and occupational violence in working women, a multicenter study: Mexico, Peru, Brazil].

    PubMed

    Alonso Castillo, Maria Magdalena; Musayon Oblitas, Flor Yesenia; David, Helena Maria Scherlowski Leal; Gómez Meza, Marco Vinicio

    2006-01-01

    The purposes of the study were: 1) Determine the proportion of working women who consume drugs; 2) identify some occupational and personal risk factors that can predict drugs consumption; 3) identify the presence of occupational violence and its relation with drugs consumption; 4) identify differences and similarities in drugs consumption and occupational violence among women from three communities in Mexico (Monterrey), Peru (Lima) and Brazil (Rio de Janeiro). A multicenter, descriptive, correlational and comparative study was carried out, with a sample of 903 women. The results show that 11% of the participants in Mexico consume alcohol, 53% in Peru and 45% in Brazil. The consumption of illicit drugs corresponded to 5% in Mexico and 6% in Peru. The presence of occupational violence was found in 16% of the Mexican participants, 24% of the Peruvians and 39% of the Brazilians.

  19. Long-Term Post-CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions.

    PubMed

    Carr, Brendan M; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C; Zhu, Wei; Shroyer, A Laurie

    2016-01-01

    Clinical risk models are commonly used to predict short-term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long-term mortality. The added value of long-term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long-term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Long-term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c-index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Mortality rates were 3%, 9%, and 17% at one-, three-, and five years, respectively (median follow-up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long-term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Long-term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long-term mortality risk can be accurately assessed and subgroups of higher-risk patients can be identified for enhanced follow-up care. More research appears warranted to refine long-term CABG clinical risk models. © 2015 The Authors. Journal of Cardiac Surgery Published by Wiley Periodicals, Inc.

  20. Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

    PubMed Central

    Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

    2015-01-01

    Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

  1. Development of a flood-induced health risk prediction model for Africa

    NASA Astrophysics Data System (ADS)

    Lee, D.; Block, P. J.

    2017-12-01

    Globally, many floods occur in developing or tropical regions where the impact on public health is substantial, including death and injury, drinking water, endemic disease, and so on. Although these flood impacts on public health have been investigated, integrated management of floods and flood-induced health risks is technically and institutionally limited. Specifically, while the use of climatic and hydrologic forecasts for disaster management has been highlighted, analogous predictions for forecasting the magnitude and impact of health risks are lacking, as is the infrastructure for health early warning systems, particularly in developing countries. In this study, we develop flood-induced health risk prediction model for African regions using season-ahead flood predictions with climate drivers and a variety of physical and socio-economic information, such as local hazard, exposure, resilience, and health vulnerability indicators. Skillful prediction of flood and flood-induced health risks can contribute to practical pre- and post-disaster responses in both local- and global-scales, and may eventually be integrated into multi-hazard early warning systems for informed advanced planning and management. This is especially attractive for areas with limited observations and/or little capacity to develop flood-induced health risk warning systems.

  2. The Risk GP Model: the standard model of prediction in medicine.

    PubMed

    Fuller, Jonathan; Flores, Luis J

    2015-12-01

    With the ascent of modern epidemiology in the Twentieth Century came a new standard model of prediction in public health and clinical medicine. In this article, we describe the structure of the model. The standard model uses epidemiological measures-most commonly, risk measures-to predict outcomes (prognosis) and effect sizes (treatment) in a patient population that can then be transformed into probabilities for individual patients. In the first step, a risk measure in a study population is generalized or extrapolated to a target population. In the second step, the risk measure is particularized or transformed to yield probabilistic information relevant to a patient from the target population. Hence, we call the approach the Risk Generalization-Particularization (Risk GP) Model. There are serious problems at both stages, especially with the extent to which the required assumptions will hold and the extent to which we have evidence for the assumptions. Given that there are other models of prediction that use different assumptions, we should not inflexibly commit ourselves to one standard model. Instead, model pluralism should be standard in medical prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. [Cesarean after labor induction: Risk factors and prediction score].

    PubMed

    Branger, B; Dochez, V; Gervier, S; Winer, N

    2018-05-01

    The objective of the study is to determine the risk factors for caesarean section at the time of labor induction, to establish a prediction algorithm, to evaluate its relevance and to compare the results with observation. A retrospective study was carried out over a year at Nantes University Hospital with 941 cervical ripening and labor inductions (24.1%) terminated by 167 caesarean sections (17.8%). Within the cohort, a case-control study was conducted with 147 caesarean sections and 148 vaginal deliveries. A multivariate analysis was carried out with a logistic regression allowing the elaboration of an equation of prediction and an ROC curve and the confrontation between the prediction and the reality. In univariate analysis, six variables were significant: nulliparity, small size of the mother, history of scarried uterus, use of prostaglandins as a mode of induction, unfavorable Bishop score<6, variety of posterior release. In multivariate analysis, five variables were significant: nulliparity, maternal size, maternal BMI, scar uterus and Bishop score. The most predictive model corresponded to an area under the curve of 0.86 (0.82-0.90) with a correct prediction percentage ("well classified") of 67.6% for a caesarean section risk of 80%. The prediction criteria would make it possible to inform the woman and the couple about the potential risk of Caesarean section in urgency or to favor a planned Caesarean section or a low-lying attempt on more objective, repeatable and transposable arguments in a medical team. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

  4. Is food insecurity associated with HIV risk? Cross-sectional evidence from sexually active women in Brazil.

    PubMed

    Tsai, Alexander C; Hung, Kristin J; Weiser, Sheri D

    2012-01-01

    Understanding how food insecurity among women gives rise to differential patterning in HIV risks is critical for policy and programming in resource-limited settings. This is particularly the case in Brazil, which has undergone successive changes in the gender and socio-geographic composition of its complex epidemic over the past three decades. We used data from a national survey of Brazilian women to estimate the relationship between food insecurity and HIV risk. We used data on 12,684 sexually active women from a national survey conducted in Brazil in 2006-2007. Self-reported outcomes were (a) consistent condom use, defined as using a condom at each occasion of sexual intercourse in the previous 12 mo; (b) recent condom use, less stringently defined as using a condom with the most recent sexual partner; and (c) itchy vaginal discharge in the previous 30 d, possibly indicating presence of a sexually transmitted infection. The primary explanatory variable of interest was food insecurity, measured using the culturally adapted and validated Escala Brasiliera de Segurança Alimentar. In multivariable logistic regression models, severe food insecurity with hunger was associated with a reduced odds of consistent condom use in the past 12 mo (adjusted odds ratio [AOR] = 0.67; 95% CI, 0.48-0.92) and condom use at last sexual intercourse (AOR = 0.75; 95% CI, 0.57-0.98). Self-reported itchy vaginal discharge was associated with all categories of food insecurity (with AORs ranging from 1.46 to 1.94). In absolute terms, the effect sizes were large in magnitude across all outcomes. Underweight and/or lack of control in sexual relations did not appear to mediate the observed associations. Severe food insecurity with hunger was associated with reduced odds of condom use and increased odds of itchy vaginal discharge, which is potentially indicative of sexually transmitted infection, among sexually active women in Brazil. Interventions targeting food insecurity may have beneficial

  5. Prediction of Coronary Artery Disease Risk Based on Multiple Longitudinal Biomarkers

    PubMed Central

    Yang, Lili; Yu, Menggang; Gao, Sujuan

    2016-01-01

    In the last decade, few topics in the area of cardiovascular disease (CVD) research have received as much attention as risk prediction. One of the well documented risk factors for CVD is high blood pressure (BP). Traditional CVD risk prediction models consider BP levels measured at a single time and such models form the basis for current clinical guidelines for CVD prevention. However, in clinical practice, BP levels are often observed and recorded in a longitudinal fashion. Information on BP trajectories can be powerful predictors for CVD events. We consider joint modeling of time to coronary artery disease and individual longitudinal measures of systolic and diastolic BPs in a primary care cohort with up to 20 years of follow-up. We applied novel prediction metrics to assess the predictive performance of joint models. Predictive performances of proposed joint models and other models were assessed via simulations and illustrated using the primary care cohort. PMID:26439685

  6. Online gaming and risks predict cyberbullying perpetration and victimization in adolescents.

    PubMed

    Chang, Fong-Ching; Chiu, Chiung-Hui; Miao, Nae-Fang; Chen, Ping-Hung; Lee, Ching-Mei; Huang, Tzu-Fu; Pan, Yun-Chieh

    2015-02-01

    The present study examined factors associated with the emergence and cessation of youth cyberbullying and victimization in Taiwan. A total of 2,315 students from 26 high schools were assessed in the 10th grade, with follow-up performed in the 11th grade. Self-administered questionnaires were collected in 2010 and 2011. Multiple logistic regression was conducted to examine the factors. Multivariate analysis results indicated that higher levels of risk factors (online game use, exposure to violence in media, internet risk behaviors, cyber/school bullying experiences) in the 10th grade coupled with an increase in risk factors from grades 10 to 11 could be used to predict the emergence of cyberbullying perpetration/victimization. In contrast, lower levels of risk factors in the 10th grade and higher levels of protective factors coupled with a decrease in risk factors predicted the cessation of cyberbullying perpetration/victimization. Online game use, exposure to violence in media, Internet risk behaviors, and cyber/school bullying experiences can be used to predict the emergence and cessation of youth cyberbullying perpetration and victimization.

  7. Predictor characteristics necessary for building a clinically useful risk prediction model: a simulation study.

    PubMed

    Schummers, Laura; Himes, Katherine P; Bodnar, Lisa M; Hutcheon, Jennifer A

    2016-09-21

    Compelled by the intuitive appeal of predicting each individual patient's risk of an outcome, there is a growing interest in risk prediction models. While the statistical methods used to build prediction models are increasingly well understood, the literature offers little insight to researchers seeking to gauge a priori whether a prediction model is likely to perform well for their particular research question. The objective of this study was to inform the development of new risk prediction models by evaluating model performance under a wide range of predictor characteristics. Data from all births to overweight or obese women in British Columbia, Canada from 2004 to 2012 (n = 75,225) were used to build a risk prediction model for preeclampsia. The data were then augmented with simulated predictors of the outcome with pre-set prevalence values and univariable odds ratios. We built 120 risk prediction models that included known demographic and clinical predictors, and one, three, or five of the simulated variables. Finally, we evaluated standard model performance criteria (discrimination, risk stratification capacity, calibration, and Nagelkerke's r 2 ) for each model. Findings from our models built with simulated predictors demonstrated the predictor characteristics required for a risk prediction model to adequately discriminate cases from non-cases and to adequately classify patients into clinically distinct risk groups. Several predictor characteristics can yield well performing risk prediction models; however, these characteristics are not typical of predictor-outcome relationships in many population-based or clinical data sets. Novel predictors must be both strongly associated with the outcome and prevalent in the population to be useful for clinical prediction modeling (e.g., one predictor with prevalence ≥20 % and odds ratio ≥8, or 3 predictors with prevalence ≥10 % and odds ratios ≥4). Area under the receiver operating characteristic curve

  8. High-risk behaviors and experiences with traffic law among night drivers in Curitiba, Brazil.

    PubMed

    Ulinski, Sandra L; Moysés, Simone T; Werneck, Renata I; Moysés, Samuel J

    2016-01-08

    To explore high-risk behaviors and experiences with traffic law among night drivers in Curitiba, Brazil. Data from 398 drivers on sociodemographic parameters, high-risk behaviors, experiences with traffic law, and traffic law violations were collected through interviews conducted at sobriety checkpoints. Exploratory-descriptive and analytical statistics were used. The mean age of the participants was 32.6±11.2 years (range, 18 to 75 years). Half of the drivers reported having driven after drinking in the last year, predominantly single men aged 18 to 29 years who drive cars and drink alcohol frequently. Only 55% of the drivers who had driven after drinking in the last year self-reported some concern about being detected in a police operation. A significant association was found between sociodemographic variables and behavior, which can help tailor public interventions to a specific group of drivers: young men who exhibit high-risk behaviors in traffic, such as driving after drinking alcohol, some of whom report heavy alcohol consumption. This group represents a challenge for educational and enforcement interventions, particularly because they admit to violating current laws and have a low perception of punishment due to the low risk of being detected by the police.

  9. Prevalence and risk factors for intestinal parasites in food handlers, southern Brazil.

    PubMed

    Colli, Cristiane Maria; Mizutani, Angelica Sayuri; Martins, Vanessa Aparecida; Ferreira, Erika Cristina; Gomes, Mônica Lúcia

    2014-01-01

    In this study, the prevalence and risk factors for enteroparasites were determined in food handlers from Maringá, Paraná State, southern Brazil. Fecal and subungual materials of 150 street food vendors were analyzed by the methods of Lutz, Faust, and Mello, respectively. A questionnaire on hygiene and sanitary conditions of the workplace and of domicile was applied. The prevalence of enteroparasites was 28%, and the protozoa infection was more expressive (21.3%) than by helminths (6.7%), but without significant difference (p > 0.05). Entamoeba coli was the most frequent species occurring in 15.3%, while the prevalence of protozoa pathogenic was low (Giardia lamblia: 2.7% and Entamoeba histolytica: 0.7%). The subungual material presented negative results. The presence of pets in domiciles has increased twice the risk of infection. The working conditions of the majority of street food vendors were inappropriate. The results highlight the need for more rigorous programs of continuing education, parasitological examination every six months, and health surveillance. In this way, the quality of the service provided to the population can be improved and the transmission of food-borne diseases can be prevented.

  10. Silicosis prevalence and risk factors in semi-precious stone mining in Brazil.

    PubMed

    Souza, Tamires P; Watte, Guilherme; Gusso, Alaíde M; Souza, Rafaela; Moreira, José da S; Knorst, Marli M

    2017-06-01

    Underground mining generates large amounts of dust and exposes workers to silica. This study aims to determine the prevalence and predictor factors for the development of silicosis among semi-precious-stone mineworkers in southern Brazil working in a self-administered cooperative. In a cross-sectional study of 348 current workers and retirees, demographic data, medical, and occupational history were collected through an interview performed by a nurse and medical record review. Risk factor associations were studied by Poisson multivariate regression. The overall prevalence of silicosis was 37%, while in current miners it was 28%. Several risk factors for silicosis were identified in the univariate analysis. Inadequate ventilation in the underground galleries combined with dry drilling, duration of silica exposure, and (inversely) education remained significant in the multivariate analysis (P < 0.05). This study is unusual in studying semi-precious stone mineworkers in a self-administered worker cooperative with limited resources. The prevalence of silicosis was very high. A number of recommendations are made-including technical support for worker cooperatives, surveillance of silica exposure and silicosis, exposure reduction measures, and benefits allowing impaired miners to leave the industry. © 2017 Wiley Periodicals, Inc.

  11. Diabetes increases the risk of recent-transmission tuberculosis in household contacts in São Paulo, Brazil.

    PubMed

    Rajan, J V; Ferrazoli, L; Waldman, E A; Simonsen, V; Ferreira, P; Telles, M A; Riley, L W

    2017-08-01

    A cohort of household contacts of tuberculosis (TB) index cases from four public health clinics in São Paulo, Brazil. To measure the association between diabetes mellitus (DM) among household contacts and recent-transmission TB (RT TB). Index TB cases (n = 263) identified from 2001 to 2002 in São Paulo, whose household contacts (n = 1383) were monitored for active TB until December 2010. From 2001 to 2010, there were 29 cases of RT TB among household contacts (cumulative incidence 2.1%, 95%CI 1.4-2.9). DM in household contacts was associated with RT TB (OR 3.96, 95%CI 1.33-11.79) even after adjustment for human immunodeficiency virus (HIV) status, smoking and alcohol use (adjusted OR [aOR] 3.21, 95%CI 1.01-10.19). HIV infection was also associated with RT TB (OR 6.40, 95%CI 1.40-29.40; aOR 4.81, 95%CI 0.96-24.18). Household contact DM was not associated with non-RT TB (OR 1.27, 95%CI 0.30-5.40). The time to diagnosis of TB was shorter in household contacts with and without DM (P = 0.035) and in household contacts with and without HIV (P = 0.0002). Household contact DM was associated with an increased risk of RT TB in a cohort in Brazil, lending support to the active screening of household contacts with DM for TB in Brazil.

  12. Polygenic risk predicts obesity in both white and black young adults.

    PubMed

    Domingue, Benjamin W; Belsky, Daniel W; Harris, Kathleen Mullan; Smolen, Andrew; McQueen, Matthew B; Boardman, Jason D

    2014-01-01

    To test transethnic replication of a genetic risk score for obesity in white and black young adults using a national sample with longitudinal data. A prospective longitudinal study using the National Longitudinal Study of Adolescent Health Sibling Pairs (n = 1,303). Obesity phenotypes were measured from anthropometric assessments when study members were aged 18-26 and again when they were 24-32. Genetic risk scores were computed based on published genome-wide association study discoveries for obesity. Analyses tested genetic associations with body-mass index (BMI), waist-height ratio, obesity, and change in BMI over time. White and black young adults with higher genetic risk scores had higher BMI and waist-height ratio and were more likely to be obese compared to lower genetic risk age-peers. Sibling analyses revealed that the genetic risk score was predictive of BMI net of risk factors shared by siblings. In white young adults only, higher genetic risk predicted increased risk of becoming obese during the study period. In black young adults, genetic risk scores constructed using loci identified in European and African American samples had similar predictive power. Cumulative information across the human genome can be used to characterize individual level risk for obesity. Measured genetic risk accounts for only a small amount of total variation in BMI among white and black young adults. Future research is needed to identify modifiable environmental exposures that amplify or mitigate genetic risk for elevated BMI.

  13. Unravelling the structure of species extinction risk for predictive conservation science.

    PubMed

    Lee, Tien Ming; Jetz, Walter

    2011-05-07

    Extinction risk varies across species and space owing to the combined and interactive effects of ecology/life history and geography. For predictive conservation science to be effective, large datasets and integrative models that quantify the relative importance of potential factors and separate rapidly changing from relatively static threat drivers are urgently required. Here, we integrate and map in space the relative and joint effects of key correlates of The International Union for Conservation of Nature-assessed extinction risk for 8700 living birds. Extinction risk varies significantly with species' broad-scale environmental niche, geographical range size, and life-history and ecological traits such as body size, developmental mode, primary diet and foraging height. Even at this broad scale, simple quantifications of past human encroachment across species' ranges emerge as key in predicting extinction risk, supporting the use of land-cover change projections for estimating future threat in an integrative setting. A final joint model explains much of the interspecific variation in extinction risk and provides a remarkably strong prediction of its observed global geography. Our approach unravels the species-level structure underlying geographical gradients in extinction risk and offers a means of disentangling static from changing components of current and future threat. This reconciliation of intrinsic and extrinsic, and of past and future extinction risk factors may offer a critical step towards a more continuous, forward-looking assessment of species' threat status based on geographically explicit environmental change projections, potentially advancing global predictive conservation science.

  14. Proarrhythmia risk prediction using human induced pluripotent stem cell-derived cardiomyocytes.

    PubMed

    Yamazaki, Daiju; Kitaguchi, Takashi; Ishimura, Masakazu; Taniguchi, Tomohiko; Yamanishi, Atsuhiro; Saji, Daisuke; Takahashi, Etsushi; Oguchi, Masao; Moriyama, Yuta; Maeda, Sanae; Miyamoto, Kaori; Morimura, Kaoru; Ohnaka, Hiroki; Tashibu, Hiroyuki; Sekino, Yuko; Miyamoto, Norimasa; Kanda, Yasunari

    2018-04-01

    Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are expected to become a useful tool for proarrhythmia risk prediction in the non-clinical drug development phase. Several features including electrophysiological properties, ion channel expression profile and drug responses were investigated using commercially available hiPSC-CMs, such as iCell-CMs and Cor.4U-CMs. Although drug-induced arrhythmia has been extensively examined by microelectrode array (MEA) assays in iCell-CMs, it has not been fully understood an availability of Cor.4U-CMs for proarrhythmia risk. Here, we evaluated the predictivity of proarrhythmia risk using Cor.4U-CMs. MEA assay revealed linear regression between inter-spike interval and field potential duration (FPD). The hERG inhibitor E-4031 induced reverse-use dependent FPD prolongation. We next evaluated the proarrhythmia risk prediction by a two-dimensional map, which we have previously proposed. We determined the relative torsade de pointes risk score, based on the extent of FPD with Fridericia's correction (FPDcF) change and early afterdepolarization occurrence, and calculated the margins normalized to free effective therapeutic plasma concentrations. The drugs were classified into three risk groups using the two-dimensional map. This risk-categorization system showed high concordance with the torsadogenic information obtained by a public database CredibleMeds. Taken together, these results indicate that Cor.4U-CMs can be used for drug-induced proarrhythmia risk prediction. Copyright © 2018 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

  15. A risk scoring system for prediction of haemorrhagic stroke.

    PubMed

    Zodpey, S P; Tiwari, R R

    2005-01-01

    The present pair-matched case control study was carried out at Government Medical College Hospital, Nagpur, India, a tertiary care hospital with the objective to devise and validate a risk scoring system for prediction of hemorrhagic stroke. The study consisted of 166 hospitalized CT scan proved cases of hemorrhagic stroke (ICD 9, 431-432), and a age and sex matched control per case. The controls were selected from patients who attended the study hospital for conditions other than stroke. On conditional multiple logistic regression five risk factors- hypertension (OR = 1.9. 95% Cl = 1.5-2.5). raised scrum total cholesterol (OR = 2.3, 95% Cl = 1.1-4.9). use of anticoagulants and antiplatelet agents (OR = 3.4, 95% Cl =1.1-10.4). past history of transient ischaemic attack (OR = 8.4, 95% Cl = 2.1- 33.6) and alcohol intake (OR = 2.1, 95% Cl = 1.3-3.6) were significant. These factors were ascribed statistical weights (based on regression coefficients) of 6, 8, 12, 21 and 8 respectively. The nonsignificant factors (diabetes mellitus, physical inactivity, obesity, smoking, type A personality, history of claudication, family history of stroke, history of cardiac diseases and oral contraceptive use in females) were not included in the development of scoring system. ROC curve suggested a total score of 21 to be the best cut-off for predicting haemorrhag stroke. At this cut-off the sensitivity, specificity, positive predictivity and Cohen's kappa were 0.74, 0.74, 0.74 and 0.48 respectively. The overall predictive accuracy of this additive risk scoring system (area under ROC curve by Wilcoxon statistic) was 0.79 (95% Cl = 0.73-0.84). Thus to conclude, if substantiated by further validation, this scorincy system can be used to predict haemorrhagic stroke, thereby helping to devise effective risk factor intervention strategy.

  16. 68Ga-PSMA-617 PET/CT: a promising new technique for predicting risk stratification and metastatic risk of prostate cancer patients.

    PubMed

    Liu, Chen; Liu, Teli; Zhang, Ning; Liu, Yiqiang; Li, Nan; Du, Peng; Yang, Yong; Liu, Ming; Gong, Kan; Yang, Xing; Zhu, Hua; Yan, Kun; Yang, Zhi

    2018-05-02

    The purpose of this study was to investigate the performance of 68 Ga-PSMA-617 PET/CT in predicting risk stratification and metastatic risk of prostate cancer. Fifty newly diagnosed patients with prostate cancer as confirmed by needle biopsy were continuously included, 40 in a train set and ten in a test set. 68 Ga-PSMA-617 PET/CT and clinical data of all patients were retrospectively analyzed. Semi-quantitative analysis of PET images provided maximum standardized uptake (SUVmax) of primary prostate cancer and volumetric parameters including intraprostatic PSMA-derived tumor volume (iPSMA-TV) and intraprostatic total lesion PSMA (iTL-PSMA). According to prostate cancer risk stratification criteria of the NCCN Guideline, all patients were simplified into a low-intermediate risk group or a high-risk group. The semi-quantitative parameters of 68 Ga-PSMA-617 PET/CT were used to establish a univariate logistic regression model for high-risk prostate cancer and its metastatic risk, and to evaluate the diagnostic efficacy of the predictive model. In the train set, 30/40 (75%) patients had high-risk prostate cancer and 10/40 (25%) patients had low-to-moderate-risk prostate cancer; in the test set, 8/10 (80%) patients had high-risk prostate cancer while 2/10 (20%) had low-intermediate risk prostate cancer. The univariate logistic regression model established with SUVmax, iPSMA-TV and iTL-PSMA could all effectively predict high-risk prostate cancer; the AUC of ROC were 0.843, 0.802 and 0.900, respectively. Based on the test set, the sensitivity and specificity of each model were 87.5% and 50% for SUVmax, 62.5% and 100% for iPSMA-TV, and 87.5% and 100% for iTL-PSMA, respectively. The iPSMA-TV and iTL-PSMA-based predictive model could predict the metastatic risk of prostate cancer, the AUC of ROC was 0.863 and 0.848, respectively, but the SUVmax-based prediction model could not predict metastatic risk. Semi-quantitative analysis indexes of 68 Ga-PSMA-617 PET/CT imaging can be

  17. Common polygenic variation enhances risk prediction for Alzheimer's disease.

    PubMed

    Escott-Price, Valentina; Sims, Rebecca; Bannister, Christian; Harold, Denise; Vronskaya, Maria; Majounie, Elisa; Badarinarayan, Nandini; Morgan, Kevin; Passmore, Peter; Holmes, Clive; Powell, John; Brayne, Carol; Gill, Michael; Mead, Simon; Goate, Alison; Cruchaga, Carlos; Lambert, Jean-Charles; van Duijn, Cornelia; Maier, Wolfgang; Ramirez, Alfredo; Holmans, Peter; Jones, Lesley; Hardy, John; Seshadri, Sudha; Schellenberg, Gerard D; Amouyel, Philippe; Williams, Julie

    2015-12-01

    The identification of subjects at high risk for Alzheimer's disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer's disease and the accuracy of Alzheimer's disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer's Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer's disease (P = 4.9 × 10(-26)). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10(-19)). The best prediction accuracy AUC = 78.2% (95% confidence interval 77-80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer's disease has a significant polygenic component, which has predictive utility for Alzheimer's disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For

  18. Prevalence of Trichomonas vaginalis and risk factors in women treated at public health units in Brazil: a transversal study.

    PubMed

    Grama, Daliane Faria; Casarotti, Leonardo da Silva; Morato, Michelle Gonçalves Vilela de Andrade; Silva, Lidyane Suellen; Mendonça, Daniella Fernandes; Limongi, Jean Ezequiel; Viana, João da Costa; Cury, Márcia Cristina

    2013-09-01

    Studies have revealed high prevalence rates of Trichomonas vaginalis in men and women worldwide. In Brazil, where reporting is not mandatory, the true prevalence rate is unknown. This study determined the prevalence of the parasite in women attending public health units in the city of Uberlândia, Minas Gerais, Brazil, identifying possible risk factors for infection, and also compared three diagnostic techniques for detecting the parasite. Samples of vaginal secretions collected from 742 women attending public health units were analyzed by direct wet mount examination, culture and smear test. Epidemiological questionnaires were administered. Of the total of 742 samples analyzed, 19 (2.6%) tested positive for T. vaginalis. The variables significantly associated with infection were: being of black ethnicity, smoking, having knowledge about sexually transmitted diseases and presenting clinical signs. The culture method was considered the gold standard test. Although there are programs to control other sexually transmitted diseases, there are none for trichomoniasis. The results of this study indicate the presence of T. vaginalis in the female population, and points to the need for more research in Brazil to gain a better understanding of the profile and epidemiology of the parasite.

  19. Habitual sleep duration and predicted 10-year cardiovascular risk using the pooled cohort risk equations among US adults.

    PubMed

    Ford, Earl S

    2014-12-02

    The association between sleep duration and predicted cardiovascular risk has been poorly characterized. The objective of this study was to examine the association between self-reported sleep duration and predicted 10-year cardiovascular risk among US adults. Data from 7690 men and nonpregnant women who were aged 40 to 79 years, who were free of self-reported heart disease and stroke, and who participated in a National Health and Nutrition Examination Survey from 2005 to 2012 were analyzed. Sleep duration was self-reported. Predicted 10-year cardiovascular risk was calculated using the pooled cohort equations. Among the included participants, 13.1% reported sleeping ≤5 hours, 24.4% reported sleeping 6 hours, 31.9% reported sleeping 7 hours, 25.2% reported sleeping 8 hours, 4.0% reported sleeping 9 hours, and 1.3% reported sleeping ≥10 hours. After adjustment for covariates, geometric mean-predicted 10-year cardiovascular risk was 4.0%, 3.6%, 3.4%, 3.5%, 3.7%, and 3.7% among participants who reported sleeping ≤5, 6, 7, 8, 9, and ≥10 hours per night, respectively (PWald chi-square<0.001). The age-adjusted percentages of predicted cardiovascular risk ≥20% for the 6 intervals of sleep duration were 14.5%, 11.9%, 11.0%, 11.4%, 11.8%, and 16.3% (PWald chi-square=0.022). After maximal adjustment, however, sleep duration was not significantly associated with cardiovascular risk ≥20% (PWald chi-square=0.698). Mean-predicted 10-year cardiovascular risk was lowest among adults who reported sleeping 7 hours per night and increased as participants reported sleeping fewer and more hours. © 2014 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

  20. Predicting neutropenia risk in patients with cancer using electronic data.

    PubMed

    Pawloski, Pamala A; Thomas, Avis J; Kane, Sheryl; Vazquez-Benitez, Gabriela; Shapiro, Gary R; Lyman, Gary H

    2017-04-01

    Clinical guidelines recommending the use of myeloid growth factors are largely based on the prescribed chemotherapy regimen. The guidelines suggest that oncologists consider patient-specific characteristics when prescribing granulocyte-colony stimulating factor (G-CSF) prophylaxis; however, a mechanism to quantify individual patient risk is lacking. Readily available electronic health record (EHR) data can provide patient-specific information needed for individualized neutropenia risk estimation. An evidence-based, individualized neutropenia risk estimation algorithm has been developed. This study evaluated the automated extraction of EHR chemotherapy treatment data and externally validated the neutropenia risk prediction model. A retrospective cohort of adult patients with newly diagnosed breast, colorectal, lung, lymphoid, or ovarian cancer who received the first cycle of a cytotoxic chemotherapy regimen from 2008 to 2013 were recruited from a single cancer clinic. Electronically extracted EHR chemotherapy treatment data were validated by chart review. Neutropenia risk stratification was conducted and risk model performance was assessed using calibration and discrimination. Chemotherapy treatment data electronically extracted from the EHR were verified by chart review. The neutropenia risk prediction tool classified 126 patients (57%) as being low risk for febrile neutropenia, 44 (20%) as intermediate risk, and 51 (23%) as high risk. The model was well calibrated (Hosmer-Lemeshow goodness-of-fit test = 0.24). Discrimination was adequate and slightly less than in the original internal validation (c-statistic 0.75 vs 0.81). Chemotherapy treatment data were electronically extracted from the EHR successfully. The individualized neutropenia risk prediction model performed well in our retrospective external cohort. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions

  1. Ethnozoology in Brazil: analysis of the methodological risks in published studies.

    PubMed

    Lyra-Neves, R M; Santos, E M; Medeiros, P M; Alves, R R N; Albuquerque, U P

    2015-11-01

    There has been a growth in the field of Ethnozoology throughout the years, especially in Brazil, where a considerable number of scientific articles pertaining to this subject has been published in recent decades. With this increase in publications comes the opportunity to assess the quality of these publications, as there are no known studies assessing the methodological risks in this area. Based on this observation, our objectives were to compile the papers published on the subject of ethnozoology and to answer the following questions: 1) Do the Brazilian ethnozoological studies use sound sampling methods?; 2) Is the sampling quality influenced by characteristics of the studies/publications? The studies found in databases and using web search engines were compiled to answer these questions. The studies were assessed based on their nature, sampling methods, use of hypotheses and tests, journal's impact factor, and animal group studied. The majority of the studies analyzed exhibited problems associated with the samples, as 144 (66.98%) studies were classified as having a high risk of bias. With regard to the characteristics analyzed, we determined that a quantitative nature and the use of tests are essential components of good sampling. Most studies classified as moderate and low risk either did not provide these data or provided data that were not clear; therefore, these studies were classified as being of a quali-quantitative nature. Studies performed with vertebrate groups were of high risk. Most of the papers analyzed here focused on fish, insects, and/or mollusks, thus highlighting the difficulties associated with conducting interviews regarding tetrapod vertebrates. Such difficulties are largely related to the extremely strict Brazilian laws, justified by the decline and extinction of some species, related to the use of wild tetrapod vertebrates.

  2. Predicting the Individual Risk of Acute Severe Colitis at Diagnosis

    PubMed Central

    Cesarini, Monica; Collins, Gary S.; Rönnblom, Anders; Santos, Antonieta; Wang, Lai Mun; Sjöberg, Daniel; Parkes, Miles; Keshav, Satish

    2017-01-01

    Abstract Background and Aims: Acute severe colitis [ASC] is associated with major morbidity. We aimed to develop and externally validate an index that predicted ASC within 3 years of diagnosis. Methods: The development cohort included patients aged 16–89 years, diagnosed with ulcerative colitis [UC] in Oxford and followed for 3 years. Primary outcome was hospitalization for ASC, excluding patients admitted within 1 month of diagnosis. Multivariable logistic regression examined the adjusted association of seven risk factors with ASC. Backwards elimination produced a parsimonious model that was simplified to create an easy-to-use index. External validation occurred in separate cohorts from Cambridge, UK, and Uppsala, Sweden. Results: The development cohort [Oxford] included 34/111 patients who developed ASC within a median 14 months [range 1–29]. The final model applied the sum of 1 point each for extensive disease, C-reactive protein [CRP] > 10mg/l, or haemoglobin < 12g/dl F or < 14g/dl M at diagnosis, to give a score from 0/3 to 3/3. This predicted a 70% risk of developing ASC within 3 years [score 3/3]. Validation cohorts included different proportions with ASC [Cambridge = 25/96; Uppsala = 18/298]. Of those scoring 3/3 at diagnosis, 18/18 [Cambridge] and 12/13 [Uppsala] subsequently developed ASC. Discriminant ability [c-index, where 1.0 = perfect discrimination] was 0.81 [Oxford], 0.95 [Cambridge], 0.97 [Uppsala]. Internal validation using bootstrapping showed good calibration, with similar predicted risk across all cohorts. A nomogram predicted individual risk. Conclusions: An index applied at diagnosis reliably predicts the risk of ASC within 3 years in different populations. Patients with a score 3/3 at diagnosis may merit early immunomodulator therapy. PMID:27647858

  3. Predicting the Individual Risk of Acute Severe Colitis at Diagnosis.

    PubMed

    Cesarini, Monica; Collins, Gary S; Rönnblom, Anders; Santos, Antonieta; Wang, Lai Mun; Sjöberg, Daniel; Parkes, Miles; Keshav, Satish; Travis, Simon P L

    2017-03-01

    Acute severe colitis [ASC] is associated with major morbidity. We aimed to develop and externally validate an index that predicted ASC within 3 years of diagnosis. The development cohort included patients aged 16-89 years, diagnosed with ulcerative colitis [UC] in Oxford and followed for 3 years. Primary outcome was hospitalization for ASC, excluding patients admitted within 1 month of diagnosis. Multivariable logistic regression examined the adjusted association of seven risk factors with ASC. Backwards elimination produced a parsimonious model that was simplified to create an easy-to-use index. External validation occurred in separate cohorts from Cambridge, UK, and Uppsala, Sweden. The development cohort [Oxford] included 34/111 patients who developed ASC within a median 14 months [range 1-29]. The final model applied the sum of 1 point each for extensive disease, C-reactive protein [CRP] > 10mg/l, or haemoglobin < 12g/dl F or < 14g/dl M at diagnosis, to give a score from 0/3 to 3/3. This predicted a 70% risk of developing ASC within 3 years [score 3/3]. Validation cohorts included different proportions with ASC [Cambridge = 25/96; Uppsala = 18/298]. Of those scoring 3/3 at diagnosis, 18/18 [Cambridge] and 12/13 [Uppsala] subsequently developed ASC. Discriminant ability [c-index, where 1.0 = perfect discrimination] was 0.81 [Oxford], 0.95 [Cambridge], 0.97 [Uppsala]. Internal validation using bootstrapping showed good calibration, with similar predicted risk across all cohorts. A nomogram predicted individual risk. An index applied at diagnosis reliably predicts the risk of ASC within 3 years in different populations. Patients with a score 3/3 at diagnosis may merit early immunomodulator therapy. Copyright © 2016 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com

  4. Spatial clustering and local risk of leprosy in São Paulo, Brazil.

    PubMed

    Ramos, Antônio Carlos Vieira; Yamamura, Mellina; Arroyo, Luiz Henrique; Popolin, Marcela Paschoal; Chiaravalloti Neto, Francisco; Palha, Pedro Fredemir; Uchoa, Severina Alice da Costa; Pieri, Flávia Meneguetti; Pinto, Ione Carvalho; Fiorati, Regina Célia; Queiroz, Ana Angélica Rêgo de; Belchior, Aylana de Souza; Dos Santos, Danielle Talita; Garcia, Maria Concebida da Cunha; Crispim, Juliane de Almeida; Alves, Luana Seles; Berra, Thaís Zamboni; Arcêncio, Ricardo Alexandre

    2017-02-01

    Although the detection rate is decreasing, the proportion of new cases with WHO grade 2 disability (G2D) is increasing, creating concern among policy makers and the Brazilian government. This study aimed to identify spatial clustering of leprosy and classify high-risk areas in a major leprosy cluster using the SatScan method. Data were obtained including all leprosy cases diagnosed between January 2006 and December 2013. In addition to the clinical variable, information was also gathered regarding the G2D of the patient at diagnosis and after treatment. The Scan Spatial statistic test, developed by Kulldorff e Nagarwalla, was used to identify spatial clustering and to measure the local risk (Relative Risk-RR) of leprosy. Maps considering these risks and their confidence intervals were constructed. A total of 434 cases were identified, including 188 (43.31%) borderline leprosy and 101 (23.28%) lepromatous leprosy cases. There was a predominance of males, with ages ranging from 15 to 59 years, and 51 patients (11.75%) presented G2D. Two significant spatial clusters and three significant spatial-temporal clusters were also observed. The main spatial cluster (p = 0.000) contained 90 census tracts, a population of approximately 58,438 inhabitants, detection rate of 22.6 cases per 100,000 people and RR of approximately 3.41 (95%CI = 2.721-4.267). Regarding the spatial-temporal clusters, two clusters were observed, with RR ranging between 24.35 (95%CI = 11.133-52.984) and 15.24 (95%CI = 10.114-22.919). These findings could contribute to improvements in policies and programming, aiming for the eradication of leprosy in Brazil. The Spatial Scan statistic test was found to be an interesting resource for health managers and healthcare professionals to map the vulnerability of areas in terms of leprosy transmission risk and areas of underreporting.

  5. A risk prediction model for xerostomia: a retrospective cohort study.

    PubMed

    Villa, Alessandro; Nordio, Francesco; Gohel, Anita

    2016-12-01

    We investigated the prevalence of xerostomia in dental patients and built a xerostomia risk prediction model by incorporating a wide range of risk factors. Socio-demographic data, past medical history, self-reported dry mouth and related symptoms were collected retrospectively from January 2010 to September 2013 for all new dental patients. A logistic regression framework was used to build a risk prediction model for xerostomia. External validation was performed using an independent data set to test the prediction power. A total of 12 682 patients were included in this analysis (54.3%, females). Xerostomia was reported by 12.2% of patients. The proportion of people reporting xerostomia was higher among those who were taking more medications (OR = 1.11, 95% CI = 1.08-1.13) or recreational drug users (OR = 1.4, 95% CI = 1.1-1.9). Rheumatic diseases (OR = 2.17, 95% CI = 1.88-2.51), psychiatric diseases (OR = 2.34, 95% CI = 2.05-2.68), eating disorders (OR = 2.28, 95% CI = 1.55-3.36) and radiotherapy (OR = 2.00, 95% CI = 1.43-2.80) were good predictors of xerostomia. For the test model performance, the ROC-AUC was 0.816 and in the external validation sample, the ROC-AUC was 0.799. The xerostomia risk prediction model had high accuracy and discriminated between high- and low-risk individuals. Clinicians could use this model to identify the classes of medications and systemic diseases associated with xerostomia. © 2015 John Wiley & Sons A/S and The Gerodontology Association. Published by John Wiley & Sons Ltd.

  6. Persisting arthralgia due to Mayaro virus infection in a traveler from Brazil: is there a risk for attendants to the 2014 FIFA World Cup?

    PubMed

    Slegers, C A D; Keuter, M; Günther, S; Schmidt-Chanasit, J; van der Ven, A J; de Mast, Q

    2014-07-01

    The 2014 FIFA World Cup and the 2016 Olympic Games will attract large groups of visitors to Brazil. These visitors will be at risk for different arboviral infections, some of which not well known outside endemic areas. We report a case of a 52-year-old Dutch woman who presented with persistent arthralgia due to a Mayaro virus (MAYV) infection which she contracted in the Amazon basin in Brazil. MAYV is a mosquito-borne alphavirus which primarily circulates in humid tropical forests of South America. Infections are rarely reported in travelers and are characterized by an acute febrile illness which is often followed by a prolonged and sometimes incapacitating polyarthralgia. Both travelers and physicians should be aware of the risk of these arboviral infections and the importance of mosquito bite prevention should be stressed. Copyright © 2014 Elsevier B.V. All rights reserved.

  7. Validation of a multifactorial risk factor model used for predicting future caries risk with Nevada adolescents.

    PubMed

    Ditmyer, Marcia M; Dounis, Georgia; Howard, Katherine M; Mobley, Connie; Cappelli, David

    2011-05-20

    The objective of this study was to measure the validity and reliability of a multifactorial Risk Factor Model developed for use in predicting future caries risk in Nevada adolescents in a public health setting. This study examined retrospective data from an oral health surveillance initiative that screened over 51,000 students 13-18 years of age, attending public/private schools in Nevada across six academic years (2002/2003-2007/2008). The Risk Factor Model included ten demographic variables: exposure to fluoridation in the municipal water supply, environmental smoke exposure, race, age, locale (metropolitan vs. rural), tobacco use, Body Mass Index, insurance status, sex, and sealant application. Multiple regression was used in a previous study to establish which significantly contributed to caries risk. Follow-up logistic regression ascertained the weight of contribution and odds ratios of the ten variables. Researchers in this study computed sensitivity, specificity, positive predictive value (PVP), negative predictive value (PVN), and prevalence across all six years of screening to assess the validity of the Risk Factor Model. Subjects' overall mean caries prevalence across all six years was 66%. Average sensitivity across all six years was 79%; average specificity was 81%; average PVP was 89% and average PVN was 67%. Overall, the Risk Factor Model provided a relatively constant, valid measure of caries that could be used in conjunction with a comprehensive risk assessment in population-based screenings by school nurses/nurse practitioners, health educators, and physicians to guide them in assessing potential future caries risk for use in prevention and referral practices.

  8. Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records

    PubMed Central

    Luo, Wei; Tran, Truyen; Berk, Michael; Venkatesh, Svetha

    2016-01-01

    Background Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk. Objective The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data. Methods We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC). Results The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians. Conclusions This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk. PMID:27400764

  9. How to make predictions about future infectious disease risks

    PubMed Central

    Woolhouse, Mark

    2011-01-01

    Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the ‘art of the possible’, which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for ‘good practice’ for the development and the use of predictive models. PMID:21624924

  10. A Novel Early Pregnancy Risk Prediction Model for Gestational Diabetes Mellitus.

    PubMed

    Sweeting, Arianne N; Wong, Jencia; Appelblom, Heidi; Ross, Glynis P; Kouru, Heikki; Williams, Paul F; Sairanen, Mikko; Hyett, Jon A

    2018-06-13

    Accurate early risk prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated novel biomarker predictors to develop a first-trimester risk prediction model in a large multiethnic cohort. Maternal clinical, aneuploidy and pre-eclampsia screening markers (PAPP-A, free hCGβ, mean arterial pressure, uterine artery pulsatility index) were measured prospectively at 11-13+6 weeks' gestation in 980 women (248 with GDM; 732 controls). Nonfasting glucose, lipids, adiponectin, leptin, lipocalin-2, and plasminogen activator inhibitor-2 were measured on banked serum. The relationship between marker multiples-of-the-median and GDM was examined with multivariate regression. Model predictive performance for early (< 24 weeks' gestation) and overall GDM diagnosis was evaluated by receiver operating characteristic curves. Glucose, triglycerides, leptin, and lipocalin-2 were higher, while adiponectin was lower, in GDM (p < 0.05). Lipocalin-2 performed best in Caucasians, and triglycerides in South Asians with GDM. Family history of diabetes, previous GDM, South/East Asian ethnicity, parity, BMI, PAPP-A, triglycerides, and lipocalin-2 were significant independent GDM predictors (all p < 0.01), achieving an area under the curve of 0.91 (95% confidence interval [CI] 0.89-0.94) overall, and 0.93 (95% CI 0.89-0.96) for early GDM, in a combined multivariate prediction model. A first-trimester risk prediction model, which incorporates novel maternal lipid markers, accurately identifies women at high risk of GDM, including early GDM. © 2018 S. Karger AG, Basel.

  11. Comparison between frailty index of deficit accumulation and fracture risk assessment tool (FRAX) in prediction of risk of fractures.

    PubMed

    Li, Guowei; Thabane, Lehana; Papaioannou, Alexandra; Adachi, Jonathan D

    2015-08-01

    A frailty index (FI) of deficit accumulation could quantify and predict the risk of fractures based on the degree of frailty in the elderly. We aimed to compare the predictive powers between the FI and the fracture risk assessment tool (FRAX) in predicting risk of major osteoporotic fracture (hip, upper arm or shoulder, spine, or wrist) and hip fracture, using the data from the Global Longitudinal Study of Osteoporosis in Women (GLOW) 3-year Hamilton cohort. There were 3985 women included in the study, with the mean age of 69.4 years (standard deviation [SD] = 8.89). During the follow-up, there were 149 (3.98%) incident major osteoporotic fractures and 18 (0.48%) hip fractures reported. The FRAX and FI were significantly related to each other. Both FRAX and FI significantly predicted risk of major osteoporotic fracture, with a hazard ratio (HR) of 1.03 (95% confidence interval [CI]: 1.02-1.05) and 1.02 (95% CI: 1.01-1.04) for per-0.01 increment for the FRAX and FI respectively. The HRs were 1.37 (95% CI: 1.19-1.58) and 1.26 (95% CI: 1.12-1.42) for an increase of per-0.10 (approximately one SD) in the FRAX and FI respectively. Similar discriminative ability of the models was found: c-index = 0.62 for the FRAX and c-index = 0.61 for the FI. When cut-points were chosen to trichotomize participants into low-risk, medium-risk and high-risk groups, a significant increase in fracture risk was found in the high-risk group (HR = 2.04, 95% CI: 1.36-3.07) but not in the medium-risk group (HR = 1.23, 95% CI: 0.82-1.84) compared with the low-risk women for the FI, while for FRAX the medium-risk (HR = 2.00, 95% CI: 1.09-3.68) and high-risk groups (HR = 2.61, 95% CI: 1.48-4.58) predicted risk of major osteoporotic fracture significantly only when survival time exceeded 18months (550 days). Similar findings were observed for hip fracture and in sensitivity analyses. In conclusion, the FI is comparable with FRAX in the prediction of risk of future fractures, indicating that

  12. Comparison between frailty index of deficit accumulation and fracture risk assessment tool (FRAX) in prediction of risk of fractures

    PubMed Central

    Li, Guowei; Thabane, Lehana; Papaioannou, Alexandra; Adachi, Jonathan D.

    2016-01-01

    A frailty index (FI) of deficit accumulation could quantify and predict the risk of fractures based on the degree of frailty in the elderly. We aimed to compare the predictive powers between the FI and the fracture risk assessment tool (FRAX) in predicting risk of major osteoporotic fracture (hip, upper arm or shoulder, spine, or wrist) and hip fracture, using the data from the Global Longitudinal Study of Osteoporosis in Women (GLOW) 3-year Hamilton cohort. There were 3985 women included in the study, with the mean age of 69.4 years (standard deviation [SD] = 8.89). During the follow-up, there were 149 (3.98%) incident major osteoporotic fractures and 18 (0.48%) hip fractures reported. The FRAX and FI were significantly related to each other. Both FRAX and FI significantly predicted risk of major osteoporotic fracture, with a hazard ratio (HR) of 1.03 (95% confidence interval [CI]: 1.02–1.05) and 1.02 (95% CI: 1.01–1.04) for per-0.01 increment for the FRAX and FI respectively. The HRs were 1.37 (95% CI: 1.19–1.58) and 1.26 (95% CI: 1.12–1.42) for an increase of per-0.10 (approximately one SD) in the FRAX and FI respectively. Similar discriminative ability of the models was found: c-index = 0.62 for the FRAX and c-index = 0.61 for the FI. When cut-points were chosen to trichotomize participants into low-risk, medium-risk and high-risk groups, a significant increase in fracture risk was found in the high-risk group (HR = 2.04, 95% CI: 1.36–3.07) but not in the medium-risk group (HR = 1.23, 95% CI: 0.82–1.84) compared with the low-risk women for the FI, while for FRAX the medium-risk (HR = 2.00, 95% CI: 1.09–3.68) and high-risk groups (HR = 2.61, 95% CI: 1.48–4.58) predicted risk of major osteoporotic fracture significantly only when survival time exceeded 18 months (550 days). Similar findings were observed for hip fracture and in sensitivity analyses. In conclusion, the FI is comparable with FRAX in the prediction of risk of future fractures

  13. Indoor tanning and the MC1R genotype: risk prediction for basal cell carcinoma risk in young people.

    PubMed

    Molinaro, Annette M; Ferrucci, Leah M; Cartmel, Brenda; Loftfield, Erikka; Leffell, David J; Bale, Allen E; Mayne, Susan T

    2015-06-01

    Basal cell carcinoma (BCC) incidence is increasing, particularly in young people, and can be associated with significant morbidity and treatment costs. To identify young individuals at risk of BCC, we assessed existing melanoma or overall skin cancer risk prediction models and built a novel risk prediction model, with a focus on indoor tanning and the melanocortin 1 receptor gene, MC1R. We evaluated logistic regression models among 759 non-Hispanic whites from a case-control study of patients seen between 2006 and 2010 in New Haven, Connecticut. In our data, the adjusted area under the receiver operating characteristic curve (AUC) for a model by Han et al. (Int J Cancer. 2006;119(8):1976-1984) with 7 MC1R variants was 0.72 (95% confidence interval (CI): 0.66, 0.78), while that by Smith et al. (J Clin Oncol. 2012;30(15 suppl):8574) with MC1R and indoor tanning had an AUC of 0.69 (95% CI: 0.63, 0.75). Our base model had greater predictive ability than existing models and was significantly improved when we added ever-indoor tanning, burns from indoor tanning, and MC1R (AUC = 0.77, 95% CI: 0.74, 0.81). Our early-onset BCC risk prediction model incorporating MC1R and indoor tanning extends the work of other skin cancer risk prediction models, emphasizes the value of both genotype and indoor tanning in skin cancer risk prediction in young people, and should be validated with an independent cohort. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  14. Application of a geographical information system approach for risk analysis of fascioliasis in southern Espírito Santo state, Brazil.

    PubMed

    Martins, Isabella Vilhena Freire; de Avelar, Barbara Rauta; Pereira, Maria Julia Salim; da Fonseca, Adevair Henrique

    2012-09-01

    A model based on geographical information systems for mapping the risk of fascioliasis was developed for the southern part of Espírito Santo state, Brazil. The determinants investigated were precipitation, temperature, elevation, slope, soil type and land use. Weightings and grades were assigned to determinants and their categories according to their relevance with respect to fascioliasis. Theme maps depicting the spatial distribution of risk areas indicate that over 50% of southern Espírito Santo is either at high or at very high risk for fascioliasis. These areas were found to be characterized by comparatively high temperature but relatively low slope, low precipitation and low elevation corresponding to periodically flooded grasslands or soils that promote water retention.

  15. In-hospital risk prediction for post-stroke depression: development and validation of the Post-stroke Depression Prediction Scale.

    PubMed

    de Man-van Ginkel, Janneke M; Hafsteinsdóttir, Thóra B; Lindeman, Eline; Ettema, Roelof G A; Grobbee, Diederick E; Schuurmans, Marieke J

    2013-09-01

    The timely detection of post-stroke depression is complicated by a decreasing length of hospital stay. Therefore, the Post-stroke Depression Prediction Scale was developed and validated. The Post-stroke Depression Prediction Scale is a clinical prediction model for the early identification of stroke patients at increased risk for post-stroke depression. The study included 410 consecutive stroke patients who were able to communicate adequately. Predictors were collected within the first week after stroke. Between 6 to 8 weeks after stroke, major depressive disorder was diagnosed using the Composite International Diagnostic Interview. Multivariable logistic regression models were fitted. A bootstrap-backward selection process resulted in a reduced model. Performance of the model was expressed by discrimination, calibration, and accuracy. The model included a medical history of depression or other psychiatric disorders, hypertension, angina pectoris, and the Barthel Index item dressing. The model had acceptable discrimination, based on an area under the receiver operating characteristic curve of 0.78 (0.72-0.85), and calibration (P value of the U-statistic, 0.96). Transforming the model to an easy-to-use risk-assessment table, the lowest risk category (sum score, <-10) showed a 2% risk of depression, which increased to 82% in the highest category (sum score, >21). The clinical prediction model enables clinicians to estimate the degree of the depression risk for an individual patient within the first week after stroke.

  16. New equations for predicting postoperative risk in patients with hip fracture.

    PubMed

    Hirose, Jun; Ide, Junji; Irie, Hiroki; Kikukawa, Kenshi; Mizuta, Hiroshi

    2009-12-01

    Predicting the postoperative course of patients with hip fractures would be helpful for surgical planning and risk management. We therefore established equations to predict the morbidity and mortality rates in candidates for hip fracture surgery using the Estimation of Physiologic Ability and Surgical Stress (E-PASS) risk-scoring system. First we evaluated the correlation between the E-PASS scores and postoperative morbidity and mortality rates in all 722 patients surgically treated for hip fractures during the study period (Group A). Next we established equations to predict morbidity and mortality rates. We then applied these equations to all 633 patients with hip fractures treated at seven other hospitals (Group B) and compared the predicted and actual morbidity and mortality rates to assess the predictive ability of the E-PASS and Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) systems. The ratio of actual to predicted morbidity and mortality rates was closer to 1.0 with the E-PASS than the POSSUM system. Our data suggest the E-PASS scoring system is useful for defining postoperative risk and its underlying algorithm accurately predicts morbidity and mortality rates in patients with hip fractures before surgery. This information then can be used to manage their condition and potentially improve treatment outcomes. Level II, prognostic study. See the Guidelines for Authors for a complete description of levels of evidence.

  17. The Reliability and Predictive Validity of the Stalking Risk Profile.

    PubMed

    McEwan, Troy E; Shea, Daniel E; Daffern, Michael; MacKenzie, Rachel D; Ogloff, James R P; Mullen, Paul E

    2018-03-01

    This study assessed the reliability and validity of the Stalking Risk Profile (SRP), a structured measure for assessing stalking risks. The SRP was administered at the point of assessment or retrospectively from file review for 241 adult stalkers (91% male) referred to a community-based forensic mental health service. Interrater reliability was high for stalker type, and moderate-to-substantial for risk judgments and domain scores. Evidence for predictive validity and discrimination between stalking recidivists and nonrecidivists for risk judgments depended on follow-up duration. Discrimination was moderate (area under the curve = 0.66-0.68) and positive and negative predictive values good over the full follow-up period ( Mdn = 170.43 weeks). At 6 months, discrimination was better than chance only for judgments related to stalking of new victims (area under the curve = 0.75); however, high-risk stalkers still reoffended against their original victim(s) 2 to 4 times as often as low-risk stalkers. Implications for the clinical utility and refinement of the SRP are discussed.

  18. Characterizing Decision-Analysis Performances of Risk Prediction Models Using ADAPT Curves.

    PubMed

    Lee, Wen-Chung; Wu, Yun-Chun

    2016-01-01

    The area under the receiver operating characteristic curve is a widely used index to characterize the performance of diagnostic tests and prediction models. However, the index does not explicitly acknowledge the utilities of risk predictions. Moreover, for most clinical settings, what counts is whether a prediction model can guide therapeutic decisions in a way that improves patient outcomes, rather than to simply update probabilities.Based on decision theory, the authors propose an alternative index, the "average deviation about the probability threshold" (ADAPT).An ADAPT curve (a plot of ADAPT value against the probability threshold) neatly characterizes the decision-analysis performances of a risk prediction model.Several prediction models can be compared for their ADAPT values at a chosen probability threshold, for a range of plausible threshold values, or for the whole ADAPT curves. This should greatly facilitate the selection of diagnostic tests and prediction models.

  19. The Stroke Assessment of Fall Risk (SAFR): predictive validity in inpatient stroke rehabilitation.

    PubMed

    Breisinger, Terry P; Skidmore, Elizabeth R; Niyonkuru, Christian; Terhorst, Lauren; Campbell, Grace B

    2014-12-01

    To evaluate relative accuracy of a newly developed Stroke Assessment of Fall Risk (SAFR) for classifying fallers and non-fallers, compared with a health system fall risk screening tool, the Fall Harm Risk Screen. Prospective quality improvement study conducted at an inpatient stroke rehabilitation unit at a large urban university hospital. Patients admitted for inpatient stroke rehabilitation (N = 419) with imaging or clinical evidence of ischemic or hemorrhagic stroke, between 1 August 2009 and 31 July 2010. Not applicable. Sensitivity, specificity, and area under the curve for Receiver Operating Characteristic Curves of both scales' classifications, based on fall risk score completed upon admission to inpatient stroke rehabilitation. A total of 68 (16%) participants fell at least once. The SAFR was significantly more accurate than the Fall Harm Risk Screen (p < 0.001), with area under the curve of 0.73, positive predictive value of 0.29, and negative predictive value of 0.94. For the Fall Harm Risk Screen, area under the curve was 0.56, positive predictive value was 0.19, and negative predictive value was 0.86. Sensitivity and specificity of the SAFR (0.78 and 0.63, respectively) was higher than the Fall Harm Risk Screen (0.57 and 0.48, respectively). An evidence-derived, population-specific fall risk assessment may more accurately predict fallers than a general fall risk screen for stroke rehabilitation patients. While the SAFR improves upon the accuracy of a general assessment tool, additional refinement may be warranted. © The Author(s) 2014.

  20. Fetal biometric parameters: Reference charts for a non-selected risk population from Uberaba, Brazil

    PubMed Central

    Peixoto, Alberto Borges; da Cunha Caldas, Taciana Mara Rodrigues; Dulgheroff, Fernando Felix; Martins, Wellington P.

    2017-01-01

    Objective To establish reference charts for fetal biometric parameters in a non-selected risk population from Uberaba, Southeast of Brazil. Methods A retrospective cross-sectional study was performed among 5656 non-selected risk singleton pregnant women between 14 and 41 weeks of gestation. The ultrasound exams were performed during routine visits of second and third trimesters. Biparietal diameter (BPD) was measured at the level of the thalami and cavum septi pellucidi. Head circumference (HC) was calculated by the following formula: HC = 1.62*(BPD + occipital frontal diameter, OFD). Abdominal circumference (AC) was measured using the following formula: AC = (anteroposterior diameter + transverse abdominal diameter) × 1.57. Femur diaphysis length (FDL) was obtained in the longest axis of femur without including the distal femoral epiphysis. The estimated fetal weight (EFW) was obtained by the Hadlock formula. Polynomial regressions were performed to obtain the best-fit model for each fetal biometric parameter as the function of gestational age (GA). Results The mean, standard deviations (SD), minimum and maximum of BPD (cm), HC (cm), AC (cm), FDL (cm) and EFW (g) were 6.9 ± 1.9 (2.3 – 10.5), 24.51 ± 6.61 (9.1 – 36.4), 22.8 ± 7.3 (7.5 – 41.1), 4.9 ± 1.6 (1.2 – 8.1) and 1365 ± 1019 (103 – 4777), respectively. Second-degree polynomial regressions between the evaluated parameters and GA resulted in the following formulas: BPD = –4.044 + 0.540 × GA – 0.0049 × GA2 (R2 = 0.97); HC= –15.420 + 2.024 GA – 0.0199 × GA2 (R2 = 0.98); AC = –9.579 + 1.329 × GA – 0.0055 × GA2 (R2 = 0.97); FDL = –3.778 + 0.416 × GA – 0.0035 × GA2 (R2 = 0.98) and EFW = 916 – 123 × GA + 4.70 × GA2 (R2 = 0.96); respectively. Conclusion Reference charts for the fetal biometric parameters in a non-selected risk population from Uberaba, Southeast of Brazil, were established. PMID:28439425

  1. Cancer risk assessment of ethyl carbamate in alcoholic beverages from Brazil with special consideration to the spirits cachaça and tiquira

    PubMed Central

    2010-01-01

    Background Ethyl carbamate (EC) is a multi-site carcinogen in experimental animals and probably carcinogenic to humans (IARC group 2A). Traces of EC below health-relevant ranges naturally occur in several fermented foods and beverages, while higher concentrations above 1 mg/l are regularly detected in only certain spirits derived from cyanogenic plants. In Brazil this concerns the sugarcane spirit cachaça and the manioc (cassava) spirit tiquira, which both regularly exceed the national EC limit of 0.15 mg/l. This study aims to estimate human exposure in Brazil and provide a quantitative risk assessment. Methods The human dietary intake of EC via alcoholic beverages was estimated based on WHO alcohol consumption data in combination with own surveys and literature data. This data comprises the EC contents of the different beverage groups cachaça, tiquira, other spirits, beer, wine, and unrecorded alcohol (as defined by the WHO; including alcohol which is not captured in routine government statistics nor taxed). The risk assessment was conducted using the margin of exposure (MOE) approach with benchmark doses obtained from dose-response modelling of animal experiments. Lifetime cancer risk was calculated using the T25 dose descriptor. Results Considering differences between pot-still and column-still cachaça, its average EC content would be 0.38 mg/l. Tiquira contained a considerably higher average EC content of 2.34 mg/l. The whole population exposure from all alcoholic beverages was calculated to be around 100 to 200 ng/kg bw/day, with cachaça and unrecorded alcohol as the major contributing factors. The MOE was calculated to range between 400 and 2,466, with the lifetime cancer risk at approximately 3 cases in 10,000. An even higher risk may exist for binge-drinkers of cachaça and tiquira with MOEs of up to 80 and 15, respectively. Conclusions According to our risk assessment, EC poses a significant cancer risk for the alcohol-drinking population in Brazil, in

  2. Prediction of breast cancer risk with volatile biomarkers in breath.

    PubMed

    Phillips, Michael; Cataneo, Renee N; Cruz-Ramos, Jose Alfonso; Huston, Jan; Ornelas, Omar; Pappas, Nadine; Pathak, Sonali

    2018-03-23

    Human breath contains volatile organic compounds (VOCs) that are biomarkers of breast cancer. We investigated the positive and negative predictive values (PPV and NPV) of breath VOC biomarkers as indicators of breast cancer risk. We employed ultra-clean breath collection balloons to collect breath samples from 54 women with biopsy-proven breast cancer and 124 cancer-free controls. Breath VOCs were analyzed with gas chromatography (GC) combined with either mass spectrometry (GC MS) or surface acoustic wave detection (GC SAW). Chromatograms were randomly assigned to a training set or a validation set. Monte Carlo analysis identified significant breath VOC biomarkers of breast cancer in the training set, and these biomarkers were incorporated into a multivariate algorithm to predict disease in the validation set. In the unsplit dataset, the predictive algorithms generated discriminant function (DF) values that varied with sensitivity, specificity, PPV and NPV. Using GC MS, test accuracy = 90% (area under curve of receiver operating characteristic in unsplit dataset) and cross-validated accuracy = 77%. Using GC SAW, test accuracy = 86% and cross-validated accuracy = 74%. With both assays, a low DF value was associated with a low risk of breast cancer (NPV > 99.9%). A high DF value was associated with a high risk of breast cancer and PPV rising to 100%. Analysis of breath VOC samples collected with ultra-clean balloons detected biomarkers that accurately predicted risk of breast cancer.

  3. Assessing risk prediction models using individual participant data from multiple studies.

    PubMed

    Pennells, Lisa; Kaptoge, Stephen; White, Ian R; Thompson, Simon G; Wood, Angela M

    2014-03-01

    Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.

  4. Is Food Insecurity Associated with HIV Risk? Cross-Sectional Evidence from Sexually Active Women in Brazil

    PubMed Central

    Tsai, Alexander C.; Hung, Kristin J.; Weiser, Sheri D.

    2012-01-01

    Background Understanding how food insecurity among women gives rise to differential patterning in HIV risks is critical for policy and programming in resource-limited settings. This is particularly the case in Brazil, which has undergone successive changes in the gender and socio-geographic composition of its complex epidemic over the past three decades. We used data from a national survey of Brazilian women to estimate the relationship between food insecurity and HIV risk. Methods and Findings We used data on 12,684 sexually active women from a national survey conducted in Brazil in 2006–2007. Self-reported outcomes were (a) consistent condom use, defined as using a condom at each occasion of sexual intercourse in the previous 12 mo; (b) recent condom use, less stringently defined as using a condom with the most recent sexual partner; and (c) itchy vaginal discharge in the previous 30 d, possibly indicating presence of a sexually transmitted infection. The primary explanatory variable of interest was food insecurity, measured using the culturally adapted and validated Escala Brasiliera de Segurança Alimentar. In multivariable logistic regression models, severe food insecurity with hunger was associated with a reduced odds of consistent condom use in the past 12 mo (adjusted odds ratio [AOR] = 0.67; 95% CI, 0.48–0.92) and condom use at last sexual intercourse (AOR = 0.75; 95% CI, 0.57–0.98). Self-reported itchy vaginal discharge was associated with all categories of food insecurity (with AORs ranging from 1.46 to 1.94). In absolute terms, the effect sizes were large in magnitude across all outcomes. Underweight and/or lack of control in sexual relations did not appear to mediate the observed associations. Conclusions Severe food insecurity with hunger was associated with reduced odds of condom use and increased odds of itchy vaginal discharge, which is potentially indicative of sexually transmitted infection, among sexually active women in Brazil

  5. Predicting risk for childhood asthma by pre-pregnancy, perinatal, and postnatal factors.

    PubMed

    Wen, Hui-Ju; Chiang, Tung-Liang; Lin, Shio-Jean; Guo, Yue Leon

    2015-05-01

    Symptoms of atopic disease start early in human life. Predicting risk for childhood asthma by early-life exposure would contribute to disease prevention. A birth cohort study was conducted to investigate early-life risk factors for childhood asthma and to develop a predictive model for the development of asthma. National representative samples of newborn babies were obtained by multistage stratified systematic sampling from the 2005 Taiwan Birth Registry. Information on potential risk factors and children's health was collected by home interview when babies were 6 months old and 5 yr old, respectively. Backward stepwise regression analysis was used to identify the risk factors of childhood asthma for predictive models that were used to calculate the probability of childhood asthma. A total of 19,192 children completed the study satisfactorily. Physician-diagnosed asthma was reported in 6.6% of 5-yr-old children. Pre-pregnancy factors (parental atopy and socioeconomic status), perinatal factors (place of residence, exposure to indoor mold and painting/renovations during pregnancy), and postnatal factors (maternal postpartum depression and the presence of atopic dermatitis before 6 months of age) were chosen for the predictive models, and the highest predicted probability of asthma in 5-yr-old children was 68.1% in boys and 78.1% in girls; the lowest probability in boys and girls was 4.1% and 3.2%, respectively. This investigation provides a technique for predicting risk of childhood asthma that can be used to developing a preventive strategy against asthma. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  6. A simple risk scoring system for prediction of relapse after inpatient alcohol treatment.

    PubMed

    Pedersen, Mads Uffe; Hesse, Morten

    2009-01-01

    Predicting relapse after alcoholism treatment can be useful in targeting patients for aftercare services. However, a valid and practical instrument for predicting relapse risk does not exist. Based on a prospective study of alcoholism treatment, we developed the Risk of Alcoholic Relapse Scale (RARS) using items taken from the Addiction Severity Index and some basic demographic information. The RARS was cross-validated using two non-overlapping samples, and tested for its ability to predict relapse across different models of treatment. The RARS predicted relapse to drinking within 6 months after alcoholism treatment in both the original and the validation sample, and in a second validation sample it predicted admission to new treatment 3 years after treatment. The RARS can identify patients at high risk of relapse who need extra aftercare and support after treatment.

  7. Spatial distribution and risk factors for Toxoplasma gondii seropositivity in cattle slaughtered for human consumption in Rondônia, North region, Brazil.

    PubMed

    de Souza, Juliana Bianca Rocha; Soares, Vando Edésio; Maia, Maerle Oliveira; Pereira, Cleidiane Magalhães; Ferraudo, Antônio Sergio; Cruz, Breno Cayeiro; Pires Teixeira, Weslen Fabrício; Felippelli, Gustavo; Maciel, Willian Giquelin; Gonçalves, Walter Antonio; da Costa, Alvimar José; Zanetti Lopes, Welber Daniel

    2016-08-15

    The present study aimed to evaluate Toxoplasma gondii seroprevalence in cattle slaughtered for human consumption from rural properties in the state of Rondônia, North region, Brazil; the seroprevalence was determined using indirect immunofluorescence assays (IFATs). Additionally, spatial distribution and risk factors associated with toxoplasmosis were also analyzed. Of the 1000 cattle serum samples examined, 53 (5.3%) were determined to be seropositive for T. gondii with antibody titers (IgG) ≥64. In regard to results of the studied risk factors (presence of cats, cats with free access to cattle, breeding system, animal's gender, consumption of raw milk by humans on the property and cattle abortion in the last 12 months) and the odds ratio (OR) of each of these factors influencing cattle to acquire toxoplasmosis, only animals raised on a feeder/stocker/backgrounder system presented a higher probability of being seropositive for T. gondii (OR≥1, P=0.04) than cattle raised only in a feeder/stocker system. There was no association between the occurrence of reproductive problems and T. gondii seropositivity. Based on results obtained in the Brazilian state of Rondônia, it could be concluded that the presence of cats and their contact with cattle on each property, cattle breeding purpose and cattle abortion in the last 12 months were not considered risk factors for T. gondii infection in cattle. Considering that the presence of T. gondii was detected in animals slaughtered in the state of Rondônia, consuming raw or undercooked meat from seropositive cattle should be considered a route of transmission of T. gondii to humans. However, the prevalence of toxoplasmosis diagnosed in cattle from this state (5.30%) is lower than the prevalence of toxoplasmosis observed in South, Southeast and Center-West regions of Brazil, which may vary between 48.5% and 71.0%. The low prevalence of toxoplasmosis in cattle is highlighted in Rondônia, which is the sixth largest state

  8. Risk factors for Toxoplasma gondii infection in sheep and cattle from Fernando de Noronha Island, Brazil.

    PubMed

    Magalhães, Fernando Jorge Rodrigues; Ribeiro-Andrade, Müller; Alcântara, Adrianne Mota de; Pinheiro, José Wilton; Sena, Maria José de; Porto, Wagnner José Nascimento; Vieira, Rafael Felipe da Costa; Mota, Rinaldo Aparecido

    2016-01-01

    Toxoplasmosis is a zoonotic disease of global distribution that affects all warm-blooded animals. The purpose of this investigation was to determine the prevalence of T. gondii infection and identify the risk factors associated with its occurrence in domestic ruminants raised on the island of Fernando de Noronha, Brazil, and to confirm that cattle and sheep raised in Fernando de Noronha Island present statistically different T. gondii prevalence rates. Serum samples were collected from sheep (n=240) and cattle (n=140) for the detection of antibodies by indirect immunofluorescence. Samples were collected from all the animals on all the farms. Risk factors were analyzed by univariate analysis and logistic regression. The prevalence rate of positive sheep was 85.0% while that of cattle was 10.7%. A multivariate analysis revealed that the site of contact of sheep with felines was a risk factor. For cattle, the risk factors identified in this study were: extensive farming system, water source, more than three cats per farm, and the presence of rats in feed storage locations. The findings revealed a significant difference in the prevalence rates in sheep and cattle raised in this insular environment.

  9. Risk Communication Concerning Welding Fumes for the Primary Preventive Care of Welding Apprentices in Southern Brazil

    PubMed Central

    Cezar-Vaz, Marta Regina; Bonow, Clarice Alves; Cezar Vaz, Joana

    2015-01-01

    This study’s aim was to assess the perceptions of welding apprentices concerning welding fumes being associated with respiratory and cardiovascular disorders and assess the implementation of risk communication as a primary prevention tool in the welding training process. This quasi-experimental, non-randomized study with before-and-after design was conducted with 84 welding apprentices in Southern Brazil. Poisson Regression analysis was used. Relative Risk was the measure used with a 95% confidence interval and 5% (p ≤ 0.05) significance level. Significant association was found between perceptions of worsened symptoms of respiratory disorders caused by welding fumes and educational level (p = 0.049), the use of goggles to protect against ultraviolet rays (p = 0.023), and access to services in private health facilities without insurance coverage (p = 0.001). Apprentices younger than 25 years old were 4.9 times more likely to perceive worsened cardiovascular symptoms caused by welding fumes after risk communication (RR = 4.91; CI 95%: 1.09 to 22.2). The conclusion is that risk communication as a primary preventive measure in continuing education processes implemented among apprentices, who are future welders, was efficacious. Thus, this study confirms that risk communication can be implemented as a primary prevention tool in welding apprenticeships. PMID:25607606

  10. Risk communication concerning welding fumes for the primary preventive care of welding apprentices in southern Brazil.

    PubMed

    Cezar-Vaz, Marta Regina; Bonow, Clarice Alves; Vaz, Joana Cezar

    2015-01-19

    This study's aim was to assess the perceptions of welding apprentices concerning welding fumes being associated with respiratory and cardiovascular disorders and assess the implementation of risk communication as a primary prevention tool in the welding training process. This quasi-experimental, non-randomized study with before-and-after design was conducted with 84 welding apprentices in Southern Brazil. Poisson Regression analysis was used. Relative Risk was the measure used with a 95% confidence interval and 5% (p ≤ 0.05) significance level. Significant association was found between perceptions of worsened symptoms of respiratory disorders caused by welding fumes and educational level (p = 0.049), the use of goggles to protect against ultraviolet rays (p = 0.023), and access to services in private health facilities without insurance coverage (p = 0.001). Apprentices younger than 25 years old were 4.9 times more likely to perceive worsened cardiovascular symptoms caused by welding fumes after risk communication (RR = 4.91; CI 95%: 1.09 to 22.2). The conclusion is that risk communication as a primary preventive measure in continuing education processes implemented among apprentices, who are future welders, was efficacious. Thus, this study confirms that risk communication can be implemented as a primary prevention tool in welding apprenticeships.

  11. Peak Pc Prediction in Conjunction Analysis: Conjunction Assessment Risk Analysis. Pc Behavior Prediction Models

    NASA Technical Reports Server (NTRS)

    Vallejo, J.J.; Hejduk, M.D.; Stamey, J. D.

    2015-01-01

    Satellite conjunction risk typically evaluated through the probability of collision (Pc). Considers both conjunction geometry and uncertainties in both state estimates. Conjunction events initially discovered through Joint Space Operations Center (JSpOC) screenings, usually seven days before Time of Closest Approach (TCA). However, JSpOC continues to track objects and issue conjunction updates. Changes in state estimate and reduced propagation time cause Pc to change as event develops. These changes a combination of potentially predictable development and unpredictable changes in state estimate covariance. Operationally useful datum: the peak Pc. If it can reasonably be inferred that the peak Pc value has passed, then risk assessment can be conducted against this peak value. If this value is below remediation level, then event intensity can be relaxed. Can the peak Pc location be reasonably predicted?

  12. Predictive Analytics for Identification of Patients at Risk for QT Interval Prolongation - A Systematic Review.

    PubMed

    Tomaselli Muensterman, Elena; Tisdale, James E

    2018-06-08

    Prolongation of the heart rate-corrected QT (QTc) interval increases the risk for torsades de pointes (TdP), a potentially fatal arrhythmia. The likelihood of TdP is higher in patients with risk factors, which include female sex, older age, heart failure with reduced ejection fraction, hypokalemia, hypomagnesemia, concomitant administration of ≥ 2 QTc interval-prolonging medications, among others. Assessment and quantification of risk factors may facilitate prediction of patients at highest risk for developing QTc interval prolongation and TdP. Investigators have utilized the field of predictive analytics, which generates predictions using techniques including data mining, modeling, machine learning, and others, to develop methods of risk quantification and prediction of QTc interval prolongation. Predictive analytics have also been incorporated into clinical decision support (CDS) tools to alert clinicians regarding patients at increased risk of developing QTc interval prolongation. The objectives of this paper are to assess the effectiveness of predictive analytics for identification of patients at risk of drug-induced QTc interval prolongation, and to discuss the efficacy of incorporation of predictive analytics into CDS tools in clinical practice. A systematic review of English language articles (human subjects only) was performed, yielding 57 articles, with an additional 4 articles identified from other sources; a total of 10 articles were included in this review. Risk scores for QTc interval prolongation have been developed in various patient populations including those in cardiac intensive care units (ICUs) and in broader populations of hospitalized or health system patients. One group developed a risk score that includes information regarding genetic polymorphisms; this score significantly predicted TdP. Development of QTc interval prolongation risk prediction models and incorporation of these models into CDS tools reduces the risk of QTc interval

  13. Gastrointestinal and ectoparasites from urban stray dogs in Fortaleza (Brazil): high infection risk for humans?

    PubMed

    Klimpel, Sven; Heukelbach, Jörg; Pothmann, David; Rückert, Sonja

    2010-08-01

    Dogs are important definite or reservoir hosts for zoonotic parasites. However, only few studies on the prevalence of intestinal parasites in urban areas in Brazil are available. We performed a comprehensive study on parasites of stray dogs in a Brazilian metropolitan area. We included 46 stray dogs caught in the urban areas of Fortaleza (northeast Brazil). After euthanization, dogs were autopsied. Ectoparasites were collected, and the intestinal content of dogs were examined for the presence of parasites. Faecal samples were collected and analysed using merthiolate iodine formaldehyde concentration method. A total of nine different parasite species were found, including five endoparasite (one protozoan, one cestode and three nematode species) and four ectoparasite species (two flea, one louse and one tick species). In the intestinal content, 3,162 specimens of four helminth species were found: Ancylostoma caninum (prevalence, 95.7%), Dipylidium caninum (45.7%), Toxocara canis (8.7%) and Trichuris vulpis (4.3%). A total of 394 ectoparasite specimens were identified, including Rhipicephalus sanguineus (prevalence, 100.0%), Heterodoxus spiniger (67.4%), Ctenocephalides canis (39.1%) and Ctenocephalides felis (17.4%). In the faeces, intestinal parasites were detected in 38 stray dogs (82.6%), including oocysts of Giardia sp. (2.2%) and eggs of the nematode A. caninum (82.6%). Neither eggs nor larval stages of D. caninum, T. canis or T. vulpis were detected in dog faeces. Sensitivity of faecal examination for A. caninum was 86.4% (95% confidence interval, 72.0-94.3) but zero percentage for the other intestinal helminth species. Our data show that stray dogs in northeast Brazil carry a multitude of zoonotic ecto- and endoparasites, posing a considerable risk for humans. With the exception of A. caninum, sensitivity of faecal examination was negligible.

  14. Cumulative risk hypothesis: Predicting and preventing child maltreatment recidivism.

    PubMed

    Solomon, David; Åsberg, Kia; Peer, Samuel; Prince, Gwendolyn

    2016-08-01

    Although Child Protective Services (CPS) and other child welfare agencies aim to prevent further maltreatment in cases of child abuse and neglect, recidivism is common. Having a better understanding of recidivism predictors could aid in preventing additional instances of maltreatment. A previous study identified two CPS interventions that predicted recidivism: psychotherapy for the parent, which was related to a reduced risk of recidivism, and temporary removal of the child from the parent's custody, which was related to an increased recidivism risk. However, counter to expectations, this previous study did not identify any other specific risk factors related to maltreatment recidivism. For the current study, it was hypothesized that (a) cumulative risk (i.e., the total number of risk factors) would significantly predict maltreatment recidivism above and beyond intervention variables in a sample of CPS case files and that (b) therapy for the parent would be related to a reduced likelihood of recidivism. Because it was believed that the relation between temporary removal of a child from the parent's custody and maltreatment recidivism is explained by cumulative risk, the study also hypothesized that that the relation between temporary removal of the child from the parent's custody and recidivism would be mediated by cumulative risk. After performing a hierarchical logistic regression analysis, the first two hypotheses were supported, and an additional predictor, psychotherapy for the child, also was related to reduced chances of recidivism. However, Hypothesis 3 was not supported, as risk did not significantly mediate the relation between temporary removal and recidivism. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Utility functions predict variance and skewness risk preferences in monkeys

    PubMed Central

    Genest, Wilfried; Stauffer, William R.; Schultz, Wolfram

    2016-01-01

    Utility is the fundamental variable thought to underlie economic choices. In particular, utility functions are believed to reflect preferences toward risk, a key decision variable in many real-life situations. To assess the validity of utility representations, it is therefore important to examine risk preferences. In turn, this approach requires formal definitions of risk. A standard approach is to focus on the variance of reward distributions (variance-risk). In this study, we also examined a form of risk related to the skewness of reward distributions (skewness-risk). Thus, we tested the extent to which empirically derived utility functions predicted preferences for variance-risk and skewness-risk in macaques. The expected utilities calculated for various symmetrical and skewed gambles served to define formally the direction of stochastic dominance between gambles. In direct choices, the animals’ preferences followed both second-order (variance) and third-order (skewness) stochastic dominance. Specifically, for gambles with different variance but identical expected values (EVs), the monkeys preferred high-variance gambles at low EVs and low-variance gambles at high EVs; in gambles with different skewness but identical EVs and variances, the animals preferred positively over symmetrical and negatively skewed gambles in a strongly transitive fashion. Thus, the utility functions predicted the animals’ preferences for variance-risk and skewness-risk. Using these well-defined forms of risk, this study shows that monkeys’ choices conform to the internal reward valuations suggested by their utility functions. This result implies a representation of utility in monkeys that accounts for both variance-risk and skewness-risk preferences. PMID:27402743

  16. Utility functions predict variance and skewness risk preferences in monkeys.

    PubMed

    Genest, Wilfried; Stauffer, William R; Schultz, Wolfram

    2016-07-26

    Utility is the fundamental variable thought to underlie economic choices. In particular, utility functions are believed to reflect preferences toward risk, a key decision variable in many real-life situations. To assess the validity of utility representations, it is therefore important to examine risk preferences. In turn, this approach requires formal definitions of risk. A standard approach is to focus on the variance of reward distributions (variance-risk). In this study, we also examined a form of risk related to the skewness of reward distributions (skewness-risk). Thus, we tested the extent to which empirically derived utility functions predicted preferences for variance-risk and skewness-risk in macaques. The expected utilities calculated for various symmetrical and skewed gambles served to define formally the direction of stochastic dominance between gambles. In direct choices, the animals' preferences followed both second-order (variance) and third-order (skewness) stochastic dominance. Specifically, for gambles with different variance but identical expected values (EVs), the monkeys preferred high-variance gambles at low EVs and low-variance gambles at high EVs; in gambles with different skewness but identical EVs and variances, the animals preferred positively over symmetrical and negatively skewed gambles in a strongly transitive fashion. Thus, the utility functions predicted the animals' preferences for variance-risk and skewness-risk. Using these well-defined forms of risk, this study shows that monkeys' choices conform to the internal reward valuations suggested by their utility functions. This result implies a representation of utility in monkeys that accounts for both variance-risk and skewness-risk preferences.

  17. Mortality determinants and prediction of outcome in high risk newborns.

    PubMed

    Dalvi, R; Dalvi, B V; Birewar, N; Chari, G; Fernandez, A R

    1990-06-01

    The aim of this study was to determine independent patient-related predictors of mortality in high risk newborns admitted at our centre. The study population comprised 100 consecutive newborns each, from the premature unit (PU) and sick baby care unit (SBCU), respectively. Thirteen high risk factors (variables) for each of the two units, were entered into a multivariate regression analysis. Variables with independent predictive value for poor outcome (i.e., death) in PU were, weight less than 1 kg, hyaline membrane disease, neurologic problems, and intravenous therapy. High risk factors in SBCU included, blood gas abnormality, bleeding phenomena, recurrent convulsions, apnea, and congenital anomalies. Identification of these factors guided us in defining priority areas for improvement in our system of neonatal care. Also, based on these variables a simple predictive score for outcome was constructed. The prediction equation and the score were cross-validated by applying them to a 'test-set' of 100 newborns each for PU and SBCU. Results showed a comparable sensitivity, specificity and error rate.

  18. Recent development of risk-prediction models for incident hypertension: An updated systematic review

    PubMed Central

    Xiao, Lei; Liu, Ya; Wang, Zuoguang; Li, Chuang; Jin, Yongxin; Zhao, Qiong

    2017-01-01

    Background Hypertension is a leading global health threat and a major cardiovascular disease. Since clinical interventions are effective in delaying the disease progression from prehypertension to hypertension, diagnostic prediction models to identify patient populations at high risk for hypertension are imperative. Methods Both PubMed and Embase databases were searched for eligible reports of either prediction models or risk scores of hypertension. The study data were collected, including risk factors, statistic methods, characteristics of study design and participants, performance measurement, etc. Results From the searched literature, 26 studies reporting 48 prediction models were selected. Among them, 20 reports studied the established models using traditional risk factors, such as body mass index (BMI), age, smoking, blood pressure (BP) level, parental history of hypertension, and biochemical factors, whereas 6 reports used genetic risk score (GRS) as the prediction factor. AUC ranged from 0.64 to 0.97, and C-statistic ranged from 60% to 90%. Conclusions The traditional models are still the predominant risk prediction models for hypertension, but recently, more models have begun to incorporate genetic factors as part of their model predictors. However, these genetic predictors need to be well selected. The current reported models have acceptable to good discrimination and calibration ability, but whether the models can be applied in clinical practice still needs more validation and adjustment. PMID:29084293

  19. Predictive and Prognostic Factors in Definition of Risk Groups in Endometrial Carcinoma

    PubMed Central

    Sorbe, Bengt

    2012-01-01

    Background. The aim was to evaluate predictive and prognostic factors in a large consecutive series of endometrial carcinomas and to discuss pre- and postoperative risk groups based on these factors. Material and Methods. In a consecutive series of 4,543 endometrial carcinomas predictive and prognostic factors were analyzed with regard to recurrence rate and survival. The patients were treated with primary surgery and adjuvant radiotherapy. Two preoperative and three postoperative risk groups were defined. DNA ploidy was included in the definitions. Eight predictive or prognostic factors were used in multivariate analyses. Results. The overall recurrence rate of the complete series was 11.4%. Median time to relapse was 19.7 months. In a multivariate logistic regression analysis, FIGO grade, myometrial infiltration, and DNA ploidy were independent and statistically predictive factors with regard to recurrence rate. The 5-year overall survival rate was 73%. Tumor stage was the single most important factor with FIGO grade on the second place. DNA ploidy was also a significant prognostic factor. In the preoperative risk group definitions three factors were used: histology, FIGO grade, and DNA ploidy. Conclusions. DNA ploidy was an important and significant predictive and prognostic factor and should be used both in preoperative and postoperative risk group definitions. PMID:23209924

  20. Common polygenic variation enhances risk prediction for Alzheimer’s disease

    PubMed Central

    Sims, Rebecca; Bannister, Christian; Harold, Denise; Vronskaya, Maria; Majounie, Elisa; Badarinarayan, Nandini; Morgan, Kevin; Passmore, Peter; Holmes, Clive; Powell, John; Brayne, Carol; Gill, Michael; Mead, Simon; Goate, Alison; Cruchaga, Carlos; Lambert, Jean-Charles; van Duijn, Cornelia; Maier, Wolfgang; Ramirez, Alfredo; Holmans, Peter; Jones, Lesley; Hardy, John; Seshadri, Sudha; Schellenberg, Gerard D.; Amouyel, Philippe

    2015-01-01

    The identification of subjects at high risk for Alzheimer’s disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer’s disease and the accuracy of Alzheimer’s disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer’s Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer’s disease (P = 4.9 × 10−26). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10−19). The best prediction accuracy AUC = 78.2% (95% confidence interval 77–80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer’s disease has a significant polygenic component, which has predictive utility for Alzheimer’s disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes. PMID:26490334

  1. Comparative Risk Predictions of Second Cancers After Carbon-Ion Therapy Versus Proton Therapy.

    PubMed

    Eley, John G; Friedrich, Thomas; Homann, Kenneth L; Howell, Rebecca M; Scholz, Michael; Durante, Marco; Newhauser, Wayne D

    2016-05-01

    This work proposes a theoretical framework that enables comparative risk predictions for second cancer incidence after particle beam therapy for different ion species for individual patients, accounting for differences in relative biological effectiveness (RBE) for the competing processes of tumor initiation and cell inactivation. Our working hypothesis was that use of carbon-ion therapy instead of proton therapy would show a difference in the predicted risk of second cancer incidence in the breast for a sample of Hodgkin lymphoma (HL) patients. We generated biologic treatment plans and calculated relative predicted risks of second cancer in the breast by using two proposed methods: a full model derived from the linear quadratic model and a simpler linear-no-threshold model. For our reference calculation, we found the predicted risk of breast cancer incidence for carbon-ion plans-to-proton plan ratio, , to be 0.75 ± 0.07 but not significantly smaller than 1 (P=.180). Our findings suggest that second cancer risks are, on average, comparable between proton therapy and carbon-ion therapy. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. Assessing Risk Prediction Models Using Individual Participant Data From Multiple Studies

    PubMed Central

    Pennells, Lisa; Kaptoge, Stephen; White, Ian R.; Thompson, Simon G.; Wood, Angela M.; Tipping, Robert W.; Folsom, Aaron R.; Couper, David J.; Ballantyne, Christie M.; Coresh, Josef; Goya Wannamethee, S.; Morris, Richard W.; Kiechl, Stefan; Willeit, Johann; Willeit, Peter; Schett, Georg; Ebrahim, Shah; Lawlor, Debbie A.; Yarnell, John W.; Gallacher, John; Cushman, Mary; Psaty, Bruce M.; Tracy, Russ; Tybjærg-Hansen, Anne; Price, Jackie F.; Lee, Amanda J.; McLachlan, Stela; Khaw, Kay-Tee; Wareham, Nicholas J.; Brenner, Hermann; Schöttker, Ben; Müller, Heiko; Jansson, Jan-Håkan; Wennberg, Patrik; Salomaa, Veikko; Harald, Kennet; Jousilahti, Pekka; Vartiainen, Erkki; Woodward, Mark; D'Agostino, Ralph B.; Bladbjerg, Else-Marie; Jørgensen, Torben; Kiyohara, Yutaka; Arima, Hisatomi; Doi, Yasufumi; Ninomiya, Toshiharu; Dekker, Jacqueline M.; Nijpels, Giel; Stehouwer, Coen D. A.; Kauhanen, Jussi; Salonen, Jukka T.; Meade, Tom W.; Cooper, Jackie A.; Cushman, Mary; Folsom, Aaron R.; Psaty, Bruce M.; Shea, Steven; Döring, Angela; Kuller, Lewis H.; Grandits, Greg; Gillum, Richard F.; Mussolino, Michael; Rimm, Eric B.; Hankinson, Sue E.; Manson, JoAnn E.; Pai, Jennifer K.; Kirkland, Susan; Shaffer, Jonathan A.; Shimbo, Daichi; Bakker, Stephan J. L.; Gansevoort, Ron T.; Hillege, Hans L.; Amouyel, Philippe; Arveiler, Dominique; Evans, Alun; Ferrières, Jean; Sattar, Naveed; Westendorp, Rudi G.; Buckley, Brendan M.; Cantin, Bernard; Lamarche, Benoît; Barrett-Connor, Elizabeth; Wingard, Deborah L.; Bettencourt, Richele; Gudnason, Vilmundur; Aspelund, Thor; Sigurdsson, Gunnar; Thorsson, Bolli; Kavousi, Maryam; Witteman, Jacqueline C.; Hofman, Albert; Franco, Oscar H.; Howard, Barbara V.; Zhang, Ying; Best, Lyle; Umans, Jason G.; Onat, Altan; Sundström, Johan; Michael Gaziano, J.; Stampfer, Meir; Ridker, Paul M.; Michael Gaziano, J.; Ridker, Paul M.; Marmot, Michael; Clarke, Robert; Collins, Rory; Fletcher, Astrid; Brunner, Eric; Shipley, Martin; Kivimäki, Mika; Ridker, Paul M.; Buring, Julie; Cook, Nancy; Ford, Ian; Shepherd, James; Cobbe, Stuart M.; Robertson, Michele; Walker, Matthew; Watson, Sarah; Alexander, Myriam; Butterworth, Adam S.; Angelantonio, Emanuele Di; Gao, Pei; Haycock, Philip; Kaptoge, Stephen; Pennells, Lisa; Thompson, Simon G.; Walker, Matthew; Watson, Sarah; White, Ian R.; Wood, Angela M.; Wormser, David; Danesh, John

    2014-01-01

    Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous. PMID:24366051

  3. The more from East-Asian, the better: risk prediction of colorectal cancer risk by GWAS-identified SNPs among Japanese.

    PubMed

    Abe, Makiko; Ito, Hidemi; Oze, Isao; Nomura, Masatoshi; Ogawa, Yoshihiro; Matsuo, Keitaro

    2017-12-01

    Little is known about the difference of genetic predisposition for CRC between ethnicities; however, many genetic traits common to colorectal cancer have been identified. This study investigated whether more SNPs identified in GWAS in East Asian population could improve the risk prediction of Japanese and explored possible application of genetic risk groups as an instrument of the risk communication. 558 Patients histologically verified colorectal cancer and 1116 first-visit outpatients were included for derivation study, and 547 cases and 547 controls were for replication study. Among each population, we evaluated prediction models for the risk of CRC that combined the genetic risk group based on SNPs from GWASs in European-population and a similarly developed model adding SNPs from GWASs in East Asian-population. We examined whether adding East Asian-specific SNPs would improve the discrimination. Six SNPs (rs6983267, rs4779584, rs4444235, rs9929218, rs10936599, rs16969681) from 23 SNPs by European-based GWAS and five SNPs (rs704017, rs11196172, rs10774214, rs647161, rs2423279) among ten SNPs by Asian-based GWAS were selected in CRC risk prediction model. Compared with a 6-SNP-based model, an 11-SNP model including Asian GWAS-SNPs showed improved discrimination capacity in Receiver operator characteristic analysis. A model with 11 SNPs resulted in statistically significant improvement in both derivation (P = 0.0039) and replication studies (P = 0.0018) compared with six SNP model. We estimated cumulative risk of CRC by using genetic risk group based on 11 SNPs and found that the cumulative risk at age 80 is approximately 13% in the high-risk group while 6% in the low-risk group. We constructed a more efficient CRC risk prediction model with 11 SNPs including newly identified East Asian-based GWAS SNPs (rs704017, rs11196172, rs10774214, rs647161, rs2423279). Risk grouping based on 11 SNPs depicted lifetime difference of CRC risk. This might be useful for

  4. Genetic risk prediction using a spatial autoregressive model with adaptive lasso.

    PubMed

    Wen, Yalu; Shen, Xiaoxi; Lu, Qing

    2018-05-31

    With rapidly evolving high-throughput technologies, studies are being initiated to accelerate the process toward precision medicine. The collection of the vast amounts of sequencing data provides us with great opportunities to systematically study the role of a deep catalog of sequencing variants in risk prediction. Nevertheless, the massive amount of noise signals and low frequencies of rare variants in sequencing data pose great analytical challenges on risk prediction modeling. Motivated by the development in spatial statistics, we propose a spatial autoregressive model with adaptive lasso (SARAL) for risk prediction modeling using high-dimensional sequencing data. The SARAL is a set-based approach, and thus, it reduces the data dimension and accumulates genetic effects within a single-nucleotide variant (SNV) set. Moreover, it allows different SNV sets having various magnitudes and directions of effect sizes, which reflects the nature of complex diseases. With the adaptive lasso implemented, SARAL can shrink the effects of noise SNV sets to be zero and, thus, further improve prediction accuracy. Through simulation studies, we demonstrate that, overall, SARAL is comparable to, if not better than, the genomic best linear unbiased prediction method. The method is further illustrated by an application to the sequencing data from the Alzheimer's Disease Neuroimaging Initiative. Copyright © 2018 John Wiley & Sons, Ltd.

  5. Genetic markers enhance coronary risk prediction in men: the MORGAM prospective cohorts.

    PubMed

    Hughes, Maria F; Saarela, Olli; Stritzke, Jan; Kee, Frank; Silander, Kaisa; Klopp, Norman; Kontto, Jukka; Karvanen, Juha; Willenborg, Christina; Salomaa, Veikko; Virtamo, Jarmo; Amouyel, Phillippe; Arveiler, Dominique; Ferrières, Jean; Wiklund, Per-Gunner; Baumert, Jens; Thorand, Barbara; Diemert, Patrick; Trégouët, David-Alexandre; Hengstenberg, Christian; Peters, Annette; Evans, Alun; Koenig, Wolfgang; Erdmann, Jeanette; Samani, Nilesh J; Kuulasmaa, Kari; Schunkert, Heribert

    2012-01-01

    More accurate coronary heart disease (CHD) prediction, specifically in middle-aged men, is needed to reduce the burden of disease more effectively. We hypothesised that a multilocus genetic risk score could refine CHD prediction beyond classic risk scores and obtain more precise risk estimates using a prospective cohort design. Using data from nine prospective European cohorts, including 26,221 men, we selected in a case-cohort setting 4,818 healthy men at baseline, and used Cox proportional hazards models to examine associations between CHD and risk scores based on genetic variants representing 13 genomic regions. Over follow-up (range: 5-18 years), 1,736 incident CHD events occurred. Genetic risk scores were validated in men with at least 10 years of follow-up (632 cases, 1361 non-cases). Genetic risk score 1 (GRS1) combined 11 SNPs and two haplotypes, with effect estimates from previous genome-wide association studies. GRS2 combined 11 SNPs plus 4 SNPs from the haplotypes with coefficients estimated from these prospective cohorts using 10-fold cross-validation. Scores were added to a model adjusted for classic risk factors comprising the Framingham risk score and 10-year risks were derived. Both scores improved net reclassification (NRI) over the Framingham score (7.5%, p = 0.017 for GRS1, 6.5%, p = 0.044 for GRS2) but GRS2 also improved discrimination (c-index improvement 1.11%, p = 0.048). Subgroup analysis on men aged 50-59 (436 cases, 603 non-cases) improved net reclassification for GRS1 (13.8%) and GRS2 (12.5%). Net reclassification improvement remained significant for both scores when family history of CHD was added to the baseline model for this male subgroup improving prediction of early onset CHD events. Genetic risk scores add precision to risk estimates for CHD and improve prediction beyond classic risk factors, particularly for middle aged men.

  6. Benign Breast Disease: Toward Molecular Prediction of Breast Cancer Risk

    DTIC Science & Technology

    2008-06-01

    of benign histology in predicting risk of future breast cancer, examining in detail the role of proliferative disease, atypia , papillomas, radial...who had proliferative disease with atypia , especially those of younger age. • We identified a marked increased risk of breast cancer in women with...imparts an increased risk of developing a subsequent carcinoma similar to other forms of proliferative breast disease without atypia . Atypical

  7. Net Reclassification Indices for Evaluating Risk-Prediction Instruments: A Critical Review

    PubMed Central

    Kerr, Kathleen F.; Wang, Zheyu; Janes, Holly; McClelland, Robyn L.; Psaty, Bruce M.; Pepe, Margaret S.

    2014-01-01

    Net reclassification indices have recently become popular statistics for measuring the prediction increment of new biomarkers. We review the various types of net reclassification indices and their correct interpretations. We evaluate the advantages and disadvantages of quantifying the prediction increment with these indices. For pre-defined risk categories, we relate net reclassification indices to existing measures of the prediction increment. We also consider statistical methodology for constructing confidence intervals for net reclassification indices and evaluate the merits of hypothesis testing based on such indices. We recommend that investigators using net reclassification indices should report them separately for events (cases) and nonevents (controls). When there are two risk categories, the components of net reclassification indices are the same as the changes in the true-positive and false-positive rates. We advocate use of true- and false-positive rates and suggest it is more useful for investigators to retain the existing, descriptive terms. When there are three or more risk categories, we recommend against net reclassification indices because they do not adequately account for clinically important differences in shifts among risk categories. The category-free net reclassification index is a new descriptive device designed to avoid pre-defined risk categories. However, it suffers from many of the same problems as other measures such as the area under the receiver operating characteristic curve. In addition, the category-free index can mislead investigators by overstating the incremental value of a biomarker, even in independent validation data. When investigators want to test a null hypothesis of no prediction increment, the well-established tests for coefficients in the regression model are superior to the net reclassification index. If investigators want to use net reclassification indices, confidence intervals should be calculated using bootstrap

  8. Infant mortality in Pelotas, Brazil: a comparison of risk factors in two birth cohorts.

    PubMed

    Menezes, Ana Maria Baptista; Hallal, Pedro Curi; Santos, Iná Silva dos; Victora, Cesar Gomes; Barros, Fernando Celso

    2005-12-01

    To compare two population-based birth cohorts to assess trends in infant mortality rates and the distribution of relevant risk factors, and how these changed after an 11-year period. Data from two population-based prospective birth cohorts (1982 and 1993) were analyzed. Both studies included all children born in a hospital (> 99% of all births) in the city of Pelotas, Southern Brazil. Infant mortality was monitored through surveillance of all maternity hospitals, mortality registries and cemeteries. There were 5,914 live-born children in 1982 and 5,249 in 1993. The infant mortality rate decreased by 41%, from 36.0 per 1,000 live births in 1982 to 21.1 per 1,000 in 1993. Socioeconomic and maternal factors tended to become more favorable during the study period, but there were unfavorable changes in birthweight and gestational age. Poverty, high parity, low birthweight, preterm delivery, and intrauterine growth restriction were the main risk factors for infant mortality in both cohorts. The 41% reduction in infant mortality between 1982 and 1993 would have been even greater had the prevalence of risk factors remained constant during the period studied here. There were impressive declines in infant mortality which were not due to changes in the risk factors we studied. Because no reduction was seen in the large social inequalities documented in the 1982 cohort, it is likely that the reduction in infant mortality resulted largely from improvements in health care.

  9. IL-8 predicts pediatric oncology patients with febrile neutropenia at low risk for bacteremia.

    PubMed

    Cost, Carrye R; Stegner, Martha M; Leonard, David; Leavey, Patrick

    2013-04-01

    Despite a low bacteremia rate, pediatric oncology patients are frequently admitted for febrile neutropenia. A pediatric risk prediction model with high sensitivity to identify patients at low risk for bacteremia is not available. We performed a single-institution prospective cohort study of pediatric oncology patients with febrile neutropenia to create a risk prediction model using clinical factors, respiratory viral infection, and cytokine expression. Pediatric oncology patients with febrile neutropenia were enrolled between March 30, 2010 and April 1, 2011 and managed per institutional protocol. Blood samples for C-reactive protein and cytokine expression and nasopharyngeal swabs for respiratory viral testing were obtained. Medical records were reviewed for clinical data. Statistical analysis utilized mixed multiple logistic regression modeling. During the 12-month period, 195 febrile neutropenia episodes were enrolled. There were 24 (12%) episodes of bacteremia. Univariate analysis revealed several factors predictive for bacteremia, and interleukin (IL)-8 was the most predictive variable in the multivariate stepwise logistic regression. Low serum IL-8 predicted patients at low risk for bacteremia with a sensitivity of 0.9 and negative predictive value of 0.98. IL-8 is a highly sensitive predictor for patients at low risk for bacteremia. IL-8 should be utilized in a multi-institution prospective trial to assign risk stratification to pediatric patients admitted with febrile neutropenia.

  10. Developing and validating risk prediction models in an individual participant data meta-analysis

    PubMed Central

    2014-01-01

    Background Risk prediction models estimate the risk of developing future outcomes for individuals based on one or more underlying characteristics (predictors). We review how researchers develop and validate risk prediction models within an individual participant data (IPD) meta-analysis, in order to assess the feasibility and conduct of the approach. Methods A qualitative review of the aims, methodology, and reporting in 15 articles that developed a risk prediction model using IPD from multiple studies. Results The IPD approach offers many opportunities but methodological challenges exist, including: unavailability of requested IPD, missing patient data and predictors, and between-study heterogeneity in methods of measurement, outcome definitions and predictor effects. Most articles develop their model using IPD from all available studies and perform only an internal validation (on the same set of data). Ten of the 15 articles did not allow for any study differences in baseline risk (intercepts), potentially limiting their model’s applicability and performance in some populations. Only two articles used external validation (on different data), including a novel method which develops the model on all but one of the IPD studies, tests performance in the excluded study, and repeats by rotating the omitted study. Conclusions An IPD meta-analysis offers unique opportunities for risk prediction research. Researchers can make more of this by allowing separate model intercept terms for each study (population) to improve generalisability, and by using ‘internal-external cross-validation’ to simultaneously develop and validate their model. Methodological challenges can be reduced by prospectively planned collaborations that share IPD for risk prediction. PMID:24397587

  11. Risk score for predicting long-term mortality after coronary artery bypass graft surgery.

    PubMed

    Wu, Chuntao; Camacho, Fabian T; Wechsler, Andrew S; Lahey, Stephen; Culliford, Alfred T; Jordan, Desmond; Gold, Jeffrey P; Higgins, Robert S D; Smith, Craig R; Hannan, Edward L

    2012-05-22

    No simplified bedside risk scores have been created to predict long-term mortality after coronary artery bypass graft surgery. The New York State Cardiac Surgery Reporting System was used to identify 8597 patients who underwent isolated coronary artery bypass graft surgery in July through December 2000. The National Death Index was used to ascertain patients' vital statuses through December 31, 2007. A Cox proportional hazards model was fit to predict death after CABG surgery using preprocedural risk factors. Then, points were assigned to significant predictors of death on the basis of the values of their regression coefficients. For each possible point total, the predicted risks of death at years 1, 3, 5, and 7 were calculated. It was found that the 7-year mortality rate was 24.2 in the study population. Significant predictors of death included age, body mass index, ejection fraction, unstable hemodynamic state or shock, left main coronary artery disease, cerebrovascular disease, peripheral arterial disease, congestive heart failure, malignant ventricular arrhythmia, chronic obstructive pulmonary disease, diabetes mellitus, renal failure, and history of open heart surgery. The points assigned to these risk factors ranged from 1 to 7; possible point totals for each patient ranged from 0 to 28. The observed and predicted risks of death at years 1, 3, 5, and 7 across patient groups stratified by point totals were highly correlated. The simplified risk score accurately predicted the risk of mortality after coronary artery bypass graft surgery and can be used for informed consent and as an aid in determining treatment choice.

  12. Comparison of Measured and Predicted Bioconcentration Estimates of Pharmaceuticals in Fish Plasma and Prediction of Chronic Risk.

    PubMed

    Nallani, Gopinath; Venables, Barney; Constantine, Lisa; Huggett, Duane

    2016-05-01

    Evaluation of the environmental risk of human pharmaceuticals is now a mandatory component in all new drug applications submitted for approval in EU. With >3000 drugs currently in use, it is not feasible to test each active ingredient, so prioritization is key. A recent review has listed nine prioritization approaches including the fish plasma model (FPM). The present paper focuses on comparison of measured and predicted fish plasma bioconcentration factors (BCFs) of four common over-the-counter/prescribed pharmaceuticals: norethindrone (NET), ibuprofen (IBU), verapamil (VER) and clozapine (CLZ). The measured data were obtained from the earlier published fish BCF studies. The measured BCF estimates of NET, IBU, VER and CLZ were 13.4, 1.4, 0.7 and 31.2, while the corresponding predicted BCFs (based log Kow at pH 7) were 19, 1.0, 7.6 and 30, respectively. These results indicate that the predicted BCFs matched well the measured values. The BCF estimates were used to calculate the human: fish plasma concentration ratios of each drug to predict potential risk to fish. The plasma ratio results show the following order of risk potential for fish: NET > CLZ > VER > IBU. The FPM has value in prioritizing pharmaceutical products for ecotoxicological assessments.

  13. Gestational Diabetes Mellitus Risk score: A practical tool to predict Gestational Diabetes Mellitus risk in Tanzania.

    PubMed

    Patrick Nombo, Anna; Wendelin Mwanri, Akwilina; Brouwer-Brolsma, Elske M; Ramaiya, Kaushik L; Feskens, Edith

    2018-05-28

    Universal screening for hyperglycemia during pregnancy may be in-practical in resource constrained countries. Therefore, the aim of this study was to develop a simple, non-invasive practical tool to predict undiagnosed Gestational diabetes mellitus (GDM) in Tanzania. We used cross-sectional data of 609 pregnant women, without known diabetes, collected in six health facilities from Dar es Salaam city (urban). Women underwent screening for GDM during ante-natal clinics visit. Smoking habit, alcohol consumption, pre-existing hypertension, birth weight of the previous child, high parity, gravida, previous caesarean section, age, MUAC ≥28 cm, previous stillbirth, haemoglobin level, gestational age (weeks), family history of type 2 diabetes, intake of sweetened drinks (soda), physical activity, vegetables and fruits consumption were considered as important predictors for GDM. Multivariate logistic regression modelling was used to create the prediction model, using a cut-off value of 2.5 to minimise the number of undiagnosed GDM (false negatives). Mid-upper arm circumference (MUAC) ≥28 cm, previous stillbirth, and family history of type 2 diabetes were identified as significant risk factors of GDM with a sensitivity, specificity, positive predictive value, and negative predictive value of 69%, 53%, 12% and 95%, respectively. Moreover, the inclusion of these three predictors resulted in an area under the curve (AUC) of 0.64 (0.56-0.72), indicating that the current tool correctly classifies 64% of high risk individuals. The findings of this study indicate that MUAC, previous stillbirth, and family history of type 2 diabetes significantly predict GDM development in this Tanzanian population. However, the developed non-invasive practical tool to predict undiagnosed GDM only identified 6 out of 10 individuals at risk of developing GDM. Thus, further development of the tool is warranted, for instance by testing the impact of other known risk factors such as maternal age

  14. Integrative genetic risk prediction using non-parametric empirical Bayes classification.

    PubMed

    Zhao, Sihai Dave

    2017-06-01

    Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find existing data that examine the target disease of interest, especially if that disease is rare or poorly studied. Furthermore, individual-level genotype data from these auxiliary studies are typically difficult to obtain. This article proposes a new approach to integrative genetic risk prediction of complex diseases with binary phenotypes. It accommodates possible heterogeneity in the genetic etiologies of the target and auxiliary diseases using a tuning parameter-free non-parametric empirical Bayes procedure, and can be trained using only auxiliary summary statistics. Simulation studies show that the proposed method can provide superior predictive accuracy relative to non-integrative as well as integrative classifiers. The method is applied to a recent study of pediatric autoimmune diseases, where it substantially reduces prediction error for certain target/auxiliary disease combinations. The proposed method is implemented in the R package ssa. © 2016, The International Biometric Society.

  15. Potential risk of re-emergence of urban transmission of Yellow Fever virus in Brazil facilitated by competent Aedes populations.

    PubMed

    Couto-Lima, Dinair; Madec, Yoann; Bersot, Maria Ignez; Campos, Stephanie Silva; Motta, Monique de Albuquerque; Santos, Flávia Barreto Dos; Vazeille, Marie; Vasconcelos, Pedro Fernando da Costa; Lourenço-de-Oliveira, Ricardo; Failloux, Anna-Bella

    2017-07-07

    Yellow fever virus (YFV) causing a deadly viral disease is transmitted by the bite of infected mosquitoes. In Brazil, YFV is restricted to a forest cycle maintained between non-human primates and forest-canopy mosquitoes, where humans can be tangentially infected. Since late 2016, a growing number of human cases have been reported in Southeastern Brazil at the gates of the most populated areas of South America, the Atlantic coast, with Rio de Janeiro state hosting nearly 16 million people. We showed that the anthropophilic mosquitoes Aedes aegypti and Aedes albopictus as well as the YFV-enzootic mosquitoes Haemagogus leucocelaenus and Sabethes albiprivus from the YFV-free region of the Atlantic coast were highly susceptible to American and African YFV strains. Therefore, the risk of reemergence of urban YFV epidemics in South America is major with a virus introduced either from a forest cycle or by a traveler returning from the YFV-endemic region of Africa.

  16. Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors.

    PubMed

    Razavian, Narges; Blecker, Saul; Schmidt, Ann Marie; Smith-McLallen, Aaron; Nigam, Somesh; Sontag, David

    2015-12-01

    We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without additional screening cost. Proposed model uncovers early and late-stage risk factors. Using administrative claims, pharmacy records, healthcare utilization, and laboratory results of 4.1 million individuals between 2005 and 2009, an initial set of 42,000 variables were derived that together describe the full health status and history of every individual. Machine learning was then used to methodically enhance predictive variable set and fit models predicting onset of type 2 diabetes in 2009-2011, 2010-2012, and 2011-2013. We compared the enhanced model with a parsimonious model consisting of known diabetes risk factors in a real-world environment, where missing values are common and prevalent. Furthermore, we analyzed novel and known risk factors emerging from the model at different age groups at different stages before the onset. Parsimonious model using 21 classic diabetes risk factors resulted in area under ROC curve (AUC) of 0.75 for diabetes prediction within a 2-year window following the baseline. The enhanced model increased the AUC to 0.80, with about 900 variables selected as predictive (p < 0.0001 for differences between AUCs). Similar improvements were observed for models predicting diabetes onset 1-3 years and 2-4 years after baseline. The enhanced model improved positive predictive value by at least 50% and identified novel surrogate risk factors for type 2 diabetes, such as chronic liver disease (odds ratio [OR] 3.71), high alanine aminotransferase (OR 2.26), esophageal reflux (OR 1.85), and history of acute bronchitis (OR 1.45). Liver risk factors emerge later in the process of diabetes development compared with obesity-related factors such as hypertension and high hemoglobin A1c. In conclusion

  17. Utilizing Dental Electronic Health Records Data to Predict Risk for Periodontal Disease.

    PubMed

    Thyvalikakath, Thankam P; Padman, Rema; Vyawahare, Karnali; Darade, Pratiksha; Paranjape, Rhucha

    2015-01-01

    Periodontal disease is a major cause for tooth loss and adversely affects individuals' oral health and quality of life. Research shows its potential association with systemic diseases like diabetes and cardiovascular disease, and social habits such as smoking. This study explores mining potential risk factors from dental electronic health records to predict and display patients' contextualized risk for periodontal disease. We retrieved relevant risk factors from structured and unstructured data on 2,370 patients who underwent comprehensive oral examinations at the Indiana University School of Dentistry, Indianapolis, IN, USA. Predicting overall risk and displaying relationships between risk factors and their influence on the patient's oral and general health can be a powerful educational and disease management tool for patients and clinicians at the point of care.

  18. Predicting adolescent's cyberbullying behavior: A longitudinal risk analysis.

    PubMed

    Barlett, Christopher P

    2015-06-01

    The current study used the risk factor approach to test the unique and combined influence of several possible risk factors for cyberbullying attitudes and behavior using a four-wave longitudinal design with an adolescent US sample. Participants (N = 96; average age = 15.50 years) completed measures of cyberbullying attitudes, perceptions of anonymity, cyberbullying behavior, and demographics four times throughout the academic school year. Several logistic regression equations were used to test the contribution of these possible risk factors. Results showed that (a) cyberbullying attitudes and previous cyberbullying behavior were important unique risk factors for later cyberbullying behavior, (b) anonymity and previous cyberbullying behavior were valid risk factors for later cyberbullying attitudes, and (c) the likelihood of engaging in later cyberbullying behavior increased with the addition of risk factors. Overall, results show the unique and combined influence of such risk factors for predicting later cyberbullying behavior. Results are discussed in terms of theory. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  19. Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study.

    PubMed

    Seyednasrollah, Fatemeh; Mäkelä, Johanna; Pitkänen, Niina; Juonala, Markus; Hutri-Kähönen, Nina; Lehtimäki, Terho; Viikari, Jorma; Kelly, Tanika; Li, Changwei; Bazzano, Lydia; Elo, Laura L; Raitakari, Olli T

    2017-06-01

    Obesity is a known risk factor for cardiovascular disease. Early prediction of obesity is essential for prevention. The aim of this study is to assess the use of childhood clinical factors and the genetic risk factors in predicting adulthood obesity using machine learning methods. A total of 2262 participants from the Cardiovascular Risk in YFS (Young Finns Study) were followed up from childhood (age 3-18 years) to adulthood for 31 years. The data were divided into training (n=1625) and validation (n=637) set. The effect of known genetic risk factors (97 single-nucleotide polymorphisms) was investigated as a weighted genetic risk score of all 97 single-nucleotide polymorphisms (WGRS97) or a subset of 19 most significant single-nucleotide polymorphisms (WGRS19) using boosting machine learning technique. WGRS97 and WGRS19 were validated using external data (n=369) from BHS (Bogalusa Heart Study). WGRS19 improved the accuracy of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744, P <0.0001) and validation data (AUC=0.769 versus AUC=0.747, P =0.026). WGRS97 improved the accuracy in training (AUC=0.782 versus AUC=0.744, P <0.0001) but not in validation data (AUC=0.749 versus AUC=0.747, P =0.785). Higher WGRS19 associated with higher body mass index at 9 years and WGRS97 at 6 years. Replication in BHS confirmed our findings that WGRS19 and WGRS97 are associated with body mass index. WGRS19 improves prediction of adulthood obesity. Predictive accuracy is highest among young children (3-6 years), whereas among older children (9-18 years) the risk can be identified using childhood clinical factors. The model is helpful in screening children with high risk of developing obesity. © 2017 American Heart Association, Inc.

  20. Prediction of Sustained Virological Response to Peginterferon-based Therapy for Chronic Hepatitis C: Regression Analysis of a Cohort from Rio Grande do Sul, Brazil.

    PubMed

    V Picon, Rafael; Fendt, Lúcia; Amaral, Karine; D Picon, Paulo

    2017-01-01

    Peginterferon plus ribavirin (peg-IFN/RBV) is still the standard of care for treatment of hepatitis C virus (HCV) in many countries. Given the high toxicity of this regimen, our study aimed to develop a prediction tool that can identify which patients are unlikely to benefit from peg-IFN/RBV and could thus postpone treatment in favor of new-generation direct-acting antivirals. Binary regression was performed using demographic, clinical, and laboratory covariates and sustained virological response (SVR) outcomes from a prospective cohort of individuals referred for therapy from 2003 to 2008 in a public HCV treatment center in Rio Grande do Sul, Brazil. Of the 743 participants analyzed, 489 completed 48 weeks of treatment (65.8%). A total of 202 of those who completed peg-IFN/RBV therapy achieved SVR (27.2% responders), 196 did not (26.4%), and 91 had missing viral load (VL) at week 72 (12.2% loss to follow-up). The remainder discontinued therapy (n = 254 [34.2%]), 78 (30.7%) doing so due to adverse effects. Baseline covariates included in the regression model were sex, age, human immunodeficiency virus, infection status, aspartate transaminase, alanine transaminase, hemoglobin, platelets, serum creatinine, prothrombin time, pretreatment VL, cirrhosis on liver biopsy, and treatment naivety. A predicted SVR of 17.9% had 90.0% sensitivity for detecting true nonresponders. The negative likelihood ratio at a predicted SVR of 17.9% was 0.16, and the negative predictive value was 92.6%. Easily obtainable variables can identify patients that will likely not benefit from peg-IFN-based therapy. This prediction model might be useful to clinicians. To our knowledge, this is the only prediction tool that can reliably help clinicians to postpone peg-IFN/RBV therapy for HCV genotype 1 patients. How to cite this article: Picon RV, Fendt L, Amaral K, Picon PD. Prediction of Sustained Virological Response to Peginterferon-based Therapy for Chronic Hepatitis C: Regression Analysis of

  1. Protective and risk factors for toxocariasis in children from two different social classes of Brazil.

    PubMed

    Santarém, Vamilton Alvares; Leli, Flávia Noris Chagas; Rubinsky-Elefant, Guita; Giuffrida, Rogério

    2011-01-01

    The aim of this study was to analyze the prevalence of Toxocara spp. antibodies in children from two different socioeconomic classes in the Presidente Prudente municipality, São Paulo State, Brazil, and the protective and risk factors associated with toxocariasis. One hundred and twenty-six middle-class (MC) and 126 disadvantaged children (DC) were included in this study. Anti-Toxocara ELISA test was performed in order to evaluate seroprevalence. A survey was applied to the children's guardians/parents in order to analyze the protective and risk factors. The overall prevalence was 11.1%, and of 9.5% (12/126) and 12.7% (16/126) for MC and DC subgroups, respectively. Toxocara seropositivity was inversely proportional to the family income. A high household income was considered a protective factor for toxocariasis in the total population and in both MC and DC subgroups. Being a girl was considered a protective factor for the total population and for both subgroups. Whilst being an owner of cat was a risk factor for children belonging to the total and for both MC and DC subgroups, having dog was considered as a risk factor for only the MC. Epidemiologic protective/factor risks can be distinct depending on the strata of the same population. Thus, it is relevant to evaluate these factors independently for different socioeconomic classes in order to design future investigations and programs for preventing the infection of human beings by Toxocara spp. and other geohelminths.

  2. The cost-effectiveness of HIV pre-exposure prophylaxis in men who have sex with men and transgender women at high risk of HIV infection in Brazil.

    PubMed

    Luz, Paula M; Osher, Benjamin; Grinsztejn, Beatriz; Maclean, Rachel L; Losina, Elena; Stern, Madeline E; Struchiner, Claudio J; Parker, Robert A; Freedberg, Kenneth A; Mesquita, Fabio; Walensky, Rochelle P; Veloso, Valdilea G; Paltiel, A David

    2018-03-01

    Men who have sex with men (MSM) and transgender women (TGW) in Brazil experience high rates of HIV infection. We examined the clinical and economic outcomes of implementing a pre-exposure prophylaxis (PrEP) programme in these populations. We used the Cost-Effectiveness of Preventing AIDS Complications (CEPAC)-International model of HIV prevention and treatment to evaluate two strategies: the current standard of care (SOC) in Brazil, including universal ART access (No PrEP strategy); and the current SOC plus daily tenofovir/emtracitabine PrEP (PrEP strategy) until age 50. Mean age (31 years, SD 8.4 years), age-stratified annual HIV incidence (age ≤ 40 years: 4.3/100 PY; age > 40 years: 1.0/100 PY), PrEP effectiveness (43% HIV incidence reduction) and PrEP drug costs ($23/month) were from Brazil-based sources. The analysis focused on direct medical costs of HIV care. We measured the comparative value of PrEP in 2015 United States dollars (USD) per year of life saved (YLS). Willingness-to-pay threshold was based on Brazil's annual per capita gross domestic product (GDP; 2015: $8540 USD). Lifetime HIV infection risk among high-risk MSM and TGW was 50.5% with No PrEP and decreased to 40.1% with PrEP. PrEP increased per-person undiscounted (discounted) life expectancy from 36.8 (20.7) years to 41.0 (22.4) years and lifetime discounted HIV-related medical costs from $4100 to $8420, which led to an incremental cost-effectiveness ratio (ICER) of $2530/YLS. PrEP remained cost-effective (<1x GDP) under plausible variation in key parameters, including PrEP effectiveness and cost, initial cohort age and HIV testing frequency on/off PrEP. Daily tenofovir/emtracitabine PrEP among MSM and TGW at high risk of HIV infection in Brazil would increase life expectancy and be highly cost-effective. © 2018 The Authors. Journal of the International AIDS Society published by John Wiley & sons Ltd on behalf of the International AIDS Society.

  3. Predicting impacts of climate change on Fasciola hepatica risk.

    PubMed

    Fox, Naomi J; White, Piran C L; McClean, Colin J; Marion, Glenn; Evans, Andy; Hutchings, Michael R

    2011-01-10

    Fasciola hepatica (liver fluke) is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

  4. Prediction of cardiovascular risk in rheumatoid arthritis: performance of original and adapted SCORE algorithms.

    PubMed

    Arts, E E A; Popa, C D; Den Broeder, A A; Donders, R; Sandoo, A; Toms, T; Rollefstad, S; Ikdahl, E; Semb, A G; Kitas, G D; Van Riel, P L C M; Fransen, J

    2016-04-01

    Predictive performance of cardiovascular disease (CVD) risk calculators appears suboptimal in rheumatoid arthritis (RA). A disease-specific CVD risk algorithm may improve CVD risk prediction in RA. The objectives of this study are to adapt the Systematic COronary Risk Evaluation (SCORE) algorithm with determinants of CVD risk in RA and to assess the accuracy of CVD risk prediction calculated with the adapted SCORE algorithm. Data from the Nijmegen early RA inception cohort were used. The primary outcome was first CVD events. The SCORE algorithm was recalibrated by reweighing included traditional CVD risk factors and adapted by adding other potential predictors of CVD. Predictive performance of the recalibrated and adapted SCORE algorithms was assessed and the adapted SCORE was externally validated. Of the 1016 included patients with RA, 103 patients experienced a CVD event. Discriminatory ability was comparable across the original, recalibrated and adapted SCORE algorithms. The Hosmer-Lemeshow test results indicated that all three algorithms provided poor model fit (p<0.05) for the Nijmegen and external validation cohort. The adapted SCORE algorithm mainly improves CVD risk estimation in non-event cases and does not show a clear advantage in reclassifying patients with RA who develop CVD (event cases) into more appropriate risk groups. This study demonstrates for the first time that adaptations of the SCORE algorithm do not provide sufficient improvement in risk prediction of future CVD in RA to serve as an appropriate alternative to the original SCORE. Risk assessment using the original SCORE algorithm may underestimate CVD risk in patients with RA. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

  5. A risk prediction score model for predicting occurrence of post-PCI vasovagal reflex syndrome: a single center study in Chinese population.

    PubMed

    Li, Hai-Yan; Guo, Yu-Tao; Tian, Cui; Song, Chao-Qun; Mu, Yang; Li, Yang; Chen, Yun-Dai

    2017-08-01

    The vasovagal reflex syndrome (VVRS) is common in the patients undergoing percutaneous coronary intervention (PCI). However, prediction and prevention of the risk for the VVRS have not been completely fulfilled. This study was conducted to develop a Risk Prediction Score Model to identify the determinants of VVRS in a large Chinese population cohort receiving PCI. From the hospital electronic medical database, we identified 3550 patients who received PCI (78.0% males, mean age 60 years) in Chinese PLA General Hospital from January 1, 2000 to August 30, 2016. The multivariate analysis and receiver operating characteristic (ROC) analysis were performed. The adverse events of VVRS in the patients were significantly increased after PCI procedure than before the operation (all P < 0.001). The rate of VVRS [95% confidence interval (CI)] in patients receiving PCI was 4.5% (4.1%-5.6%). Compared to the patients suffering no VVRS, incidence of VVRS involved the following factors, namely female gender, primary PCI, hypertension, over two stents implantation in the left anterior descending (LAD), and the femoral puncture site. The multivariate analysis suggested that they were independent risk factors for predicting the incidence of VVRS (all P < 0.001). We developed a risk prediction score model for VVRS. ROC analysis showed that the risk prediction score model was effectively predictive of the incidence of VVRS in patients receiving PCI (c-statistic 0.76, 95% CI: 0.72-0.79, P < 0.001). There were decreased events of VVRS in the patients receiving PCI whose diastolic blood pressure dropped by more than 30 mmHg and heart rate reduced by 10 times per minute (AUC: 0.84, 95% CI: 0.81-0.87, P < 0.001). The risk prediction score is quite efficient in predicting the incidence of VVRS in patients receiving PCI. In which, the following factors may be involved, the femoral puncture site, female gender, hypertension, primary PCI, and over 2 stents implanted in LAD.

  6. Risk Prediction Models in Psychiatry: Toward a New Frontier for the Prevention of Mental Illnesses.

    PubMed

    Bernardini, Francesco; Attademo, Luigi; Cleary, Sean D; Luther, Charles; Shim, Ruth S; Quartesan, Roberto; Compton, Michael T

    2017-05-01

    We conducted a systematic, qualitative review of risk prediction models designed and tested for depression, bipolar disorder, generalized anxiety disorder, posttraumatic stress disorder, and psychotic disorders. Our aim was to understand the current state of research on risk prediction models for these 5 disorders and thus future directions as our field moves toward embracing prediction and prevention. Systematic searches of the entire MEDLINE electronic database were conducted independently by 2 of the authors (from 1960 through 2013) in July 2014 using defined search criteria. Search terms included risk prediction, predictive model, or prediction model combined with depression, bipolar, manic depressive, generalized anxiety, posttraumatic, PTSD, schizophrenia, or psychosis. We identified 268 articles based on the search terms and 3 criteria: published in English, provided empirical data (as opposed to review articles), and presented results pertaining to developing or validating a risk prediction model in which the outcome was the diagnosis of 1 of the 5 aforementioned mental illnesses. We selected 43 original research reports as a final set of articles to be qualitatively reviewed. The 2 independent reviewers abstracted 3 types of data (sample characteristics, variables included in the model, and reported model statistics) and reached consensus regarding any discrepant abstracted information. Twelve reports described models developed for prediction of major depressive disorder, 1 for bipolar disorder, 2 for generalized anxiety disorder, 4 for posttraumatic stress disorder, and 24 for psychotic disorders. Most studies reported on sensitivity, specificity, positive predictive value, negative predictive value, and area under the (receiver operating characteristic) curve. Recent studies demonstrate the feasibility of developing risk prediction models for psychiatric disorders (especially psychotic disorders). The field must now advance by (1) conducting more large

  7. We are all people living with AIDS: myths and realities of AIDS in Brazil.

    PubMed

    Daniel, H

    1991-01-01

    Although AIDS was expected in Brazil, no serious efforts were undertaken to prevent AIDS from taking root. Irresponsible press and media coverage highlighted the spread of AIDS within the gay community of the United States, creating an aura of immunity in Brazil to what was characterized as a "foreign" disorder. When AIDS did surface in 1983, the official response was to adopt an abstract, inappropriate, and ideological "Western" model, in which only stigmatized "others" and "minorities" were at risk of HIV infection. Brazilian health authorities subsequently downplayed the significance of the sale of contaminated blood in HIV transmission, and likewise ignored the rising rates of AIDS among Brazil's one unarguable majority group: the poor. An analysis of efforts to force the "facts" of AIDS to fit a false model's predictions leads to a clearer definition of the broader context of the Brazilian epidemic: we all are people living with AIDS, precisely because we live in this age of AIDS; it is sheer folly to discriminate against persons infected by HIV and to obstruct their participation in efforts to curtail the epidemic's spread; and the necessary response to AIDS is solidarity, not because it is poetic, but because no other response will suffice.

  8. Biomarker-based risk prediction in the community.

    PubMed

    AbouEzzeddine, Omar F; McKie, Paul M; Scott, Christopher G; Rodeheffer, Richard J; Chen, Horng H; Michael Felker, G; Jaffe, Allan S; Burnett, John C; Redfield, Margaret M

    2016-11-01

    Guided by predictive characteristics of cardiovascular biomarkers, we explored the clinical implications of a simulated biomarker-guided heart failure (HF) and major adverse cardiovascular events (MACE) prevention strategy in the community. In a community cohort (n = 1824), the predictive characteristics for HF and MACE of galectin-3 (Gal-3), ST2, high-sensitivity cardiac troponin I (hscTnI), high-sensitivity C-reactive protein (hsCRP), N-terminal pro-brain natriuretic peptide (NT-proBNP) and B-type natriuretic peptide (BNP) were established. We performed number needed to screen (NNS) and treat (NNT) with the intervention analyses according to biomarker screening strategy and intervention efficacy in persons with at least one cardiovascular risk factor. In the entire cohort, for both HF and MACE, the predictive characteristics of NT-proBNP and hscTnI were superior to other biomarkers; alone, in a multimarker model, and adjusting for clinical risk factors. An NT-proBNP-guided preventative intervention with an intervention effect size (4-year hazard ratio for intervention in biomarker positive cohort) of ≤0.7 would reduce the global burden of HF by ≥20% and MACE by ≥15%. From this simulation, the NNS to prevent one HF event or MACE in 4 years would be ≤100 with a NNT to prevent one HF event of ≤20 and one MACE of ≤10. The predictive characteristics of NT-proBNP and hscTnI for HF or MACE in the community are superior to other biomarkers. Biomarker-guided preventative interventions with reasonable efficacy would compare favourably to established preventative interventions. This data provides a framework for biomarker selection which may inform design of biomarker-guided preventative intervention trials. © 2016 The Authors. European Journal of Heart Failure © 2016 European Society of Cardiology.

  9. Chapter 4. Predicting post-fire erosion and sedimentation risk on a landscape scale

    USGS Publications Warehouse

    MacDonald, L.H.; Sampson, R.; Brady, D.; Juarros, L.; Martin, Deborah

    2000-01-01

    Historic fire suppression efforts have increased the likelihood of large wildfires in much of the western U.S. Post-fire soil erosion and sedimentation risks are important concerns to resource managers. In this paper we develop and apply procedures to predict post-fire erosion and sedimentation risks on a pixel-, catchment-, and landscape-scale in central and western Colorado.Our model for predicting post-fire surface erosion risk is conceptually similar to the Revised Universal Soil Loss Equation (RUSLE). One key addition is the incorporation of a hydrophobicity risk index (HY-RISK) based on vegetation type, predicted fire severity, and soil texture. Post-fire surface erosion risk was assessed for each 90-m pixel by combining HYRISK, slope, soil erodibility, and a factor representing the likely increase in soil wetness due to removal of the vegetation. Sedimentation risk was a simple function of stream gradient. Composite surface erosion and sedimentation risk indices were calculated and compared across the 72 catchments in the study area.When evaluated on a catchment scale, two-thirds of the catchments had relatively little post-fire erosion risk. Steeper catchments with higher fuel loadings typically had the highest post-fire surface erosion risk. These were generally located along the major north-south mountain chains and, to a lesser extent, in west-central Colorado. Sedimentation risks were usually highest in the eastern part of the study area where a higher proportion of streams had lower gradients. While data to validate the predicted erosion and sedimentation risks are lacking, the results appear reasonable and are consistent with our limited field observations. The models and analytic procedures can be readily adapted to other locations and should provide useful tools for planning and management at both the catchment and landscape scale.

  10. Risk prediction models for graft failure in kidney transplantation: a systematic review.

    PubMed

    Kaboré, Rémi; Haller, Maria C; Harambat, Jérôme; Heinze, Georg; Leffondré, Karen

    2017-04-01

    Risk prediction models are useful for identifying kidney recipients at high risk of graft failure, thus optimizing clinical care. Our objective was to systematically review the models that have been recently developed and validated to predict graft failure in kidney transplantation recipients. We used PubMed and Scopus to search for English, German and French language articles published in 2005-15. We selected studies that developed and validated a new risk prediction model for graft failure after kidney transplantation, or validated an existing model with or without updating the model. Data on recipient characteristics and predictors, as well as modelling and validation methods were extracted. In total, 39 articles met the inclusion criteria. Of these, 34 developed and validated a new risk prediction model and 5 validated an existing one with or without updating the model. The most frequently predicted outcome was graft failure, defined as dialysis, re-transplantation or death with functioning graft. Most studies used the Cox model. There was substantial variability in predictors used. In total, 25 studies used predictors measured at transplantation only, and 14 studies used predictors also measured after transplantation. Discrimination performance was reported in 87% of studies, while calibration was reported in 56%. Performance indicators were estimated using both internal and external validation in 13 studies, and using external validation only in 6 studies. Several prediction models for kidney graft failure in adults have been published. Our study highlights the need to better account for competing risks when applicable in such studies, and to adequately account for post-transplant measures of predictors in studies aiming at improving monitoring of kidney transplant recipients. © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

  11. [Lifetime socioeconomic status and health-related risk behaviors: the ELSA-Brazil study].

    PubMed

    Faleiro, Jéssica Costa; Giatti, Luana; Barreto, Sandhi Maria; Camelo, Lidyane do Valle; Griep, Rosane Härter; Guimarães, Joanna M N; Fonseca, Maria de Jesus Mendes da; Chor, Dóra; Chagas, Maria da Conceição Almeida

    2017-04-03

    Our objective was to investigate the association between lifetime socioeconomic status and intra-generational social mobility and low consumption of fruits and vegetables, leisure-time physical inactivity, and smoking among 13,216 men and women participating in the baseline of the ELSA-Brazil study (2008-2010). Socioeconomic status in childhood, adolescence, and adulthood was measured by maternal schooling, socio-occupational class of the first occupation, and socio-occupational class of the current occupation, respectively. Social disadvantages in adulthood were consistently associated with higher prevalence of the three behaviors analyzed in men and women. However, socioeconomic status in youth and childhood was less consistently associated with the behaviors. For example, while low maternal schooling reduced the odds of past smoking (women) and current smoking (men and women), it was associated with higher odds of leisure-time physical inactivity in women. Meanwhile, low socioeconomic status in youth increased the odds of past smoking (men and women) and current smoking (women). Analysis of social trajectories lent additional support to the relevance of disadvantages in adulthood for risk behaviors, since only individuals that rose to the high socio-occupational class did not show higher odds of these behaviors when compared to participants that had always belonged to the high socio-occupational class. Our findings indicate that socioeconomic disadvantages in adulthood appear to be more relevant for risk behaviors than disadvantages in childhood and adolescence.

  12. Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions.

    PubMed

    Sakoda, Lori C; Henderson, Louise M; Caverly, Tanner J; Wernli, Karen J; Katki, Hormuzd A

    2017-12-01

    Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice. Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening. Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.

  13. Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.

    PubMed

    Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J

    2017-07-01

    To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

  14. Two criteria for evaluating risk prediction models

    PubMed Central

    Pfeiffer, R.M.; Gail, M.H.

    2010-01-01

    SUMMARY We propose and study two criteria to assess the usefulness of models that predict risk of disease incidence for screening and prevention, or the usefulness of prognostic models for management following disease diagnosis. The first criterion, the proportion of cases followed PCF(q), is the proportion of individuals who will develop disease who are included in the proportion q of individuals in the population at highest risk. The second criterion is the proportion needed to follow-up, PNF(p), namely the proportion of the general population at highest risk that one needs to follow in order that a proportion p of those destined to become cases will be followed. PCF(q) assesses the effectiveness of a program that follows 100q% of the population at highest risk. PNF(p) assess the feasibility of covering 100p% of cases by indicating how much of the population at highest risk must be followed. We show the relationship of those two criteria to the Lorenz curve and its inverse, and present distribution theory for estimates of PCF and PNF. We develop new methods, based on influence functions, for inference for a single risk model, and also for comparing the PCFs and PNFs of two risk models, both of which were evaluated in the same validation data. PMID:21155746

  15. Leptospirosis in a subsistence farming community in Brazil

    PubMed Central

    Lacerda, Hênio G.; Monteiro, Gloria R.; Oliveira, Carlos C.G.; Suassuna, Fernando B.; Queiroz, Jose W.; Barbosa, James D.A.; Martins, Daniella R.; Reis, Mitemayer G.; Ko, Albert I.; Jeronimo, Selma M.B.

    2014-01-01

    Summary Leptospirosis has been reported in rural areas of Brazil. However, there is limited information about the exposure risk or the risk of Leptospira infection for rural-based populations. A cross-sectional study was carried out in order to determine the prevalence and risk factors for prior Leptospira infection in a rural subsistence farming region of the state of Rio Grande do Norte, an area in which outbreaks of leptospirosis have occurred. Among 290 individuals enrolled, 44 (15.2%) had anti-Leptospira IgM antibodies as determined by IgM ELISA. Infection tended to occur with activities related to the rice fields (P = 0.08). Our findings indicate that Leptospira infection occurs even in years of low rainfall, and may have an important impact among poor rural-based subsistence farmers in Brazil. Additional studies are needed to characterize the mode of transmission in this region. PMID:18599101

  16. Two-Step Approach for the Prediction of Future Type 2 Diabetes Risk

    PubMed Central

    Abdul-Ghani, Muhammad A.; Abdul-Ghani, Tamam; Stern, Michael P.; Karavic, Jasmina; Tuomi, Tiinamaija; Bo, Insoma; DeFronzo, Ralph A.; Groop, Leif

    2011-01-01

    OBJECTIVE To develop a model for the prediction of type 2 diabetes mellitus (T2DM) risk on the basis of a multivariate logistic model and 1-h plasma glucose concentration (1-h PG). RESEARCH DESIGN AND METHODS The model was developed in a cohort of 1,562 nondiabetic subjects from the San Antonio Heart Study (SAHS) and validated in 2,395 nondiabetic subjects in the Botnia Study. A risk score on the basis of anthropometric parameters, plasma glucose and lipid profile, and blood pressure was computed for each subject. Subjects with a risk score above a certain cut point were considered to represent high-risk individuals, and their 1-h PG concentration during the oral glucose tolerance test was used to further refine their future T2DM risk. RESULTS We used the San Antonio Diabetes Prediction Model (SADPM) to generate the initial risk score. A risk-score value of 0.065 was found to be an optimal cut point for initial screening and selection of high-risk individuals. A 1-h PG concentration >140 mg/dL in high-risk individuals (whose risk score was >0.065) was the optimal cut point for identification of subjects at increased risk. The two cut points had 77.8, 77.4, and 44.8% (for the SAHS) and 75.8, 71.6, and 11.9% (for the Botnia Study) sensitivity, specificity, and positive predictive value, respectively, in the SAHS and Botnia Study. CONCLUSIONS A two-step model, based on the combination of the SADPM and 1-h PG, is a useful tool for the identification of high-risk Mexican-American and Caucasian individuals. PMID:21788628

  17. Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks.

    PubMed

    Coiera, Enrico; Wang, Ying; Magrabi, Farah; Concha, Oscar Perez; Gallego, Blanca; Runciman, William

    2014-05-21

    Current prognostic models factor in patient and disease specific variables but do not consider cumulative risks of hospitalization over time. We developed risk models of the likelihood of death associated with cumulative exposure to hospitalization, based on time-varying risks of hospitalization over any given day, as well as day of the week. Model performance was evaluated alone, and in combination with simple disease-specific models. Patients admitted between 2000 and 2006 from 501 public and private hospitals in NSW, Australia were used for training and 2007 data for evaluation. The impact of hospital care delivered over different days of the week and or times of the day was modeled by separating hospitalization risk into 21 separate time periods (morning, day, night across the days of the week). Three models were developed to predict death up to 7-days post-discharge: 1/a simple background risk model using age, gender; 2/a time-varying risk model for exposure to hospitalization (admission time, days in hospital); 3/disease specific models (Charlson co-morbidity index, DRG). Combining these three generated a full model. Models were evaluated by accuracy, AUC, Akaike and Bayesian information criteria. There was a clear diurnal rhythm to hospital mortality in the data set, peaking in the evening, as well as the well-known 'weekend-effect' where mortality peaks with weekend admissions. Individual models had modest performance on the test data set (AUC 0.71, 0.79 and 0.79 respectively). The combined model which included time-varying risk however yielded an average AUC of 0.92. This model performed best for stays up to 7-days (93% of admissions), peaking at days 3 to 5 (AUC 0.94). Risks of hospitalization vary not just with the day of the week but also time of the day, and can be used to make predictions about the cumulative risk of death associated with an individual's hospitalization. Combining disease specific models with such time varying- estimates appears to

  18. Comparative Risk Predictions of Second Cancers After Carbon-Ion Therapy Versus Proton Therapy

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Eley, John G., E-mail: jeley@som.umaryland.edu; University of Texas Graduate School of Biomedical Sciences, Houston, Texas; Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland

    Purpose: This work proposes a theoretical framework that enables comparative risk predictions for second cancer incidence after particle beam therapy for different ion species for individual patients, accounting for differences in relative biological effectiveness (RBE) for the competing processes of tumor initiation and cell inactivation. Our working hypothesis was that use of carbon-ion therapy instead of proton therapy would show a difference in the predicted risk of second cancer incidence in the breast for a sample of Hodgkin lymphoma (HL) patients. Methods and Materials: We generated biologic treatment plans and calculated relative predicted risks of second cancer in the breastmore » by using two proposed methods: a full model derived from the linear quadratic model and a simpler linear-no-threshold model. Results: For our reference calculation, we found the predicted risk of breast cancer incidence for carbon-ion plans-to-proton plan ratio, , to be 0.75 ± 0.07 but not significantly smaller than 1 (P=.180). Conclusions: Our findings suggest that second cancer risks are, on average, comparable between proton therapy and carbon-ion therapy.« less

  19. Gender differences in predicting high-risk drinking among undergraduate students.

    PubMed

    Wilke, Dina J; Siebert, Darcy Clay; Delva, Jorge; Smith, Michael P; Howell, Richard L

    2005-01-01

    The purpose of this study was to examine gender differences in college students' high-risk drinking as measured by an estimated blood alcohol concentration (eBAC) based on gender, height, weight, self-reported number of drinks, and hours spent drinking. Using a developmental/contextual framework, high-risk drinking is conceptualized as a function of relevant individual characteristics, interpersonal factors, and contextual factors regularly mentioned in the college drinking literature. Individual characteristics include race, gender, and age; interpersonal characteristics include number of sexual partners and having experienced forced sexual contact. Finally, contextual factors include Greek membership, living off-campus, and perception of peer drinking behavior. This study is a secondary data analysis of 1,422 students at a large university in the Southeast. Data were gathered from a probability sample of students through a mail survey. A three-step hierarchical logistic regression analysis showed gender differences in the pathway for high-risk drinking. For men, high-risk drinking was predicted by a combination of individual characteristics and contextual factors. For women, interpersonal factors, along with individual characteristics and contextual factors, predicted high-risk drinking, highlighting the importance of understanding female sexual relationships and raising questions about women's risk-taking behavior. Implications for prevention and assessment are discussed.

  20. Predicting dementia risk in primary care: development and validation of the Dementia Risk Score using routinely collected data.

    PubMed

    Walters, K; Hardoon, S; Petersen, I; Iliffe, S; Omar, R Z; Nazareth, I; Rait, G

    2016-01-21

    Existing dementia risk scores require collection of additional data from patients, limiting their use in practice. Routinely collected healthcare data have the potential to assess dementia risk without the need to collect further information. Our objective was to develop and validate a 5-year dementia risk score derived from primary healthcare data. We used data from general practices in The Health Improvement Network (THIN) database from across the UK, randomly selecting 377 practices for a development cohort and identifying 930,395 patients aged 60-95 years without a recording of dementia, cognitive impairment or memory symptoms at baseline. We developed risk algorithm models for two age groups (60-79 and 80-95 years). An external validation was conducted by validating the model on a separate cohort of 264,224 patients from 95 randomly chosen THIN practices that did not contribute to the development cohort. Our main outcome was 5-year risk of first recorded dementia diagnosis. Potential predictors included sociodemographic, cardiovascular, lifestyle and mental health variables. Dementia incidence was 1.88 (95% CI, 1.83-1.93) and 16.53 (95% CI, 16.15-16.92) per 1000 PYAR for those aged 60-79 (n = 6017) and 80-95 years (n = 7104), respectively. Predictors for those aged 60-79 included age, sex, social deprivation, smoking, BMI, heavy alcohol use, anti-hypertensive drugs, diabetes, stroke/TIA, atrial fibrillation, aspirin, depression. The discrimination and calibration of the risk algorithm were good for the 60-79 years model; D statistic 2.03 (95% CI, 1.95-2.11), C index 0.84 (95% CI, 0.81-0.87), and calibration slope 0.98 (95% CI, 0.93-1.02). The algorithm had a high negative predictive value, but lower positive predictive value at most risk thresholds. Discrimination and calibration were poor for the 80-95 years model. Routinely collected data predicts 5-year risk of recorded diagnosis of dementia for those aged 60-79, but not those aged 80+. This

  1. A Knowledge-Base for a Personalized Infectious Disease Risk Prediction System.

    PubMed

    Vinarti, Retno; Hederman, Lucy

    2018-01-01

    We present a knowledge-base to represent collated infectious disease risk (IDR) knowledge. The knowledge is about personal and contextual risk of contracting an infectious disease obtained from declarative sources (e.g. Atlas of Human Infectious Diseases). Automated prediction requires encoding this knowledge in a form that can produce risk probabilities (e.g. Bayesian Network - BN). The knowledge-base presented in this paper feeds an algorithm that can auto-generate the BN. The knowledge from 234 infectious diseases was compiled. From this compilation, we designed an ontology and five rule types for modelling IDR knowledge in general. The evaluation aims to assess whether the knowledge-base structure, and its application to three disease-country contexts, meets the needs of personalized IDR prediction system. From the evaluation results, the knowledge-base conforms to the system's purpose: personalization of infectious disease risk.

  2. Developing Risk Prediction Models for Postoperative Pancreatic Fistula: a Systematic Review of Methodology and Reporting Quality.

    PubMed

    Wen, Zhang; Guo, Ya; Xu, Banghao; Xiao, Kaiyin; Peng, Tao; Peng, Minhao

    2016-04-01

    Postoperative pancreatic fistula is still a major complication after pancreatic surgery, despite improvements of surgical technique and perioperative management. We sought to systematically review and critically access the conduct and reporting of methods used to develop risk prediction models for predicting postoperative pancreatic fistula. We conducted a systematic search of PubMed and EMBASE databases to identify articles published before January 1, 2015, which described the development of models to predict the risk of postoperative pancreatic fistula. We extracted information of developing a prediction model including study design, sample size and number of events, definition of postoperative pancreatic fistula, risk predictor selection, missing data, model-building strategies, and model performance. Seven studies of developing seven risk prediction models were included. In three studies (42 %), the number of events per variable was less than 10. The number of candidate risk predictors ranged from 9 to 32. Five studies (71 %) reported using univariate screening, which was not recommended in building a multivariate model, to reduce the number of risk predictors. Six risk prediction models (86 %) were developed by categorizing all continuous risk predictors. The treatment and handling of missing data were not mentioned in all studies. We found use of inappropriate methods that could endanger the development of model, including univariate pre-screening of variables, categorization of continuous risk predictors, and model validation. The use of inappropriate methods affects the reliability and the accuracy of the probability estimates of predicting postoperative pancreatic fistula.

  3. Critical points of Brazil nuts: a beginning for food safety, quality control and Amazon sustainability.

    PubMed

    Lima, Andriele M; Gonçalves, Evonnildo C; Andrade, Soraya S; Barbosa, Maria S R; Barroso, Karla F P; de Sousa, Mayara B; Borges, Larissa; Vieira, Jozé L F; Teixeira, Francisco M

    2013-03-15

    One difficulty of self-sustainability is the quality assurance of native products. This research was designed to study the risks and critical control points in the collection, handling and marketing of Brazil nuts from native forests and urban fairs in the Brazilian Amazon by characterisation of morphological aspects of fungi and posterior identification by molecular biology and determination of aflatoxins by high-performance liquid chromatography. Several corrective actions to improve product quality were found to be necessary in both sites. Growth of fungi was observed in 95% of fragments of Brazil nuts from both sites during the between-harvest period. Aflatoxin levels indicated that, although fungal growth was observed in both sites, only Brazil nuts from the native forest showed a high risk to human health (total aflatoxin level of 471.69 µg kg(-1)). This study has shown the main issues related to the process design of Brazil nuts, supporting the necessity for research on new strategies to improve the quality of nuts. Also, the habit of eating Brazil nuts stored throughout the year may represent a risk to farmers. © 2012 Society of Chemical Industry.

  4. Predictive risk mapping of West Nile virus (WNV) infection in Saskatchewan horses.

    PubMed

    Epp, Tasha Y; Waldner, Cheryl; Berke, Olaf

    2011-07-01

    The objective of this study was to develop a model using equine data from geographically limited surveillance locations to predict risk categories for West Nile virus (WNV) infection in horses in all geographic locations across the province of Saskatchewan. The province was divided geographically into low-, medium-, or high-risk categories for WNV, based on available serology information from 923 horses obtained through 4 studies of WNV infection in horse populations in Saskatchewan. Discriminant analysis was used to build models using the observed risk of WNV in horses and geographic division-specific environmental data as well as to predict the risk category for all areas, including those beyond the surveillance zones. High-risk areas were indicated by relatively lower rainfall, higher temperatures, and a lower percentage of area covered in trees, water, and wetland. These conditions were most often identified in the southwest corner of the province. Environmental conditions can be used to identify those areas that are at highest risk for WNV. Public health managers could use prediction maps, which are based on animal or human information and developed from annual early season meteorological information, to guide ongoing decisions about when and where to focus intervention strategies for WNV.

  5. Risk factors associated with leptospirosis in dairy goats under tropical conditions in Brazil.

    PubMed

    Lilenbaum, Walter; Varges, Renato; Medeiros, Luciana; Cordeiro, Ana Gabriela; Cavalcanti, Amanda; Souza, Guilherme N; Richtzenhain, Leonardo; Vasconcellos, Silvio A

    2008-02-01

    Serum samples from 248 adult dairy goats from 13 flocks with lowered fertility farmed in the Rio de Janeiro region of Brazil were examined for Leptospira antibodies by MAT with 24 serovars, cut off 100. A questionnaire was completed for each herd. Antibodies were detected in 20.8% of these goats, mainly to serovar Hardjo. Risk factors associated with seroprevalence to leptospirosis were the frequency of professional veterinary supervision (OR = 2.35), climate (OR = 2.63) and grazing for more than 2h a day. Flock factors as size, type of milking and offering of food supplementation, as well as the location and topography, the type of animal housing or the presence of silos did not significantly affect seroprevalence. We suggest that a successful control program for goat leptospirosis should include a complete investigation of herd management practices, which could influence in the occurrence of the infection.

  6. Quantitative prediction of oral cancer risk in patients with oral leukoplakia.

    PubMed

    Liu, Yao; Li, Yicheng; Fu, Yue; Liu, Tong; Liu, Xiaoyong; Zhang, Xinyan; Fu, Jie; Guan, Xiaobing; Chen, Tong; Chen, Xiaoxin; Sun, Zheng

    2017-07-11

    Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma. We have developed an oral cancer risk index using DNA index value to quantitatively assess cancer risk in patients with oral leukoplakia, but with limited success. In order to improve the performance of the risk index, we collected exfoliative cytology, histopathology, and clinical follow-up data from two independent cohorts of normal, leukoplakia and cancer subjects (training set and validation set). Peaks were defined on the basis of first derivatives with positives, and modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data. Random forest was found to be the best model with high sensitivity (100%) and specificity (99.2%). Using the Peaks-Random Forest model, we constructed an index (OCRI2) as a quantitative measurement of cancer risk. Among 11 leukoplakia patients with an OCRI2 over 0.5, 4 (36.4%) developed cancer during follow-up (23 ± 20 months), whereas 3 (5.3%) of 57 leukoplakia patients with an OCRI2 less than 0.5 developed cancer (32 ± 31 months). OCRI2 is better than other methods in predicting oral squamous cell carcinoma during follow-up. In conclusion, we have developed an exfoliative cytology-based method for quantitative prediction of cancer risk in patients with oral leukoplakia.

  7. A Bayesian framework for early risk prediction in traumatic brain injury

    NASA Astrophysics Data System (ADS)

    Chaganti, Shikha; Plassard, Andrew J.; Wilson, Laura; Smith, Miya A.; Patel, Mayur B.; Landman, Bennett A.

    2016-03-01

    Early detection of risk is critical in determining the course of treatment in traumatic brain injury (TBI). Computed tomography (CT) acquired at admission has shown latent prognostic value in prior studies; however, no robust clinical risk predictions have been achieved based on the imaging data in large-scale TBI analysis. The major challenge lies in the lack of consistent and complete medical records for patients, and an inherent bias associated with the limited number of patients samples with high-risk outcomes in available TBI datasets. Herein, we propose a Bayesian framework with mutual information-based forward feature selection to handle this type of data. Using multi-atlas segmentation, 154 image-based features (capturing intensity, volume and texture) were computed over 22 ROIs in 1791 CT scans. These features were combined with 14 clinical parameters and converted into risk likelihood scores using Bayes modeling. We explore the prediction power of the image features versus the clinical measures for various risk outcomes. The imaging data alone were more predictive of outcomes than the clinical data (including Marshall CT classification) for discharge disposition with an area under the curve of 0.81 vs. 0.67, but less predictive than clinical data for discharge Glasgow Coma Scale (GCS) score with an area under the curve of 0.65 vs. 0.85. However, in both cases, combining imaging and clinical data increased the combined area under the curve with 0.86 for discharge disposition and 0.88 for discharge GCS score. In conclusion, CT data have meaningful prognostic value for TBI patients beyond what is captured in clinical measures and the Marshall CT classification.

  8. The increase in domestic violence in Brazil from 2009-2014.

    PubMed

    Rodrigues, Nádia Cristina Pinheiro; O'Dwyer, Gisele; Andrade, Mônica Kramer de Noronha; Flynn, Matthew Brian; Monteiro, Denise Leite Maia; Lino, Valéria Teresa Saraiva

    2017-09-01

    In recent decades, the rise violent phenomena in Brazil has reached epidemic proportions. However, the prevalence of domestic violence (DV) across different states in the country is not well established. The objective of this study was to describe the distribution of DV across Brazilian states from 2009 to 2014. An ecological study based on spatial analysis techniques was performed using Brazilian states as geographical units of analysis. A multilevel Poisson model was used to explain the risk of DV in Brazil according to age, sex, period (fixed effects), the Human Developing Index, and the victim's residence state (random effects). The overall average rate of DV almost tripled from 2009-2010 to 2013-2014. The rate of DV in Brazil in the 2013-2014 period was 3.52 times greater than the 2009-2010 period. The risk of DV in men was 74% lower than in women. The increase of DV against women during period under study occurred mainly in the Southeast, South, and Midwest. DV was more frequent in adolescence and adulthood. DV is gradually increasing in recent years in Brazil. More legislation and government programs are needed to combat the growth of violence in society.

  9. [Statistical prediction methods in violence risk assessment and its application].

    PubMed

    Liu, Yuan-Yuan; Hu, Jun-Mei; Yang, Min; Li, Xiao-Song

    2013-06-01

    It is an urgent global problem how to improve the violence risk assessment. As a necessary part of risk assessment, statistical methods have remarkable impacts and effects. In this study, the predicted methods in violence risk assessment from the point of statistics are reviewed. The application of Logistic regression as the sample of multivariate statistical model, decision tree model as the sample of data mining technique, and neural networks model as the sample of artificial intelligence technology are all reviewed. This study provides data in order to contribute the further research of violence risk assessment.

  10. Risk Factors for Bartonella species Infection in Blood Donors from Southeast Brazil.

    PubMed

    Diniz, Pedro Paulo Vissotto de Paiva; Velho, Paulo Eduardo Neves Ferreira; Pitassi, Luiza Helena Urso; Drummond, Marina Rovani; Lania, Bruno Grosselli; Barjas-Castro, Maria Lourdes; Sowy, Stanley; Breitschwerdt, Edward B; Scorpio, Diana Gerardi

    2016-03-01

    Bacteria from the genus Bartonella are emerging blood-borne bacteria, capable of causing long-lasting infection in marine and terrestrial mammals, including humans. Bartonella are generally well adapted to their main host, causing persistent infection without clinical manifestation. However, these organisms may cause severe disease in natural or accidental hosts. In humans, Bartonella species have been detected from sick patients presented with diverse disease manifestations, including cat scratch disease, trench fever, bacillary angiomatosis, endocarditis, polyarthritis, or granulomatous inflammatory disease. However, with the advances in diagnostic methods, subclinical bloodstream infection in humans has been reported, with the potential for transmission through blood transfusion been recently investigated by our group. The objective of this study was to determine the risk factors associated with Bartonella species infection in asymptomatic blood donors presented at a major blood bank in Southeastern Brazil. Five hundred blood donors were randomly enrolled and tested for Bartonella species infection by specialized blood cultured coupled with high-sensitive PCR assays. Epidemiological questionnaires were designed to cover major potential risk factors, such as age, gender, ethnicity, contact with companion animals, livestock, or wild animals, bites from insects or animal, economical status, among other factors. Based on multivariate logistic regression, bloodstream infection with B. henselae or B. clarridgeiae was associated with cat contact (adjusted OR: 3.4, 95% CI: 1.1-9.6) or history of tick bite (adjusted OR: 3.7, 95% CI: 1.3-13.4). These risk factors should be considered during donor screening, as bacteremia by these Bartonella species may not be detected by traditional laboratory screening methods, and it may be transmitted by blood transfusion.

  11. HIV infection and related risk behaviours in a disadvantaged youth institution of São Paulo, Brazil.

    PubMed

    Zanetta, D M; Strazza, L; Azevedo, R S; Carvalho, H B; Massad, E; Menezes, R X; Ferreira, D P; Burattini, M N

    1999-02-01

    In order to study the prevalence of HIV and related risky behaviours among disadvantaged youth, we interviewed and bled, between December 1994 and April 1995, 1122 young males and 93 young females who were serving time in FEBEM, a state institution that cares for homeless and offender youth of São Paulo, Brazil. Our questionnaire covered the following areas: sexual practices and use of illicit drugs; knowledge of HIV and STDs and their prevention; and myths and beliefs about AIDS. Seroprevalence of HIV was assessed and related with risk-taking behaviours by means of uni-, bi- and multivariate analysis. We found 2.6% of the males and 10.3% of the females to be positive to HIV. The prevalence of hepatitis C virus (HCV) antibodies resulted in 5.9% for males and 4.6% for females, respectively. The risk for parenterally transmitted HIV among the males was higher than that for sexually related transmission. The inverse relationship was found among the females.

  12. Methods and Techniques for Risk Prediction of Space Shuttle Upgrades

    NASA Technical Reports Server (NTRS)

    Hoffman, Chad R.; Pugh, Rich; Safie, Fayssal

    1998-01-01

    Since the Space Shuttle Accident in 1986, NASA has been trying to incorporate probabilistic risk assessment (PRA) in decisions concerning the Space Shuttle and other NASA projects. One major study NASA is currently conducting is in the PRA area in establishing an overall risk model for the Space Shuttle System. The model is intended to provide a tool to predict the Shuttle risk and to perform sensitivity analyses and trade studies including evaluation of upgrades. Marshall Space Flight Center (MSFC) and its prime contractors including Pratt and Whitney (P&W) are part of the NASA team conducting the PRA study. MSFC responsibility involves modeling the External Tank (ET), the Solid Rocket Booster (SRB), the Reusable Solid Rocket Motor (RSRM), and the Space Shuttle Main Engine (SSME). A major challenge that faced the PRA team is modeling the shuttle upgrades. This mainly includes the P&W High Pressure Fuel Turbopump (HPFTP) and the High Pressure Oxidizer Turbopump (HPOTP). The purpose of this paper is to discuss the various methods and techniques used for predicting the risk of the P&W redesigned HPFTP and HPOTP.

  13. Contamination of port zone sediments by metals from Large Marine Ecosystems of Brazil.

    PubMed

    Buruaem, Lucas M; Hortellani, Marcos A; Sarkis, Jorge E; Costa-Lotufo, Leticia V; Abessa, Denis M S

    2012-03-01

    Sediment contamination by metals poses risks to coastal ecosystems and is considered to be problematic to dredging operations. In Brazil, there are differences in sedimentology along the Large Marine Ecosystems in relation to the metal distributions. We aimed to assess the extent of Al, Fe, Hg, Cd, Cr, Cu, Ni, Pb and Zn contamination in sediments from port zones in northeast (Mucuripe and Pecém) and southeast (Santos) Brazil through geochemical analyses and sediment quality ratings. The metal concentrations found in these port zones were higher than those observed in the continental shelf or the background values in both regions. In the northeast, metals were associated with carbonate, while in Santos, they were associated with mud. Geochemical analyses showed enrichments in Hg, Cd, Cu, Ni and Zn, and a simple application of international sediment quality guidelines failed to predict their impacts, whereas the use of site-specific values that were derived by geochemical and ecotoxicological approaches seemed to be more appropriate in the management of the dredged sediments. Copyright © 2012 Elsevier Ltd. All rights reserved.

  14. Aftereffect Calculation and Prediction of Methanol Tank Leak’s Environmental Risk Accident

    NASA Astrophysics Data System (ADS)

    Lang, Yueting; Zheng, Lina; Chen, Henan; Wang, Qiushi; Jiang, Hui; Pan, Yiwen

    2018-01-01

    With the increasing frequency of environmental risk accidents, more emphasis was placed on environmental risk assessment. In this article, the aftermath of an Environmental Risk Accident on Methanol Tank Leakage occurred on a cryogenic unit area in a certain oilfield processing plant have been mainly calculated and predicted. Major hazards were identified through the major hazards identification on dangerous chemicals, which could afterwards analyze maximum credible accident and confirm source item and the source intensity. In the end, the consequence of the accident has been calculated so that the impact on surrounding environment can be predicted after the accident.

  15. Different type 2 diabetes risk assessments predict dissimilar numbers at ‘high risk’: a retrospective analysis of diabetes risk-assessment tools

    PubMed Central

    Gray, Benjamin J; Bracken, Richard M; Turner, Daniel; Morgan, Kerry; Thomas, Michael; Williams, Sally P; Williams, Meurig; Rice, Sam; Stephens, Jeffrey W

    2015-01-01

    Background Use of a validated risk-assessment tool to identify individuals at high risk of developing type 2 diabetes is currently recommended. It is under-reported, however, whether a different risk tool alters the predicted risk of an individual. Aim This study explored any differences between commonly used validated risk-assessment tools for type 2 diabetes. Design and setting Cross-sectional analysis of individuals who participated in a workplace-based risk assessment in Carmarthenshire, South Wales. Method Retrospective analysis of 676 individuals (389 females and 287 males) who participated in a workplace-based diabetes risk-assessment initiative. Ten-year risk of type 2 diabetes was predicted using the validated QDiabetes®, Leicester Risk Assessment (LRA), FINDRISC, and Cambridge Risk Score (CRS) algorithms. Results Differences between the risk-assessment tools were apparent following retrospective analysis of individuals. CRS categorised the highest proportion (13.6%) of individuals at ‘high risk’ followed by FINDRISC (6.6%), QDiabetes (6.1%), and, finally, the LRA was the most conservative risk tool (3.1%). Following further analysis by sex, over one-quarter of males were categorised at high risk using CRS (25.4%), whereas a greater percentage of females were categorised as high risk using FINDRISC (7.8%). Conclusion The adoption of a different valid risk-assessment tool can alter the predicted risk of an individual and caution should be used to identify those individuals who really are at high risk of type 2 diabetes. PMID:26541180

  16. [Surveillance of risk factors for non-communicable diseases among adolescents: the experience in Rio de Janeiro, Brazil].

    PubMed

    Castro, Inês Rugani Ribeiro de; Cardoso, Letícia Oliveira; Engstrom, Elyne Montenegro; Levy, Renata Bertazzi; Monteiro, Carlos Augusto

    2008-10-01

    This paper presents the methodology and results of the implementation of a Surveillance System for Non-Communicable Disease Risk Factors in Adolescents. A random sample of 8th-grade students (n = 1,684) enrolled in municipal schools in Rio de Janeiro, Brazil, was studied. Students were asked to complete a confidential questionnaire on food consumption, physical activity, sedentary leisure-time activities, and tobacco consumption. Prevalence estimates of risk factors were calculated for the entire sample and by gender. Non-response rates ranged from 1.1 to 8.9%. The findings included low consumption of fruits (45.8%) and vegetables (20.0% and 16.5% for salads and cooked vegetables, respectively), regular consumption of soft drinks (36.7%) and candies (46.7%), extensive time on TV, computer, and videogames (71.7% spend at least 4h/day at these activities), low frequency of regular physical activity (40%), and 6.4% prevalence of smoking. Girls showed less physical activity and more smoking. The system appeared to be feasible and indicated high prevalence of risk factors for non-communicable diseases.

  17. Comparison of RISK-PCI, GRACE, TIMI risk scores for prediction of major adverse cardiac events in patients with acute coronary syndrome.

    PubMed

    Jakimov, Tamara; Mrdović, Igor; Filipović, Branka; Zdravković, Marija; Djoković, Aleksandra; Hinić, Saša; Milić, Nataša; Filipović, Branislav

    2017-12-31

    To compare the prognostic performance of three major risk scoring systems including global registry for acute coronary events (GRACE), thrombolysis in myocardial infarction (TIMI), and prediction of 30-day major adverse cardiovascular events after primary percutaneous coronary intervention (RISK-PCI). This single-center retrospective study involved 200 patients with acute coronary syndrome (ACS) who underwent invasive diagnostic approach, ie, coronary angiography and myocardial revascularization if appropriate, in the period from January 2014 to July 2014. The GRACE, TIMI, and RISK-PCI risk scores were compared for their predictive ability. The primary endpoint was a composite 30-day major adverse cardiovascular event (MACE), which included death, urgent target-vessel revascularization (TVR), stroke, and non-fatal recurrent myocardial infarction (REMI). The c-statistics of the tested scores for 30-day MACE or area under the receiver operating characteristic curve (AUC) with confidence intervals (CI) were as follows: RISK-PCI (AUC=0.94; 95% CI 1.790-4.353), the GRACE score on admission (AUC=0.73; 95% CI 1.013-1.045), the GRACE score on discharge (AUC=0.65; 95% CI 0.999-1.033). The RISK-PCI score was the only score that could predict TVR (AUC=0.91; 95% CI 1.392-2.882). The RISK-PCI scoring system showed an excellent discriminative potential for 30-day death (AUC=0.96; 95% CI 1.339-3.548) in comparison with the GRACE scores on admission (AUC=0.88; 95% CI 1.018-1.072) and on discharge (AUC=0.78; 95% CI 1.000-1.058). In comparison with the GRACE and TIMI scores, RISK-PCI score showed a non-inferior ability to predict 30-day MACE and death in ACS patients. Moreover, RISK-PCI was the only scoring system that could predict recurrent ischemia requiring TVR.

  18. Comparison of RISK-PCI, GRACE, TIMI risk scores for prediction of major adverse cardiac events in patients with acute coronary syndrome

    PubMed Central

    Jakimov, Tamara; Mrdović, Igor; Filipović, Branka; Zdravković, Marija; Djoković, Aleksandra; Hinić, Saša; Milić, Nataša; Filipović, Branislav

    2017-01-01

    Aim To compare the prognostic performance of three major risk scoring systems including global registry for acute coronary events (GRACE), thrombolysis in myocardial infarction (TIMI), and prediction of 30-day major adverse cardiovascular events after primary percutaneous coronary intervention (RISK-PCI). Methods This single-center retrospective study involved 200 patients with acute coronary syndrome (ACS) who underwent invasive diagnostic approach, ie, coronary angiography and myocardial revascularization if appropriate, in the period from January 2014 to July 2014. The GRACE, TIMI, and RISK-PCI risk scores were compared for their predictive ability. The primary endpoint was a composite 30-day major adverse cardiovascular event (MACE), which included death, urgent target-vessel revascularization (TVR), stroke, and non-fatal recurrent myocardial infarction (REMI). Results The c-statistics of the tested scores for 30-day MACE or area under the receiver operating characteristic curve (AUC) with confidence intervals (CI) were as follows: RISK-PCI (AUC = 0.94; 95% CI 1.790-4.353), the GRACE score on admission (AUC = 0.73; 95% CI 1.013-1.045), the GRACE score on discharge (AUC = 0.65; 95% CI 0.999-1.033). The RISK-PCI score was the only score that could predict TVR (AUC = 0.91; 95% CI 1.392-2.882). The RISK-PCI scoring system showed an excellent discriminative potential for 30-day death (AUC = 0.96; 95% CI 1.339-3.548) in comparison with the GRACE scores on admission (AUC = 0.88; 95% CI 1.018-1.072) and on discharge (AUC = 0.78; 95% CI 1.000-1.058). Conclusions In comparison with the GRACE and TIMI scores, RISK-PCI score showed a non-inferior ability to predict 30-day MACE and death in ACS patients. Moreover, RISK-PCI was the only scoring system that could predict recurrent ischemia requiring TVR. PMID:29308832

  19. Risk prediction score for death of traumatised and injured children

    PubMed Central

    2014-01-01

    Background Injury prediction scores facilitate the development of clinical management protocols to decrease mortality. However, most of the previously developed scores are limited in scope and are non-specific for use in children. We aimed to develop and validate a risk prediction model of death for injured and Traumatised Thai children. Methods Our cross-sectional study included 43,516 injured children from 34 emergency services. A risk prediction model was derived using a logistic regression analysis that included 15 predictors. Model performance was assessed using the concordance statistic (C-statistic) and the observed per expected (O/E) ratio. Internal validation of the model was performed using a 200-repetition bootstrap analysis. Results Death occurred in 1.7% of the injured children (95% confidence interval [95% CI]: 1.57–1.82). Ten predictors (i.e., age, airway intervention, physical injury mechanism, three injured body regions, the Glasgow Coma Scale, and three vital signs) were significantly associated with death. The C-statistic and the O/E ratio were 0.938 (95% CI: 0.929–0.947) and 0.86 (95% CI: 0.70–1.02), respectively. The scoring scheme classified three risk stratifications with respective likelihood ratios of 1.26 (95% CI: 1.25–1.27), 2.45 (95% CI: 2.42–2.52), and 4.72 (95% CI: 4.57–4.88) for low, intermediate, and high risks of death. Internal validation showed good model performance (C-statistic = 0.938, 95% CI: 0.926–0.952) and a small calibration bias of 0.002 (95% CI: 0.0005–0.003). Conclusions We developed a simplified Thai pediatric injury death prediction score with satisfactory calibrated and discriminative performance in emergency room settings. PMID:24575982

  20. Prediction of Adult Dyslipidemia Using Genetic and Childhood Clinical Risk Factors: The Cardiovascular Risk in Young Finns Study.

    PubMed

    Nuotio, Joel; Pitkänen, Niina; Magnussen, Costan G; Buscot, Marie-Jeanne; Venäläinen, Mikko S; Elo, Laura L; Jokinen, Eero; Laitinen, Tomi; Taittonen, Leena; Hutri-Kähönen, Nina; Lyytikäinen, Leo-Pekka; Lehtimäki, Terho; Viikari, Jorma S; Juonala, Markus; Raitakari, Olli T

    2017-06-01

    Dyslipidemia is a major modifiable risk factor for cardiovascular disease. We examined whether the addition of novel single-nucleotide polymorphisms for blood lipid levels enhances the prediction of adult dyslipidemia in comparison to childhood lipid measures. Two thousand four hundred and twenty-two participants of the Cardiovascular Risk in Young Finns Study who had participated in 2 surveys held during childhood (in 1980 when aged 3-18 years and in 1986) and at least once in a follow-up study in adulthood (2001, 2007, and 2011) were included. We examined whether inclusion of a lipid-specific weighted genetic risk score based on 58 single-nucleotide polymorphisms for low-density lipoprotein cholesterol, 71 single-nucleotide polymorphisms for high-density lipoprotein cholesterol, and 40 single-nucleotide polymorphisms for triglycerides improved the prediction of adult dyslipidemia compared with clinical childhood risk factors. Adjusting for age, sex, body mass index, physical activity, and smoking in childhood, childhood lipid levels, and weighted genetic risk scores were associated with an increased risk of adult dyslipidemia for all lipids. Risk assessment based on 2 childhood lipid measures and the lipid-specific weighted genetic risk scores improved the accuracy of predicting adult dyslipidemia compared with the approach using only childhood lipid measures for low-density lipoprotein cholesterol (area under the receiver-operating characteristic curve 0.806 versus 0.811; P =0.01) and triglycerides (area under the receiver-operating characteristic curve 0.740 versus area under the receiver-operating characteristic curve 0.758; P <0.01). The overall net reclassification improvement and integrated discrimination improvement were significant for all outcomes. The inclusion of weighted genetic risk scores to lipid-screening programs in childhood could modestly improve the identification of those at highest risk of dyslipidemia in adulthood. © 2017 American Heart

  1. Evaluation of fetal anthropometric measures to predict the risk for shoulder dystocia.

    PubMed

    Burkhardt, T; Schmidt, M; Kurmanavicius, J; Zimmermann, R; Schäffer, L

    2014-01-01

    To evaluate the quality of anthropometric measures to improve the prediction of shoulder dystocia by combining different sonographic biometric parameters. This was a retrospective cohort study of 12,794 vaginal deliveries with complete sonographic biometry data obtained within 7 days before delivery. Receiver-operating characteristics (ROC) curves of various combinations of the biometric parameters, namely, biparietal diameter (BPD), occipitofrontal diameter (OFD), head circumference, abdominal diameter (AD), abdominal circumference (AC) and femur length were analyzed. The influences of independent risk factors were calculated and their combination used in a predictive model. The incidence of shoulder dystocia was 1.14%. Different combinations of sonographic parameters showed comparable ROC curves without advantage for a particular combination. The difference between AD and BPD (AD - BPD) (area under the curve (AUC) = 0.704) revealed a significant increase in risk (odds ratio (OR) 7.6 (95% CI 4.2-13.9), sensitivity 8.2%, specificity 98.8%) at a suggested cut-off ≥ 2.6 cm. However, the positive predictive value (PPV) was low (7.5%). The AC as a single parameter (AUC = 0.732) with a cut-off ≥ 35 cm performed worse (OR 4.6 (95% CI 3.3-6.5), PPV 2.6%). BPD/OFD (a surrogate for fetal cranial shape) was not significantly different between those with and those without shoulder dystocia. The combination of estimated fetal weight, maternal diabetes, gender and AD - BPD provided a reasonable estimate of the individual risk. Sonographic fetal anthropometric measures appear not to be a useful tool to screen for the risk of shoulder dystocia due to a low PPV. However, AD - BPD appears to be a relevant risk factor. While risk stratification including different known risk factors may aid in counseling, shoulder dystocia cannot effectively be predicted. Copyright © 2013 ISUOG. Published by John Wiley & Sons Ltd.

  2. Predicting the Risk of Breakthrough Urinary Tract Infections: Primary Vesicoureteral Reflux.

    PubMed

    Hidas, Guy; Billimek, John; Nam, Alexander; Soltani, Tandis; Kelly, Maryellen S; Selby, Blake; Dorgalli, Crystal; Wehbi, Elias; McAleer, Irene; McLorie, Gordon; Greenfield, Sheldon; Kaplan, Sherrie H; Khoury, Antoine E

    2015-11-01

    We constructed a risk prediction instrument stratifying patients with primary vesicoureteral reflux into groups according to their 2-year probability of breakthrough urinary tract infection. Demographic and clinical information was retrospectively collected in children diagnosed with primary vesicoureteral reflux and followed for 2 years. Bivariate and binary logistic regression analyses were performed to identify factors associated with breakthrough urinary tract infection. The final regression model was used to compute an estimation of the 2-year probability of breakthrough urinary tract infection for each subject. Accuracy of the binary classifier for breakthrough urinary tract infection was evaluated using receiver operator curve analysis. Three distinct risk groups were identified. The model was then validated in a prospective cohort. A total of 252 bivariate analyses showed that high grade (IV or V) vesicoureteral reflux (OR 9.4, 95% CI 3.8-23.5, p <0.001), presentation after urinary tract infection (OR 5.3, 95% CI 1.1-24.7, p = 0.034) and female gender (OR 2.6, 95% CI 0.097-7.11, p <0.054) were important risk factors for breakthrough urinary tract infection. Subgroup analysis revealed bladder and bowel dysfunction was a significant risk factor more pronounced in low grade (I to III) vesicoureteral reflux (OR 2.8, p = 0.018). The estimation model was applied for prospective validation, which demonstrated predicted vs actual 2-year breakthrough urinary tract infection rates of 19% vs 21%. Stratifying the patients into 3 risk groups based on parameters in the risk model showed 2-year risk for breakthrough urinary tract infection was 8.6%, 26.0% and 62.5% in the low, intermediate and high risk groups, respectively. This proposed risk stratification and probability model allows prediction of 2-year risk of patient breakthrough urinary tract infection to better inform parents of possible outcomes and treatment strategies. Copyright © 2015 American Urological

  3. Risk prediction with triglycerides in patients with stable coronary disease on statin treatment.

    PubMed

    Werner, Christian; Filmer, Anja; Fritsch, Marco; Groenewold, Stephanie; Gräber, Stefan; Böhm, Michael; Laufs, Ulrich

    2014-12-01

    The aim of the prospective Homburg Cream and Sugar study was to analyze the role of fasting and postprandial serum triglycerides (TG) as risk modifiers in patients with coronary artery disease (CAD). A sequential oral triglyceride and glucose tolerance test was developed to obtain standardized measurements of postprandial TG kinetics and glucose in 514 consecutive patients with stable CAD confirmed by angiography (95% were treated with a statin). Fasting and postprandial TG predicted the primary outcome measure of cardiovascular death and hospitalizations after 48 months follow-up (fasting TG >150 vs. <106 mg/dl: Hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.31-2.45, p = 0.0001; area under the curve >1120 vs. <750 mg/dl/5 hr: HR 1.78, 95% CI 1.29-2.45, p = 0.0003). Parameters of the postprandial TG increase did not improve risk prediction compared to fasting TG. The number of cardiovascular deaths and myocardial infarctions was higher in the upper tertile of fasting TG (HR 1.79, 95%-CI 1.04-3.09, p = 0.03). Risk prediction by TG was independent of traditional risk factors, medication, glucose metabolism, LDL- and HDL-cholesterol. Total cholesterol, LDL- and HDL-cholesterol concentrations were not associated with the primary outcome. Fasting serum triglycerides >150 mg/dl independently predict cardiovascular events in patients with coronary artery disease on guideline-recommended medication. Assessment of postprandial TG does not improve risk prediction compared to fasting TG in these patients.

  4. A risk score for predicting near-term incidence of hypertension: the Framingham Heart Study.

    PubMed

    Parikh, Nisha I; Pencina, Michael J; Wang, Thomas J; Benjamin, Emelia J; Lanier, Katherine J; Levy, Daniel; D'Agostino, Ralph B; Kannel, William B; Vasan, Ramachandran S

    2008-01-15

    Studies suggest that targeting high-risk, nonhypertensive individuals for treatment may delay hypertension onset, thereby possibly mitigating vascular complications. Risk stratification may facilitate cost-effective approaches to management. To develop a simple risk score for predicting hypertension incidence by using measures readily obtained in the physician's office. Longitudinal cohort study. Framingham Heart Study, Framingham, Massachusetts. 1717 nonhypertensive white individuals 20 to 69 years of age (mean age, 42 years; 54% women), without diabetes and with both parents in the original cohort of the Framingham Heart Study, contributed 5814 person-examinations. Scores were developed for predicting the 1-, 2-, and 4-year risk for new-onset hypertension, and performance characteristics of the prediction algorithm were assessed by using calibration and discrimination measures. Parental hypertension was ascertained from examinations of the original cohort of the Framingham Heart Study. During follow-up (median time over all person-examinations, 3.8 years), 796 persons (52% women) developed new-onset hypertension. In multivariable analyses, age, sex, systolic and diastolic blood pressure, body mass index, parental hypertension, and cigarette smoking were significant predictors of hypertension. According to the risk score based on these factors, the 4-year risk for incident hypertension was classified as low (<5%) in 34% of participants, medium (5% to 10%) in 19%, and high (>10%) in 47%. The c-statistic for the prediction model was 0.788, and calibration was very good. The risk score findings may not be generalizable to persons of nonwhite race or ethnicity or to persons with diabetes. The risk score algorithm has not been validated in an independent cohort and is based on single measurements of risk factors and blood pressure. The hypertension risk prediction score can be used to estimate an individual's absolute risk for hypertension on short-term follow-up, and

  5. Determinants of tuberculosis transmission and treatment abandonment in Fortaleza, Brazil.

    PubMed

    Harling, Guy; Lima Neto, Antonio S; Sousa, Geziel S; Machado, Marcia M T; Castro, Marcia C

    2017-05-25

    Tuberculosis (TB) remains a public health problem, despite recent achievements in reducing incidence and mortality rates. In Brazil, these achievements were above the worldwide average, but marked by large regional heterogeneities. In Fortaleza (5th largest city in Brazil), the tuberculosis cure rate has been declining and treatment abandonment has been increasing in the past decade, despite a reduction in incidence and an increase in directly observed therapy (DOT). These trends put efforts to eliminate tuberculosis at risk. We therefore sought to determine social and programmatic determinants of tuberculosis incidence and treatment abandonment in Fortaleza. We analyzed sociodemographic and clinical data for all new tuberculosis cases notified in the Notifiable Diseases Information System (SINAN) from Fortaleza between 2007 and 2014. We calculated incidence rates for 117 neighborhoods in Fortaleza, assessed their spatial clustering, and used spatial regression models to quantify associations between neighborhood-level covariates and incidence rates. We used hierarchical logistic regression models to evaluate how individual- and neighborhood-level covariates predicted tuberculosis treatment abandonment. There were 12,338 new cases reported during the study period. Case rates across neighborhoods were significantly positively clustered in two low-income areas close to the city center. In an adjusted model, tuberculosis rates were significantly higher in neighborhoods with lower literacy, higher sewerage access and homicide rates, and a greater proportion of self-reported black residents. Treatment was abandoned in 1901 cases (15.4%), a rate that rose by 71% between 2007 and 2014. Abandonment was significantly associated with many individual sociodemographic and clinical factors. Notably, being recommended for DOT was protective for those who completed DOT, but associated with abandonment for those who did not. Low socioeconomic status areas have higher tuberculosis

  6. A Comparative Study of Adolescent Risk Assessment Instruments: Predictive and Incremental Validity

    ERIC Educational Resources Information Center

    Welsh, Jennifer L.; Schmidt, Fred; McKinnon, Lauren; Chattha, H. K.; Meyers, Joanna R.

    2008-01-01

    Promising new adolescent risk assessment tools are being incorporated into clinical practice but currently possess limited evidence of predictive validity regarding their individual and/or combined use in risk assessments. The current study compares three structured adolescent risk instruments, Youth Level of Service/Case Management Inventory…

  7. Presence of high-risk clones of OXA-23-producing Acinetobacter baumannii (ST79) and SPM-1-producing Pseudomonas aeruginosa (ST277) in environmental water samples in Brazil.

    PubMed

    Turano, Helena; Gomes, Fernando; Medeiros, Micheli; Oliveira, Silvane; Fontes, Lívia C; Sato, Maria I Z; Lincopan, Nilton

    2016-09-01

    This study reports the presence of hospital-associated high-risk lineages of OXA-23-producing ST79 Acinetobacter baumannii and SPM-1-producing ST277 Pseudomonas aeruginosa in urban rivers in Brazil. These findings indicate that urban rivers can act as reservoirs of clinically important multidrug-resistant bacteria, which constitute a potential risk to human and animal health. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. Development and Preliminary Performance of a Risk Factor Screen to Predict Posttraumatic Psychological Disorder After Trauma Exposure

    PubMed Central

    Carlson, Eve B.; Palmieri, Patrick A.; Spain, David A.

    2017-01-01

    Objective We examined data from a prospective study of risk factors that increase vulnerability or resilience, exacerbate distress, or foster recovery to determine whether risk factors accurately predict which individuals will later have high posttraumatic (PT) symptom levels and whether brief measures of risk factors also accurately predict later symptom elevations. Method Using data from 129 adults exposed to traumatic injury of self or a loved one, we conducted receiver operating characteristic (ROC) analyses of 14 risk factors assessed by full-length measures, determined optimal cutoff scores and calculated predictive performance for the nine that were most predictive. For five risk factors, we identified sets of items that accounted for 90% of variance in total scores and calculated predictive performance for sets of brief risk measures. Results A set of nine risk factors assessed by full measures identified 89% of those who later had elevated PT symptoms (sensitivity) and 78% of those who did not (specificity). A set of four brief risk factor measures assessed soon after injury identified 86% of those who later had elevated PT symptoms and 72% of those who did not. Conclusions Use of sets of brief risk factor measures shows promise of accurate prediction of PT psychological disorder and probable PTSD or depression. Replication of predictive accuracy is needed in a new and larger sample. PMID:28622811

  9. Predicting Drug Safety and Communicating Risk: Benefits of a Bayesian Approach.

    PubMed

    Lazic, Stanley E; Edmunds, Nicholas; Pollard, Christopher E

    2018-03-01

    Drug toxicity is a major source of attrition in drug discovery and development. Pharmaceutical companies routinely use preclinical data to predict clinical outcomes and continue to invest in new assays to improve predictions. However, there are many open questions about how to make the best use of available data, combine diverse data, quantify risk, and communicate risk and uncertainty to enable good decisions. The costs of suboptimal decisions are clear: resources are wasted and patients may be put at risk. We argue that Bayesian methods provide answers to all of these problems and use hERG-mediated QT prolongation as a case study. Benefits of Bayesian machine learning models include intuitive probabilistic statements of risk that incorporate all sources of uncertainty, the option to include diverse data and external information, and visualizations that have a clear link between the output from a statistical model and what this means for risk. Furthermore, Bayesian methods are easy to use with modern software, making their adoption for safety screening straightforward. We include R and Python code to encourage the adoption of these methods.

  10. Predictions of space radiation fatality risk for exploration missions.

    PubMed

    Cucinotta, Francis A; To, Khiet; Cacao, Eliedonna

    2017-05-01

    In this paper we describe revisions to the NASA Space Cancer Risk (NSCR) model focusing on updates to probability distribution functions (PDF) representing the uncertainties in the radiation quality factor (QF) model parameters and the dose and dose-rate reduction effectiveness factor (DDREF). We integrate recent heavy ion data on liver, colorectal, intestinal, lung, and Harderian gland tumors with other data from fission neutron experiments into the model analysis. In an earlier work we introduced distinct QFs for leukemia and solid cancer risk predictions, and here we consider liver cancer risks separately because of the higher RBE's reported in mouse experiments compared to other tumors types, and distinct risk factors for liver cancer for astronauts compared to the U.S. The revised model is used to make predictions of fatal cancer and circulatory disease risks for 1-year deep space and International Space Station (ISS) missions, and a 940 day Mars mission. We analyzed the contribution of the various model parameter uncertainties to the overall uncertainty, which shows that the uncertainties in relative biological effectiveness (RBE) factors at high LET due to statistical uncertainties and differences across tissue types and mouse strains are the dominant uncertainty. NASA's exposure limits are approached or exceeded for each mission scenario considered. Two main conclusions are made: 1) Reducing the current estimate of about a 3-fold uncertainty to a 2-fold or lower uncertainty will require much more expansive animal carcinogenesis studies in order to reduce statistical uncertainties and understand tissue, sex and genetic variations. 2) Alternative model assumptions such as non-targeted effects, increased tumor lethality and decreased latency at high LET, and non-cancer mortality risks from circulatory diseases could significantly increase risk estimates to several times higher than the NASA limits. Copyright © 2017 The Committee on Space Research (COSPAR

  11. Predictions of space radiation fatality risk for exploration missions

    NASA Astrophysics Data System (ADS)

    Cucinotta, Francis A.; To, Khiet; Cacao, Eliedonna

    2017-05-01

    In this paper we describe revisions to the NASA Space Cancer Risk (NSCR) model focusing on updates to probability distribution functions (PDF) representing the uncertainties in the radiation quality factor (QF) model parameters and the dose and dose-rate reduction effectiveness factor (DDREF). We integrate recent heavy ion data on liver, colorectal, intestinal, lung, and Harderian gland tumors with other data from fission neutron experiments into the model analysis. In an earlier work we introduced distinct QFs for leukemia and solid cancer risk predictions, and here we consider liver cancer risks separately because of the higher RBE's reported in mouse experiments compared to other tumors types, and distinct risk factors for liver cancer for astronauts compared to the U.S. population. The revised model is used to make predictions of fatal cancer and circulatory disease risks for 1-year deep space and International Space Station (ISS) missions, and a 940 day Mars mission. We analyzed the contribution of the various model parameter uncertainties to the overall uncertainty, which shows that the uncertainties in relative biological effectiveness (RBE) factors at high LET due to statistical uncertainties and differences across tissue types and mouse strains are the dominant uncertainty. NASA's exposure limits are approached or exceeded for each mission scenario considered. Two main conclusions are made: 1) Reducing the current estimate of about a 3-fold uncertainty to a 2-fold or lower uncertainty will require much more expansive animal carcinogenesis studies in order to reduce statistical uncertainties and understand tissue, sex and genetic variations. 2) Alternative model assumptions such as non-targeted effects, increased tumor lethality and decreased latency at high LET, and non-cancer mortality risks from circulatory diseases could significantly increase risk estimates to several times higher than the NASA limits.

  12. Blood lead and cadmium levels in preschool children and associated risk factors in São Paulo, Brazil.

    PubMed

    Olympio, Kelly Polido Kaneshiro; Silva, Júlia Prestes da Rocha; Silva, Agnes Soares da; Souza, Vanessa Cristina de Oliveira; Buzalaf, Marília Afonso Rabelo; Barbosa, Fernando; Cardoso, Maria Regina Alves

    2018-05-18

    In Brazil, there are scarce data on lead (Pb) and cadmium (Cd) contamination, especially for more vulnerable populations such as preschool children. In this paper, we answer two questions: (1) What are the exposure levels of lead and cadmium in preschool children, in Sao Paulo, Brazil? and (2) What are the risk factors associated with this exposure? This cross-sectional study included 50 day care centers (DCCs), totaling 2463 children aged 1-4 years. Venous blood samples were analyzed by ICP-MS. Questionnaires were administered to the parents. Multiple logistic regression models were used to identify associations between blood lead levels (BLLs) and blood cadmium levels (BCLs) and potential risk factors. The geometric mean for BLLs was 2.16 μg/dL (95% CI: 2.10-2.22 μg/dL), and the 97.5th percentile was 13.9 μg/dL (95% CI: 10.0-17.3 μg/dL). For cadmium exposure, the geometric mean for BCLs was 0.48 μg/L (95% CI: 0.47-0.50 μg/L), and the 95th percentile was 2.57 μg/L (95% CI: 2.26-2.75 μg/L). The DCCs' geographic region was associated with high BLLs and BCLs, indicating hot spots for lead and cadmium exposures. In addition, it was found that the higher the vehicles flow, the higher were the BLLs in children. Red lead in household gates was also an important risk factor for lead exposure. Comparing these results with the findings of the Fourth National Report on Human Exposure to Environmental Chemicals by CDC-2013, it was found that in Brazilian preschool children the BLLs are almost three times higher (97.5th percentile) and the BCLs are almost twelve times higher (95th percentile) than those in U.S. children. This information is essential to formulate public health policies. Copyright © 2018 Elsevier Ltd. All rights reserved.

  13. Development of a Korean Fracture Risk Score (KFRS) for Predicting Osteoporotic Fracture Risk: Analysis of Data from the Korean National Health Insurance Service

    PubMed Central

    Jang, Eun Jin; Park, ByeongJu; Kim, Tae-Young; Shin, Soon-Ae

    2016-01-01

    Background Asian-specific prediction models for estimating individual risk of osteoporotic fractures are rare. We developed a Korean fracture risk prediction model using clinical risk factors and assessed validity of the final model. Methods A total of 718,306 Korean men and women aged 50–90 years were followed for 7 years in a national system-based cohort study. In total, 50% of the subjects were assigned randomly to the development dataset and 50% were assigned to the validation dataset. Clinical risk factors for osteoporotic fracture were assessed at the biennial health check. Data on osteoporotic fractures during the follow-up period were identified by ICD-10 codes and the nationwide database of the National Health Insurance Service (NHIS). Results During the follow-up period, 19,840 osteoporotic fractures were reported (4,889 in men and 14,951 in women) in the development dataset. The assessment tool called the Korean Fracture Risk Score (KFRS) is comprised of a set of nine variables, including age, body mass index, recent fragility fracture, current smoking, high alcohol intake, lack of regular exercise, recent use of oral glucocorticoid, rheumatoid arthritis, and other causes of secondary osteoporosis. The KFRS predicted osteoporotic fractures over the 7 years. This score was validated using an independent dataset. A close relationship with overall fracture rate was observed when we compared the mean predicted scores after applying the KFRS with the observed risks after 7 years within each 10th of predicted risk. Conclusion We developed a Korean specific prediction model for osteoporotic fractures. The KFRS was able to predict risk of fracture in the primary population without bone mineral density testing and is therefore suitable for use in both clinical setting and self-assessment. The website is available at http://www.nhis.or.kr. PMID:27399597

  14. Development of a Korean Fracture Risk Score (KFRS) for Predicting Osteoporotic Fracture Risk: Analysis of Data from the Korean National Health Insurance Service.

    PubMed

    Kim, Ha Young; Jang, Eun Jin; Park, ByeongJu; Kim, Tae-Young; Shin, Soon-Ae; Ha, Yong-Chan; Jang, Sunmee

    2016-01-01

    Asian-specific prediction models for estimating individual risk of osteoporotic fractures are rare. We developed a Korean fracture risk prediction model using clinical risk factors and assessed validity of the final model. A total of 718,306 Korean men and women aged 50-90 years were followed for 7 years in a national system-based cohort study. In total, 50% of the subjects were assigned randomly to the development dataset and 50% were assigned to the validation dataset. Clinical risk factors for osteoporotic fracture were assessed at the biennial health check. Data on osteoporotic fractures during the follow-up period were identified by ICD-10 codes and the nationwide database of the National Health Insurance Service (NHIS). During the follow-up period, 19,840 osteoporotic fractures were reported (4,889 in men and 14,951 in women) in the development dataset. The assessment tool called the Korean Fracture Risk Score (KFRS) is comprised of a set of nine variables, including age, body mass index, recent fragility fracture, current smoking, high alcohol intake, lack of regular exercise, recent use of oral glucocorticoid, rheumatoid arthritis, and other causes of secondary osteoporosis. The KFRS predicted osteoporotic fractures over the 7 years. This score was validated using an independent dataset. A close relationship with overall fracture rate was observed when we compared the mean predicted scores after applying the KFRS with the observed risks after 7 years within each 10th of predicted risk. We developed a Korean specific prediction model for osteoporotic fractures. The KFRS was able to predict risk of fracture in the primary population without bone mineral density testing and is therefore suitable for use in both clinical setting and self-assessment. The website is available at http://www.nhis.or.kr.

  15. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

    PubMed

    Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

    2018-05-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P < .001). The model was able to distinguish well among three risk groups based on tertiles of the risk score. Adding treatment modality to the model did not decrease the predictive power. As a post hoc analysis, we tested the added value of comorbidity as scored by American Society of Anesthesiologists score in a subsample, which increased the C statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

  16. Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration

    PubMed Central

    Janssens, A Cecile JW; Ioannidis, John PA; Bedrosian, Sara; Boffetta, Paolo; Dolan, Siobhan M; Dowling, Nicole; Fortier, Isabel; Freedman, Andrew N; Grimshaw, Jeremy M; Gulcher, Jeffrey; Gwinn, Marta; Hlatky, Mark A; Janes, Holly; Kraft, Peter; Melillo, Stephanie; O'Donnell, Christopher J; Pencina, Michael J; Ransohoff, David; Schully, Sheri D; Seminara, Daniela; Winn, Deborah M; Wright, Caroline F; van Duijn, Cornelia M; Little, Julian; Khoury, Muin J

    2011-01-01

    The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis. PMID:21407270

  17. Risk factors associated with cluster size of Mycobacterium tuberculosis (Mtb) of different RFLP lineages in Brazil.

    PubMed

    Peres, Renata Lyrio; Vinhas, Solange Alves; Ribeiro, Fabíola Karla Correa; Palaci, Moisés; do Prado, Thiago Nascimento; Reis-Santos, Bárbara; Zandonade, Eliana; Suffys, Philip Noel; Golub, Jonathan E; Riley, Lee W; Maciel, Ethel Leonor

    2018-02-08

    Tuberculosis (TB) transmission is influenced by patient-related risk, environment and bacteriological factors. We determined the risk factors associated with cluster size of IS6110 RFLP based genotypes of Mycobacterium tuberculosis (Mtb) isolates from Vitoria, Espirito Santo, Brazil. Cross-sectional study of new TB cases identified in the metropolitan area of Vitoria, Brazil between 2000 and 2010. Mtb isolates were genotyped by the IS6110 RFLP, spoligotyping and RD Rio . The isolates were classified according to genotype cluster sizes by three genotyping methods and associated patient epidemiologic characteristics. Regression Model was performed to identify factors associated with cluster size. Among 959 Mtb isolates, 461 (48%) cases had an isolate that belonged to an RFLP cluster, and six clusters with ten or more isolates were identified. Of the isolates spoligotyped, 448 (52%) were classified as LAM and 412 (48%) as non-LAM. Our regression model found that 6-9 isolates/RFLP cluster were more likely belong to the LAM family, having the RD Rio genotype and to be smear-positive (adjusted OR = 1.17, 95% CI 1.08-1.26; adjusted OR = 1.25, 95% CI 1.14-1.37; crude OR = 2.68, 95% IC 1.13-6.34; respectively) and living in a Serra city neighborhood decrease the risk of being in the 6-9 isolates/RFLP cluster (adjusted OR = 0.29, 95% CI, 0.10-0.84), than in the others groups. Individuals aged 21 to 30, 31 to 40 and > 50 years were less likely of belonging the 2-5 isolates/RFLP cluster than unique patterns compared to individuals < 20 years of age (adjusted OR = 0.49, 95% CI 0.28-0.85, OR = 0.43 95% CI 0.24-0.77and OR = 0. 49, 95% CI 0.26-0.91), respectively. The extrapulmonary disease was less likely to occur in those infected with strains in the 2-5 isolates/cluster group (adjustment OR = 0.45, 95% CI 0.24-0.85) than unique patterns. We found that a large proportion of new TB infections in Vitoria is caused by prevalent Mtb genotypes

  18. Predicting risk for medical malpractice claims using quality-of-care characteristics.

    PubMed Central

    Charles, S C; Gibbons, R D; Frisch, P R; Pyskoty, C E; Hedeker, D; Singha, N K

    1992-01-01

    The current fault-based tort system assumes that claims made against physicians are inversely related to the quality of care they provide. In this study we identified physician characteristics associated with elements of medical care that make physicians vulnerable to malpractice claims. A sample of physicians (n = 248) thought to be at high or low risk for claims was surveyed on various personal and professional characteristics. Statistical analysis showed that 9 characteristics predicted risk group. High risk was associated with increased age, surgical specialty, emergency department coverage, increased days away from practice, and the feeling that the litigation climate was "unfair." Low risk was associated with scheduling enough time to talk with patients, answering patients' telephone calls directly, feeling "satisfied" with practice arrangements, and acknowledging greater emotional distress. Prediction was more accurate for physicians in practice 15 years or less. We conclude that a relationship exists between a history of malpractice claims and selected physician characteristics. PMID:1462538

  19. Web-based decision support system to predict risk level of long term rice production

    NASA Astrophysics Data System (ADS)

    Mukhlash, Imam; Maulidiyah, Ratna; Sutikno; Setiyono, Budi

    2017-09-01

    Appropriate decision making in risk management of rice production is very important in agricultural planning, especially for Indonesia which is an agricultural country. Good decision would be obtained if the supporting data required are satisfied and using appropriate methods. This study aims to develop a Decision Support System that can be used to predict the risk level of rice production in some districts which are central of rice production in East Java. Web-based decision support system is constructed so that the information can be easily accessed and understood. Components of the system are data management, model management, and user interface. This research uses regression models of OLS and Copula. OLS model used to predict rainfall while Copula model used to predict harvested area. Experimental results show that the models used are successfully predict the harvested area of rice production in some districts which are central of rice production in East Java at any given time based on the conditions and climate of a region. Furthermore, it can predict the amount of rice production with the level of risk. System generates prediction of production risk level in the long term for some districts that can be used as a decision support for the authorities.

  20. Factors predictive of risk for complications in patients with oesophageal foreign bodies.

    PubMed

    Sung, Sang Hun; Jeon, Seong Woo; Son, Hyuk Su; Kim, Sung Kook; Jung, Min Kyu; Cho, Chang Min; Tak, Won Young; Kweon, Young Oh

    2011-08-01

    Reports on predictive risk factors associated with complications of ingested oesophageal foreign bodies are rare. The aim of this study was to determine the predictive risk factors associated with the complications of oesophageal foreign bodies. Three hundred sixteen cases with foreign bodies in the oesophagus were retrospectively investigated. The predictive risk factors for complications after foreign body ingestion were analysed by multivariate logistic regression, and included age, size and type of foreign body ingested, duration of impaction, and the level of foreign body impaction. The types of oesophageal foreign bodies included fish bones (37.0%), food (19.0%), and metals (18.4%). The complications associated with foreign bodies were ulcers (21.2%), lacerations (14.9%), erosions (12.0%), and perforation (1.9%). Multivariate analysis showed that the duration of impaction (p<0.001), and the type (p<0.001) and size of the foreign bodies (p<0.001) were significant independent risk factors associated with the development of complications in patients with oesophageal foreign bodies. In patients with oesophageal foreign bodies, the risk of complications was increased with a longer duration of impaction, bone type, and larger size. Copyright © 2011 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

  1. Cardiovascular risk prediction in HIV-infected patients: comparing the Framingham, atherosclerotic cardiovascular disease risk score (ASCVD), Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) and Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) risk prediction models.

    PubMed

    Krikke, M; Hoogeveen, R C; Hoepelman, A I M; Visseren, F L J; Arends, J E

    2016-04-01

    The aim of the study was to compare the predictions of five popular cardiovascular disease (CVD) risk prediction models, namely the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) model, the Framingham Heart Study (FHS) coronary heart disease (FHS-CHD) and general CVD (FHS-CVD) models, the American Heart Association (AHA) atherosclerotic cardiovascular disease risk score (ASCVD) model and the Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) model. A cross-sectional design was used to compare the cumulative CVD risk predictions of the models. Furthermore, the predictions of the general CVD models were compared with those of the HIV-specific D:A:D model using three categories (< 10%, 10-20% and > 20%) to categorize the risk and to determine the degree to which patients were categorized similarly or in a higher/lower category. A total of 997 HIV-infected patients were included in the study: 81% were male and they had a median age of 46 [interquartile range (IQR) 40-52] years, a known duration of HIV infection of 6.8 (IQR 3.7-10.9) years, and a median time on ART of 6.4 (IQR 3.0-11.5) years. The D:A:D, ASCVD and SCORE-NL models gave a lower cumulative CVD risk, compared with that of the FHS-CVD and FHS-CHD models. Comparing the general CVD models with the D:A:D model, the FHS-CVD and FHS-CHD models only classified 65% and 79% of patients, respectively, in the same category as did the D:A:D model. However, for the ASCVD and SCORE-NL models, this percentage was 89% and 87%, respectively. Furthermore, FHS-CVD and FHS-CHD attributed a higher CVD risk to 33% and 16% of patients, respectively, while this percentage was < 6% for ASCVD and SCORE-NL. When using FHS-CVD and FHS-CHD, a higher overall CVD risk was attributed to the HIV-infected patients than when using the D:A:D, ASCVD and SCORE-NL models. This could have consequences regarding overtreatment, drug-related adverse events and drug-drug interactions. © 2015 British HIV Association.

  2. Osteoporosis risk prediction using machine learning and conventional methods.

    PubMed

    Kim, Sung Kean; Yoo, Tae Keun; Oh, Ein; Kim, Deok Won

    2013-01-01

    A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women, and compared with the ability of a conventional clinical decision tool, osteoporosis self-assessment tool (OST). We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Surveys (KNHANES V-1). The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and logistic regression (LR) based on various predictors associated with low bone density. The learning models were compared with OST. SVM had significantly better area under the curve (AUC) of the receiver operating characteristic (ROC) than ANN, LR, and OST. Validation on the test set showed that SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0%. We were the first to perform comparisons of the performance of osteoporosis prediction between the machine learning and conventional methods using population-based epidemiological data. The machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.

  3. Commentary on Holmes et al. (2007): resolving the debate on when extinction risk is predictable.

    PubMed

    Ellner, Stephen P; Holmes, Elizabeth E

    2008-08-01

    We reconcile the findings of Holmes et al. (Ecology Letters, 10, 2007, 1182) that 95% confidence intervals for quasi-extinction risk were narrow for many vertebrates of conservation concern, with previous theory predicting wide confidence intervals. We extend previous theory, concerning the precision of quasi-extinction estimates as a function of population dynamic parameters, prediction intervals and quasi-extinction thresholds, and provide an approximation that specifies the prediction interval and threshold combinations where quasi-extinction estimates are precise (vs. imprecise). This allows PVA practitioners to define the prediction interval and threshold regions of safety (low risk with high confidence), danger (high risk with high confidence), and uncertainty.

  4. Early-onset Conduct Problems: Predictions from daring temperament and risk taking behavior.

    PubMed

    Bai, Sunhye; Lee, Steve S

    2017-12-01

    Given its considerable public health significance, identifying predictors of early expressions of conduct problems is a priority. We examined the predictive validity of daring, a key dimension of temperament, and the Balloon Analog Risk Task (BART), a laboratory-based measure of risk taking behavior, with respect to two-year change in parent, teacher-, and youth self-reported oppositional defiant disorder (ODD), conduct disorder (CD), and antisocial behavior. At baseline, 150 ethnically diverse 6- to 10-year old (M=7.8, SD=1.1; 69.3% male) youth with ( n =82) and without ( n =68) DSM-IV ADHD completed the BART whereas parents rated youth temperament (i.e., daring); parents and teachers also independently rated youth ODD and CD symptoms. Approximately 2 years later, multi-informant ratings of youth ODD, CD, and antisocial behavior were gathered from rating scales and interviews. Whereas risk taking on the BART was unrelated to conduct problems, individual differences in daring prospectively predicted multi-informant rated conduct problems, independent of baseline risk taking, conduct problems, and ADHD diagnostic status. Early differences in the propensity to show positive socio-emotional responses to risky or novel experiences uniquely predicted escalating conduct problems in childhood, even with control of other potent clinical correlates. We consider the role of temperament in the origins and development of significant conduct problems from childhood to adolescence, including possible explanatory mechanisms underlying these predictions.

  5. Feasibility of dynamic risk prediction for hepatocellular carcinoma development in patients with chronic hepatitis B.

    PubMed

    Jeon, Mi Young; Lee, Hye Won; Kim, Seung Up; Kim, Beom Kyung; Park, Jun Yong; Kim, Do Young; Han, Kwang-Hyub; Ahn, Sang Hoon

    2018-04-01

    Several risk prediction models for hepatocellular carcinoma (HCC) development are available. We explored whether the use of risk prediction models can dynamically predict HCC development at different time points in chronic hepatitis B (CHB) patients. Between 2006 and 2014, 1397 CHB patients were recruited. All patients underwent serial transient elastography at intervals of >6 months. The median age of this study population (931 males and 466 females) was 49.0 years. The median CU-HCC, REACH-B, LSM-HCC and mREACH-B score at enrolment were 4.0, 9.0, 10.0 and 8.0 respectively. During the follow-up period (median, 68.0 months), 87 (6.2%) patients developed HCC. All risk prediction models were successful in predicting HCC development at both the first liver stiffness (LS) measurement (hazard ratio [HR] = 1.067-1.467 in the subgroup without antiviral therapy [AVT] and 1.096-1.458 in the subgroup with AVT) and second LS measurement (HR = 1.125-1.448 in the subgroup without AVT and 1.087-1.249 in the subgroup with AVT). In contrast, neither the absolute nor percentage change in the scores from the risk prediction models predicted HCC development (all P > .05). The mREACH-B score performed similarly or significantly better than did the other scores (AUROCs at 5 years, 0.694-0.862 vs 0.537-0.875). Dynamic prediction of HCC development at different time points was achieved using four risk prediction models, but not using the changes in the absolute and percentage values between two time points. The mREACH-B score was the most appropriate prediction model of HCC development among four prediction models. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  6. Stress and anger as contextual factors and preexisting cognitive schemas: predicting parental child maltreatment risk.

    PubMed

    Rodriguez, Christina M; Richardson, Michael J

    2007-11-01

    Progress in the child maltreatment field depends on refinements in leading models. This study examines aspects of social information processing theory (Milner, 2000) in predicting physical maltreatment risk in a community sample. Consistent with this theory, selected preexisting schema (external locus-of-control orientation, inappropriate developmental expectations, low empathic perspective-taking ability, and low perceived attachment relationship to child) were expected to predict child abuse risk beyond contextual factors (parenting stress and anger expression). Based on 115 parents' self-report, results from this study support cognitive factors that predict abuse risk (with locus of control, perceived attachment, or empathy predicting different abuse risk measures, but not developmental expectations), although the broad contextual factors involving negative affectivity and stress were consistent predictors across abuse risk markers. Findings are discussed with regard to implications for future model evaluations, with indications the model may apply to other forms of maltreatment, such as psychological maltreatment or neglect.

  7. The Functional Movement Screen and Injury Risk: Association and Predictive Value in Active Men.

    PubMed

    Bushman, Timothy T; Grier, Tyson L; Canham-Chervak, Michelle; Anderson, Morgan K; North, William J; Jones, Bruce H

    2016-02-01

    The Functional Movement Screen (FMS) is a series of 7 tests used to assess the injury risk in active populations. To determine the association of the FMS with the injury risk, assess predictive values, and identify optimal cut points using 3 injury types. Cohort study; Level of evidence, 2. Physically active male soldiers aged 18 to 57 years (N = 2476) completed the FMS. Demographic and fitness data were collected by survey. Medical record data for overuse injuries, traumatic injuries, and any injury 6 months after the FMS assessment were obtained. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated along with the receiver operating characteristic (ROC) to determine the area under the curve (AUC) and identify optimal cut points for the risk assessment. Risks, risk ratios (RRs), odds ratios (ORs), and 95% CIs were calculated to assess injury risks. Soldiers who scored ≤14 were at a greater risk for injuries compared with those who scored >14 using the composite score for overuse injuries (RR, 1.84; 95% CI, 1.63-2.09), traumatic injuries (RR, 1.26; 95% CI, 1.03-1.54), and any injury (RR, 1.60; 95% CI, 1.45-1.77). When controlling for other known injury risk factors, multivariate logistic regression analysis identified poor FMS performance (OR [score ≤14/19-21], 2.00; 95% CI, 1.42-2.81) as an independent risk factor for injuries. A cut point of ≤14 registered low measures of predictive value for all 3 injury types (sensitivity, 28%-37%; PPV, 19%-52%; AUC, 54%-61%). Shifting the injury risk cut point of ≤14 to the optimal cut points indicated by the ROC did not appreciably improve sensitivity or the PPV. Although poor FMS performance was associated with a higher risk of injuries, it displayed low sensitivity, PPV, and AUC. On the basis of these findings, the use of the FMS to screen for the injury risk is not recommended in this population because of the low predictive value and misclassification of the

  8. Longitudinal Prediction of Disruptive Behavior Disorders in Adolescent Males from Multiple Risk Domains

    PubMed Central

    Trentacosta, Christopher J.; Hyde, Luke W.; Goodlett, Benjamin D.; Shaw, Daniel S.

    2012-01-01

    The disruptive behavior disorders are among the most prevalent youth psychiatric disorders, and they predict numerous problematic outcomes in adulthood. This study examined multiple domains of risk during early childhood and early adolescence as longitudinal predictors of disruptive behavior disorder diagnoses among adolescent males. Early adolescent risks in the domains of sociodemographic factors, the caregiving context, and youth attributes were examined as mediators of associations between early childhood risks and disruptive behavior disorder diagnoses. Participants were 309 males from a longitudinal study of low-income mothers and their sons. Caregiving and youth risk during early adolescence each predicted the likelihood of receiving a disruptive behavior disorder diagnosis. Furthermore, sociodemographic and caregiving risk during early childhood were indirectly associated with disruptive behavior disorder diagnoses via their association with early adolescent risk. The findings suggest that preventive interventions targeting risk across domains may reduce the prevalence of disruptive behavior disorders. PMID:23239427

  9. Occupational health and safety in Brazil.

    PubMed Central

    Frumkin, H; Câmara, V de M

    1991-01-01

    BACKGROUND. Brazil is the world's fifth largest and sixth most populous nation. Its economy is varied, with strong manufacturing, agriculture, mining, and service sectors. Therefore, a wide variety of workplace hazards confronts its work force. This paper describes Brazil's occupational safety and health regulatory scheme, workers' compensation system, plant-level practices, training, and data collection. METHODS. We reviewed and analyzed Brazilian regulatory legislation and government and non-governmental organization (NGO) activity in occupational safety and health, as well as the structure and function of the workers' compensation system. We also reviewed available data on injuries and diseases from major sources, including the now-defunct Instituto Nacional do Previdencia Social (INPS) and the workers' compensation scheme, Seguro de Acidente de Trabalho (SAT). RESULTS. The incidence of workplace injuries has decreased in recent years and is now reported to be about 5 per 100 workers per year. The case fatality rate has been constant at about 5 fatalities per 1000 injuries. Less than 6% of reported injuries are classified as "diseases." Brazil's rates are comparable to those of Mexico and Zimbabwe, and two to four times higher than in most industrialized countries. CONCLUSIONS. Brazil has a high incidence of occupational injuries and diseases; these injuries and diseases are underreported; there is a large informal sector at special risk; and Brazil illustrates the disparity that exists in many countries between legislation on the books and legislation that is actually implemented. PMID:1836110

  10. Occupational health and safety in Brazil.

    PubMed

    Frumkin, H; Câmara, V de M

    1991-12-01

    Brazil is the world's fifth largest and sixth most populous nation. Its economy is varied, with strong manufacturing, agriculture, mining, and service sectors. Therefore, a wide variety of workplace hazards confronts its work force. This paper describes Brazil's occupational safety and health regulatory scheme, workers' compensation system, plant-level practices, training, and data collection. We reviewed and analyzed Brazilian regulatory legislation and government and non-governmental organization (NGO) activity in occupational safety and health, as well as the structure and function of the workers' compensation system. We also reviewed available data on injuries and diseases from major sources, including the now-defunct Instituto Nacional do Previdencia Social (INPS) and the workers' compensation scheme, Seguro de Acidente de Trabalho (SAT). The incidence of workplace injuries has decreased in recent years and is now reported to be about 5 per 100 workers per year. The case fatality rate has been constant at about 5 fatalities per 1000 injuries. Less than 6% of reported injuries are classified as "diseases." Brazil's rates are comparable to those of Mexico and Zimbabwe, and two to four times higher than in most industrialized countries. Brazil has a high incidence of occupational injuries and diseases; these injuries and diseases are underreported; there is a large informal sector at special risk; and Brazil illustrates the disparity that exists in many countries between legislation on the books and legislation that is actually implemented.

  11. [Triatominae and Cactaceae: a risk for the transmission of the American trypanosomiasis in the peridomicilary space (Northeast Brazil)].

    PubMed

    Emperaire, L; Romaña, C A

    2006-06-01

    Field observations carried in semi-arid Brazil Northeast point out the frequent association, in the peridomiciliary space, between a cactus, Cereus jamacaru, the occurrence of nests in its branches and the occurrence of two species of insects vectors of Trypanosoma cruzi, pathogenic agent of Chagas disease: Rhodnius neglectus and Triatoma pseudomaculata. The analysis of the architectural variables of this Cactaceae shows that the presence of nests, and thus of insects, depends on the traditional practices of management of this cactus. This study underlines the relevance of an integrated approach of the ecology of Triatominae for the identification of factors of risk.

  12. Prediction of Sustained Virological Response to Peginterferon-based Therapy for Chronic Hepatitis C: Regression Analysis of a Cohort from Rio Grande do Sul, Brazil

    PubMed Central

    Fendt, Lúcia; Amaral, Karine; D Picon, Paulo

    2017-01-01

    Aim: Peginterferon plus ribavirin (peg-IFN/RBV) is still the standard of care for treatment of hepatitis C virus (HCV) in many countries. Given the high toxicity of this regimen, our study aimed to develop a prediction tool that can identify which patients are unlikely to benefit from peg-IFN/RBV and could thus postpone treatment in favor of new-generation direct-acting antivirals. Materials and methods: Binary regression was performed using demographic, clinical, and laboratory covariates and sustained virological response (SVR) outcomes from a prospective cohort of individuals referred for therapy from 2003 to 2008 in a public HCV treatment center in Rio Grande do Sul, Brazil. Results: Of the 743 participants analyzed, 489 completed 48 weeks of treatment (65.8%). A total of 202 of those who completed peg-IFN/RBV therapy achieved SVR (27.2% responders), 196 did not (26.4%), and 91 had missing viral load (VL) at week 72 (12.2% loss to follow-up). The remainder discontinued therapy (n = 254 [34.2%]), 78 (30.7%) doing so due to adverse effects. Baseline covariates included in the regression model were sex, age, human immunodeficiency virus, infection status, aspartate transaminase, alanine transaminase, hemoglobin, platelets, serum creatinine, prothrombin time, pretreatment VL, cirrhosis on liver biopsy, and treatment naivety. A predicted SVR of 17.9% had 90.0% sensitivity for detecting true nonresponders. The negative likelihood ratio at a predicted SVR of 17.9% was 0.16, and the negative predictive value was 92.6%. Conclusion: Easily obtainable variables can identify patients that will likely not benefit from peg-IFN-based therapy. This prediction model might be useful to clinicians. Clinical significance: To our knowledge, this is the only prediction tool that can reliably help clinicians to postpone peg-IFN/RBV therapy for HCV genotype 1 patients. How to cite this article: Picon RV, Fendt L, Amaral K, Picon PD. Prediction of Sustained Virological Response to

  13. The Efficacy of Violence Prediction: A Meta-Analytic Comparison of Nine Risk Assessment Tools

    ERIC Educational Resources Information Center

    Yang, Min; Wong, Stephen C. P.; Coid, Jeremy

    2010-01-01

    Actuarial risk assessment tools are used extensively to predict future violence, but previous studies comparing their predictive accuracies have produced inconsistent findings as a result of various methodological issues. We conducted meta-analyses of the effect sizes of 9 commonly used risk assessment tools and their subscales to compare their…

  14. Multifactorial risk index for prediction of intraoperative blood transfusion in endovascular aneurysm repair.

    PubMed

    Mahmood, Eitezaz; Matyal, Robina; Mueller, Ariel; Mahmood, Feroze; Tung, Avery; Montealegre-Gallegos, Mario; Schermerhorn, Marc; Shahul, Sajid

    2018-03-01

    In some institutions, the current blood ordering practice does not discriminate minimally invasive endovascular aneurysm repair (EVAR) from open procedures, with consequent increasing costs and likelihood of blood product wastage for EVARs. This limitation in practice can possibly be addressed with the development of a reliable prediction model for transfusion risk in EVAR patients. We used the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database to create a model for prediction of intraoperative blood transfusion occurrence in patients undergoing EVAR. Afterward, we tested our predictive model on the Vascular Study Group of New England (VSGNE) database. We used the ACS NSQIP database for patients who underwent EVAR from 2011 to 2013 (N = 4709) as our derivation set for identifying a risk index for predicting intraoperative blood transfusion. We then developed a clinical risk score and validated this model using patients who underwent EVAR from 2003 to 2014 in the VSGNE database (N = 4478). The transfusion rates were 8.4% and 6.1% for the ACS NSQIP (derivation set) and VSGNE (validation) databases, respectively. Hemoglobin concentration, American Society of Anesthesiologists class, age, and aneurysm diameter predicted blood transfusion in the derivation set. When it was applied on the validation set, our risk index demonstrated good discrimination in both the derivation and validation set (C statistic = 0.73 and 0.70, respectively) and calibration using the Hosmer-Lemeshow test (P = .27 and 0.31) for both data sets. We developed and validated a risk index for predicting the likelihood of intraoperative blood transfusion in EVAR patients. Implementation of this index may facilitate the blood management strategies specific for EVAR. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

  15. Stillbirth prevalence in Brazil: an exploration of regional differences.

    PubMed

    Carvalho, Taiana Silva; Pellanda, Lucia Campos; Doyle, Pat

    Brazil is a large, heterogeneous, and diverse country, marked by social, economic, and regional inequalities. Stillbirth is a global concern, especially in low- and middle-income countries. This study investigated the prevalence and possible determinants of stillbirth in different regions of Brazil. This is a cross-sectional study including all women of reproductive age who had had a pregnancy in the last five years, enrolled in the most recent Brazilian Demographic and Health Survey (DHS/PNDS-2006/07). Logistic regression was used to assess the association between region and other maternal characteristics and stillbirth risk. The prevalence of stillbirth in Brazil was 14.82 per 1000 births, with great variation by region of the country, and a higher prevalence among the most deprived. The North and Northeast regions had the highest odds of stillbirth compared to the Center-West, which persisted after adjustment for multiple confounders - including deprivation level and ethnicity. Low maternal age and maternal obesity were also related to higher odds of stillbirth. In Brazil, the region influences stillbirth risk, with much higher risk in the North and Northeast. Variation in socioeconomic level does not explain this finding. Further research on the subject should explore other possible explanations, such as antenatal care and type of delivery, as well as the role of the private and public health systems in determining stillbirth. Preventive strategies should be directed to these historically disadvantaged regions, such as guaranteeing access and quality of care during pregnancy and around the time of birth. Copyright © 2017 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

  16. Predicting Family Burden Following Childhood Traumatic Brain Injury: A Cumulative Risk Approach

    PubMed Central

    Josie, Katherine Leigh; Peterson, Catherine Cant; Burant, Christopher; Drotar, Dennis; Stancin, Terry; Wade, Shari L.; Yeates, Keith; Taylor, H. Gerry

    2015-01-01

    Objective To examine the utility of a cumulative risk index (CRI) in predicting the family burden of injury (FBI) over time in families of children with traumatic brain injury (TBI). Participants One hundred eight children with severe or moderate TBI and their families participated in the study. Measures The measures used in the study include the Socioeconomic Composite Index, Life Stressors and Social Resources Inventory—Adult Form, Vineland Adaptive Behavior Scales, Child Behavior Checklist, Children’s Depression Inventory, McMaster Family Assessment Device, Brief Symptom Inventory, and Family Burden of Injury Interview. In addition, information on injury-related risk was obtained via medical charts. Methods Participants were assessed immediately, 6, and 12 months postinjury and at a 4-year extended follow-up. Results Risk variables were dichotomized (ie, high- or low-risk) and summed to create a CRI for each child. The CRI predicted the FBI at all assessments, even after accounting for autocorrelations across repeated assessments. Path coefficients between the outcome measures at each time point were significant, as were all path coefficients from the CRI to family burden at each time point. In addition, all fit indices were above the recommended guidelines, and the χ2 statistic indicated a good fit to the data. Conclusions The current study provides initial support for the utility of a CRI (ie, an index of accumulated risk factors) in predicting family outcomes over time for children with TBI. The time period immediately after injury best predicts the future levels of FBI; however, cumulative risk continues to influence the change across successive postinjury assessments. These results suggest that clinical interventions could be proactive or preventive by intervening with identified “at-risk” subgroups immediately following injury. PMID:19033828

  17. Canine vector-borne diseases in Brazil

    PubMed Central

    Dantas-Torres, Filipe

    2008-01-01

    Canine vector-borne diseases (CVBDs) are highly prevalent in Brazil and represent a challenge to veterinarians and public health workers, since some diseases are of great zoonotic potential. Dogs are affected by many protozoa (e.g., Babesia vogeli, Leishmania infantum, and Trypanosoma cruzi), bacteria (e.g., Anaplasma platys and Ehrlichia canis), and helminths (e.g., Dirofilaria immitis and Dipylidium caninum) that are transmitted by a diverse range of arthropod vectors, including ticks, fleas, lice, triatomines, mosquitoes, tabanids, and phlebotomine sand flies. This article focuses on several aspects (etiology, transmission, distribution, prevalence, risk factors, diagnosis, control, prevention, and public health significance) of CVBDs in Brazil and discusses research gaps to be addressed in future studies. PMID:18691408

  18. Application of Physically based landslide susceptibility models in Brazil

    NASA Astrophysics Data System (ADS)

    Carvalho Vieira, Bianca; Martins, Tiago D.

    2017-04-01

    Shallow landslides and floods are the processes responsible for most material and environmental damages in Brazil. In the last decades, some landslides events induce a high number of deaths (e.g. Over 1000 deaths in one event) and incalculable social and economic losses. Therefore, the prediction of those processes is considered an important tool for land use planning tools. Among different methods the physically based landslide susceptibility models having been widely used in many countries, but in Brazil it is still incipient when compared to other ones, like statistical tools and frequency analyses. Thus, the main objective of this research was to assess the application of some Physically based landslide susceptibility models in Brazil, identifying their main results, the efficiency of susceptibility mapping, parameters used and limitations of the tropical humid environment. In order to achieve that, it was evaluated SHALSTAB, SINMAP and TRIGRS models in some studies in Brazil along with the Geotechnical values, scales, DEM grid resolution and the results based on the analysis of the agreement between predicted susceptibility and the landslide scar's map. Most of the studies in Brazil applied SHALSTAB, SINMAP and to a lesser extent the TRIGRS model. The majority researches are concentrated in the Serra do Mar mountain range, that is a system of escarpments and rugged mountains that extends more than 1,500 km along the southern and southeastern Brazilian coast, and regularly affected by heavy rainfall that generates widespread mass movements. Most part of these studies used conventional topographic maps with scales ranging from 1:2000 to 1:50000 and DEM-grid resolution between 2 and 20m. Regarding the Geotechnical and hydrological values, a few studies use field collected data which could produce more efficient results, as indicated by international literature. Therefore, even though they have enormous potential in the susceptibility mapping, even for comparison

  19. External validation of the Garvan nomograms for predicting absolute fracture risk: the Tromsø study.

    PubMed

    Ahmed, Luai A; Nguyen, Nguyen D; Bjørnerem, Åshild; Joakimsen, Ragnar M; Jørgensen, Lone; Størmer, Jan; Bliuc, Dana; Center, Jacqueline R; Eisman, John A; Nguyen, Tuan V; Emaus, Nina

    2014-01-01

    Absolute risk estimation is a preferred approach for assessing fracture risk and treatment decision making. This study aimed to evaluate and validate the predictive performance of the Garvan Fracture Risk Calculator in a Norwegian cohort. The analysis included 1637 women and 1355 aged 60+ years from the Tromsø study. All incident fragility fractures between 2001 and 2009 were registered. The predicted probabilities of non-vertebral osteoporotic and hip fractures were determined using models with and without BMD. The discrimination and calibration of the models were assessed. Reclassification analysis was used to compare the models performance. The incidence of osteoporotic and hip fracture was 31.5 and 8.6 per 1000 population in women, respectively; in men the corresponding incidence was 12.2 and 5.1. The predicted 5-year and 10-year probability of fractures was consistently higher in the fracture group than the non-fracture group for all models. The 10-year predicted probabilities of hip fracture in those with fracture was 2.8 (women) to 3.1 times (men) higher than those without fracture. There was a close agreement between predicted and observed risk in both sexes and up to the fifth quintile. Among those in the highest quintile of risk, the models over-estimated the risk of fracture. Models with BMD performed better than models with body weight in correct classification of risk in individuals with and without fracture. The overall net decrease in reclassification of the model with weight compared to the model with BMD was 10.6% (p = 0.008) in women and 17.2% (p = 0.001) in men for osteoporotic fractures, and 13.3% (p = 0.07) in women and 17.5% (p = 0.09) in men for hip fracture. The Garvan Fracture Risk Calculator is valid and clinically useful in identifying individuals at high risk of fracture. The models with BMD performed better than those with body weight in fracture risk prediction.

  20. External Validation of the Garvan Nomograms for Predicting Absolute Fracture Risk: The Tromsø Study

    PubMed Central

    Ahmed, Luai A.; Nguyen, Nguyen D.; Bjørnerem, Åshild; Joakimsen, Ragnar M.; Jørgensen, Lone; Størmer, Jan; Bliuc, Dana; Center, Jacqueline R.; Eisman, John A.; Nguyen, Tuan V.; Emaus, Nina

    2014-01-01

    Background Absolute risk estimation is a preferred approach for assessing fracture risk and treatment decision making. This study aimed to evaluate and validate the predictive performance of the Garvan Fracture Risk Calculator in a Norwegian cohort. Methods The analysis included 1637 women and 1355 aged 60+ years from the Tromsø study. All incident fragility fractures between 2001 and 2009 were registered. The predicted probabilities of non-vertebral osteoporotic and hip fractures were determined using models with and without BMD. The discrimination and calibration of the models were assessed. Reclassification analysis was used to compare the models performance. Results The incidence of osteoporotic and hip fracture was 31.5 and 8.6 per 1000 population in women, respectively; in men the corresponding incidence was 12.2 and 5.1. The predicted 5-year and 10-year probability of fractures was consistently higher in the fracture group than the non-fracture group for all models. The 10-year predicted probabilities of hip fracture in those with fracture was 2.8 (women) to 3.1 times (men) higher than those without fracture. There was a close agreement between predicted and observed risk in both sexes and up to the fifth quintile. Among those in the highest quintile of risk, the models over-estimated the risk of fracture. Models with BMD performed better than models with body weight in correct classification of risk in individuals with and without fracture. The overall net decrease in reclassification of the model with weight compared to the model with BMD was 10.6% (p = 0.008) in women and 17.2% (p = 0.001) in men for osteoporotic fractures, and 13.3% (p = 0.07) in women and 17.5% (p = 0.09) in men for hip fracture. Conclusions The Garvan Fracture Risk Calculator is valid and clinically useful in identifying individuals at high risk of fracture. The models with BMD performed better than those with body weight in fracture risk prediction. PMID:25255221

  1. Metal concentrations in surface water and sediments from Pardo River, Brazil: human health risks.

    PubMed

    Alves, Renato I S; Sampaio, Carolina F; Nadal, Martí; Schuhmacher, Marta; Domingo, José L; Segura-Muñoz, Susana I

    2014-08-01

    Pardo River (Brazil) is suffering from an important anthropogenic impact due to the pressure of highly populated areas and the influence of sugarcane cultivation. The objective of the present study was to determine the levels of 13 trace elements (As, Be, Cd, Cr, Cu, Pb, Mn, Hg, Ni, Tl, Sn, V and Zn) in samples of surface water and sediments from the Pardo River. Furthermore, the human health risks associated with exposure to those metals through oral intake and dermal absorption were also evaluated. Spatial and seasonal trends of the data were closely analyzed from a probabilistic approach. Manganese showed the highest mean concentrations in both water and sediments, remarking the incidence of the agricultural activity and the geological characteristics within the basin. Thallium and arsenic were identified as two priority pollutants, being the most important contributors to the Hazard Index (HI). Since non-carcinogenic risks due to thallium exposure slightly exceeded international guidelines (HI>1), a special effort should be made on this trace element. However, the current concentrations of arsenic, a carcinogenic element, were in accordance to acceptable lifetime risks. Nowadays, there is a clear increasing growth in human population and economic activities in the Pardo River, whose waters have become a serious strategic alternative for the potential supply of drinking water. Therefore, environmental monitoring studies are required not only to assure that the current state of pollution of Pardo River does not mean a risk for the riverside population, but also to assess the potential trends in the environmental levels of those elements. Copyright © 2014 Elsevier Inc. All rights reserved.

  2. Can we improve clinical prediction of at-risk older drivers?

    PubMed Central

    Bowers, Alex R.; Anastasio, R. Julius; Sheldon, Sarah S.; O’Connor, Margaret G.; Hollis, Ann M.; Howe, Piers D.; Horowitz, Todd S.

    2013-01-01

    Objectives To conduct a pilot study to evaluate the predictive value of the Montreal Cognitive Assessment test (MoCA) and a brief test of multiple object tracking (MOT) relative to other tests of cognition and attention in identifying at-risk older drivers, and to determine which combination of tests provided the best overall prediction. Methods Forty-seven currently-licensed drivers (58 to 95 years), primarily from a clinical driving evaluation program, participated. Their performance was measured on: (1) a screening test battery, comprising MoCA, MOT, MiniMental State Examination (MMSE), Trail-Making Test, visual acuity, contrast sensitivity, and Useful Field of View (UFOV); and (2) a standardized road test. Results Eighteen participants were rated at-risk on the road test. UFOV subtest 2 was the best single predictor with an area under the curve (AUC) of .84. Neither MoCA nor MOT was a better predictor of the at-risk outcome than either MMSE or UFOV, respectively. The best four-test combination (MMSE, UFOV subtest 2, visual acuity and contrast sensitivity) was able to identify at-risk drivers with 95% specificity and 80% sensitivity (.91 AUC). Conclusions Although the best four-test combination was much better than a single test in identifying at-risk drivers, there is still much work to do in this field to establish test batteries that have both high sensitivity and specificity. PMID:23954688

  3. Predicting survival using clinical risk scores and non-HLA immunogenetics.

    PubMed

    Balavarca, Y; Pearce, K; Norden, J; Collin, M; Jackson, G; Holler, E; Dressel, R; Kolb, H-J; Greinix, H; Socie, G; Toubert, A; Rocha, V; Gluckman, E; Hromadnikova, I; Sedlacek, P; Wolff, D; Holtick, U; Dickinson, A; Bickeböller, H

    2015-11-01

    Previous studies of non-histocompatibility leukocyte antigen (HLA) gene single-nucleotide polymorphisms (SNPs) on subgroups of patients undergoing allogeneic haematopoietic stem cell transplantation (HSCT) revealed an association with transplant outcome. This study further evaluated the association of non-HLA polymorphisms with overall survival in a cohort of 762 HSCT patients using data on 26 polymorphisms in 16 non-HLA genes. When viewed in addition to an already established clinical risk score (EBMT-score), three polymorphisms: rs8177374 in the gene for MyD88-adapter-like (MAL; P=0.026), rs9340799 in the oestrogen receptor gene (ESR; P=0.003) and rs1800795 in interleukin-6 (IL-6; P=0.007) were found to be associated with reduced overall survival, whereas the haplo-genotype (ACC/ACC) in IL-10 was protective (P=0.02). The addition of these non-HLA polymorphisms in a Cox regression model alongside the EBMT-score improved discrimination between risk groups and increased the level of prediction compared with the EBMT-score alone (gain in prediction capability for EBMT-genetic-score 10.8%). Results also demonstrated how changes in clinical practice through time have altered the effects of non-HLA analysis. The study illustrates the significance of non-HLA genotyping prior to HSCT and the importance of further investigation into non-HLA gene polymorphisms in risk prediction.

  4. Cannabis use in children with individualized risk profiles: Predicting the effect of universal prevention intervention.

    PubMed

    Miovský, Michal; Vonkova, Hana; Čablová, Lenka; Gabrhelík, Roman

    2015-11-01

    To study the effect of a universal prevention intervention targeting cannabis use in individual children with different risk profiles. A school-based randomized controlled prevention trial was conducted over a period of 33 months (n=1874 sixth-graders, baseline mean age 11.82). We used a two-level random intercept logistic model for panel data to predict the probabilities of cannabis use for each child. Specifically, we used eight risk/protective factors to characterize each child and then predicted two probabilities of cannabis use for each child if the child had the intervention or not. Using the two probabilities, we calculated the absolute and relative effect of the intervention for each child. According to the two probabilities, we also divided the sample into a low-risk group (the quarter of the children with the lowest probabilities), a moderate-risk group, and a high-risk group (the quarter of the children with the highest probabilities) and showed the average effect of the intervention on these groups. The differences between the intervention group and the control group were statistically significant in each risk group. The average predicted probabilities of cannabis use for a child from the low-risk group were 4.3% if the child had the intervention and 6.53% if no intervention was provided. The corresponding probabilities for a child from the moderate-risk group were 10.91% and 15.34% and for a child from the high-risk group 25.51% and 32.61%. School grades, thoughts of hurting oneself, and breaking the rules were the three most important factors distinguishing high-risk and low-risk children. We predicted the effect of the intervention on individual children, characterized by their risk/protective factors. The predicted absolute effect and relative effect of any intervention for any selected risk/protective profile of a given child may be utilized in both prevention practice and research. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. An Empiric HIV Risk Scoring Tool to Predict HIV-1 Acquisition in African Women.

    PubMed

    Balkus, Jennifer E; Brown, Elizabeth; Palanee, Thesla; Nair, Gonasagrie; Gafoor, Zakir; Zhang, Jingyang; Richardson, Barbra A; Chirenje, Zvavahera M; Marrazzo, Jeanne M; Baeten, Jared M

    2016-07-01

    To develop and validate an HIV risk assessment tool to predict HIV acquisition among African women. Data were analyzed from 3 randomized trials of biomedical HIV prevention interventions among African women (VOICE, HPTN 035, and FEM-PrEP). We implemented standard methods for the development of clinical prediction rules to generate a risk-scoring tool to predict HIV acquisition over the course of 1 year. Performance of the score was assessed through internal and external validations. The final risk score resulting from multivariable modeling included age, married/living with a partner, partner provides financial or material support, partner has other partners, alcohol use, detection of a curable sexually transmitted infection, and herpes simplex virus 2 serostatus. Point values for each factor ranged from 0 to 2, with a maximum possible total score of 11. Scores ≥5 were associated with HIV incidence >5 per 100 person-years and identified 91% of incident HIV infections from among only 64% of women. The area under the curve (AUC) for predictive ability of the score was 0.71 (95% confidence interval [CI]: 0.68 to 0.74), indicating good predictive ability. Risk score performance was generally similar with internal cross-validation (AUC = 0.69; 95% CI: 0.66 to 0.73) and external validation in HPTN 035 (AUC = 0.70; 95% CI: 0.65 to 0.75) and FEM-PrEP (AUC = 0.58; 95% CI: 0.51 to 0.65). A discrete set of characteristics that can be easily assessed in clinical and research settings was predictive of HIV acquisition over 1 year. The use of a validated risk score could improve efficiency of recruitment into HIV prevention research and inform scale-up of HIV prevention strategies in women at highest risk.

  6. Using the Lorenz Curve to Characterize Risk Predictiveness and Etiologic Heterogeneity

    PubMed Central

    Mauguen, Audrey; Begg, Colin B.

    2017-01-01

    The Lorenz curve is a graphical tool that is used widely in econometrics. It represents the spread of a probability distribution, and its traditional use has been to characterize population distributions of wealth or income, or more specifically, inequalities in wealth or income. However, its utility in public health research has not been broadly established. The purpose of this article is to explain its special usefulness for characterizing the population distribution of disease risks, and in particular for identifying the precise disease burden that can be predicted to occur in segments of the population that are known to have especially high (or low) risks, a feature that is important for evaluating the yield of screening or other disease prevention initiatives. We demonstrate that, although the Lorenz curve represents the distribution of predicted risks in a population at risk for the disease, in fact it can be estimated from a case–control study conducted in the population without the need for information on absolute risks. We explore two different estimation strategies and compare their statistical properties using simulations. The Lorenz curve is a statistical tool that deserves wider use in public health research. PMID:27096256

  7. Mapping Global Potential Risk of Mango Sudden Decline Disease Caused by Ceratocystis fimbriata.

    PubMed

    Galdino, Tarcísio Visintin da Silva; Kumar, Sunil; Oliveira, Leonardo S S; Alfenas, Acelino C; Neven, Lisa G; Al-Sadi, Abdullah M; Picanço, Marcelo C

    2016-01-01

    The Mango Sudden Decline (MSD), also referred to as Mango Wilt, is an important disease of mango in Brazil, Oman and Pakistan. This fungus is mainly disseminated by the mango bark beetle, Hypocryphalus mangiferae (Stebbing), by infected plant material, and the infested soils where it is able to survive for long periods. The best way to avoid losses due to MSD is to prevent its establishment in mango production areas. Our objectives in this study were to: (1) predict the global potential distribution of MSD, (2) identify the mango growing areas that are under potential risk of MSD establishment, and (3) identify climatic factors associated with MSD distribution. Occurrence records were collected from Brazil, Oman and Pakistan where the disease is currently known to occur in mango. We used the correlative maximum entropy based model (MaxEnt) algorithm to assess the global potential distribution of MSD. The MaxEnt model predicted suitable areas in countries where the disease does not already occur in mango, but where mango is grown. Among these areas are the largest mango producers in the world including India, China, Thailand, Indonesia, and Mexico. The mean annual temperature, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest month variables contributed most to the potential distribution of MSD disease. The mango bark beetle vector is known to occur beyond the locations where MSD currently exists and where the model predicted suitable areas, thus showing a high likelihood for disease establishment in areas predicted by our model. Our study is the first to map the potential risk of MSD establishment on a global scale. This information can be used in designing strategies to prevent introduction and establishment of MSD disease, and in preparation of efficient pest risk assessments and monitoring programs.

  8. Mapping Global Potential Risk of Mango Sudden Decline Disease Caused by Ceratocystis fimbriata

    PubMed Central

    Oliveira, Leonardo S. S.; Alfenas, Acelino C.; Neven, Lisa G.; Al-Sadi, Abdullah M.

    2016-01-01

    The Mango Sudden Decline (MSD), also referred to as Mango Wilt, is an important disease of mango in Brazil, Oman and Pakistan. This fungus is mainly disseminated by the mango bark beetle, Hypocryphalus mangiferae (Stebbing), by infected plant material, and the infested soils where it is able to survive for long periods. The best way to avoid losses due to MSD is to prevent its establishment in mango production areas. Our objectives in this study were to: (1) predict the global potential distribution of MSD, (2) identify the mango growing areas that are under potential risk of MSD establishment, and (3) identify climatic factors associated with MSD distribution. Occurrence records were collected from Brazil, Oman and Pakistan where the disease is currently known to occur in mango. We used the correlative maximum entropy based model (MaxEnt) algorithm to assess the global potential distribution of MSD. The MaxEnt model predicted suitable areas in countries where the disease does not already occur in mango, but where mango is grown. Among these areas are the largest mango producers in the world including India, China, Thailand, Indonesia, and Mexico. The mean annual temperature, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest month variables contributed most to the potential distribution of MSD disease. The mango bark beetle vector is known to occur beyond the locations where MSD currently exists and where the model predicted suitable areas, thus showing a high likelihood for disease establishment in areas predicted by our model. Our study is the first to map the potential risk of MSD establishment on a global scale. This information can be used in designing strategies to prevent introduction and establishment of MSD disease, and in preparation of efficient pest risk assessments and monitoring programs. PMID:27415625

  9. Epidemiology of childhood conduct problems in Brazil: systematic review and meta-analysis.

    PubMed

    Murray, Joseph; Anselmi, Luciana; Gallo, Erika Alejandra Giraldo; Fleitlich-Bilyk, Bacy; Bordin, Isabel A

    2013-10-01

    This study aimed to review evidence on the prevalence of and risk factors for conduct problems in Brazil. We searched electronic databases and contacted Brazilian researchers up to 05/2012. Studies were included in the review if they reported the prevalence of or risk factors for conduct problems, conduct disorder, or oppositional defiant disorder for 100 + Brazilian children aged ≤18 years, systematically sampled in schools or the community. Prevalence rates and sex differences were meta-analysed. Risk factor studies were reviewed one by one. The average prevalence of conduct problems in screening questionnaires was 20.8%, and the average prevalence of conduct disorder/oppositional defiant disorder was 4.1%. There was systematic variation in the results of screening studies according to methodology: recruitment location, informants, instruments, impairment criterion for case definition, and response rates. Risk factors previously identified in high-income countries were mainly replicated in Brazil, including comorbid mental health problems, educational failure, low religiosity, harsh physical punishment and abuse, parental mental health problems, single parent family, and low socioeconomic status. However, boys did not always have higher risk for conduct problems than girls. Studies using screening questionnaires suggest that Brazilian children have higher rates of conduct problems than children in other countries, but diagnostic studies do not show this difference. Risk factors in Brazil were similar to those in high-income countries, apart from child sex. Future research should investigate developmental patterns of antisocial behaviour, employ a variety of research designs to identify causal risk mechanisms, and examine a broader range of risk factors.

  10. [Religion and fertility among adolescents in Brazil].

    PubMed

    Verona, Ana Paula de Andrade; Dias Júnior, Cláudio Santiago

    2012-01-01

    The objective of this study was to examine the association between the age of having one's first child in adolescence and before marriage and religious involvement in Brazil, measured by religious affiliation and frequency of attendance at religious services or masses. The objective of this study was to examine the association between the age of having one's first child in adolescence and before marriage and religious involvement in Brazil, measured by religious affiliation and frequency of attendance at religious services or masses. Transverse data obtained from the National Survey of Demographics and Health of 1996 and the National Survey of Demographics and Health of Women and Children of 2006 were utilized. Cox proportional risks models were employed to estimate the association between religion and age of having one's first child premaritally and during adolescence. The results indicate a strong association between premarital fertility in adolescence and religious involvement in both 1996 and 2006. In 1996, frequency of attendance at religious service s or masses was more important than religious affiliation in explaining the age at which one had her first child. In 2006, belonging to a Pentecostal church comes to predominate. The results presented in this study are encouraging insofar as they show that Protestant adolescents, particularly Pentecostals, have a reduced risk of adolescent premarital motherhood. This result was not expected, given that Pentecostalism predominates in the less advantaged population groups, with lower incomes and levels of education and residence in urban areas, where adolescent fertility is also concentrated in Brazil. Future studies must be undertaken with the purpose of understanding how the various mechanisms of religious influence operate in the life and behavior of adolescents in Brazil.

  11. Improving coeliac disease risk prediction by testing non-HLA variants additional to HLA variants.

    PubMed

    Romanos, Jihane; Rosén, Anna; Kumar, Vinod; Trynka, Gosia; Franke, Lude; Szperl, Agata; Gutierrez-Achury, Javier; van Diemen, Cleo C; Kanninga, Roan; Jankipersadsing, Soesma A; Steck, Andrea; Eisenbarth, Georges; van Heel, David A; Cukrowska, Bozena; Bruno, Valentina; Mazzilli, Maria Cristina; Núñez, Concepcion; Bilbao, Jose Ramon; Mearin, M Luisa; Barisani, Donatella; Rewers, Marian; Norris, Jill M; Ivarsson, Anneli; Boezen, H Marieke; Liu, Edwin; Wijmenga, Cisca

    2014-03-01

    The majority of coeliac disease (CD) patients are not being properly diagnosed and therefore remain untreated, leading to a greater risk of developing CD-associated complications. The major genetic risk heterodimer, HLA-DQ2 and DQ8, is already used clinically to help exclude disease. However, approximately 40% of the population carry these alleles and the majority never develop CD. We explored whether CD risk prediction can be improved by adding non-HLA-susceptible variants to common HLA testing. We developed an average weighted genetic risk score with 10, 26 and 57 single nucleotide polymorphisms (SNP) in 2675 cases and 2815 controls and assessed the improvement in risk prediction provided by the non-HLA SNP. Moreover, we assessed the transferability of the genetic risk model with 26 non-HLA variants to a nested case-control population (n=1709) and a prospective cohort (n=1245) and then tested how well this model predicted CD outcome for 985 independent individuals. Adding 57 non-HLA variants to HLA testing showed a statistically significant improvement compared to scores from models based on HLA only, HLA plus 10 SNP and HLA plus 26 SNP. With 57 non-HLA variants, the area under the receiver operator characteristic curve reached 0.854 compared to 0.823 for HLA only, and 11.1% of individuals were reclassified to a more accurate risk group. We show that the risk model with HLA plus 26 SNP is useful in independent populations. Predicting risk with 57 additional non-HLA variants improved the identification of potential CD patients. This demonstrates a possible role for combined HLA and non-HLA genetic testing in diagnostic work for CD.

  12. Applying artificial neural networks to predict communication risks in the emergency department.

    PubMed

    Bagnasco, Annamaria; Siri, Anna; Aleo, Giuseppe; Rocco, Gennaro; Sasso, Loredana

    2015-10-01

    To describe the utility of artificial neural networks in predicting communication risks. In health care, effective communication reduces the risk of error. Therefore, it is important to identify the predictive factors of effective communication. Non-technical skills are needed to achieve effective communication. This study explores how artificial neural networks can be applied to predict the risk of communication failures in emergency departments. A multicentre observational study. Data were collected between March-May 2011 by observing the communication interactions of 840 nurses with their patients during their routine activities in emergency departments. The tools used for our observation were a questionnaire to collect personal and descriptive data, level of training and experience and Guilbert's observation grid, applying the Situation-Background-Assessment-Recommendation technique to communication in emergency departments. A total of 840 observations were made on the nurses working in the emergency departments. Based on Guilbert's observation grid, the output variables is likely to influence the risk of communication failure were 'terminology'; 'listening'; 'attention' and 'clarity', whereas nurses' personal characteristics were used as input variables in the artificial neural network model. A model based on the multilayer perceptron topology was developed and trained. The receiver operator characteristic analysis confirmed that the artificial neural network model correctly predicted the performance of more than 80% of the communication failures. The application of the artificial neural network model could offer a valid tool to forecast and prevent harmful communication errors in the emergency department. © 2015 John Wiley & Sons Ltd.

  13. Body composition indices and predicted cardiovascular disease risk profile among urban dwellers in Malaysia.

    PubMed

    Su, Tin Tin; Amiri, Mohammadreza; Mohd Hairi, Farizah; Thangiah, Nithiah; Dahlui, Maznah; Majid, Hazreen Abdul

    2015-01-01

    This study aims to compare various body composition indices and their association with a predicted cardiovascular disease (CVD) risk profile in an urban population in Kuala Lumpur, Malaysia. A cross-sectional survey was conducted in metropolitan Kuala Lumpur, Malaysia, in 2012. Households were selected using a simple random-sampling method, and adult members were invited for medical screening. The Framingham Risk Scoring algorithm was used to predict CVD risk, which was then analyzed in association with body composition measurements, including waist circumference, waist-hip ratio, waist-height ratio, body fat percentage, and body mass index. Altogether, 882 individuals were included in our analyses. Indices that included waist-related measurements had the strongest association with CVD risk in both genders. After adjusting for demographic and socioeconomic variables, waist-related measurements retained the strongest correlations with predicted CVD risk in males. However, body mass index, waist-height ratio, and waist circumference had the strongest correlation with CVD risk in females. The waist-related indicators of abdominal obesity are important components of CVD risk profiles. As waist-related parameters can quickly and easily be measured, they should be routinely obtained in primary care settings and population health screens in order to assess future CVD risk profiles and design appropriate interventions.

  14. Body Composition Indices and Predicted Cardiovascular Disease Risk Profile among Urban Dwellers in Malaysia

    PubMed Central

    Su, Tin Tin; Amiri, Mohammadreza; Mohd Hairi, Farizah; Thangiah, Nithiah; Dahlui, Maznah; Majid, Hazreen Abdul

    2015-01-01

    Objectives. This study aims to compare various body composition indices and their association with a predicted cardiovascular disease (CVD) risk profile in an urban population in Kuala Lumpur, Malaysia. Methods. A cross-sectional survey was conducted in metropolitan Kuala Lumpur, Malaysia, in 2012. Households were selected using a simple random-sampling method, and adult members were invited for medical screening. The Framingham Risk Scoring algorithm was used to predict CVD risk, which was then analyzed in association with body composition measurements, including waist circumference, waist-hip ratio, waist-height ratio, body fat percentage, and body mass index. Results. Altogether, 882 individuals were included in our analyses. Indices that included waist-related measurements had the strongest association with CVD risk in both genders. After adjusting for demographic and socioeconomic variables, waist-related measurements retained the strongest correlations with predicted CVD risk in males. However, body mass index, waist-height ratio, and waist circumference had the strongest correlation with CVD risk in females. Conclusions. The waist-related indicators of abdominal obesity are important components of CVD risk profiles. As waist-related parameters can quickly and easily be measured, they should be routinely obtained in primary care settings and population health screens in order to assess future CVD risk profiles and design appropriate interventions. PMID:25710002

  15. Yellow Fever outbreaks in unvaccinated populations, Brazil, 2008-2009.

    PubMed

    Romano, Alessandro Pecego Martins; Costa, Zouraide Guerra Antunes; Ramos, Daniel Garkauskas; Andrade, Maria Auxiliadora; Jayme, Valéria de Sá; Almeida, Marco Antônio Barreto de; Vettorello, Kátia Campomar; Mascheretti, Melissa; Flannery, Brendan

    2014-03-01

    Due to the risk of severe vaccine-associated adverse events, yellow fever vaccination in Brazil is only recommended in areas considered at risk for disease. From September 2008 through June 2009, two outbreaks of yellow fever in previously unvaccinated populations resulted in 21 confirmed cases with 9 deaths (case-fatality, 43%) in the southern state of Rio Grande do Sul and 28 cases with 11 deaths (39%) in Sao Paulo state. Epizootic deaths of non-human primates were reported before and during the outbreak. Over 5.5 million doses of yellow fever vaccine were administered in the two most affected states. Vaccine-associated adverse events were associated with six deaths due to acute viscerotropic disease (0.8 deaths per million doses administered) and 45 cases of acute neurotropic disease (5.6 per million doses administered). Yellow fever vaccine recommendations were revised to include areas in Brazil previously not considered at risk for yellow fever.

  16. The utility of absolute risk prediction using FRAX® and Garvan Fracture Risk Calculator in daily practice.

    PubMed

    van Geel, Tineke A C M; Eisman, John A; Geusens, Piet P; van den Bergh, Joop P W; Center, Jacqueline R; Dinant, Geert-Jan

    2014-02-01

    There are two commonly used fracture risk prediction tools FRAX(®) and Garvan Fracture Risk Calculator (GARVAN-FRC). The objective of this study was to investigate the utility of these tools in daily practice. A prospective population-based 5-year follow-up study was conducted in ten general practice centres in the Netherlands. For the analyses, the FRAX(®) and GARVAN-FRC 10-year absolute risks (FRAX(®) does not have 5-year risk prediction) for all fractures were used. Among 506 postmenopausal women aged ≥60 years (mean age: 67.8±5.8 years), 48 (9.5%) sustained a fracture during follow-up. Both tools, using BMD values, distinguish between women who did and did not fracture (10.2% vs. 6.8%, respectively for FRAX(®) and 32.4% vs. 39.1%, respectively for GARVAN-FRC, p<0.0001) at group level. However, only 8.9% of those who sustained a fracture had an estimated fracture risk ≥20% using FRAX(®) compared with 53.3% using GARVAN-FRC. Although both underestimated the observed fracture risk, the GARVAN-FRC performed significantly better for women who sustained a fracture (higher sensitivity) and FRAX(®) for women who did not sustain a fracture (higher specificity). Similar results were obtained using age related cut off points. The discriminant value of both models is at least as good as models used in other medical conditions; hence they can be used to communicate the fracture risk to patients. However, given differences in the estimated risks between FRAX(®) and GARVAN-FRC, the significance of the absolute risk must be related to country-specific recommended intervention thresholds to inform the patient. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  17. Evidence on existing caries risk assessment systems: are they predictive of future caries?

    PubMed

    Tellez, M; Gomez, J; Pretty, I; Ellwood, R; Ismail, A I

    2013-02-01

    To critically appraise evidence for the prediction of caries using four caries risk assessment (CRA) systems/guidelines (Cariogram, Caries Management by Risk Assessment (CAMBRA), American Dental Association (ADA), and American Academy of Pediatric Dentistry (AAPD)). This review focused on prospective cohort studies or randomized controlled trials. A systematic search strategy was developed to locate papers published in Medline Ovid and Cochrane databases. The search identified 539 scientific reports, and after title and abstract review, 137 were selected for full review and 14 met the following inclusion criteria: (i) used as validating criterion caries incidence/increment, (ii) involved human subjects and natural carious lesions, and (iii) published in peer-reviewed journals. In addition, papers were excluded if they met one or more of the following criteria: (i) incomplete description of sample selection, outcomes, or small sample size and (ii) not meeting the criteria for best evidence under the prognosis category of the Oxford Centre for Evidence-Based Medicine. There are wide variations among the systems in terms of definitions of caries risk categories, type and number of risk factors/markers, and disease indicators. The Cariogram combined sensitivity and specificity for predicting caries in permanent dentition ranges from 110 to 139 and is the only system for which prospective studies have been conducted to assess its validity. The Cariogram had limited prediction utility in preschool children, and a moderate to good performance for sorting out elderly individuals into caries risk groups. One retrospective analysis on CAMBRA's CRA reported higher incidence of cavitated lesions among those assessed as extreme-risk patients when compared with those at low risk. The evidence on the validity for existing systems for CRA is limited. It is unknown if the identification of high-risk individuals can lead to more effective long-term patient management that prevents

  18. Deconstructing Pretest Risk Enrichment to Optimize Prediction of Psychosis in Individuals at Clinical High Risk.

    PubMed

    Fusar-Poli, Paolo; Rutigliano, Grazia; Stahl, Daniel; Schmidt, André; Ramella-Cravaro, Valentina; Hitesh, Shetty; McGuire, Philip

    2016-12-01

    Pretest risk estimation is routinely used in clinical medicine to inform further diagnostic testing in individuals with suspected diseases. To our knowledge, the overall characteristics and specific determinants of pretest risk of psychosis onset in individuals undergoing clinical high risk (CHR) assessment are unknown. To investigate the characteristics and determinants of pretest risk of psychosis onset in individuals undergoing CHR assessment and to develop and externally validate a pretest risk stratification model. Clinical register-based cohort study. Individuals were drawn from electronic, real-world, real-time clinical records relating to routine mental health care of CHR services in South London and the Maudsley National Health Service Trust in London, United Kingdom. The study included nonpsychotic individuals referred on suspicion of psychosis risk and assessed by the Outreach and Support in South London CHR service from 2002 to 2015. Model development and validation was performed with machine-learning methods based on Least Absolute Shrinkage and Selection Operator for Cox proportional hazards model. Pretest risk of psychosis onset in individuals undergoing CHR assessment. Predictors included age, sex, age × sex interaction, race/ethnicity, socioeconomic status, marital status, referral source, and referral year. A total of 710 nonpsychotic individuals undergoing CHR assessment were included. The mean age was 23 years. Three hundred ninety-nine individuals were men (56%), their race/ethnicity was heterogenous, and they were referred from a variety of sources. The cumulative 6-year pretest risk of psychosis was 14.55% (95% CI, 11.71% to 17.99%), confirming substantial pretest risk enrichment during the recruitment of individuals undergoing CHR assessment. Race/ethnicity and source of referral were associated with pretest risk enrichment. The predictive model based on these factors was externally validated, showing moderately good discrimination and

  19. Higher risk of common mental disorders after experiencing physical violence in Rio de Janeiro, Brazil: the Pró-Saúde Study.

    PubMed

    Lopes, Claudia S; Faerstein, Eduardo; Chor, Dóra; Werneck, Guilherme L

    2008-03-01

    In Brazil, violence is a major public health problem. However, up until now, the extent to which violence acts as a risk factor for mental disorders has not been investigated prospectively. We determined the risk of common mental disorders (CMD) associated with personal experience with physical violence (PV). A cohort of 3253 public employees in Rio de Janeiro completed questionnaires that measured CMD (GHQ-12), experience with PV and stressful life events (SLE). After adjusting for age, income and SLE, those who experienced PV in either 1999 or 2001 and those who experienced PV in both years had, respectively, 1.2-fold (95% CI; 1.0-1.4) and 2.1-fold (1.6-2.6) increased risks of CMD. Direct exposure to violence may act cumulatively on the risk of developing CMD. The absence of association for reporting CMD both in 1999 and 2001 suggests that other determinants may be more relevant for chronic mental disorders.

  20. Can theory predict the process of suicide on entry to prison? Predicting dynamic risk factors for suicide ideation in a high-risk prison population.

    PubMed

    Slade, Karen; Edelman, Robert

    2014-01-01

    Each year approximately 110,000 people are imprisoned in England and Wales and new prisoners remain one of the highest risk groups for suicide across the world. The reduction of suicide in prisoners remains difficult as assessments and interventions tend to rely on static risk factors with few theoretical or integrated models yet evaluated. To identify the dynamic factors that contribute to suicide ideation in this population based on Williams and Pollock's (2001) Cry of Pain (CoP) model. New arrivals (N = 198) into prison were asked to complete measures derived from the CoP model plus clinical and prison-specific factors. It was hypothesized that the factors of the CoP model would be predictive of suicide ideation. Support was provided for the defeat and entrapment aspects of the CoP model with previous self-harm, repeated times in prison, and suicide-permissive cognitions also key in predicting suicide ideation for prisoners on entry to prison. An integrated and dynamic model was developed that has utility in predicting suicide in early-stage prisoners. Implications for both theory and practice are discussed along with recommendations for future research.

  1. Echocardiography and risk prediction in advanced heart failure: incremental value over clinical markers.

    PubMed

    Agha, Syed A; Kalogeropoulos, Andreas P; Shih, Jeffrey; Georgiopoulou, Vasiliki V; Giamouzis, Grigorios; Anarado, Perry; Mangalat, Deepa; Hussain, Imad; Book, Wendy; Laskar, Sonjoy; Smith, Andrew L; Martin, Randolph; Butler, Javed

    2009-09-01

    Incremental value of echocardiography over clinical parameters for outcome prediction in advanced heart failure (HF) is not well established. We evaluated 223 patients with advanced HF receiving optimal therapy (91.9% angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, 92.8% beta-blockers, 71.8% biventricular pacemaker, and/or defibrillator use). The Seattle Heart Failure Model (SHFM) was used as the reference clinical risk prediction scheme. The incremental value of echocardiographic parameters for event prediction (death or urgent heart transplantation) was measured by the improvement in fit and discrimination achieved by addition of standard echocardiographic parameters to the SHFM. After a median follow-up of 2.4 years, there were 38 (17.0%) events (35 deaths; 3 urgent transplants). The SHFM had likelihood ratio (LR) chi(2) 32.0 and C statistic 0.756 for event prediction. Left ventricular end-systolic volume, stroke volume, and severe tricuspid regurgitation were independent echocardiographic predictors of events. The addition of these parameters to SHFM improved LR chi(2) to 72.0 and C statistic to 0.866 (P < .001 and P=.019, respectively). Reclassifying the SHFM-predicted risk with use of the echocardiography-added model resulted in improved prognostic separation. Addition of standard echocardiographic variables to the SHFM results in significant improvement in risk prediction for patients with advanced HF.

  2. Predicting high-risk preterm birth using artificial neural networks.

    PubMed

    Catley, Christina; Frize, Monique; Walker, C Robin; Petriu, Dorina C

    2006-07-01

    A reengineered approach to the early prediction of preterm birth is presented as a complimentary technique to the current procedure of using costly and invasive clinical testing on high-risk maternal populations. Artificial neural networks (ANNs) are employed as a screening tool for preterm birth on a heterogeneous maternal population; risk estimations use obstetrical variables available to physicians before 23 weeks gestation. The objective was to assess if ANNs have a potential use in obstetrical outcome estimations in low-risk maternal populations. The back-propagation feedforward ANN was trained and tested on cases with eight input variables describing the patient's obstetrical history; the output variables were: 1) preterm birth; 2) high-risk preterm birth; and 3) a refined high-risk preterm birth outcome excluding all cases where resuscitation was delivered in the form of free flow oxygen. Artificial training sets were created to increase the distribution of the underrepresented class to 20%. Training on the refined high-risk preterm birth model increased the network's sensitivity to 54.8%, compared to just over 20% for the nonartificially distributed preterm birth model.

  3. Mortality risk score prediction in an elderly population using machine learning.

    PubMed

    Rose, Sherri

    2013-03-01

    Standard practice for prediction often relies on parametric regression methods. Interesting new methods from the machine learning literature have been introduced in epidemiologic studies, such as random forest and neural networks. However, a priori, an investigator will not know which algorithm to select and may wish to try several. Here I apply the super learner, an ensembling machine learning approach that combines multiple algorithms into a single algorithm and returns a prediction function with the best cross-validated mean squared error. Super learning is a generalization of stacking methods. I used super learning in the Study of Physical Performance and Age-Related Changes in Sonomans (SPPARCS) to predict death among 2,066 residents of Sonoma, California, aged 54 years or more during the period 1993-1999. The super learner for predicting death (risk score) improved upon all single algorithms in the collection of algorithms, although its performance was similar to that of several algorithms. Super learner outperformed the worst algorithm (neural networks) by 44% with respect to estimated cross-validated mean squared error and had an R2 value of 0.201. The improvement of super learner over random forest with respect to R2 was approximately 2-fold. Alternatives for risk score prediction include the super learner, which can provide improved performance.

  4. Risk factors for childhood pneumonia among the urban poor in Fortaleza, Brazil: a case--control study.

    PubMed

    Fonseca, W; Kirkwood, B R; Victora, C G; Fuchs, S R; Flores, J A; Misago, C

    1996-01-01

    Reported are the results of a case-control study carried out between July 1989 and June 1990 in Fortaleza city, Ceará State, Brazil, to determine the factors that place young children living in urban slum conditions at increased risk of contracting pneumonia. Cases were 650 under-2-year-olds with a radiological diagnosis of pneumonia who were recruited at the main paediatric hospital in the city over a full calendar year. Age-matched controls were recruited from the neighbourhood where the cases lived. Cases and controls were compared with respect to a variety of sociodemographic, environmental, reproductive, nutritional, and morbidity factors, and a risk factor questionnaire was administered to the mother of each child or to the child's normal guardian. Cases and controls were also weighed and measured. Malnutrition was the most important risk factor for childhood pneumonia in the study population, with weight-for-age, height-for-age, and weight-for-height also being important risk factors. In view of the high prevalence of stunting in the study population, there is an urgent need to reduce the level of malnutrition as a priority. Attendance at a day care centre was also associated with a high odds ratio. In view of the growing numbers of children attending day care centres in both developing and developed countries, it is essential that ways be identified to improve the design and management of such centres in order to minimize the risk of pneumonia. Increased risks of childhood pneumonia were also associated with low birth weight, non-breast-feeding, crowding, high parity, and incomplete vaccination status, but not with socioeconomic status or environmental variables. Finally, children who had suffered from previous episodes of wheezing or been hospitalized for pneumonia had a greater than threefold increased risk of contracting the disease.

  5. Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm.

    PubMed

    Bellón, Juan Ángel; de Dios Luna, Juan; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Moreno-Peral, Patricia

    2017-04-01

    Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers. To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care. Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months. Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT. From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The 'predictAL-10' risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the 'predictAL-9'), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9. The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking. © British Journal of General Practice 2017.

  6. Individual risk of cutaneous melanoma in New Zealand: developing a clinical prediction aid.

    PubMed

    Sneyd, Mary Jane; Cameron, Claire; Cox, Brian

    2014-05-22

    New Zealand and Australia have the highest melanoma incidence rates worldwide. In New Zealand, both the incidence and thickness have been increasing. Clinical decisions require accurate risk prediction but a simple list of genetic, phenotypic and behavioural risk factors is inadequate to estimate individual risk as the risk factors for melanoma have complex interactions. In order to offer tailored clinical management strategies, we developed a New Zealand prediction model to estimate individual 5-year absolute risk of melanoma. A population-based case-control study (368 cases and 270 controls) of melanoma risk factors provided estimates of relative risks for fair-skinned New Zealanders aged 20-79 years. Model selection techniques and multivariate logistic regression were used to determine the important predictors. The relative risks for predictors were combined with baseline melanoma incidence rates and non-melanoma mortality rates to calculate individual probabilities of developing melanoma within 5 years. For women, the best model included skin colour, number of moles > =5 mm on the right arm, having a 1st degree relative with large moles, and a personal history of non-melanoma skin cancer (NMSC). The model correctly classified 68% of participants; the C-statistic was 0.74. For men, the best model included age, place of occupation up to age 18 years, number of moles > =5 mm on the right arm, birthplace, and a history of NMSC. The model correctly classified 67% of cases; the C-statistic was 0.71. We have developed the first New Zealand risk prediction model that calculates individual absolute 5-year risk of melanoma. This model will aid physicians to identify individuals at high risk, allowing them to individually target surveillance and other management strategies, and thereby reduce the high melanoma burden in New Zealand.

  7. Longitudinal study of mammographic density measures that predict breast cancer risk

    PubMed Central

    Krishnan, Kavitha; Baglietto, Laura; Stone, Jennifer; Simpson, Julie A; Severi, Gianluca; Evans, Christopher F; MacInnis, Robert J; Giles, Graham G; Apicella, Carmel; Hopper, John L

    2016-01-01

    Background After adjusting for age and body mass index (BMI), mammographic measures - dense area (DA), percent dense area (PDA) and non-dense area (NDA) - are associated with breast cancer risk. Our aim was to use longitudinal data to estimate the extent to which these risk-predicting measures track over time. Methods We collected 4,320 mammograms (age range, 24-83 years) from 970 women in the Melbourne Collaborative Cohort Study and the Australian Breast Cancer Family Registry. Women had on average 4.5 mammograms (range, 1-14). DA, PDA and NDA were measured using the Cumulus software and normalised using the Box-Cox method. Correlations in the normalised risk-predicting measures over time intervals of different lengths were estimated using nonlinear mixed-effects modelling of Gompertz curves. Results Mean normalised DA and PDA were constant with age to the early 40s, decreased over the next two decades, and were almost constant from the mid 60s onwards. Mean normalised NDA increased non-linearly with age. After adjusting for age and BMI, the within-woman correlation estimates for normalised DA were 0.94, 0.93, 0.91, 0.91 and 0.91 for mammograms taken 2, 4, 6, 8 and 10 years apart, respectively. Similar correlations were estimated for the age and BMI adjusted normalized PDA and NDA. Conclusion The mammographic measures that predict breast cancer risk are highly correlated over time. Impact This has implications for etiologic research and clinical management whereby women at increased risk could be identified at a young age (e.g. early 40s or even younger) and recommended appropriate screening and prevention strategies. PMID:28062399

  8. From the lab - Predicting Autism in High-Risk Infants | NIH MedlinePlus the Magazine

    MedlinePlus

    ... High-Risk Infants Follow us Photo: iStock Predicting Autism in High-Risk Infants AN NIH-SUPPORTED STUDY ... high-risk, 6-month-old infants will develop autism spectrum disorder by age 2. Such a tool ...

  9. A Model to Predict the Risk of Keratinocyte Carcinomas.

    PubMed

    Whiteman, David C; Thompson, Bridie S; Thrift, Aaron P; Hughes, Maria-Celia; Muranushi, Chiho; Neale, Rachel E; Green, Adele C; Olsen, Catherine M

    2016-06-01

    Basal cell and squamous cell carcinomas of the skin are the commonest cancers in humans, yet no validated tools exist to estimate future risks of developing keratinocyte carcinomas. To develop a prediction tool, we used baseline data from a prospective cohort study (n = 38,726) in Queensland, Australia, and used data linkage to capture all surgically excised keratinocyte carcinomas arising within the cohort. Predictive factors were identified through stepwise logistic regression models. In secondary analyses, we derived separate models within strata of prior skin cancer history, age, and sex. The primary model included terms for 10 items. Factors with the strongest effects were >20 prior skin cancers excised (odds ratio 8.57, 95% confidence interval [95% CI] 6.73-10.91), >50 skin lesions destroyed (odds ratio 3.37, 95% CI 2.85-3.99), age ≥ 70 years (odds ratio 3.47, 95% CI 2.53-4.77), and fair skin color (odds ratio 1.75, 95% CI 1.42-2.15). Discrimination in the validation dataset was high (area under the receiver operator characteristic curve 0.80, 95% CI 0.79-0.81) and the model appeared well calibrated. Among those reporting no prior history of skin cancer, a similar model with 10 factors predicted keratinocyte carcinoma events with reasonable discrimination (area under the receiver operator characteristic curve 0.72, 95% CI 0.70-0.75). Algorithms using self-reported patient data have high accuracy for predicting risks of keratinocyte carcinomas. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  10. Correlates of tuberculosis risk: predictive biomarkers for progression to active tuberculosis

    PubMed Central

    Petruccioli, Elisa; Scriba, Thomas J.; Petrone, Linda; Hatherill, Mark; Cirillo, Daniela M.; Joosten, Simone A.; Ottenhoff, Tom H.; Denkinger, Claudia M.; Goletti, Delia

    2016-01-01

    New approaches to control the spread of tuberculosis (TB) are needed, including tools to predict development of active TB from latent TB infection (LTBI). Recent studies have described potential correlates of risk, in order to inform the development of prognostic tests for TB disease progression. These efforts have included unbiased approaches employing “omics” technologies, as well as more directed, hypothesis-driven approaches assessing a small set or even individual selected markers as candidate correlates of TB risk. Unbiased high-throughput screening of blood RNAseq profiles identified signatures of active TB risk in individuals with LTBI, ≥1 year before diagnosis. A recent infant vaccination study identified enhanced expression of T-cell activation markers as a correlate of risk prior to developing TB; conversely, high levels of Ag85A antibodies and high frequencies of interferon (IFN)-γ specific T-cells were associated with reduced risk of disease. Others have described CD27−IFN-γ+CD4+ T-cells as possibly predictive markers of TB disease. T-cell responses to TB latency antigens, including heparin-binding haemagglutinin and DosR-regulon-encoded antigens have also been correlated with protection. Further studies are needed to determine whether correlates of risk can be used to prevent active TB through targeted prophylactic treatment, or to allow targeted enrolment into efficacy trials of new TB vaccines and therapeutic drugs. PMID:27836953

  11. Prediction of psychosis across protocols and risk cohorts using automated language analysis

    PubMed Central

    Corcoran, Cheryl M.; Carrillo, Facundo; Fernández‐Slezak, Diego; Bedi, Gillinder; Klim, Casimir; Javitt, Daniel C.; Bearden, Carrie E.; Cecchi, Guillermo A.

    2018-01-01

    Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer‐based natural language processing analyses, we previously showed that, among English‐speaking clinical (e.g., ultra) high‐risk youths, baseline reduction in semantic coherence (the flow of meaning in speech) and in syntactic complexity could predict subsequent psychosis onset with high accuracy. Herein, we aimed to cross‐validate these automated linguistic analytic methods in a second larger risk cohort, also English‐speaking, and to discriminate speech in psychosis from normal speech. We identified an automated machine‐learning speech classifier – comprising decreased semantic coherence, greater variance in that coherence, and reduced usage of possessive pronouns – that had an 83% accuracy in predicting psychosis onset (intra‐protocol), a cross‐validated accuracy of 79% of psychosis onset prediction in the original risk cohort (cross‐protocol), and a 72% accuracy in discriminating the speech of recent‐onset psychosis patients from that of healthy individuals. The classifier was highly correlated with previously identified manual linguistic predictors. Our findings support the utility and validity of automated natural language processing methods to characterize disturbances in semantics and syntax across stages of psychotic disorder. The next steps will be to apply these methods in larger risk cohorts to further test reproducibility, also in languages other than English, and identify sources of variability. This technology has the potential to improve prediction of psychosis outcome among at‐risk youths and identify

  12. Prediction of psychosis across protocols and risk cohorts using automated language analysis.

    PubMed

    Corcoran, Cheryl M; Carrillo, Facundo; Fernández-Slezak, Diego; Bedi, Gillinder; Klim, Casimir; Javitt, Daniel C; Bearden, Carrie E; Cecchi, Guillermo A

    2018-02-01

    Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer-based natural language processing analyses, we previously showed that, among English-speaking clinical (e.g., ultra) high-risk youths, baseline reduction in semantic coherence (the flow of meaning in speech) and in syntactic complexity could predict subsequent psychosis onset with high accuracy. Herein, we aimed to cross-validate these automated linguistic analytic methods in a second larger risk cohort, also English-speaking, and to discriminate speech in psychosis from normal speech. We identified an automated machine-learning speech classifier - comprising decreased semantic coherence, greater variance in that coherence, and reduced usage of possessive pronouns - that had an 83% accuracy in predicting psychosis onset (intra-protocol), a cross-validated accuracy of 79% of psychosis onset prediction in the original risk cohort (cross-protocol), and a 72% accuracy in discriminating the speech of recent-onset psychosis patients from that of healthy individuals. The classifier was highly correlated with previously identified manual linguistic predictors. Our findings support the utility and validity of automated natural language processing methods to characterize disturbances in semantics and syntax across stages of psychotic disorder. The next steps will be to apply these methods in larger risk cohorts to further test reproducibility, also in languages other than English, and identify sources of variability. This technology has the potential to improve prediction of psychosis outcome among at-risk youths and identify linguistic targets for remediation

  13. Clinical Utility of Five Genetic Variants for Predicting Prostate Cancer Risk and Mortality

    PubMed Central

    Salinas, Claudia A.; Koopmeiners, Joseph S.; Kwon, Erika M.; FitzGerald, Liesel; Lin, Daniel W.; Ostrander, Elaine A.; Feng, Ziding; Stanford, Janet L.

    2009-01-01

    Background A recent report suggests that the combination of five single-nucleotide polymorphisms (SNPs) at 8q24, 17q12, 17q24.3 and a family history of the disease may predict risk of prostate cancer. The present study tests the performance of these factors in prediction models for prostate cancer risk and prostate cancer-specific mortality. Methods SNPs were genotyped in population-based samples from Caucasians in King County, Washington. Incident cases (n=1308), aged 35–74, were compared to age-matched controls (n=1266) using logistic regression to estimate odds ratios (OR) associated with genotypes and family history. Cox proportional hazards models estimated hazard ratios for prostate cancer-specific mortality according to genotypes. Results The combination of SNP genotypes and family history was significantly associated with prostate cancer risk (ptrend=1.5 × 10−20). Men with ≥ five risk factors had an OR of 4.9 (95% CI 1.6 to 18.5) compared to men with none. However, this combination of factors did not improve the ROC curve after accounting for known risk predictors (i.e., age, serum PSA, family history). Neither the individual nor combined risk factors was associated with prostate cancer-specific mortality. Conclusion Genotypes for five SNPs plus family history are associated with a significant elevation in risk for prostate cancer and may explain up to 45% of prostate cancer in our population. However, they do not improve prediction models for assessing who is at risk of getting or dying from the disease, once known risk or prognostic factors are taken into account. Thus, this SNP panel may have limited clinical utility. PMID:19058137

  14. Risk Prediction Models for Acute Kidney Injury in Critically Ill Patients: Opus in Progressu.

    PubMed

    Neyra, Javier A; Leaf, David E

    2018-05-31

    Acute kidney injury (AKI) is a complex systemic syndrome associated with high morbidity and mortality. Among critically ill patients admitted to intensive care units (ICUs), the incidence of AKI is as high as 50% and is associated with dismal outcomes. Thus, the development and validation of clinical risk prediction tools that accurately identify patients at high risk for AKI in the ICU is of paramount importance. We provide a comprehensive review of 3 clinical risk prediction tools that have been developed for incident AKI occurring in the first few hours or days following admission to the ICU. We found substantial heterogeneity among the clinical variables that were examined and included as significant predictors of AKI in the final models. The area under the receiver operating characteristic curves was ∼0.8 for all 3 models, indicating satisfactory model performance, though positive predictive values ranged from only 23 to 38%. Hence, further research is needed to develop more accurate and reproducible clinical risk prediction tools. Strategies for improved assessment of AKI susceptibility in the ICU include the incorporation of dynamic (time-varying) clinical parameters, as well as biomarker, functional, imaging, and genomic data. © 2018 S. Karger AG, Basel.

  15. Predicting Parent-Child Aggression Risk: Cognitive Factors and Their Interaction With Anger.

    PubMed

    Rodriguez, Christina M

    2018-02-01

    Several cognitive elements have previously been proposed to elevate risk for physical child abuse. To predict parent-child aggression risk, the current study evaluated the role of approval of parent-child aggression, perceptions of children as poorly behaved, and discipline attributions. Several dimensions of attributions specifically tied to parents' discipline practices were targeted. In addition, anger experienced during discipline episodes was considered a potential moderator of these cognitive processes. Using a largely multiple-indicator approach, a sample of 110 mothers reported on these cognitive and affective aspects that may occur when disciplining their children as well as responding to measures of parent-child aggression risk. Findings suggest that greater approval of parent-child aggression, negative perceptions of their child's behavior, and discipline attributions independently predicted parent-child aggression risk, with anger significantly interacting with mothers' perception of their child as more poorly behaved to exacerbate their parent-child aggression risk. Of the discipline attribution dimensions evaluated, mothers' sense of external locus of control and believing their child deserved their discipline were related to increase parent-child aggression risk. Future work is encouraged to comprehensively evaluate how cognitive and affective components contribute and interact to increase risk for parent-child aggression.

  16. Gender inequalities in external cause mortality in Brazil, 2010.

    PubMed

    de Moura, Erly Catarina; Gomes, Romeu; Falcão, Marcia Thereza Couto; Schwarz, Eduardo; das Neves, Alice Cristina Medeiros; Santos, Wallace

    2015-03-01

    To estimate mortality rate by external causes in Brazil. Mortality national 2010's data corrected by underreport and adjusted by direct method were evaluated by sex according to age, region of residence, race/skin color, education and conjugal situation. The standardized mortality coefficient of external causes is higher among men (178 per thousand inhabitants) than among women (24 per thousand inhabitants), being higher among young men (20 to 29 years old) in all regions and decreasing with aging. The mortality rate reaches almost nine times higher among men comparably to women, being higher in North and Northeast regions. The death incidence by external causes is higher among men (36.4%) than among women (10.9%), meaning 170% more risk for men. The risk is also higher among the youngest: 6.00 for men and 7.36 for women. The main kind of death by external causes among men is aggressions, followed by transport accidents, the opposite of women. Besides sex, age is the more important predictive factor of precocious death by external causes, pointing the need of many and various sectors in order to construct new identities of non violence.

  17. A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute Coronary Syndrome Using Electronic Health Records.

    PubMed

    Huang, Zhengxing; Dong, Wei; Duan, Huilong; Liu, Jiquan

    2018-05-01

    Acute coronary syndrome (ACS), as a common and severe cardiovascular disease, is a leading cause of death and the principal cause of serious long-term disability globally. Clinical risk prediction of ACS is important for early intervention and treatment. Existing ACS risk scoring models are based mainly on a small set of hand-picked risk factors and often dichotomize predictive variables to simplify the score calculation. This study develops a regularized stacked denoising autoencoder (SDAE) model to stratify clinical risks of ACS patients from a large volume of electronic health records (EHR). To capture characteristics of patients at similar risk levels, and preserve the discriminating information across different risk levels, two constraints are added on SDAE to make the reconstructed feature representations contain more risk information of patients, which contribute to a better clinical risk prediction result. We validate our approach on a real clinical dataset consisting of 3464 ACS patient samples. The performance of our approach for predicting ACS risk remains robust and reaches 0.868 and 0.73 in terms of both AUC and accuracy, respectively. The obtained results show that the proposed approach achieves a competitive performance compared to state-of-the-art models in dealing with the clinical risk prediction problem. In addition, our approach can extract informative risk factors of ACS via a reconstructive learning strategy. Some of these extracted risk factors are not only consistent with existing medical domain knowledge, but also contain suggestive hypotheses that could be validated by further investigations in the medical domain.

  18. Child undernutrition in one of the cities with greater nutritional risk in Brazil: population-based study in the Western Brazilian Amazon.

    PubMed

    Araújo, Thiago Santos de; Oliveira, Cristieli Sérgio de Menezes; Muniz, Pascoal Torres; Silva-Nunes, Mônica da; Cardoso, Marly Augusto

    2016-01-01

    To estimate the prevalence of child undernutrition and associated factors in a municipality with high nutritional risk in Brazil. This cross-sectional, population-based study was conducted with a sample of 478 children aged under 5 years in the city of Jordão, Acre, Brazil. The following indicators were calculated: weight for age (W/A), height for age (H/A), and weight for height (W/H), using the growth curves of the WHO as reference, which adopts a cutoff of -2 z scores for identification of malnourished children. Adjusted prevalence ratios (PRs) were obtained using multiple Poisson regression models with robust error estimate (p < 0.05). A high prevalence of stunting (35.8%) was observed. Children with indigenous ancestry living in rural areas showed the highest prevalence of malnutrition (59.4%). After controlling for age, gender, and indigenous ancestry, the factors associated with stunting risk were: living in rural area (PR = 1.6; 95%CI 1.2 - 2.1); lower tertile of household wealth index (PR = 1.6; 95%CI 1.1 - 2.3); living in houses made of walking palm (PR = 1.6; 95%CI 1.1 - 2.4); maternal height less than or equal to 146.4 cm (PR = 3.1; 95%CI 1.9 - 5.0); and history of introduction of cow's milk before 30 days of age (PR = 1.4; 95%CI 1.0 - 1.8). Children with updated vaccination cards were inversely associated with stunting risk (PR = 0.7; 95%CI 0.5 - 0.9). Child undernutrition remains a serious public health problem in the Amazon, indicating additional difficulties in facing the problem in this region of the country.

  19. Utility of genetic and non-genetic risk factors in predicting coronary heart disease in Singaporean Chinese.

    PubMed

    Chang, Xuling; Salim, Agus; Dorajoo, Rajkumar; Han, Yi; Khor, Chiea-Chuen; van Dam, Rob M; Yuan, Jian-Min; Koh, Woon-Puay; Liu, Jianjun; Goh, Daniel Yt; Wang, Xu; Teo, Yik-Ying; Friedlander, Yechiel; Heng, Chew-Kiat

    2017-01-01

    Background Although numerous phenotype based equations for predicting risk of 'hard' coronary heart disease are available, data on the utility of genetic information for such risk prediction is lacking in Chinese populations. Design Case-control study nested within the Singapore Chinese Health Study. Methods A total of 1306 subjects comprising 836 men (267 incident cases and 569 controls) and 470 women (128 incident cases and 342 controls) were included. A Genetic Risk Score comprising 156 single nucleotide polymorphisms that have been robustly associated with coronary heart disease or its risk factors ( p < 5 × 10 -8 ) in at least two independent cohorts of genome-wide association studies was built. For each gender, three base models were used: recalibrated Adult Treatment Panel III (ATPIII) Model (M 1 ); ATP III model fitted using Singapore Chinese Health Study data (M 2 ) and M 3 : M 2 + C-reactive protein + creatinine. Results The Genetic Risk Score was significantly associated with incident 'hard' coronary heart disease ( p for men: 1.70 × 10 -10 -1.73 × 10 -9 ; p for women: 0.001). The inclusion of the Genetic Risk Score in the prediction models improved discrimination in both genders (c-statistics: 0.706-0.722 vs. 0.663-0.695 from base models for men; 0.788-0.790 vs. 0.765-0.773 for women). In addition, the inclusion of the Genetic Risk Score also improved risk classification with a net gain of cases being reclassified to higher risk categories (men: 12.4%-16.5%; women: 10.2% (M 3 )), while not significantly reducing the classification accuracy in controls. Conclusions The Genetic Risk Score is an independent predictor for incident 'hard' coronary heart disease in our ethnic Chinese population. Inclusion of genetic factors into coronary heart disease prediction models could significantly improve risk prediction performance.

  20. Controlling behaviours and technology‐facilitated abuse perpetrated by men receiving substance use treatment in England and Brazil: Prevalence and risk factors

    PubMed Central

    Canfield, Martha; Radcliffe, Polly; D'Oliveira, Ana Flavia Pires Lucas

    2017-01-01

    'Oliveira AFPL. Controlling behaviours and technology‐facilitated abuse perpetrated by men receiving substance use treatment in England and Brazil: Prevalence and risk factors. Drug Alcohol Rev 2017;36:52–63] PMID:28134494

  1. Predictive accuracy of risk scales following self-harm: multicentre, prospective cohort study†

    PubMed Central

    Quinlivan, Leah; Cooper, Jayne; Meehan, Declan; Longson, Damien; Potokar, John; Hulme, Tom; Marsden, Jennifer; Brand, Fiona; Lange, Kezia; Riseborough, Elena; Page, Lisa; Metcalfe, Chris; Davies, Linda; O'Connor, Rory; Hawton, Keith; Gunnell, David; Kapur, Nav

    2017-01-01

    Background Scales are widely used in psychiatric assessments following self-harm. Robust evidence for their diagnostic use is lacking. Aims To evaluate the performance of risk scales (Manchester Self-Harm Rule, ReACT Self-Harm Rule, SAD PERSONS scale, Modified SAD PERSONS scale, Barratt Impulsiveness Scale); and patient and clinician estimates of risk in identifying patients who repeat self-harm within 6 months. Method A multisite prospective cohort study was conducted of adults aged 18 years and over referred to liaison psychiatry services following self-harm. Scale a priori cut-offs were evaluated using diagnostic accuracy statistics. The area under the curve (AUC) was used to determine optimal cut-offs and compare global accuracy. Results In total, 483 episodes of self-harm were included in the study. The episode-based 6-month repetition rate was 30% (n = 145). Sensitivity ranged from 1% (95% CI 0–5) for the SAD PERSONS scale, to 97% (95% CI 93–99) for the Manchester Self-Harm Rule. Positive predictive values ranged from 13% (95% CI 2–47) for the Modified SAD PERSONS Scale to 47% (95% CI 41–53) for the clinician assessment of risk. The AUC ranged from 0.55 (95% CI 0.50–0.61) for the SAD PERSONS scale to 0.74 (95% CI 0.69–0.79) for the clinician global scale. The remaining scales performed significantly worse than clinician and patient estimates of risk (P<0.001). Conclusions Risk scales following self-harm have limited clinical utility and may waste valuable resources. Most scales performed no better than clinician or patient ratings of risk. Some performed considerably worse. Positive predictive values were modest. In line with national guidelines, risk scales should not be used to determine patient management or predict self-harm. PMID:28302702

  2. Predictive accuracy of risk scales following self-harm: multicentre, prospective cohort study.

    PubMed

    Quinlivan, Leah; Cooper, Jayne; Meehan, Declan; Longson, Damien; Potokar, John; Hulme, Tom; Marsden, Jennifer; Brand, Fiona; Lange, Kezia; Riseborough, Elena; Page, Lisa; Metcalfe, Chris; Davies, Linda; O'Connor, Rory; Hawton, Keith; Gunnell, David; Kapur, Nav

    2017-06-01

    Background Scales are widely used in psychiatric assessments following self-harm. Robust evidence for their diagnostic use is lacking. Aims To evaluate the performance of risk scales (Manchester Self-Harm Rule, ReACT Self-Harm Rule, SAD PERSONS scale, Modified SAD PERSONS scale, Barratt Impulsiveness Scale); and patient and clinician estimates of risk in identifying patients who repeat self-harm within 6 months. Method A multisite prospective cohort study was conducted of adults aged 18 years and over referred to liaison psychiatry services following self-harm. Scale a priori cut-offs were evaluated using diagnostic accuracy statistics. The area under the curve (AUC) was used to determine optimal cut-offs and compare global accuracy. Results In total, 483 episodes of self-harm were included in the study. The episode-based 6-month repetition rate was 30% ( n = 145). Sensitivity ranged from 1% (95% CI 0-5) for the SAD PERSONS scale, to 97% (95% CI 93-99) for the Manchester Self-Harm Rule. Positive predictive values ranged from 13% (95% CI 2-47) for the Modified SAD PERSONS Scale to 47% (95% CI 41-53) for the clinician assessment of risk. The AUC ranged from 0.55 (95% CI 0.50-0.61) for the SAD PERSONS scale to 0.74 (95% CI 0.69-0.79) for the clinician global scale. The remaining scales performed significantly worse than clinician and patient estimates of risk ( P <0.001). Conclusions Risk scales following self-harm have limited clinical utility and may waste valuable resources. Most scales performed no better than clinician or patient ratings of risk. Some performed considerably worse. Positive predictive values were modest. In line with national guidelines, risk scales should not be used to determine patient management or predict self-harm. © The Royal College of Psychiatrists 2017.

  3. Mortality Risk After Transcatheter Aortic Valve Implantation: Analysis of the Predictive Accuracy of the Transcatheter Valve Therapy Registry Risk Assessment Model.

    PubMed

    Codner, Pablo; Malick, Waqas; Kouz, Remi; Patel, Amisha; Chen, Cheng-Han; Terre, Juan; Landes, Uri; Vahl, Torsten Peter; George, Isaac; Nazif, Tamim; Kirtane, Ajay J; Khalique, Omar K; Hahn, Rebecca T; Leon, Martin B; Kodali, Susheel

    2018-05-08

    Risk assessment tools currently used to predict mortality in transcatheter aortic valve implantation (TAVI) were designed for patients undergoing cardiac surgery. We aim to assess the accuracy of the TAVI dedicated American College of Cardiology / Transcatheter Valve Therapies (ACC/TVT) risk score in predicting mortality outcomes. Consecutive patients (n=1038) undergoing TAVI at a single institution from 2014 to 2016 were included. The ACC/TVT registry mortality risk score, the Society of Thoracic Surgeons - Patient Reported Outcomes (STS-PROM) score and the EuroSCORE II were calculated for all patients. In hospital and 30-day all-cause mortality rates were 1.3% and 2.9%, respectively. The ACC/TVT risk stratification tool scored higher for patients who died in-hospital than in those who survived the index hospitalization (6.4 ± 4.6 vs. 3.5 ± 1.6, p = 0.03; respectively). The ACC/TVT score showed a high level of discrimination, C-index for in-hospital mortality 0.74, 95% CI [0.59 - 0.88]. There were no significant differences between the performance of the ACC/TVT registry risk score, the EuroSCORE II and the STS-PROM for in hospital and 30-day mortality rates. The ACC/TVT registry risk model is a dedicated tool to aid in the prediction of in-hospital mortality risk after TAVI.

  4. G6PD as a predictive marker for glioma risk, prognosis and chemosensitivity.

    PubMed

    Yang, Chin-An; Huang, Hsi-Yuan; Lin, Cheng-Li; Chang, Jan-Gowth

    2018-05-29

    Glucose-6-phosphate dehydrogenase (G6PD) is a key enzyme preventing cells from oxidative damage and has been reported to have tumor-promoting roles. This study aims to comprehensively evaluate the predictive values of G6PD on brain tumor risk, prognosis and chemo-resistance. A retrospective 13-year cohort study analyzing cancer risk using the Taiwan National Health Insurance Research Database (4066 G6PD deficiency patients and 16,264 controls) was conducted. Furthermore, RNAseq and clinical data of grade II-III glioma (LGG, n = 515) and glioblastoma (GBM, n = 155) were downloaded from The Cancer Genome Atlas (TCGA) and analyzed. Bioinformatics methods were applied to build a glioma prognostication model and to predict response to chemotherapy based on tumor G6PD-related gene expressions. The predicted results were validated in another glioma cohort GSE 16011 and in KALS1 cell line. G6PD-dificient patients were found to have an increased risk for cancers, especially for brain tumor (adjusted hazard ratio (HR) 10.5, 95% CI 1.03-7.60). Furthermore, higher tumor G6PD expression was associated with poor patient survival in LGG, but not in GBM. A prognostication model using expression levels of G6PD and 9 related genes (PSMA2, PSMB8, SHFM1, GSS, GSTK1, MGST2, POLD3, MSH2, MSH6) could independently predict LGG patient survival. Boosted decision tree analysis on 213 cancer cell line database revealed predictive values of G6PD expression on response to gemcitabine and bortezomib. Knockdown of G6PD in KALS1 cell line enhanced its sensitivity to both chemotherapeutic agents. Our study suggests that G6PD could be a marker predicting glioma risk, prognosis and chemo-sensitivity.

  5. Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

    PubMed Central

    2012-01-01

    Background Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). Methods A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. Results The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Conclusions Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk

  6. Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups.

    PubMed

    Marschollek, Michael; Gövercin, Mehmet; Rust, Stefan; Gietzelt, Matthias; Schulze, Mareike; Wolf, Klaus-Hendrik; Steinhagen-Thiessen, Elisabeth

    2012-03-14

    Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified

  7. Predicting the risk for colorectal cancer with personal characteristics and fecal immunochemical test.

    PubMed

    Li, Wen; Zhao, Li-Zhong; Ma, Dong-Wang; Wang, De-Zheng; Shi, Lei; Wang, Hong-Lei; Dong, Mo; Zhang, Shu-Yi; Cao, Lei; Zhang, Wei-Hua; Zhang, Xi-Peng; Zhang, Qing-Huai; Yu, Lin; Qin, Hai; Wang, Xi-Mo; Chen, Sam Li-Sheng

    2018-05-01

    We aimed to predict colorectal cancer (CRC) based on the demographic features and clinical correlates of personal symptoms and signs from Tianjin community-based CRC screening data.A total of 891,199 residents who were aged 60 to 74 and were screened in 2012 were enrolled. The Lasso logistic regression model was used to identify the predictors for CRC. Predictive validity was assessed by the receiver operating characteristic (ROC) curve. Bootstrapping method was also performed to validate this prediction model.CRC was best predicted by a model that included age, sex, education level, occupations, diarrhea, constipation, colon mucosa and bleeding, gallbladder disease, a stressful life event, family history of CRC, and a positive fecal immunochemical test (FIT). The area under curve (AUC) for the questionnaire with a FIT was 84% (95% CI: 82%-86%), followed by 76% (95% CI: 74%-79%) for a FIT alone, and 73% (95% CI: 71%-76%) for the questionnaire alone. With 500 bootstrap replications, the estimated optimism (<0.005) shows good discrimination in validation of prediction model.A risk prediction model for CRC based on a series of symptoms and signs related to enteric diseases in combination with a FIT was developed from first round of screening. The results of the current study are useful for increasing the awareness of high-risk subjects and for individual-risk-guided invitations or strategies to achieve mass screening for CRC.

  8. Childhood behaviour problems predict crime and violence in late adolescence: Brazilian and British birth cohort studies.

    PubMed

    Murray, Joseph; Menezes, Ana M B; Hickman, Matthew; Maughan, Barbara; Gallo, Erika Alejandra Giraldo; Matijasevich, Alicia; Gonçalves, Helen; Anselmi, Luciana; Assunção, Maria Cecília F; Barros, Fernando C; Victora, Cesar G

    2015-04-01

    Most children live in low- and middle-income countries (LMICs), many of which have high levels of violence. Research in high-income countries (HICs) shows that childhood behaviour problems are important precursors of crime and violence. Evidence is lacking on whether this is also true in LMICs. This study examines prevalence rates and associations between conduct problems and hyperactivity and crime and violence in Brazil and Britain. A comparison was made of birth cohorts in Brazil and Britain, including measures of behaviour problems based on parental report at age 11, and self-reports of crime at age 18 (N = 3,618 Brazil; N = 4,103 Britain). Confounders were measured in the perinatal period and at age 11 in questionnaires completed by the mother and, in Brazil, searches of police records regarding parental crime. Conduct problems, hyperactivity and violent crime were more prevalent in Brazil than in Britain, but nonviolent crime was more prevalent in Britain. Sex differences in prevalence rates were larger where behaviours were less common: larger for conduct problems, hyperactivity, and violent crime in Britain, and larger for nonviolent crime in Brazil. Conduct problems and hyperactivity predicted nonviolent and violent crime similarly in both countries; the effects were partly explained by perinatal health factors and childhood family environments. Conduct problems and hyperactivity are similar precursors of crime and violence across different social settings. Early crime and violence prevention programmes could target these behavioural difficulties and associated risks in LMICs as well as in HICs.

  9. Risk Preferences and Predictions about Others: No Association with 2D:4D Ratio

    PubMed Central

    Lima de Miranda, Katharina; Neyse, Levent; Schmidt, Ulrich

    2018-01-01

    Prenatal androgen exposure affects the brain development of the fetus which may facilitate certain behaviors and decision patterns in the later life. The ratio between the lengths of second and the fourth fingers (2D:4D) is a negative biomarker of the ratio between prenatal androgen and estrogen exposure and men typically have lower ratios than women. In line with the typical findings suggesting that women are more risk averse than men, several studies have also shown negative relationships between 2D:4D and risk taking although the evidence is not conclusive. Previous studies have also reported that both men and women believe women are more risk averse than men. In the current study, we re-test the relationship between 2D:4D and risk preferences in a German student sample and also investigate whether the 2D:4D ratio is associated with people’s perceptions about others’ risk preferences. Following an incentivized risk elicitation task, we asked all participants their predictions about (i) others’ responses (without sex specification), (ii) men’s responses, and (iii) women’s responses; then measured their 2D:4D ratios. In line with the previous findings, female participants in our sample were more risk averse. While both men and women underestimated other participants’ (non sex-specific) and women’s risky decisions on average, their predictions about men were accurate. We also found evidence for the false consensus effect, as risky choices are positively correlated with predictions about other participants’ risky choices. The 2D:4D ratio was not directly associated either with risk preferences or the predictions of other participants’ choices. An unexpected finding was that women with mid-range levels of 2D:4D estimated significantly larger sex differences in participants’ decisions. This finding needs further testing in future studies. PMID:29472846

  10. A novel neural-inspired learning algorithm with application to clinical risk prediction.

    PubMed

    Tay, Darwin; Poh, Chueh Loo; Kitney, Richard I

    2015-04-01

    Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for individuals at risk of cardiovascular disease (CVD) given the fact that it is the leading causes of death in many developed counties. To this end, we introduce a novel learning algorithm - a key factor that influences the performance of machine learning-based prediction models - and utilities it to develop CVD risk prediction tool. This novel neural-inspired algorithm, called the Artificial Neural Cell System for classification (ANCSc), is inspired by mechanisms that develop the brain and empowering it with capabilities such as information processing/storage and recall, decision making and initiating actions on external environment. Specifically, we exploit on 3 natural neural mechanisms responsible for developing and enriching the brain - namely neurogenesis, neuroplasticity via nurturing and apoptosis - when implementing ANCSc algorithm. Benchmark testing was conducted using the Honolulu Heart Program (HHP) dataset and results are juxtaposed with 2 other algorithms - i.e. Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS). Empirical experiments indicate that ANCSc algorithm (statistically) outperforms both SVM and EDC-AIRS algorithms. Key clinical markers identified by ANCSc algorithm include risk factors related to diet/lifestyle, pulmonary function, personal/family/medical history, blood data, blood pressure, and electrocardiography. These clinical markers, in general, are also found to be clinically significant - providing a promising avenue for identifying potential cardiovascular risk factors to be evaluated in clinical trials. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. AN APPROACH TO PREDICT RISKS TO WILDLIFE POPULATIONS FROM MERCURY AND OTHER STRESSORS

    EPA Science Inventory

    The U.S. Environmental Protection Agency's National Health and Environmental Effects Research Laboratory (NHEERL) is developing tools for predicting risks of multiple stressors to wildlife populations, which support the development of risk-based protective criteria. NHEERL's res...

  12. When does risk perception predict protection motivation for health threats? A person-by-situation analysis.

    PubMed

    Ferrer, Rebecca A; Klein, William M P; Avishai, Aya; Jones, Katelyn; Villegas, Megan; Sheeran, Paschal

    2018-01-01

    Although risk perception is a key concept in many health behavior theories, little research has explicitly tested when risk perception predicts motivation to take protective action against a health threat (protection motivation). The present study tackled this question by (a) adopting a multidimensional model of risk perception that comprises deliberative, affective, and experiential components (the TRIRISK model), and (b) taking a person-by-situation approach. We leveraged a highly intensive within-subjects paradigm to test features of the health threat (i.e., perceived severity) and individual differences (e.g., emotion reappraisal) as moderators of the relationship between the three types of risk perception and protection motivation in a within-subjects design. Multi-level modeling of 2968 observations (32 health threats across 94 participants) showed interactions among the TRIRISK components and moderation both by person-level and situational factors. For instance, affective risk perception better predicted protection motivation when deliberative risk perception was high, when the threat was less severe, and among participants who engage less in emotional reappraisal. These findings support the TRIRISK model and offer new insights into when risk perceptions predict protection motivation.

  13. When does risk perception predict protection motivation for health threats? A person-by-situation analysis

    PubMed Central

    Klein, William M. P.; Avishai, Aya; Jones, Katelyn; Villegas, Megan; Sheeran, Paschal

    2018-01-01

    Although risk perception is a key concept in many health behavior theories, little research has explicitly tested when risk perception predicts motivation to take protective action against a health threat (protection motivation). The present study tackled this question by (a) adopting a multidimensional model of risk perception that comprises deliberative, affective, and experiential components (the TRIRISK model), and (b) taking a person-by-situation approach. We leveraged a highly intensive within-subjects paradigm to test features of the health threat (i.e., perceived severity) and individual differences (e.g., emotion reappraisal) as moderators of the relationship between the three types of risk perception and protection motivation in a within-subjects design. Multi-level modeling of 2968 observations (32 health threats across 94 participants) showed interactions among the TRIRISK components and moderation both by person-level and situational factors. For instance, affective risk perception better predicted protection motivation when deliberative risk perception was high, when the threat was less severe, and among participants who engage less in emotional reappraisal. These findings support the TRIRISK model and offer new insights into when risk perceptions predict protection motivation. PMID:29494705

  14. Utilizing environmental, socioeconomic data and GIS techniques to estimate the risk for ascariasis and trichuriasis in Minas Gerais, Brazil.

    PubMed

    Scholte, Ronaldo G C; Freitas, Corina C; Dutra, Luciano V; Guimaraes, Ricardo J P S; Drummond, Sandra C; Oliveira, Guilherme; Carvalho, Omar S

    2012-02-01

    The impact of intestinal helminths on human health is well known among the population and health authorities because of their wide geographic distribution and the serious problems they cause. Geohelminths are highly prevalent and have a big impact on public health, mainly in underdeveloped and developing countries. Geohelminths are responsible for the high levels of debility found in the younger population and are often related to cases of chronic diarrhea and malnutrition, which put the physical and intellectual development of children at risk. These geohelminths have not been sufficiently studied. One obstacle in implementing a control program is the lack of knowledge of the prevalence and geographical distribution. Geographical information systems (GIS) and remote sensing (RS) have been utilized to improve understanding of infectious disease distribution and climatic patterns. In this study, GIS and RS technologies, as well as meteorological, social, and environmental variables were utilized for the modeling and prediction of ascariasis and trichuriasis. The GIS and RS technologies specifically used were those produced by orbital sensing including the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Shuttle Radar Topography Mission (SRTM). The results of this study demonstrated important factors related to the transmission of ascariasis and trichuriasis and confirmed the key association between environmental variables and the poverty index, which enabled us to identify priority areas for intervention planning in the state of Minas Gerais in Brazil. Copyright © 2011 Elsevier B.V. All rights reserved.

  15. Impact of diet on the cardiovascular risk profile of Japanese immigrants living in Brazil: contributions of World Health Organization CARDIAC and MONALISA studies.

    PubMed

    Moriguchi, E H; Moriguchi, Y; Yamori, Y

    2004-12-01

    1. Japanese immigrants from Okinawa living in Brazil have a higher mortality from cardiovascular diseases and have their mean life expectancy shortened compared with their counterparts living in Japan. 2. A cross-sectional study comparing Okinawans living in Okinawa (OO) and Okinawan immigrants living in Brazil (OB) was designed to characterize the dietary factors that could interfere with the profile of cardiovascular risk factors and with this reduction on the life expectancy when Okinawans emigrate to Brazil. 3. In total, 234 OO and 160 OB (aged 45-59 years) were recruited to the present study to undergo medical and dietary history, blood pressure measurement, electrocardiograph (ECG), blood tests and 24 h food/urine collection. 4. In the present study, OO subjects presented with 37% less obesity and 50% less systemic hypertension than OB. The OB subjects used threefold more antihypertensive medication than OO. Meat intake was 34% higher in OB than OO, whereas fish intake was sevenfold higher in OO than OB. Serum potassium levels were 10% higher in OO than OB. Urinary taurine (an index of seafood intake) was 43% higher in OO than OB. Urinary isoflavones (an index of the intake of soy products) were significantly lower in OB than in OO. Of acid (20:5) and docosahexaenoic acid (22:6) were two- and threefold higher in OO than OB, respectively. 5. The rate of ischaemic ECG changes in OO subjects was only 50% of that of OB subjects. 6. There were no differences in the smoking rate between OO and OB subjects. 7. The results of the present study suggest that coronary risk factors and cardiovascular health are not only regulated by genetic factors, but that the impact of lifestyle (mainly diet) can be large enough to modulate the expression of genes.

  16. A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.

    PubMed

    Wolfson, Julian; Bandyopadhyay, Sunayan; Elidrisi, Mohamed; Vazquez-Benitez, Gabriela; Vock, David M; Musgrove, Donald; Adomavicius, Gediminas; Johnson, Paul E; O'Connor, Patrick J

    2015-09-20

    Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records provide an alternative to the longitudinal cohort studies traditionally used to construct risk models, bringing with them both opportunities and challenges. Large sample sizes and detailed covariate histories enable the use of sophisticated machine learning techniques to uncover complex associations and interactions, but observational databases are often 'messy', with high levels of missing data and incomplete patient follow-up. In this paper, we propose an adaptation of the well-known Naive Bayes machine learning approach to time-to-event outcomes subject to censoring. We compare the predictive performance of our method with the Cox proportional hazards model which is commonly used for risk prediction in healthcare populations, and illustrate its application to prediction of cardiovascular risk using an electronic health record dataset from a large Midwest integrated healthcare system. Copyright © 2015 John Wiley & Sons, Ltd.

  17. Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm

    PubMed Central

    Bellón, Juan Ángel; de Dios Luna, Juan; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Moreno-Peral, Patricia

    2017-01-01

    Background Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers. Aim To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care. Design and setting Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months. Method Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT. Results From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The ‘predictAL-10’ risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the ‘predictAL-9’), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9. Conclusion The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking. PMID:28360074

  18. Identifying the necessary and sufficient number of risk factors for predicting academic failure.

    PubMed

    Lucio, Robert; Hunt, Elizabeth; Bornovalova, Marina

    2012-03-01

    Identifying the point at which individuals become at risk for academic failure (grade point average [GPA] < 2.0) involves an understanding of which and how many factors contribute to poor outcomes. School-related factors appear to be among the many factors that significantly impact academic success or failure. This study focused on 12 school-related factors. Using a thorough 5-step process, we identified which unique risk factors place one at risk for academic failure. Academic engagement, academic expectations, academic self-efficacy, homework completion, school relevance, school safety, teacher relationships (positive relationship), grade retention, school mobility, and school misbehaviors (negative relationship) were uniquely related to GPA even after controlling for all relevant covariates. Next, a receiver operating characteristic curve was used to determine a cutoff point for determining how many risk factors predict academic failure (GPA < 2.0). Results yielded a cutoff point of 2 risk factors for predicting academic failure, which provides a way for early identification of individuals who are at risk. Further implications of these findings are discussed. PsycINFO Database Record (c) 2012 APA, all rights reserved.

  19. Phase angle assessment by bioelectrical impedance analysis and its predictive value for malnutrition risk in hospitalized geriatric patients.

    PubMed

    Varan, Hacer Dogan; Bolayir, Basak; Kara, Ozgur; Arik, Gunes; Kizilarslanoglu, Muhammet Cemal; Kilic, Mustafa Kemal; Sumer, Fatih; Kuyumcu, Mehmet Emin; Yesil, Yusuf; Yavuz, Burcu Balam; Halil, Meltem; Cankurtaran, Mustafa

    2016-12-01

    Phase angle (PhA) value determined by bioelectrical impedance analysis (BIA) is an indicator of cell membrane damage and body cell mass. Recent studies have shown that low PhA value is associated with increased nutritional risk in various group of patients. However, there have been only a few studies performed globally assessing the relationship between nutritional risk and PhA in hospitalized geriatric patients. The aim of the study is to evaluate the predictive value of the PhA for malnutrition risk in hospitalized geriatric patients. One hundred and twenty-two hospitalized geriatric patients were included in this cross-sectional study. Comprehensive geriatric assessment tests and BIA measurements were performed within the first 48 h after admission. Nutritional risk state of the patients was determined with NRS-2002. Phase angle values of the patients with malnutrition risk were compared with the patients that did not have the same risk. The independent variables for predicting malnutrition risk were determined. SPSS version 15 was utilized for the statistical analyzes. The patients with malnutrition risk had significantly lower phase angle values than the patients without malnutrition risk (p = 0.003). ROC curve analysis suggested that the optimum PhA cut-off point for malnutrition risk was 4.7° with 79.6 % sensitivity, 64.6 % specificity, 73.9 % positive predictive value, and 73.9 % negative predictive value. BMI, prealbumin, PhA, and Mini Mental State Examination Test scores were the independent variables for predicting malnutrition risk. PhA can be a useful, independent indicator for predicting malnutrition risk in hospitalized geriatric patients.

  20. Yellow Fever Outbreaks in Unvaccinated Populations, Brazil, 2008–2009

    PubMed Central

    Romano, Alessandro Pecego Martins; Costa, Zouraide Guerra Antunes; Ramos, Daniel Garkauskas; Andrade, Maria Auxiliadora; Jayme, Valéria de Sá; de Almeida, Marco Antônio Barreto; Vettorello, Kátia Campomar; Mascheretti, Melissa; Flannery, Brendan

    2014-01-01

    Due to the risk of severe vaccine-associated adverse events, yellow fever vaccination in Brazil is only recommended in areas considered at risk for disease. From September 2008 through June 2009, two outbreaks of yellow fever in previously unvaccinated populations resulted in 21 confirmed cases with 9 deaths (case-fatality, 43%) in the southern state of Rio Grande do Sul and 28 cases with 11 deaths (39%) in Sao Paulo state. Epizootic deaths of non-human primates were reported before and during the outbreak. Over 5.5 million doses of yellow fever vaccine were administered in the two most affected states. Vaccine-associated adverse events were associated with six deaths due to acute viscerotropic disease (0.8 deaths per million doses administered) and 45 cases of acute neurotropic disease (5.6 per million doses administered). Yellow fever vaccine recommendations were revised to include areas in Brazil previously not considered at risk for yellow fever. PMID:24625634

  1. Prevalence and risk factors for giardiasis and soil-transmitted helminthiasis in three municipalities of Southeastern Minas Gerais State, Brazil: risk factors for giardiasis and soil-transmitted helminthiasis.

    PubMed

    Pinheiro, Izabella de Oliveira; de Castro, Milton Ferreira; Mitterofhe, Adalberto; Pires, Flávia Alves Condé; Abramo, Clarice; Ribeiro, Luiz Cláudio; Tibiriçá, Sandra Helena Cerrato; Coimbra, Elaine Soares

    2011-05-01

    Giardiasis and soil-transmitted helminthiasis (STH) are parasitic diseases that are among the major health concerns observed in economically disadvantaged populations of developing countries, and have clear social and environmental bases. In Brazil, there is a lack of epidemiologic data concerning these infections in the study area, whose inhabitants have plenty of access to health care services, including good dwelling and adequate sanitary conditions. In this survey we investigated the risk factors for giardiasis and STH in three municipalities with good sanitation, situated in Minas Gerais state, Brazil. A cross-sectional survey was conducted in the municipalities of Piau, Coronel Pacheco and Goianá, in both urban and rural areas. The fieldwork consisted of a questionnaire and the examination of 2,367 stool samples using the Hoffmann, Pons and Janer method. Of all individuals from the population sample, 6.1% were found infected with the parasitic diseases included in this work. Hookworm infection was the most prevalent disease, followed by giardiasis, trichuriasis and ascariasis. Infection was more prevalent in males (8.1%, p < 0.001; odds ratio [OR] = 1.975) and in individuals living in rural areas (8.6%, p = 0.003; OR = 1.693). Multivariate analysis showed that variables such as inadequate sewage discharge (p < 0.001), drinking of unsafe water (p < 0.001), lack of sanitary infrastructure (p = 0.015), and host sex (p < 0.001) were the risk factors more strongly associated with infection status (95% confidence interval [CI]). In this study we demonstrate that giardiasis and STH still persist, infecting people who have good housing conditions and free access to public health care and education.

  2. Phosphorus saturation and superficial fertilizer application as key parameters to assess the risk of diffuse phosphorus losses from agricultural soils in Brazil.

    PubMed

    Fischer, P; Pöthig, R; Gücker, B; Venohr, M

    2018-07-15

    In Brazil, a steady increase in phosphorus (P) fertilizer application and agricultural intensification has been reported for recent decades. The concomitant P accumulation in soils potentially threatens surface water bodies with eutrophication through diffuse P losses. Here, we demonstrated the applicability of a soil type-independent approach for estimating the degree of P saturation (DPS; a risk parameter of P loss) by a standard method of water-soluble phosphorus (WSP) for two major soil types (Oxisols, Entisols) of the São Francisco catchment in Brazil. Subsequently, soil Mehlich-1P (M1P) levels recommended by Brazilian agricultural institutions were transformed into DPS values. Recommended M1P values for optimal agronomic production corresponded to DPS values below critical thresholds of high risks of P losses (DPS=80%) for major crops of the catchment. Higher risks of reaching critical DPS values due to P accumulation were found for Entisols due to their total sorption capacities being only half those of Oxisols. For complementary information on soil mineralogy and its influence on P sorption and P binding forms, Fourier transformation infrared (FTIR) spectroscopic analyses were executed. FTIR analyses suggested the occurrence of the clay minerals palygorskite and sepiolite in some of the analyzed Entisols and the formation of crandallite as the soil specific P binding form in the investigated Oxisols. Palygorskite and sepiolite can enhance P solubility and hence the risk of P losses. In contrast, the reshaping of superphosphate grains into crandallite may explain the chemical processes leading to previously observed low dissolved P concentrations in surface runoff from Oxisols. To prevent high risk of P losses, we recommend avoiding superficial fertilizer application and establishing environmental thresholds for soil M1P based on DPS. These measures could help to prevent eutrophication of naturally oligotrophic surface waters, and subsequent adverse effects

  3. Major bleeding and intracranial hemorrhage risk prediction in patients with atrial fibrillation: Attention to modifiable bleeding risk factors or use of a bleeding risk stratification score? A nationwide cohort study.

    PubMed

    Chao, Tze-Fan; Lip, Gregory Y H; Lin, Yenn-Jiang; Chang, Shih-Lin; Lo, Li-Wei; Hu, Yu-Feng; Tuan, Ta-Chuan; Liao, Jo-Nan; Chung, Fa-Po; Chen, Tzeng-Ji; Chen, Shih-Ann

    2018-03-01

    While modifiable bleeding risks should be addressed in all patients with atrial fibrillation (AF), use of a bleeding risk score enables clinicians to 'flag up' those at risk of bleeding for more regular patient contact reviews. We compared a risk assessment strategy for major bleeding and intracranial hemorrhage (ICH) based on modifiable bleeding risk factors (referred to as a 'MBR factors' score) against established bleeding risk stratification scores (HEMORR 2 HAGES, HAS-BLED, ATRIA, ORBIT). A nationwide cohort study of 40,450 AF patients who received warfarin for stroke prevention was performed. The clinical endpoints included ICH and major bleeding. Bleeding scores were compared using receiver operating characteristic (ROC) curves (areas under the ROC curves [AUCs], or c-index) and the net reclassification index (NRI). During a follow up of 4.60±3.62years, 1581 (3.91%) patients sustained ICH and 6889 (17.03%) patients sustained major bleeding events. All tested bleeding risk scores at baseline were higher in those sustaining major bleeds. When compared to no ICH, patients sustaining ICH had higher baseline HEMORR 2 HAGES (p=0.003), HAS-BLED (p<0.001) and MBR factors score (p=0.013) but not ATRIA and ORBIT scores. When HAS-BLED was compared to other bleeding scores, c-indexes were significantly higher compared to MBR factors (p<0.001) and ORBIT (p=0.05) scores for major bleeding. C-indexes for the MBR factors score was significantly lower compared to all other scores (De long test, all p<0.001). When NRI was performed, HAS-BLED outperformed all other bleeding risk scores for major bleeding (all p<0.001). C-indexes for ATRIA and ORBIT scores suggested no significant prediction for ICH. All contemporary bleeding risk scores had modest predictive value for predicting major bleeding but the best predictive value and NRI was found for the HAS-BLED score. Simply depending on modifiable bleeding risk factors had suboptimal predictive value for the prediction of major

  4. The predictive performance of a path-dependent exotic-option credit risk model in the emerging market

    NASA Astrophysics Data System (ADS)

    Chen, Dar-Hsin; Chou, Heng-Chih; Wang, David; Zaabar, Rim

    2011-06-01

    Most empirical research of the path-dependent, exotic-option credit risk model focuses on developed markets. Taking Taiwan as an example, this study investigates the bankruptcy prediction performance of the path-dependent, barrier option model in the emerging market. We adopt Duan's (1994) [11], (2000) [12] transformed-data maximum likelihood estimation (MLE) method to directly estimate the unobserved model parameters, and compare the predictive ability of the barrier option model to the commonly adopted credit risk model, Merton's model. Our empirical findings show that the barrier option model is more powerful than Merton's model in predicting bankruptcy in the emerging market. Moreover, we find that the barrier option model predicts bankruptcy much better for highly-leveraged firms. Finally, our findings indicate that the prediction accuracy of the credit risk model can be improved by higher asset liquidity and greater financial transparency.

  5. A Risk Prediction Model for Sporadic CRC Based on Routine Lab Results.

    PubMed

    Boursi, Ben; Mamtani, Ronac; Hwang, Wei-Ting; Haynes, Kevin; Yang, Yu-Xiao

    2016-07-01

    Current risk scores for colorectal cancer (CRC) are based on demographic and behavioral factors and have limited predictive values. To develop a novel risk prediction model for sporadic CRC using clinical and laboratory data in electronic medical records. We conducted a nested case-control study in a UK primary care database. Cases included those with a diagnostic code of CRC, aged 50-85. Each case was matched with four controls using incidence density sampling. CRC predictors were examined using univariate conditional logistic regression. Variables with p value <0.25 in the univariate analysis were further evaluated in multivariate models using backward elimination. Discrimination was assessed using receiver operating curve. Calibration was evaluated using the McFadden's R2. Net reclassification index (NRI) associated with incorporation of laboratory results was calculated. Results were internally validated. A model similar to existing CRC prediction models which included age, sex, height, obesity, ever smoking, alcohol dependence, and previous screening colonoscopy had an AUC of 0.58 (0.57-0.59) with poor goodness of fit. A laboratory-based model including hematocrit, MCV, lymphocytes, and neutrophil-lymphocyte ratio (NLR) had an AUC of 0.76 (0.76-0.77) and a McFadden's R2 of 0.21 with a NRI of 47.6 %. A combined model including sex, hemoglobin, MCV, white blood cells, platelets, NLR, and oral hypoglycemic use had an AUC of 0.80 (0.79-0.81) with a McFadden's R2 of 0.27 and a NRI of 60.7 %. Similar results were shown in an internal validation set. A laboratory-based risk model had good predictive power for sporadic CRC risk.

  6. Understanding Interrater Reliability and Validity of Risk Assessment Tools Used to Predict Adverse Clinical Events.

    PubMed

    Siedlecki, Sandra L; Albert, Nancy M

    This article will describe how to assess interrater reliability and validity of risk assessment tools, using easy-to-follow formulas, and to provide calculations that demonstrate principles discussed. Clinical nurse specialists should be able to identify risk assessment tools that provide high-quality interrater reliability and the highest validity for predicting true events of importance to clinical settings. Making best practice recommendations for assessment tool use is critical to high-quality patient care and safe practices that impact patient outcomes and nursing resources. Optimal risk assessment tool selection requires knowledge about interrater reliability and tool validity. The clinical nurse specialist will understand the reliability and validity issues associated with risk assessment tools, and be able to evaluate tools using basic calculations. Risk assessment tools are developed to objectively predict quality and safety events and ultimately reduce the risk of event occurrence through preventive interventions. To ensure high-quality tool use, clinical nurse specialists must critically assess tool properties. The better the tool's ability to predict adverse events, the more likely that event risk is mediated. Interrater reliability and validity assessment is relatively an easy skill to master and will result in better decisions when selecting or making recommendations for risk assessment tool use.

  7. Prevalence of risk factors for hepatitis C and associated factors: a population-based study in southern Brazil.

    PubMed

    Kvitko, David Timm; Bastos, Gisele Alsina Nader; Pinto, Maria Eugênia Bresolin

    2013-04-01

    The hepatitis C is a severe public health problem worldwide because its consequences. Studies which aim at determining the prevalence of risk factors are really important to understand the problem. To estimate the prevalence and factors associated with some risk factors for the disease in a community, called Restinga, located in the city of Porto Alegre, RS, Brazil. This paper is based on a population-based cross-sectional study, with systematic sampling and proportional to the size of census tracts in which 3,391 adults answered a standardized questionnaire. The prevalence of blood transfusion among the people who were interviewed was 14.98%, 60.83% of those had it before 1993. A total of 16.16% of the people had a tattoo, 7.23% wore a piercing, 1.09% said they had already injected illicit drugs and 12.39% reported previous hospitalization. Prevalence ratios showed that tattoos were more common among young people, piercings among women and illicit drugs among men. To summarize, the recognition of risk factors for hepatitis C enables proper screening of possible carriers of the hepatitis C virus, thus enabling a reduction in virus shedding. However, being only possible if health services are prepared to deal with hepatitis C virus, through education and public awareness.

  8. Flock-level risk factors associated with leptospirosis in dairy goats in a semiarid region of Northeastern Brazil.

    PubMed

    Higino, Severino S S; Santos, Fabrine A; Costa, Diego F; Santos, Carolina S A B; Silva, Maria L C R; Alves, Clebert J; Azevedo, Sérgio S

    2013-04-01

    A cross-sectional study based on a planned sampling was carried out to determine flock-level risk factors associated to Leptospira spp. infection in dairy goat flocks in a semiarid region of Northeastern Brazil. Serum samples from 975 adult dairy goats from 110 flocks were examined for Leptospira spp. antibodies by MAT using 24 serovars. A structured questionnaire focusing on risk factors for leptospirosis was completed for each flock. Of the 110 flocks 48 (43.6%; 95% CI: 34.2-53.4%) presented at least one seropositive animal, and most frequent serovar was Autumnalis (10.9%). Ninety-eight (8.7%; 95% CI: 5.7-12.9%; design effect=4.23) of the 975 goats tested seropositive at MAT, and serovar Autumnalis was also the most frequent (1.74%). Presence of rodents (OR=2.78; P=0.015) was identified as a risk factor. There was also association between history of infertility (OR=14.74; P=0.015) and prevalence of positive flocks. We suggest that a program of rodent control should be included in the flock management practices aiming to reduce transmission of the agent and then to reduce prevalence of positive flocks and occurrence of reproductive disorders such as impaired fertility. Copyright © 2012 Elsevier B.V. All rights reserved.

  9. Herd-level risk factors for Neospora caninum seroprevalence in dairy farms in southern Brazil.

    PubMed

    Corbellini, Luis G; Smith, David R; Pescador, Caroline A; Schmitz, Milene; Correa, Andre; Steffen, David J; Driemeier, David

    2006-05-17

    A cross-sectional study was used to test the relationship between herd seroprevalence to Neospora caninum and various potential herd-level risk factors in 60 dairy farms located in two distinct regions in southern Brazil. Thirty farms were randomly selected from within each region. A questionnaire was designed to summarize each farm's production system as it might relate to N. caninum transmission. The questionnaire contained 105 closed questions relating to general characteristics of the farms, farm facilities, management, source of food and water, herd health, environment and biosecurity, which included questions relevant to N. caninum transmission, including presence and number of dogs and other animals, purchase of animals and contact with man. Serum samples were collected from 40% of animals in each farm and N. caninum antibodies were detected by immunofluorescent antibody test (IFAT). The association between potential risk factors and the probability of an animal being seropositive was modeled using a generalized estimation equations (GEE) logistic regression model. The model accounted for multilevel correlation of data from multiple animals within herds. The mean (+/-S.D.) number of animals in the 60 herds was 64.5 (+/-45.6), ranging from 20 to 280 females. Blood samples were collected from 1549 animals. The size of the farms varied from 4 to 100 ha (mean 30.1+/-25.9 ha). At least one dog was found in 57 of the 60 dairy farms (95%). The mean number of dogs was 3.1 (+/-1.9), ranging from 0 to 10. All females were raised on pasture. For all cattle sampled, N. caninum seroprevalence was 17.8%. Overall, 93.3% of herds (56/60) had at least one seropositive animal identified. Four variables were significantly associated with N. caninum sero-response in the 57 dairy farms, which were included in the final multivariable model: the number of dogs on the farm, farm area (hectares), feeding pooled sources of colostrum and region. The odds of a cow being seropositive

  10. Contemporary model for cardiovascular risk prediction in people with type 2 diabetes.

    PubMed

    Kengne, Andre Pascal; Patel, Anushka; Marre, Michel; Travert, Florence; Lievre, Michel; Zoungas, Sophia; Chalmers, John; Colagiuri, Stephen; Grobbee, Diederick E; Hamet, Pavel; Heller, Simon; Neal, Bruce; Woodward, Mark

    2011-06-01

    Existing cardiovascular risk prediction equations perform non-optimally in different populations with diabetes. Thus, there is a continuing need to develop new equations that will reliably estimate cardiovascular disease (CVD) risk and offer flexibility for adaptation in various settings. This report presents a contemporary model for predicting cardiovascular risk in people with type 2 diabetes mellitus. A 4.5-year follow-up of the Action in Diabetes and Vascular disease: preterax and diamicron-MR controlled evaluation (ADVANCE) cohort was used to estimate coefficients for significant predictors of CVD using Cox models. Similar Cox models were used to fit the 4-year risk of CVD in 7168 participants without previous CVD. The model's applicability was tested on the same sample and another dataset. A total of 473 major cardiovascular events were recorded during follow-up. Age at diagnosis, known duration of diabetes, sex, pulse pressure, treated hypertension, atrial fibrillation, retinopathy, HbA1c, urinary albumin/creatinine ratio and non-HDL cholesterol at baseline were significant predictors of cardiovascular events. The model developed using these predictors displayed an acceptable discrimination (c-statistic: 0.70) and good calibration during internal validation. The external applicability of the model was tested on an independent cohort of individuals with type 2 diabetes, where similar discrimination was demonstrated (c-statistic: 0.69). Major cardiovascular events in contemporary populations with type 2 diabetes can be predicted on the basis of routinely measured clinical and biological variables. The model presented here can be used to quantify risk and guide the intensity of treatment in people with diabetes.

  11. Prediction of individual genetic risk to prostate cancer using a polygenic score.

    PubMed

    Szulkin, Robert; Whitington, Thomas; Eklund, Martin; Aly, Markus; Eeles, Rosalind A; Easton, Douglas; Kote-Jarai, Z Sofia; Amin Al Olama, Ali; Benlloch, Sara; Muir, Kenneth; Giles, Graham G; Southey, Melissa C; Fitzgerald, Liesel M; Henderson, Brian E; Schumacher, Fredrick; Haiman, Christopher A; Schleutker, Johanna; Wahlfors, Tiina; Tammela, Teuvo L J; Nordestgaard, Børge G; Key, Tim J; Travis, Ruth C; Neal, David E; Donovan, Jenny L; Hamdy, Freddie C; Pharoah, Paul; Pashayan, Nora; Khaw, Kay-Tee; Stanford, Janet L; Thibodeau, Stephen N; McDonnell, Shannon K; Schaid, Daniel J; Maier, Christiane; Vogel, Walther; Luedeke, Manuel; Herkommer, Kathleen; Kibel, Adam S; Cybulski, Cezary; Lubiński, Jan; Kluźniak, Wojciech; Cannon-Albright, Lisa; Brenner, Hermann; Butterbach, Katja; Stegmaier, Christa; Park, Jong Y; Sellers, Thomas; Lin, Hui-Yi; Lim, Hui-Yi; Slavov, Chavdar; Kaneva, Radka; Mitev, Vanio; Batra, Jyotsna; Clements, Judith A; Spurdle, Amanda; Teixeira, Manuel R; Paulo, Paula; Maia, Sofia; Pandha, Hardev; Michael, Agnieszka; Kierzek, Andrzej; Gronberg, Henrik; Wiklund, Fredrik

    2015-09-01

    Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction. © 2015 Wiley Periodicals, Inc.

  12. One vs. Two Breast Density Measures to Predict 5- and 10- Year Breast Cancer Risk

    PubMed Central

    Kerlikowske, Karla; Gard, Charlotte C.; Sprague, Brian L.; Tice, Jeffrey A.; Miglioretti, Diana L.

    2015-01-01

    Background One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined if two BI-RADS density measures improves the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared to one measure. Methods We included 722,654 women aged 35–74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000–2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death. Results The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC=0.640 vs. 0.635). Of 18.6% of women (134,404/722,654) who decreased density categories, 15.4% (20,741/134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model. Conclusion The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density. Impact A two-density model should be considered for women whose density decreases when calculating breast cancer risk. PMID:25824444

  13. One versus Two Breast Density Measures to Predict 5- and 10-Year Breast Cancer Risk.

    PubMed

    Kerlikowske, Karla; Gard, Charlotte C; Sprague, Brian L; Tice, Jeffrey A; Miglioretti, Diana L

    2015-06-01

    One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined whether two BI-RADS density measures improve the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared with one measure. We included 722,654 women of ages 35 to 74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000-2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death. The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC, 0.640 vs. 0.635). Of 18.6% of women (134,404 of 722,654) who decreased density categories, 15.4% (20,741 of 134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model. The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density. A two-density model should be considered for women whose density decreases when calculating breast cancer risk. ©2015 American Association for Cancer Research.

  14. Undisclosed Human Immunodeficiency Virus Risk Factors Identified through a Computer-based Questionnaire Program among Blood Donors in Brazil

    PubMed Central

    Blatyta, Paula Fraiman; Custer, Brian; Gonçalez, Thelma Terezinha; Birch, Rebecca; Lopes, Maria Esther; Ferreira, Maria Ines Lopes; Proietti, Anna Barbara Carneiro; Sabino, Ester Cerdeira; Page, Kimberly; de Almeida Neto, Cesar

    2013-01-01

    Background HIV risk factor screening among blood donors remains a cornerstone for the safety of blood supply and is dependent on prospective donor self-disclosure and an attentive predonation interview. Residual risk of HIV transmission through blood transfusion is higher in Brazil than in many other countries. Audio computer-assisted structured-interview (ACASI) has been shown to increase self-reporting of risk behaviors. Study design and methods This cross-sectional study was conducted between January 2009 and March 2011 at four Brazilian blood centers to identify the population of HIV-negative eligible blood donors that answered face-to-face interviews without disclosing risks, but subsequently disclosed deferrable risk factors by ACASI. Compared to the donor interview, the ACASI contained expanded content on demographics, sexual behavior and other HIV risk factors questions. Results 901 HIV-negative blood donors were interviewed. On the ACASI, 13% of donors (N=120) declared a risk factor that would have resulted in deferral that was not disclosed during the face-to-face assessment. The main risk factors identified were recent unprotected sex with an unknown or irregular partner (49 donors), sex with a person with exposure to blood/ fluids (26 donors), multiple sexual partners (19 donors), and male-male sexual behavior (10 donors). Independent factors associated with the disclosure of any risk factor for HIV were age (≥40 years vs. 18–25 years, AOR=0.45; 95% CI 0.23–0.88) and blood center (Hemope vs. Hemominas, AOR=2.51; 95% CI 1.42–4.44). Conclusion ACASI elicited increased disclosure of HIV risk factors among blood donors. ACASI may be a valuable modality of interview to be introduced in Brazilian blood banks. PMID:23521083

  15. Risk Prediction of New Adjacent Vertebral Fractures After PVP for Patients with Vertebral Compression Fractures: Development of a Prediction Model

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Zhong, Bin-Yan; He, Shi-Cheng; Zhu, Hai-Dong

    PurposeWe aim to determine the predictors of new adjacent vertebral fractures (AVCFs) after percutaneous vertebroplasty (PVP) in patients with osteoporotic vertebral compression fractures (OVCFs) and to construct a risk prediction score to estimate a 2-year new AVCF risk-by-risk factor condition.Materials and MethodsPatients with OVCFs who underwent their first PVP between December 2006 and December 2013 at Hospital A (training cohort) and Hospital B (validation cohort) were included in this study. In training cohort, we assessed the independent risk predictors and developed the probability of new adjacent OVCFs (PNAV) score system using the Cox proportional hazard regression analysis. The accuracy ofmore » this system was then validated in both training and validation cohorts by concordance (c) statistic.Results421 patients (training cohort: n = 256; validation cohort: n = 165) were included in this study. In training cohort, new AVCFs after the first PVP treatment occurred in 33 (12.9%) patients. The independent risk factors were intradiscal cement leakage and preexisting old vertebral compression fracture(s). The estimated 2-year absolute risk of new AVCFs ranged from less than 4% in patients with neither independent risk factors to more than 45% in individuals with both factors.ConclusionsThe PNAV score is an objective and easy approach to predict the risk of new AVCFs.« less

  16. Childhood trauma and suicide risk in a sample of young individuals aged 14-35 years in southern Brazil.

    PubMed

    Barbosa, Luana Porto; Quevedo, Luciana; da Silva, Giovanna Del Grande; Jansen, Karen; Pinheiro, Ricardo Tavares; Branco, Jerônimo; Lara, Diogo; Oses, Jean; da Silva, Ricardo Azevedo

    2014-07-01

    Suicide is among the main causes of death of people aged between 15 and 44 years old. Childhood trauma is an important risk factor for suicide. Hence, the objective of this study was to verify the relationship between childhood trauma and current suicide risk (suicidal behavior and ideation) in individuals aged 14-35 years, in the city of Pelotas, Brazil. This is a cross-sectional, population-based study. Sample selection was performed by clusters. Suicide risk was evaluated using the Mini International Neuropsychiatric Interview (MINI) and Childhood trauma was assessed with the Childhood Trauma Questionnaire (CTQ). Moreover, the participants responded to a questionnaire concerning socioeconomic status, work, and substance use. The sample was composed of 1,380 individuals. The prevalence of suicide risk was 11.5%. The prevalence figures of childhood trauma were 15.2% (emotional neglect), 13.5% (physical neglect), 7.6% (sexual abuse), 10.1% (physical abuse), and 13.8% (emotional abuse). Suicide risk was associated (p<.001) with gender, work, alcohol abuse, tobacco use, and all types of childhood trauma. The odds of suicide risk were higher in women (OR=1.8), people who were not currently working (OR=2.3), individuals who presented alcohol abuse (OR=2.6), and among tobacco smokers (OR=3.4). Moreover, suicide risk was increased in all types of trauma: emotional neglect (OR=3.7), physical neglect (OR=2.8), sexual abuse (OR=3.4), physical abuse (OR=3.1), and emotional abuse (OR=6.6). Thus, preventing early trauma may reduce suicide risk in young individuals. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Long-Term Survival Prediction for Coronary Artery Bypass Grafting: Validation of the ASCERT Model Compared With The Society of Thoracic Surgeons Predicted Risk of Mortality.

    PubMed

    Lancaster, Timothy S; Schill, Matthew R; Greenberg, Jason W; Ruaengsri, Chawannuch; Schuessler, Richard B; Lawton, Jennifer S; Maniar, Hersh S; Pasque, Michael K; Moon, Marc R; Damiano, Ralph J; Melby, Spencer J

    2018-05-01

    The recently developed American College of Cardiology Foundation-Society of Thoracic Surgeons (STS) Collaboration on the Comparative Effectiveness of Revascularization Strategy (ASCERT) Long-Term Survival Probability Calculator is a valuable addition to existing short-term risk-prediction tools for cardiac surgical procedures but has yet to be externally validated. Institutional data of 654 patients aged 65 years or older undergoing isolated coronary artery bypass grafting between 2005 and 2010 were reviewed. Predicted survival probabilities were calculated using the ASCERT model. Survival data were collected using the Social Security Death Index and institutional medical records. Model calibration and discrimination were assessed for the overall sample and for risk-stratified subgroups based on (1) ASCERT 7-year survival probability and (2) the predicted risk of mortality (PROM) from the STS Short-Term Risk Calculator. Logistic regression analysis was performed to evaluate additional perioperative variables contributing to death. Overall survival was 92.1% (569 of 597) at 1 year and 50.5% (164 of 325) at 7 years. Calibration assessment found no significant differences between predicted and actual survival curves for the overall sample or for the risk-stratified subgroups, whether stratified by predicted 7-year survival or by PROM. Discriminative performance was comparable between the ASCERT and PROM models for 7-year survival prediction (p < 0.001 for both; C-statistic = 0.815 for ASCERT and 0.781 for PROM). Prolonged ventilation, stroke, and hospital length of stay were also predictive of long-term death. The ASCERT survival probability calculator was externally validated for prediction of long-term survival after coronary artery bypass grafting in all risk groups. The widely used STS PROM performed comparably as a predictor of long-term survival. Both tools provide important information for preoperative decision making and patient counseling about potential

  18. The 2014 FIFA World Cup: communicable disease risks and advice for visitors to Brazil--a review from the Latin American Society for Travel Medicine (SLAMVI).

    PubMed

    Gallego, Viviana; Berberian, Griselda; Lloveras, Susana; Verbanaz, Sergio; Chaves, Tania S S; Orduna, Tomas; Rodriguez-Morales, Alfonso J

    2014-01-01

    The next FIFA World Cup will be held in Brazil in June-July 2014. Around 600,000 international visitors and participants (as well over 3 million domestic travelers) are expected. This event will take place in twelve cities. This event poses specific challenges, given its size and the diversity of attendees, including the potential for the transmission of imported or endemic communicable diseases, especially those that have an increased transmission rate as a result of close human proximity, eg, seasonal influenza, measles but also tropical endemic diseases. In anticipation of increased travel, a panel of experts from the Latin American Society for Travel Medicine (SLAMVI) developed the current recommendations regarding the epidemiology and risks of the main communicable diseases in the major potential destinations, recommended immunizations and other preventives measures to be used as a basis for advice for travelers and travel medicine practitioners. Mosquito-borne infections also pose a challenge. Dengue poses a significant risk in all states, including the host cities. Vaccination against yellow fever is recommended except for travelers who will only visit coastal areas. Travelers visiting high-risk areas for malaria (Amazon) should be assessed regarding the need for chemoprophylaxis. Chikunguya fever may be a threat for Brazil, given the presence of Aedes aegypti, vector of dengue, and the possibility of travelers bringing the virus with them when attending the event. Advice on the correct timing and use of repellents and other personal protection measures is key to preventing these vector-borne infections. Other important recommendations for travelers should focus on preventing water and food-borne diseases such as hepatitis A, typhoid fever, giardiasis and traveler's diarrhea. Sexually transmitted diseases (STD) should be also mentioned and the use of condoms advocated. This review addresses pre-travel, preventive strategies to reduce the risk of acquiring

  19. Blood test could predict risk of heart attack and subsequent death.

    PubMed

    2017-01-18

    A high-sensitivity blood test, known as a troponin test, could predict the risk of heart attack and death and patients' response to statins, say researchers from the Universities of Edinburgh and Glasgow.

  20. A novel risk score model for prediction of contrast-induced nephropathy after emergent percutaneous coronary intervention.

    PubMed

    Lin, Kai-Yang; Zheng, Wei-Ping; Bei, Wei-Jie; Chen, Shi-Qun; Islam, Sheikh Mohammed Shariful; Liu, Yong; Xue, Lin; Tan, Ning; Chen, Ji-Yan

    2017-03-01

    A few studies developed simple risk model for predicting CIN with poor prognosis after emergent PCI. The study aimed to develop and validate a novel tool for predicting the risk of contrast-induced nephropathy (CIN) in patients undergoing emergent percutaneous coronary intervention (PCI). 692 consecutive patients undergoing emergent PCI between January 2010 and December 2013 were randomly (2:1) assigned to a development dataset (n=461) and a validation dataset (n=231). Multivariate logistic regression was applied to identify independent predictors of CIN, and established CIN predicting model, whose prognostic accuracy was assessed using the c-statistic for discrimination and the Hosmere Lemeshow test for calibration. The overall incidence of CIN was 55(7.9%). A total of 11 variables were analyzed, including age >75years old, baseline serum creatinine (SCr)>1.5mg/dl, hypotension and the use of intra-aortic balloon pump(IABP), which were identified to enter risk score model (Chen). The incidence of CIN was 32(6.9%) in the development dataset (in low risk (score=0), 1.0%, moderate risk (score:1-2), 13.4%, high risk (score≥3), 90.0%). Compared to the classical Mehran's and ACEF CIN risk score models, the risk score (Chen) across the subgroup of the study population exhibited similar discrimination and predictive ability on CIN (c-statistic:0.828, 0.776, 0.853, respectively), in-hospital mortality, 2, 3-years mortality (c-statistic:0.738.0.750, 0.845, respectively) in the validation population. Our data showed that this simple risk model exhibited good discrimination and predictive ability on CIN, similar to Mehran's and ACEF score, and even on long-term mortality after emergent PCI. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  1. Time trends in adult chronic disease inequalities by education in Brazil: 1998-2013.

    PubMed

    Beltrán-Sánchez, Hiram; Andrade, Flavia C D

    2016-11-17

    Socioeconomic differences in health in Brazil are largely driven by differences in educational attainment. In this paper, we assess whether educational gradients in chronic disease prevalence have narrowed in Brazil from 1998 to 2013, a period of a booming economy accompanied by major investments in public health in the country. Individual-level data came from the 1998, 2003 and 2008 Brazilian National Household Survey and the 2013 National Health Survey. We first evaluate age-standardized prevalence rates of chronic disease by education and second, we predict the estimated prevalence rate between those in low vs. high education to assess if relative changes in chronic disease have narrowed over time. Third, we estimate the slope index of inequality (SII) that evaluates the absolute change in the predicted prevalence of a disease between those in low vs. high education. Finally, we tested for statistically significant time trends in adult chronic disease inequalities by education. Prevalence of diabetes and hypertension have increased over the period, whereas the prevalence of heart disease decreased. Brazilian adults with no education had higher levels of diabetes, hypertension and heart disease than those with some college or more. Adjusted prevalence for hypertension and heart disease indicate some progress in reducing educational disparities over time. However, for diabetes, adjusted results show a continuously increasing educational disparity from 1998 to 2013. By 2013, individuals with no education had about two times higher diabetes prevalence than those with higher education with larger disparity among women. Results confirm findings from previous work that educational inequalities in health are large in Brazil but also provide evidence suggesting some improvement in narrowing these differentials in recent times. Recent policies aiming at reducing the prevalence of obesity, smoking and alcohol consumption, and increasing physical activity and consumption of

  2. Anthropometric measurements as predictive indicators of metabolic risk in a Mexican population

    PubMed

    Domínguez-Reyes, Teresa; Quiroz-Vargas, Irma; Salgado-Bernabé, Aralia Berenice; Salgado-Goytia, Lorenzo; Muñoz-Valle, José Francisco; Parra-Rojas, Isela

    2017-02-01

    Introduction: Currently, it is considered that the body fat accumulation at central level is associated with the presence of hypertriglyceridemia, hypertension and diabetes. The body mass index (BMI) has been used to identify obesity in the general population, but can not detect the distribution of body fat, so that can be used other anthropometric measures to assess adiposity and determine their relationship with the presence of metabolic disorders that present people with excess weight. Objective: To evaluate anthropometric measurements such as waist-hip ratio (WHR), BMI and waist circumference (WC) as predictive indicators of metabolic risk factors in Mexican adults. Methods:A descriptive cross-sectional study was conducted in a total of 490 subjects (27-46 years), grouped by gender. All participants were determined anthropometric measurements and biochemical parameters. ROC curves of anthropometric parameters were set to identify the best predictive indicator of metabolic risk. Results: The metabolic risk factor most prevalent after abdominal obesity in women was hypertriglyceridemia, followed by hyperglycemia, hypercholesterolemia and high blood pressure, which are found most often in men than in women, although the presence of abdominal obesity was found most frequently in women (73.9% vs.37.3%). WC was the best predictive indicator to have one or more metabolic risk factors [area under the curve AUC = 0.85 (95% CI, 0.78 to 0.92)], followed by the BMI [AUC = 0.79 (95% CI, 0.72 to 0.88)], and finally the WHC [AUC = 0.63 (95% CI, 0.52 to 0.74)]. Also shows that abdominal obesity duplicate the risk of metabolic syndrome. Conclusion: Waist circumference is a better indicator of metabolic risk in both genders compared with BMI and the WHC.

  3. Assessing and predicting drug-induced anticholinergic risks: an integrated computational approach.

    PubMed

    Xu, Dong; Anderson, Heather D; Tao, Aoxiang; Hannah, Katia L; Linnebur, Sunny A; Valuck, Robert J; Culbertson, Vaughn L

    2017-11-01

    Anticholinergic (AC) adverse drug events (ADEs) are caused by inhibition of muscarinic receptors as a result of designated or off-target drug-receptor interactions. In practice, AC toxicity is assessed primarily based on clinician experience. The goal of this study was to evaluate a novel concept of integrating big pharmacological and healthcare data to assess clinical AC toxicity risks. AC toxicity scores (ATSs) were computed using drug-receptor inhibitions identified through pharmacological data screening. A longitudinal retrospective cohort study using medical claims data was performed to quantify AC clinical risks. ATS was compared with two previously reported toxicity measures. A quantitative structure-activity relationship (QSAR) model was established for rapid assessment and prediction of AC clinical risks. A total of 25 common medications, and 575,228 exposed and unexposed patients were analyzed. Our data indicated that ATS is more consistent with the trend of AC outcomes than other toxicity methods. Incorporating drug pharmacokinetic parameters to ATS yielded a QSAR model with excellent correlation to AC incident rate ( R 2 = 0.83) and predictive performance (cross validation Q 2 = 0.64). Good correlation and predictive performance ( R 2 = 0.68/ Q 2 = 0.29) were also obtained for an M2 receptor-specific QSAR model and tachycardia, an M2 receptor-specific ADE. Albeit using a small medication sample size, our pilot data demonstrated the potential and feasibility of a new computational AC toxicity scoring approach driven by underlying pharmacology and big data analytics. Follow-up work is under way to further develop the ATS scoring approach and clinical toxicity predictive model using a large number of medications and clinical parameters.

  4. Could the Recent Zika Epidemic Have Been Predicted?

    NASA Astrophysics Data System (ADS)

    Vecchi, G. A.; Munoz, A. G.; Thomson, M. C.; Stewart-Ibarra, A. M.; Chourio, X.; Nájera, P.; Moran, Z.; Yang, X.

    2017-12-01

    Given knowledge at the time, the recent 2015-2016 zika virus (ZIKV) epidemic probably could not have been predicted. Without the prior knowledge of ZIKV being already present in South America, and given the lack of understanding of key epidemiologic processes and long-term records of ZIKV cases in the continent, the best related prediction could be carried out for the potential risk of a generic Aedes-borne disease epidemic. Here we use a recently published two-vector basic reproduction number model to assess the predictability of the conditions conducive to epidemics of diseases like zika, chikungunya, or dengue, transmitted by the independent or concurrent presence of Aedes aegypti and Aedes albopictus. We compare the potential risk of transmission forcing the model with the observed climate and with state-of-the-art operational forecasts from the North American Multi Model Ensemble (NMME), finding that the predictive skill of this new seasonal forecast system is highest for multiple countries in Latin America and the Caribbean during the December-February and March-May seasons, and slightly lower—but still of potential use to decision-makers—for the rest of the year. In particular, we find that above-normal suitable conditions for the occurrence of the zika epidemic at the beginning of 2015 could have been successfully predicted at least 1 month in advance for several zika hotspots, and in particular for Northeast Brazil: the heart of the epidemic. Nonetheless, the initiation and spread of an epidemic depends on the effect of multiple factors beyond climate conditions, and thus this type of approach must be considered as a guide and not as a formal predictive tool of vector-borne epidemics.

  5. Could the Recent Zika Epidemic Have Been Predicted?

    PubMed

    Muñoz, Ángel G; Thomson, Madeleine C; Stewart-Ibarra, Anna M; Vecchi, Gabriel A; Chourio, Xandre; Nájera, Patricia; Moran, Zelda; Yang, Xiaosong

    2017-01-01

    Given knowledge at the time, the recent 2015-2016 zika virus (ZIKV) epidemic probably could not have been predicted. Without the prior knowledge of ZIKV being already present in South America, and given the lack of understanding of key epidemiologic processes and long-term records of ZIKV cases in the continent, the best related prediction could be carried out for the potential risk of a generic Aedes -borne disease epidemic. Here we use a recently published two-vector basic reproduction number model to assess the predictability of the conditions conducive to epidemics of diseases like zika, chikungunya, or dengue, transmitted by the independent or concurrent presence of Aedes aegypti and Aedes albopictus . We compare the potential risk of transmission forcing the model with the observed climate and with state-of-the-art operational forecasts from the North American Multi Model Ensemble (NMME), finding that the predictive skill of this new seasonal forecast system is highest for multiple countries in Latin America and the Caribbean during the December-February and March-May seasons, and slightly lower-but still of potential use to decision-makers-for the rest of the year. In particular, we find that above-normal suitable conditions for the occurrence of the zika epidemic at the beginning of 2015 could have been successfully predicted at least 1 month in advance for several zika hotspots, and in particular for Northeast Brazil: the heart of the epidemic. Nonetheless, the initiation and spread of an epidemic depends on the effect of multiple factors beyond climate conditions, and thus this type of approach must be considered as a guide and not as a formal predictive tool of vector-borne epidemics.

  6. Risk factors predict post-traumatic stress disorder differently in men and women

    PubMed Central

    Christiansen, Dorte M; Elklit, Ask

    2008-01-01

    Background About twice as many women as men develop post-traumatic stress disorder (PTSD), even though men as a group are exposed to more traumatic events. Exposure to different trauma types does not sufficiently explain why women are more vulnerable. Methods The present work examines the effect of age, previous trauma, negative affectivity (NA), anxiety, depression, persistent dissociation, and social support on PTSD separately in men and women. Subjects were exposed to either a series of explosions in a firework factory near a residential area or to a high school stabbing incident. Results Some gender differences were found in the predictive power of well known risk factors for PTSD. Anxiety predicted PTSD in men, but not in women, whereas the opposite was found for depression. Dissociation was a better predictor for PTSD in women than in men in the explosion sample but not in the stabbing sample. Initially, NA predicted PTSD better in women than men in the explosion sample, but when compared only to other significant risk factors, it significantly predicted PTSD for both men and women in both studies. Previous traumatic events and age did not significantly predict PTSD in either gender. Conclusion Gender differences in the predictive value of social support on PTSD appear to be very complex, and no clear conclusions can be made based on the two studies included in this article. PMID:19017412

  7. A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.

    PubMed

    Geng, Yuan; Lu, Wenbin; Zhang, Hao Helen

    2014-01-01

    Risk classification and survival probability prediction are two major goals in survival data analysis since they play an important role in patients' risk stratification, long-term diagnosis, and treatment selection. In this article, we propose a new model-free machine learning framework for risk classification and survival probability prediction based on weighted support vector machines. The new procedure does not require any specific parametric or semiparametric model assumption on data, and is therefore capable of capturing nonlinear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumor data and a breast cancer gene expression survival data are shown to illustrate the new methodology in real data analysis.

  8. Risk approximation in decision making: approximative numeric abilities predict advantageous decisions under objective risk.

    PubMed

    Mueller, Silke M; Schiebener, Johannes; Delazer, Margarete; Brand, Matthias

    2018-01-22

    Many decision situations in everyday life involve mathematical considerations. In decisions under objective risk, i.e., when explicit numeric information is available, executive functions and abilities to handle exact numbers and ratios are predictors of objectively advantageous choices. Although still debated, exact numeric abilities, e.g., normative calculation skills, are assumed to be related to approximate number processing skills. The current study investigates the effects of approximative numeric abilities on decision making under objective risk. Participants (N = 153) performed a paradigm measuring number-comparison, quantity-estimation, risk-estimation, and decision-making skills on the basis of rapid dot comparisons. Additionally, a risky decision-making task with exact numeric information was administered, as well as tasks measuring executive functions and exact numeric abilities, e.g., mental calculation and ratio processing skills, were conducted. Approximative numeric abilities significantly predicted advantageous decision making, even beyond the effects of executive functions and exact numeric skills. Especially being able to make accurate risk estimations seemed to contribute to superior choices. We recommend approximation skills and approximate number processing to be subject of future investigations on decision making under risk.

  9. Smoking and Adverse Maternal and Child Health Outcomes in Brazil

    PubMed Central

    2013-01-01

    Introduction: Numerous studies from high-income countries document the causal relationship between cigarette smoking during pregnancy and adverse maternal and child health (MCH) outcomes. Less research has been conducted in low and middle income countries, but a burgeoning literature can be found for Brazil. Methods: We review Brazilian studies of the prevalence of maternal smoking, the relative risk of smoking-attributable adverse MCH outcomes, and present new estimates for these outcomes, using the attributable fraction method. Results: We found that Brazilian studies of the relative risks of smoking-attributable adverse MCH outcomes were broadly consistent with previous reviews. Based on a comparison of maternal smoking over time, smoking during pregnancy has declined by about 50% over the last 20 years in Brazil. For 2008, we estimate that 5,352 cases of spontaneous abortion, 10,929 cases of preterm birth, 20,717 cases of low birth weight, and 29 cases of sudden infant death syndrome are attributable to maternal smoking. Between 1989 and 2008, the percent of smoking-attributable adverse MCH outcomes in Brazil was at least halved. Conclusions: The results show that over a 20-year period, during which Brazil implemented numerous effective tobacco control measures, the country experienced a dramatic decrease in both maternal smoking prevalence and smoking-attributable adverse MCH outcomes. Countries that implement effective tobacco control measures can expect to reduce both maternal smoking and adverse MCH outcomes, thereby improving the public health. PMID:23873977

  10. Predicting invasion risk using measures of introduction effort and environmental niche models.

    PubMed

    Herborg, Leif-Matthias; Jerde, Christopher L; Lodge, David M; Ruiz, Gregory M; MacIsaac, Hugh J

    2007-04-01

    The Chinese mitten crab (Eriocheir sinensis) is native to east Asia, is established throughout Europe, and is introduced but geographically restricted in North America. We developed and compared two separate environmental niche models using genetic algorithm for rule set prediction (GARP) and mitten crab occurrences in Asia and Europe to predict the species' potential distribution in North America. Since mitten crabs must reproduce in water with >15% per hundred salinity, we limited the potential North American range to freshwater habitats within the highest documented dispersal distance (1260 km) and a more restricted dispersal limit (354 km) from the sea. Applying the higher dispersal distance, both models predicted the lower Great Lakes, most of the eastern seaboard, the Gulf of Mexico and southern extent of the Mississippi River watershed, and the Pacific northwest as suitable environment for mitten crabs, but environmental match for southern states (below 35 degrees N) was much lower for the European model. Use of the lower range with both models reduced the expected range, especially in the Great Lakes, Mississippi drainage, and inland areas of the Pacific Northwest. To estimate the risk of introduction of mitten crabs, the amount of reported ballast water discharge into major United States ports from regions in Asia and Europe with established mitten crab populations was used as an index of introduction effort. Relative risk of invasion was estimated based on a combination of environmental match and volume of unexchanged ballast water received (July 1999-December 2003) for major ports. The ports of Norfolk and Baltimore were most vulnerable to invasion and establishment, making Chesapeake Bay the most likely location to be invaded by mitten crabs in the United States. The next highest risk was predicted for Portland, Oregon. Interestingly, the port of Los Angeles/Long Beach, which has a large shipping volume, had a low risk of invasion. Ports such as

  11. As of 2012, what are the key predictive risk factors for pressure ulcers? Developing French guidelines for clinical practice.

    PubMed

    Michel, J-M; Willebois, S; Ribinik, P; Barrois, B; Colin, D; Passadori, Y

    2012-10-01

    An evaluation of predictive risk factors for pressure ulcers is essential in development of a preventive strategy on admission to hospitals and/or nursing homes. Identification of the predictive factors for pressure ulcers as of 2012. Systematic review of the literature querying the databases PASCAL Biomed, Cochrane Library and PubMed from 2000 through 2010. Immobility should be considered as a predictive risk factor for pressure ulcers (grade B). Undernutrition/malnutrition may also be a predictive risk factor for pressure ulcers (grade C). Even if the level of evidence is low, once these risk factors have been detected, management is essential. Sensitizing and mobilizing health care teams requires training in ways of tracking and screening. According to the experts, risk scales should be used. As decision aids, they should always be balanced and complemented by the clinical judgment of the treatment team. According to experts, it is important to know and predictively evaluate risk of pressure ulcers at the time of hospital admission. The predictive risk factors found in this study are identical to those highlighted at the 2001 consensus conference of which was PERSE was the promoter. Copyright © 2012. Published by Elsevier Masson SAS.

  12. Problems With Risk Reclassification Methods for Evaluating Prediction Models

    PubMed Central

    Pepe, Margaret S.

    2011-01-01

    For comparing the performance of a baseline risk prediction model with one that includes an additional predictor, a risk reclassification analysis strategy has been proposed. The first step is to cross-classify risks calculated according to the 2 models for all study subjects. Summary measures including the percentage of reclassification and the percentage of correct reclassification are calculated, along with 2 reclassification calibration statistics. The author shows that interpretations of the proposed summary measures and P values are problematic. The author's recommendation is to display the reclassification table, because it shows interesting information, but to use alternative methods for summarizing and comparing model performance. The Net Reclassification Index has been suggested as one alternative method. The author argues for reporting components of the Net Reclassification Index because they are more clinically relevant than is the single numerical summary measure. PMID:21555714

  13. SU-E-T-128: Applying Failure Modes and Effects Analysis to a Risk-Based Quality Management for Stereotactic Radiosurgery in Brazil

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Teixeira, F; Universidade do Estado do Rio de Janeiro, Rio De Janeiro, RJ; Almeida, C de

    2015-06-15

    Purpose: The goal of the present work was to evaluate the process maps for stereotactic radiosurgery (SRS) treatment at three radiotherapy centers in Brazil and apply the FMEA technique to evaluate similarities and differences, if any, of the hazards and risks associated with these processes. Methods: A team, consisting of professionals from different disciplines and involved in the SRS treatment, was formed at each center. Each team was responsible for the development of the process map, and performance of FMEA and FTA. A facilitator knowledgeable in these techniques led the work at each center. The TG100 recommended scales were usedmore » for the evaluation of hazard and severity for each step for the major process “treatment planning”. Results: Hazard index given by the Risk Priority Number (RPN) is found to range from 4–270 for various processes and the severity (S) index is found to range from 1–10. The RPN values > 100 and severity value ≥ 7 were chosen to flag safety improvement interventions. Number of steps with RPN ≥100 were found to be 6, 59 and 45 for the three centers. The corresponding values for S ≥ 7 are 24, 21 and 25 respectively. The range of RPN and S values for each center belong to different process steps and failure modes. Conclusion: These results show that interventions to improve safety is different for each center and it is associated with the skill level of the professional team as well as the technology used to provide radiosurgery treatment. The present study will very likely be a model for implementation of risk-based prospective quality management program for SRS treatment in Brazil where currently there are 28 radiotherapy centers performing SRS. A complete FMEA for SRS for these three radiotherapy centers is currently under development.« less

  14. Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach.

    PubMed

    Batterham, Philip J; Christensen, Helen; Mackinnon, Andrew J

    2009-11-22

    Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

  15. A Predictive Risk Model for A(H7N9) Human Infections Based on Spatial-Temporal Autocorrelation and Risk Factors: China, 2013–2014

    PubMed Central

    Dong, Wen; Yang, Kun; Xu, Quan-Li; Yang, Yu-Lian

    2015-01-01

    This study investigated the spatial distribution, spatial autocorrelation, temporal cluster, spatial-temporal autocorrelation and probable risk factors of H7N9 outbreaks in humans from March 2013 to December 2014 in China. The results showed that the epidemic spread with significant spatial-temporal autocorrelation. In order to describe the spatial-temporal autocorrelation of H7N9, an improved model was developed by introducing a spatial-temporal factor in this paper. Logistic regression analyses were utilized to investigate the risk factors associated with their distribution, and nine risk factors were significantly associated with the occurrence of A(H7N9) human infections: the spatial-temporal factor φ (OR = 2546669.382, p < 0.001), migration route (OR = 0.993, p < 0.01), river (OR = 0.861, p < 0.001), lake(OR = 0.992, p < 0.001), road (OR = 0.906, p < 0.001), railway (OR = 0.980, p < 0.001), temperature (OR = 1.170, p < 0.01), precipitation (OR = 0.615, p < 0.001) and relative humidity (OR = 1.337, p < 0.001). The improved model obtained a better prediction performance and a higher fitting accuracy than the traditional model: in the improved model 90.1% (91/101) of the cases during February 2014 occurred in the high risk areas (the predictive risk > 0.70) of the predictive risk map, whereas 44.6% (45/101) of which overlaid on the high risk areas (the predictive risk > 0.70) for the traditional model, and the fitting accuracy of the improved model was 91.6% which was superior to the traditional model (86.1%). The predictive risk map generated based on the improved model revealed that the east and southeast of China were the high risk areas of A(H7N9) human infections in February 2014. These results provided baseline data for the control and prevention of future human infections. PMID:26633446

  16. Clinical score to predict the risk of bile leakage after liver resection.

    PubMed

    Kajiwara, Takahiro; Midorikawa, Yutaka; Yamazaki, Shintaro; Higaki, Tokio; Nakayama, Hisashi; Moriguchi, Masamichi; Tsuji, Shingo; Takayama, Tadatoshi

    2016-05-06

    In liver resection, bile leakage remains the most common cause of operative morbidity. In order to predict the risk of this complication on the basis of various factors, we developed a clinical score system to predict the potential risk of bile leakage after liver resection. We analyzed the postoperative course in 518 patients who underwent liver resection for malignancy to identify independent predictors of bile leakage, which was defined as "a drain fluid bilirubin concentration at least three times the serum bilirubin concentration on or after postoperative day 3," as proposed by the International Study Group of Liver Surgery. To confirm the robustness of the risk score system for bile leakage, we analyzed the independent series of 289 patients undergoing liver resection for malignancy. Among 81 (15.6 %) patients with bile leakage, 76 had grade A bile leakage, and five had grade C leakage and underwent reoperation. The median postoperative hospital stay was significantly longer in patients with bile leakage (median, 14 days; range, 8 to 34) than in those without bile leakage (11 days; 5 to 62; P = 0.001). There was no hepatic insufficiency or in-hospital death. The risk score model was based on the four independent predictors of postoperative bile leakage: non-anatomical resection (odds ratio, 3.16; 95 % confidence interval [CI], 1.72 to 6.07; P < 0.001), indocyanine green clearance rate (2.43; 1.32 to 7.76; P = 0.004), albumin level (2.29; 1.23 to 4.22; P = 0.01), and weight of resected specimen (1.97; 1.11 to 3.51; P = 0.02). When this risk score system was used to assign patients to low-, middle-, and high-risk groups, the frequency of bile leakage in the high-risk group was 2.64 (95 % CI, 1.12 to 6.41; P = 0.04) than that in the low-risk group. Among the independent series for validation, 4 (5.7 %), 16 (10.0 %), and 10 (16.6 %) patients in low-, middle, and high-risk groups were given a diagnosis of bile leakage after

  17. Perceived extrinsic mortality risk and reported effort in looking after health: testing a behavioral ecological prediction.

    PubMed

    Pepper, Gillian V; Nettle, Daniel

    2014-09-01

    Socioeconomic gradients in health behavior are pervasive and well documented. Yet, there is little consensus on their causes. Behavioral ecological theory predicts that, if people of lower socioeconomic position (SEP) perceive greater personal extrinsic mortality risk than those of higher SEP, they should disinvest in their future health. We surveyed North American adults for reported effort in looking after health, perceived extrinsic and intrinsic mortality risks, and measures of SEP. We examined the relationships between these variables and found that lower subjective SEP predicted lower reported health effort. Lower subjective SEP was also associated with higher perceived extrinsic mortality risk, which in turn predicted lower reported health effort. The effect of subjective SEP on reported health effort was completely mediated by perceived extrinsic mortality risk. Our findings indicate that perceived extrinsic mortality risk may be a key factor underlying SEP gradients in motivation to invest in future health.

  18. Prediction impact curve is a new measure integrating intervention effects in the evaluation of risk models.

    PubMed

    Campbell, William; Ganna, Andrea; Ingelsson, Erik; Janssens, A Cecile J W

    2016-01-01

    We propose a new measure of assessing the performance of risk models, the area under the prediction impact curve (auPIC), which quantifies the performance of risk models in terms of their average health impact in the population. Using simulated data, we explain how the prediction impact curve (PIC) estimates the percentage of events prevented when a risk model is used to assign high-risk individuals to an intervention. We apply the PIC to the Atherosclerosis Risk in Communities (ARIC) Study to illustrate its application toward prevention of coronary heart disease. We estimated that if the ARIC cohort received statins at baseline, 5% of events would be prevented when the risk model was evaluated at a cutoff threshold of 20% predicted risk compared to 1% when individuals were assigned to the intervention without the use of a model. By calculating the auPIC, we estimated that an average of 15% of events would be prevented when considering performance across the entire interval. We conclude that the PIC is a clinically meaningful measure for quantifying the expected health impact of risk models that supplements existing measures of model performance. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Configural approaches to temperament assessment: implications for predicting risk of unintentional injury in children.

    PubMed

    Berry, Jack W; Schwebel, David C

    2009-10-01

    This study used two configural approaches to understand how temperament factors (surgency/extraversion, negative affect, and effortful control) might predict child injury risk. In the first approach, clustering procedures were applied to trait dimensions to identify discrete personality prototypes. In the second approach, two- and three-way trait interactions were considered dimensionally in regression models predicting injury outcomes. Injury risk was assessed through four measures: lifetime prevalence of injuries requiring professional medical attention, scores on the Injury Behavior Checklist, and frequency and severity of injuries reported in a 2-week injury diary. In the prototype analysis, three temperament clusters were obtained, which resembled resilient, overcontrolled, and undercontrolled types found in previous research. Undercontrolled children had greater risk of injury than children in the other groups. In the dimensional interaction analyses, an interaction between surgency/extraversion and negative affect tended to predict injury, especially when children lacked capacity for effortful control.

  20. Personality patterns predict the risk of antisocial behavior in Spanish-speaking adolescents.

    PubMed

    Alcázar-Córcoles, Miguel A; Verdejo-García, Antonio; Bouso-Sáiz, José C; Revuelta-Menéndez, Javier; Ramírez-Lira, Ezequiel

    2017-05-01

    There is a renewed interest in incorporating personality variables in criminology theories in order to build models able to integrate personality variables and biological factors with psychosocial and sociocultural factors. The aim of this article is the assessment of personality dimensions that contribute to the prediction of antisocial behavior in adolescents. For this purpose, a sample of adolescents from El Salvador, Mexico, and Spain was obtained. The sample consisted of 1035 participants with a mean age of 16.2. There were 450 adolescents from a forensic population (those who committed a crime) and 585 adolescents from the normal population (no crime committed). All of participants answered personality tests about neuroticism, extraversion, psychoticism, sensation seeking, impulsivity, and violence risk. Principal component analysis of the data identified two independent factors: (i) the disinhibited behavior pattern (PDC), formed by the dimensions of neuroticism, psychoticism, impulsivity and risk of violence; and (ii) the extrovert behavior pattern (PEC), formed by the dimensions of sensation risk and extraversion. Both patterns significantly contributed to the prediction of adolescent antisocial behavior in a logistic regression model which properly classifies a global percentage of 81.9%, 86.8% for non-offense and 72.5% for offense behavior. The classification power of regression equations allows making very satisfactory predictions about adolescent offense commission. Educational level has been classified as a protective factor, while age and gender (male) have been classified as risk factors.

  1. Deep learning architectures for multi-label classification of intelligent health risk prediction.

    PubMed

    Maxwell, Andrew; Li, Runzhi; Yang, Bei; Weng, Heng; Ou, Aihua; Hong, Huixiao; Zhou, Zhaoxian; Gong, Ping; Zhang, Chaoyang

    2017-12-28

    Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases. Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable. Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient's risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging

  2. Risk prediction models of breast cancer: a systematic review of model performances.

    PubMed

    Anothaisintawee, Thunyarat; Teerawattananon, Yot; Wiratkapun, Chollathip; Kasamesup, Vijj; Thakkinstian, Ammarin

    2012-05-01

    The number of risk prediction models has been increasingly developed, for estimating about breast cancer in individual women. However, those model performances are questionable. We therefore have conducted a study with the aim to systematically review previous risk prediction models. The results from this review help to identify the most reliable model and indicate the strengths and weaknesses of each model for guiding future model development. We searched MEDLINE (PubMed) from 1949 and EMBASE (Ovid) from 1974 until October 2010. Observational studies which constructed models using regression methods were selected. Information about model development and performance were extracted. Twenty-five out of 453 studies were eligible. Of these, 18 developed prediction models and 7 validated existing prediction models. Up to 13 variables were included in the models and sample sizes for each study ranged from 550 to 2,404,636. Internal validation was performed in four models, while five models had external validation. Gail and Rosner and Colditz models were the significant models which were subsequently modified by other scholars. Calibration performance of most models was fair to good (expected/observe ratio: 0.87-1.12), but discriminatory accuracy was poor to fair both in internal validation (concordance statistics: 0.53-0.66) and in external validation (concordance statistics: 0.56-0.63). Most models yielded relatively poor discrimination in both internal and external validation. This poor discriminatory accuracy of existing models might be because of a lack of knowledge about risk factors, heterogeneous subtypes of breast cancer, and different distributions of risk factors across populations. In addition the concordance statistic itself is insensitive to measure the improvement of discrimination. Therefore, the new method such as net reclassification index should be considered to evaluate the improvement of the performance of a new develop model.

  3. Environmental Drivers and Predicted Risk of Bacillary Dysentery in Southwest China.

    PubMed

    Zhang, Han; Si, Yali; Wang, Xiaofeng; Gong, Peng

    2017-07-14

    Bacillary dysentery has long been a considerable health problem in southwest China, however, the quantitative relationship between anthropogenic and physical environmental factors and the disease is not fully understand. It is also not clear where exactly the bacillary dysentery risk is potentially high. Based on the result of hotspot analysis, we generated training samples to build a spatial distribution model. Univariate analyses, autocorrelation and multi-collinearity examinations and stepwise selection were then applied to screen the potential causative factors. Multiple logistic regressions were finally applied to quantify the effects of key factors. A bootstrapping strategy was adopted while fitting models. The model was evaluated by area under the receiver operating characteristic curve (AUC), Kappa and independent validation samples. Hotspot counties were mainly mountainous lands in southwest China. Higher risk of bacillary dysentery was found associated with underdeveloped socio-economy, proximity to farmland or water bodies, higher environmental temperature, medium relative humidity and the distribution of the Tibeto-Burman ethnicity. A predictive risk map with high accuracy (88.19%) was generated. The high-risk areas are mainly located in the mountainous lands where the Tibeto-Burman people live, especially in the basins, river valleys or other flat places in the mountains with relatively lower elevation and a warmer climate. In the high-risk areas predicted by this study, improving the economic development, investment in health care and the construction of infrastructures for safe water supply, waste treatment and sewage disposal, and improving health related education could reduce the disease risk.

  4. Environmental Drivers and Predicted Risk of Bacillary Dysentery in Southwest China

    PubMed Central

    Si, Yali; Gong, Peng

    2017-01-01

    Bacillary dysentery has long been a considerable health problem in southwest China, however, the quantitative relationship between anthropogenic and physical environmental factors and the disease is not fully understand. It is also not clear where exactly the bacillary dysentery risk is potentially high. Based on the result of hotspot analysis, we generated training samples to build a spatial distribution model. Univariate analyses, autocorrelation and multi-collinearity examinations and stepwise selection were then applied to screen the potential causative factors. Multiple logistic regressions were finally applied to quantify the effects of key factors. A bootstrapping strategy was adopted while fitting models. The model was evaluated by area under the receiver operating characteristic curve (AUC), Kappa and independent validation samples. Hotspot counties were mainly mountainous lands in southwest China. Higher risk of bacillary dysentery was found associated with underdeveloped socio-economy, proximity to farmland or water bodies, higher environmental temperature, medium relative humidity and the distribution of the Tibeto-Burman ethnicity. A predictive risk map with high accuracy (88.19%) was generated. The high-risk areas are mainly located in the mountainous lands where the Tibeto-Burman people live, especially in the basins, river valleys or other flat places in the mountains with relatively lower elevation and a warmer climate. In the high-risk areas predicted by this study, improving the economic development, investment in health care and the construction of infrastructures for safe water supply, waste treatment and sewage disposal, and improving health related education could reduce the disease risk. PMID:28708077

  5. Risk Factors for Mycobacterium abscessus subsp. bolletii infection after laparoscopic surgery during an outbreak in Brazil.

    PubMed

    Baruque Villar, Gabriela; de Mello Freitas, Felipe Teixeira; Pais Ramos, Jesus; Dias Campos, Carlos Eduardo; de Souza Caldas, Paulo Cesar; Santos Bordalo, Fernanda; Amorim Ramos, Tatyana Costa; do Nascimento Pereira, Vívian; Cordeiro-Santos, Marcelo; Abdalla Santos, Joao Hugo; Coelho Motta, Glauco; Gomes, Suzie Marie; Mendes de Souza, Verena Maria; de Araujo, Wildo Navegantes

    2015-01-01

    OBJECTIVE To identify risk factors related to Mycobacterium abscessus subsp. bolletii infection during an outbreak, associated with laparoscopic surgery and to propose recommendations for preventing new cases. DESIGN A retrospective cohort study. SETTING A private hospital in Manaus, Brazil. PATIENTS A cohort of 222 patients who underwent laparoscopic surgery between July 2009 and August 2010 by a single surgical team. METHODS We collected information about the patients and the surgical procedure using a standard form. We included sex, age, and variables with P≤0.2 in the bivariate analysis in a logistic regression model. Additionally, we reviewed the procedures for reprocessing the laparoscopic surgery equipment, and the strains obtained with culture were identified by molecular methods. RESULTS We recorded 60 (27%) cases of infection. After multivariate analysis, the duration of surgery beyond 1 hour (odds ratio [OR] 2.4; 95% confidence interval [CI] 1.2-4.5), not to have been the first operated patient on a given day (OR, 2.7; 95% CI, 1.4-5.2), and the use of permanent trocar (OR, 2.2; 95% CI, 1.1-4.2) were associated with infection. We observed that the surgical team attempted to sterilize the equipment in glutaraldehyde solution when sanitary authorities had already prohibited it. Eleven strains presented 100% DNA identity with a single strain, known as BRA100 clone. CONCLUSIONS Because contaminated material can act as vehicle for infection, ensuring adequate sterilization processing of video-assisted surgery equipment was crucial to stopping this single clonal outbreak of nonturbeculous mycobacteria in Brazil.

  6. A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance.

    PubMed

    Meads, Catherine; Ahmed, Ikhlaaq; Riley, Richard D

    2012-04-01

    A risk prediction model is a statistical tool for estimating the probability that a currently healthy individual with specific risk factors will develop a condition in the future such as breast cancer. Reliably accurate prediction models can inform future disease burdens, health policies and individual decisions. Breast cancer prediction models containing modifiable risk factors, such as alcohol consumption, BMI or weight, condom use, exogenous hormone use and physical activity, are of particular interest to women who might be considering how to reduce their risk of breast cancer and clinicians developing health policies to reduce population incidence rates. We performed a systematic review to identify and evaluate the performance of prediction models for breast cancer that contain modifiable factors. A protocol was developed and a sensitive search in databases including MEDLINE and EMBASE was conducted in June 2010. Extensive use was made of reference lists. Included were any articles proposing or validating a breast cancer prediction model in a general female population, with no language restrictions. Duplicate data extraction and quality assessment were conducted. Results were summarised qualitatively, and where possible meta-analysis of model performance statistics was undertaken. The systematic review found 17 breast cancer models, each containing a different but often overlapping set of modifiable and other risk factors, combined with an estimated baseline risk that was also often different. Quality of reporting was generally poor, with characteristics of included participants and fitted model results often missing. Only four models received independent validation in external data, most notably the 'Gail 2' model with 12 validations. None of the models demonstrated consistently outstanding ability to accurately discriminate between those who did and those who did not develop breast cancer. For example, random-effects meta-analyses of the performance of the

  7. Lung cancer in never smokers Epidemiology and risk prediction models

    PubMed Central

    McCarthy, William J.; Meza, Rafael; Jeon, Jihyoun; Moolgavkar, Suresh

    2012-01-01

    In this chapter we review the epidemiology of lung cancer incidence and mortality among never smokers/ nonsmokers and describe the never smoker lung cancer risk models used by CISNET modelers. Our review focuses on those influences likely to have measurable population impact on never smoker risk, such as secondhand smoke, even though the individual-level impact may be small. Occupational exposures may also contribute importantly to the population attributable risk of lung cancer. We examine the following risk factors in this chapter: age, environmental tobacco smoke, cooking fumes, ionizing radiation including radon gas, inherited genetic susceptibility, selected occupational exposures, preexisting lung disease, and oncogenic viruses. We also compare the prevalence of never smokers between the three CISNET smoking scenarios and present the corresponding lung cancer mortality estimates among never smokers as predicted by a typical CISNET model. PMID:22882894

  8. Arsenic speciation in fish and shellfish from the North Sea (Southern bight) and Açu Port area (Brazil) and health risks related to seafood consumption.

    PubMed

    Gao, Yue; Baisch, Paulo; Mirlean, Nicolai; Rodrigues da Silva Júnior, Flavio Manoel; Van Larebeke, Nik; Baeyens, Willy; Leermakers, Martine

    2018-01-01

    In North Sea and Port Açu (Brazil) coastal areas, high arsenic (As) concentrations were observed in water, soil and sediments. Therefore, the impact of this contamination on fish and shellfish species bought from local fishermen was studied. Total As was assessed with ICP-OES (Brazil) and ICP-MS (North Sea) after microwave digestion. Toxic As was assessed with liquid chromatography-ICP-MS (Brazil) and hydride generation-atomic fluorescence spectrometry (North Sea). All analytical methods comply with Quality Assurance/Quality Control procedures. Several fish species have average Total As concentrations above 1 μg g-1 wet weight (ww), but the highest concentrations are found in less spotted dogfish, lemon sole and whelks from the North Sea, with respectively 50, 49 and 50 μg g-1 ww. High Total As concentrations correspond to high Toxic As concentrations, except for scallops having increased Toxic As concentrations. Toxic As fractions are highest in scallops (almost 10%) but rarely exceeds 2% in all other species. Liver samples were only analyzed in ray, dogfish and catfish and their Toxic As fractions are between 2 and 4 times higher than in muscle. For a consumption of 150 g of seafood, only 3 samples exceed the provisional total daily intake of 2 μg kg-1 bw, however, cancer risks are non-negligible. Using mean Toxic As concentrations for each of the different fish and shellfish species studied, Lifetime Cancer Risk values at the actual global seafood consumption rate of 54 g day-1 are above 10-4 for whelks, scallops, dogfish, ray and lemon sole. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Development and validation of the ORACLE score to predict risk of osteoporosis.

    PubMed

    Richy, Florent; Deceulaer, Fréderic; Ethgen, Olivier; Bruyère, Olivier; Reginster, Jean-Yves

    2004-11-01

    To develop and validate a composite index, the Osteoporosis Risk Assessment by Composite Linear Estimate (ORACLE), that includes risk factors and ultrasonometric outcomes to screen for osteoporosis. Two cohorts of postmenopausal women aged 45 years and older participated in the development (n = 407) and the validation (n = 202) of ORACLE. Their bone mineral density was determined by dual energy x-ray absorptiometry and quantitative ultrasonometry (QUS), and their historical and clinical risk factors were assessed (January to June 2003). Logistic regression analysis was used to select significant predictors of bone mineral density, whereas receiver operating characteristic (ROC) analysis was used to assess the discriminatory performance of ORACLE. The final logistic regression model retained 4 biometric or historical variables and 1 ultrasonometric outcome. The ROC areas under the curves (AUCs) for ORACLE were 84% for the prediction of osteoporosis and 78% for low bone mass. A sensitivity of 90% corresponded to a specificity of 50% for identification of women at risk of developing osteoporosis. The corresponding positive and negative predictive values were 86% and 54%, respectively, in the development cohort. In the validation cohort, the AUCs for identification of osteoporosis and low bone mass were 81% and 76% for ORACLE, 69% and 64% for QUS T score, 71% and 68% for QUS ultrasonometric bone profile index, and 76% and 75% for Osteoporosis Self-assessment Tool, respectively. ORACLE had the best discriminatory performance in identifying osteoporosis compared with the other approaches (P < .05). ORACLE exhibited the highest discriminatory properties compared with ultrasonography alone or other previously validated risk indices. It may be helpful to enhance the predictive value of QUS.

  10. Improving default risk prediction using Bayesian model uncertainty techniques.

    PubMed

    Kazemi, Reza; Mosleh, Ali

    2012-11-01

    Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from "nominal predictions" due to "upsetting events" such as the 2008 global banking crisis. © 2012 Society for Risk Analysis.

  11. Quantifying and estimating the predictive accuracy for censored time-to-event data with competing risks.

    PubMed

    Wu, Cai; Li, Liang

    2018-05-15

    This paper focuses on quantifying and estimating the predictive accuracy of prognostic models for time-to-event outcomes with competing events. We consider the time-dependent discrimination and calibration metrics, including the receiver operating characteristics curve and the Brier score, in the context of competing risks. To address censoring, we propose a unified nonparametric estimation framework for both discrimination and calibration measures, by weighting the censored subjects with the conditional probability of the event of interest given the observed data. The proposed method can be extended to time-dependent predictive accuracy metrics constructed from a general class of loss functions. We apply the methodology to a data set from the African American Study of Kidney Disease and Hypertension to evaluate the predictive accuracy of a prognostic risk score in predicting end-stage renal disease, accounting for the competing risk of pre-end-stage renal disease death, and evaluate its numerical performance in extensive simulation studies. Copyright © 2018 John Wiley & Sons, Ltd.

  12. Potential risks of the residue from Samarco's mine dam burst (Bento Rodrigues, Brazil).

    PubMed

    Segura, Fabiana Roberta; Nunes, Emilene Arusievicz; Paniz, Fernanda Pollo; Paulelli, Ana Carolina Cavalheiro; Rodrigues, Gabriela Braga; Braga, Gilberto Úbida Leite; Dos Reis Pedreira Filho, Walter; Barbosa, Fernando; Cerchiaro, Giselle; Silva, Fábio Ferreira; Batista, Bruno Lemos

    2016-11-01

    On November 5th, 2015, Samarco's iron mine dam - called Fundão - spilled 50-60 million m 3 of mud into Gualaxo do Norte, a river that belongs to Rio Doce Basin. Approximately 15 km 2 were flooded along the rivers Gualaxo do Norte, Carmo and Doce, reaching the Atlantic Ocean on November 22nd, 2015. Six days after, our group collected mud, soil and water samples in Bento Rodrigues (Minas Gerais, Brazil), which was the first impacted area. Overall, the results, water samples - potable and surface water from river - presented chemical elements concentration according to Brazilian environmental legislations, except silver concentration in surface water that ranged from 1.5 to 1087 μg L -1 . In addition, water mud-containing presented Fe and Mn concentrations approximately 4-fold higher than the maximum limit for water bodies quality assessment, according to Brazilian laws. Mud particle size ranged from 1 to 200 μm. SEM-EDS spot provided us some semi quantitative data. Leaching/extraction tests suggested that Ba, Pb, As, Sr, Fe, Mn and Al have high potential mobilization from mud to water. Low microbial diversity in mud samples compared to background soil samples. Toxicological bioassays (HepG2 and Allium cepa) indicated potential risks of cytotoxicity and DNA damage in mud and soil samples used in both assays. The present study provides preliminary information aiming to collaborate to the development of future works for monitoring and risk assessment. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Prediction of Febrile Neutropenia after Chemotherapy Based on Pretreatment Risk Factors among Cancer Patients

    PubMed Central

    Aagaard, Theis; Roen, Ashley; Daugaard, Gedske; Brown, Peter; Sengeløv, Henrik; Mocroft, Amanda; Lundgren, Jens; Helleberg, Marie

    2017-01-01

    Abstract Background Febrile neutropenia (FN) is a common complication to chemotherapy associated with a high burden of morbidity and mortality. Reliable prediction of individual risk based on pretreatment risk factors allows for stratification of preventive interventions. We aimed to develop such a risk stratification model to predict FN in the 30 days after initiation of chemotherapy. Methods We included consecutive treatment-naïve patients with solid cancers and diffuse large B-cell lymphomas at Copenhagen University Hospital, 2010–2015. Data were obtained from the PERSIMUNE repository of electronic health records. FN was defined as neutrophils ≤0.5 × 10E9/L ​at the time of either a blood culture sample or death. Time from initiation of chemotherapy to FN was analyzed using Fine-Gray models with death as a competing event. Risk factors investigated were: age, sex, body surface area, haemoglobin, albumin, neutrophil-to-lymphocyte ratio, Charlson Comorbidity Index (CCI) and chemotherapy drugs. Parameter estimates were scaled and summed to create the risk score. The scores were grouped into four: low, intermediate, high and very high risk. Results Among 8,585 patients, 467 experienced FN, incidence rate/30 person-days 0.05 (95% CI, 0.05–0.06). Age (1 point if > 65 years), albumin (1 point if < 39 g/L), CCI (1 point if > 2) and chemotherapy (range -5 to 6 points/drug) predicted FN. Median score at inclusion was 2 points (range –5 to 9). The cumulative incidence and the incidence rates and hazard ratios of FN are shown in Figure 1 and Table 1, respectively. Conclusion We developed a risk score to predict FN the first month after initiation of chemotherapy. The score is easy to use and provides good differentiation of risk groups; the score needs independent validation before routine use. Disclosures All authors: No reported disclosures.

  14. Maternal Alcohol Consumption during Pregnancy and Early Age Leukemia Risk in Brazil.

    PubMed

    Ferreira, Jeniffer Dantas; Couto, Arnaldo Cézar; Emerenciano, Mariana; Pombo-de-Oliveira, Maria S; Koifman, Sergio

    2015-01-01

    Objectives. To investigate the association between the maternal alcohol consumption during pregnancy and early age leukemia (EAL) in offspring. Methods. Datasets were analyzed from a case-control study carried out in Brazil during 1999-2007. Data were obtained by maternal interviews using a standardized questionnaire. The present study included 675 children (193 acute lymphoid leukemia (ALL), 59 acute myeloid leukemia (AML), and 423 controls). Unconditional logistic regression was performed, and adjusted odds ratios (adj. OR) on the association between alcohol consumption and EAL were ascertained. Results. Alcohol consumption was reported by 43% of ALL and 39% of AML case mothers and 35.5% of controls'. Beer consumption before and during pregnancy was associated with ALL in crude analysis (OR = 1.54, 95% CI, 1.08-2.19), although in adjusted analysis no statistical significance was found. For weekly intake of ≤1 glass (adj. OR = 1.30, 95% CI, 0.71-2.36) and ≥1 glass/week (adj. OR = 1.47, 95% CI, 0.88-2.46) a potential dose-response was observed (P trend < 0.03). Conclusion. This study failed to support the hypothesis of an increased risk of EAL associated with maternal alcohol intake during pregnancy, neither with the interaction with tobacco nor with alcohol consumption.

  15. Prediction of 30-year risk for cardiovascular mortality by fitness and risk factor levels: the Cooper Center Longitudinal Study.

    PubMed

    Wickramasinghe, Chanaka D; Ayers, Colby R; Das, Sandeep; de Lemos, James A; Willis, Benjamin L; Berry, Jarett D

    2014-07-01

    Fitness and traditional risk factors have well-known associations with cardiovascular disease (CVD) death in both short-term (10 years) and across the remaining lifespan. However, currently available short-term and long-term risk prediction tools do not incorporate measured fitness. We included 16 533 participants from the Cooper Center Longitudinal Study (CCLS) without prior CVD. Fitness was measured using the Balke protocol. Sex-specific fitness levels were derived from the Balke treadmill times and categorized into low, intermediate, and high fit according to age- and sex-specific treadmill times. Sex-specific 30-year risk estimates for CVD death adjusted for competing risk of non-CVD death were estimated using the cause-specific hazards model and included age, body mass index, systolic blood pressure, fitness, diabetes mellitus, total cholesterol, and smoking. During a median follow-up period of 28 years, there were 1123 CVD deaths. The 30-year risk estimates for CVD mortality derived from the cause-specific hazards model demonstrated overall good calibration (Nam-D'Agostino χ(2) [men, P=0.286; women, P=0.664] and discrimination (c statistic; men, 0.81 [0.80-0.82] and women, 0.86 [0.82-0.91]). Across all risk factor strata, the presence of low fitness was associated with a greater 30-year risk for CVD death. Fitness represents an important additional covariate in 30-year risk prediction functions that may serve as a useful tool in clinical practice. © 2014 American Heart Association, Inc.

  16. Predicting Risk of Type 2 Diabetes Mellitus with Genetic Risk Models on the Basis of Established Genome-wide Association Markers: A Systematic Review

    PubMed Central

    Bao, Wei; Hu, Frank B.; Rong, Shuang; Rong, Ying; Bowers, Katherine; Schisterman, Enrique F.; Liu, Liegang; Zhang, Cuilin

    2013-01-01

    This study aimed to evaluate the predictive performance of genetic risk models based on risk loci identified and/or confirmed in genome-wide association studies for type 2 diabetes mellitus. A systematic literature search was conducted in the PubMed/MEDLINE and EMBASE databases through April 13, 2012, and published data relevant to the prediction of type 2 diabetes based on genome-wide association marker–based risk models (GRMs) were included. Of the 1,234 potentially relevant articles, 21 articles representing 23 studies were eligible for inclusion. The median area under the receiver operating characteristic curve (AUC) among eligible studies was 0.60 (range, 0.55–0.68), which did not differ appreciably by study design, sample size, participants’ race/ethnicity, or the number of genetic markers included in the GRMs. In addition, the AUCs for type 2 diabetes did not improve appreciably with the addition of genetic markers into conventional risk factor–based models (median AUC, 0.79 (range, 0.63–0.91) vs. median AUC, 0.78 (range, 0.63–0.90), respectively). A limited number of included studies used reclassification measures and yielded inconsistent results. In conclusion, GRMs showed a low predictive performance for risk of type 2 diabetes, irrespective of study design, participants’ race/ethnicity, and the number of genetic markers included. Moreover, the addition of genome-wide association markers into conventional risk models produced little improvement in predictive performance. PMID:24008910

  17. Spatial distribution of vehicle emission inventories in the Federal District, Brazil

    NASA Astrophysics Data System (ADS)

    Réquia, Weeberb João; Koutrakis, Petros; Roig, Henrique Llacer

    2015-07-01

    Air pollution poses an important public health risk, especially in large urban areas. Information about the spatial distribution of air pollutants can be used as a tool for developing public policies to reduce source emissions. Air pollution monitoring networks provide information about pollutant concentrations; however, they are not available in every urban area. Among the 5570 cities in Brazil, for example, only 1.7% of them have air pollution monitoring networks. In this study we assess vehicle emissions for main traffic routes of the Federal District (state of Brazil) and characterize their spatial patterns. Toward this end, we used a bottom-up method to predict emissions and to characterize their spatial patterns using Global Moran's (Spatial autocorrelation analysis) and Getis-Ord General G (High/Low cluster analysis). Our findings suggested that light duty vehicles are primarily responsible for the vehicular emissions of CO (68.9%), CH4 (93.6%), and CO2 (57.9%), whereas heavy duty vehicles are primarily responsible for the vehicular emissions of NMHC (92.9%), NOx (90.7%), and PM (97.4%). Furthermore, CO2 is the pollutant with the highest emissions, over 30 million tons/year. In the spatial autocorrelation analysis was identified cluster (p < 0.01) for all types of vehicles and for all pollutants. However, we identified high cluster only for the light vehicles.

  18. Perspectives on invasive amphibians in Brazil

    PubMed Central

    Forti, Lucas Rodriguez; Becker, C. Guilherme; Tacioli, Leandro; Pereira, Vânia Rosa; Santos, André Cid F. A.; Oliveira, Igor; Haddad, Célio F. B.; Toledo, Luís Felipe

    2017-01-01

    Introduced species have the potential to become invasive and jeopardize entire ecosystems. The success of species establishing viable populations outside their original extent depends primarily on favorable climatic conditions in the invasive ranges. Species distribution modeling (SDM) can thus be used to estimate potential habitat suitability for populations of invasive species. Here we review the status of six amphibian species with invasive populations in Brazil (four domestic species and two imported species). We (i) modeled the current habitat suitability and future potential distribution of these six focal species, (ii) reported on the disease status of Eleutherodactylus johnstonei and Phyllodytes luteolus, and (iii) quantified the acoustic overlap of P. luteolus and Leptodactylus labyrinthicus with three co-occurring native species. Our models indicated that all six invasive species could potentially expand their ranges in Brazil within the next few decades. In addition, our SDMs predicted important expansions in available habitat for 2 out of 6 invasive species under future (2100) climatic conditions. We detected high acoustic niche overlap between invasive and native amphibian species, underscoring that acoustic interference might reduce mating success in local frogs. Despite the American bullfrog Lithobates catesbeianus being recognized as a potential reservoir for the frog-killing fungus Batrachochytrium dendrobatidis (Bd) in Brazil, we did not detect Bd in the recently introduced population of E. johnstonei and P. luteolus in the State of São Paulo. We emphasize that the number of invasive amphibian species in Brazil is increasing exponentially, highlighting the urgent need to monitor and control these populations and decrease potential impacts on the locally biodiverse wildlife. PMID:28938024

  19. Perspectives on invasive amphibians in Brazil.

    PubMed

    Forti, Lucas Rodriguez; Becker, C Guilherme; Tacioli, Leandro; Pereira, Vânia Rosa; Santos, André Cid F A; Oliveira, Igor; Haddad, Célio F B; Toledo, Luís Felipe

    2017-01-01

    Introduced species have the potential to become invasive and jeopardize entire ecosystems. The success of species establishing viable populations outside their original extent depends primarily on favorable climatic conditions in the invasive ranges. Species distribution modeling (SDM) can thus be used to estimate potential habitat suitability for populations of invasive species. Here we review the status of six amphibian species with invasive populations in Brazil (four domestic species and two imported species). We (i) modeled the current habitat suitability and future potential distribution of these six focal species, (ii) reported on the disease status of Eleutherodactylus johnstonei and Phyllodytes luteolus, and (iii) quantified the acoustic overlap of P. luteolus and Leptodactylus labyrinthicus with three co-occurring native species. Our models indicated that all six invasive species could potentially expand their ranges in Brazil within the next few decades. In addition, our SDMs predicted important expansions in available habitat for 2 out of 6 invasive species under future (2100) climatic conditions. We detected high acoustic niche overlap between invasive and native amphibian species, underscoring that acoustic interference might reduce mating success in local frogs. Despite the American bullfrog Lithobates catesbeianus being recognized as a potential reservoir for the frog-killing fungus Batrachochytrium dendrobatidis (Bd) in Brazil, we did not detect Bd in the recently introduced population of E. johnstonei and P. luteolus in the State of São Paulo. We emphasize that the number of invasive amphibian species in Brazil is increasing exponentially, highlighting the urgent need to monitor and control these populations and decrease potential impacts on the locally biodiverse wildlife.

  20. Risk and other factors associated with toxoplasmosis and toxocariasis in pregnant women from southern Brazil.

    PubMed

    Santos, P C; Telmo, P L; Lehmann, L M; Mattos, G T; Klafke, G B; Lorenzi, C; Hirsch, C; Lemos, L; Berne, M E A; Gonçalves, C V; Scaini, C J

    2017-09-01

    Toxoplasmosis causes complications during pregnancy that have serious effects on fetal development. Thus far, toxocariasis has been reported to spread only via vertical transmission. Nonetheless, the population of pregnant women is also exposed to this infection. Co-infection with both Toxoplasma gondii and Toxocara spp. has been reported in children, but there are no reports of co-infection in the population of pregnant women. The aim of this study was to determine the prevalence of co-infection with T. gondii and Toxocara spp. in pregnant women at a university hospital in southern Brazil, and to identify the risk factors associated with infection by both parasites. Two hundred pregnant women were tested for the presence of anti-T. gondii and anti-Toxocara spp. antibodies and were asked to complete an epidemiological questionnaire. In this study, the co-infection rate observed in the total population of pregnant women was 8%. In addition, women with a positive result for a serology test for Toxocara spp. were at increased risk of infection by T. gondii (P = 0.019). Co-infection with both parasites in pregnant women was associated with low birth weights in neonates. The similar modes of transmission of both parasites could explain the co-infection. Only a few previous studies have investigated this phenomenon. The findings of the present study emphasize the importance of serological diagnosis during prenatal care and further research in this area to identify risk factors associated with this co-infection, and the possible implications of this co-infection during pregnancy and on the health of newborns.

  1. Predicting the Risk of Clostridium difficile Infection upon Admission: A Score to Identify Patients for Antimicrobial Stewardship Efforts.

    PubMed

    Kuntz, Jennifer L; Smith, David H; Petrik, Amanda F; Yang, Xiuhai; Thorp, Micah L; Barton, Tracy; Barton, Karen; Labreche, Matthew; Spindel, Steven J; Johnson, Eric S

    2016-01-01

    Increasing morbidity and health care costs related to Clostridium difficile infection (CDI) have heightened interest in methods to identify patients who would most benefit from interventions to mitigate the likelihood of CDI. To develop a risk score that can be calculated upon hospital admission and used by antimicrobial stewards, including pharmacists and clinicians, to identify patients at risk for CDI who would benefit from enhanced antibiotic review and patient education. We assembled a cohort of Kaiser Permanente Northwest patients with a hospital admission from July 1, 2005, through December 30, 2012, and identified CDI in the six months following hospital admission. Using Cox regression, we constructed a score to identify patients at high risk for CDI on the basis of preadmission characteristics. We calculated and plotted the observed six-month CDI risk for each decile of predicted risk. We identified 721 CDIs following 54,186 hospital admissions-a 6-month incidence of 13.3 CDIs/1000 patient admissions. Patients with the highest predicted risk of CDI had an observed incidence of 53 CDIs/1000 patient admissions. The score differentiated between patients who do and do not develop CDI, with values for the extended C-statistic of 0.75. Predicted risk for CDI agreed closely with observed risk. Our risk score accurately predicted six-month risk for CDI using preadmission characteristics. Accurate predictions among the highest-risk patient subgroups allow for the identification of patients who could be targeted for and who would likely benefit from review of inpatient antibiotic use or enhanced educational efforts at the time of discharge planning.

  2. Using global maps to predict the risk of dengue in Europe.

    PubMed

    Rogers, David J; Suk, Jonathan E; Semenza, Jan C

    2014-01-01

    This article attempts to quantify the risk to Europe of dengue, following the arrival and spread there of one of dengue's vector species Aedes (Stegomyia) albopictus. A global risk map for dengue is presented, based on a global database of the occurrence of this disease, derived from electronic literature searches. Remotely sensed satellite data (from NASA's MODIS series), interpolated meteorological data, predicted distribution maps of dengue's two main vector species, Aedes aegypti and Aedes albopictus, a digital elevation surface and human population density data were all used as potential predictor variables in a non-linear discriminant analysis modelling framework. One hundred bootstrap models were produced by randomly sub-sampling three different training sets for dengue fever, severe dengue (i.e. dengue haemorrhagic fever, DHF) and all-dengue, and output predictions were averaged to produce a single global risk map for each type of dengue. This paper concentrates on the all-dengue models. Key predictor variables were various thermal data layers, including both day- and night-time Land Surface Temperature, human population density, and a variety of rainfall variables. The relative importance of each may be shown visually using rainbow files and quantitatively using a ranking system. Vegetation Index variables (a common proxy for humidity or saturation deficit) were rarely chosen in the models. The kappa index of agreement indicated an excellent (dengue haemorrhagic fever, Cohen's kappa=0.79 ± 0.028, AUC=0.96 ± 0.007) or good fit of the top ten models in each series to the data (Cohen's kappa=0.73 ± 0.018, AUC=0.94 ± 0.007 for dengue fever and 0.74 ± 0.017, AUC=0.95 ± 0.005 for all dengue). The global risk map predicts widespread dengue risk in SE Asia and India, in Central America and parts of coastal South America, but in relatively few regions of Africa. In many cases these are less extensive predictions than those of other published dengue risk maps

  3. Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study

    PubMed Central

    ten Haaf, Kevin; Tammemägi, Martin C.; Han, Summer S.; Kong, Chung Yin; Plevritis, Sylvia K.; de Koning, Harry J.; Steyerberg, Ewout W.

    2017-01-01

    Background Selection of candidates for lung cancer screening based on individual risk has been proposed as an alternative to criteria based on age and cumulative smoking exposure (pack-years). Nine previously established risk models were assessed for their ability to identify those most likely to develop or die from lung cancer. All models considered age and various aspects of smoking exposure (smoking status, smoking duration, cigarettes per day, pack-years smoked, time since smoking cessation) as risk predictors. In addition, some models considered factors such as gender, race, ethnicity, education, body mass index, chronic obstructive pulmonary disease, emphysema, personal history of cancer, personal history of pneumonia, and family history of lung cancer. Methods and findings Retrospective analyses were performed on 53,452 National Lung Screening Trial (NLST) participants (1,925 lung cancer cases and 884 lung cancer deaths) and 80,672 Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) ever-smoking participants (1,463 lung cancer cases and 915 lung cancer deaths). Six-year lung cancer incidence and mortality risk predictions were assessed for (1) calibration (graphically) by comparing the agreement between the predicted and the observed risks, (2) discrimination (area under the receiver operating characteristic curve [AUC]) between individuals with and without lung cancer (death), and (3) clinical usefulness (net benefit in decision curve analysis) by identifying risk thresholds at which applying risk-based eligibility would improve lung cancer screening efficacy. To further assess performance, risk model sensitivities and specificities in the PLCO were compared to those based on the NLST eligibility criteria. Calibration was satisfactory, but discrimination ranged widely (AUCs from 0.61 to 0.81). The models outperformed the NLST eligibility criteria over a substantial range of risk thresholds in decision curve analysis, with a higher

  4. Occurrence and risk assessment of population exposed to deoxynivalenol in foods derived from wheat flour in Brazil.

    PubMed

    Silva, Milena Veronezi; Pante, Giseli Cristina; Romoli, Jéssica Cristina Zoratto; de Souza, Alexandra Perdigão Maia; Rocha, Gustavo Henrique Oliveira da; Ferreira, Flavio Dias; Feijó, Adriane Lettnin Roll; Moscardi, Salesia Maria Prodócimo; de Paula, Karina Ruaro; Bando, Erika; Nerilo, Samuel Botião; Machinski, Miguel

    2018-03-01

    Deoxynivalenol (DON) is the most important of the trichothecenes in terms of amounts and occurrence in wheat. This compound was shown to be associated with a glomerulonephropathy involving an increase of immunoglobulin A in humans. This study assessed the occurrence of DON in wheat flour and the exposure of Brazilian teenagers, adults and elderly to this mycotoxin due to intake of wheat flour-based products. DON extraction in wheat flour was carried out by solid phase extraction and the quantification was performed by ultra-high proficiency liquid chromatography with diode-array detection. A total of 77.9% of all samples were positive for DON, with concentrations ranging from 73.50 to 2794.63 µg kg -1 . The intake was calculated for the average and 90th percentile of the contamination levels of DON in foods based-wheat for teenagers, adults and elderly in Brazil, and compared with the provisional maximum tolerable daily intakes (PMTDI). Females of all age groups were exposed to DON at higher levels when compared to males in regard of consumption of breads and pastas. Teenagers were the main consumers of foods derived from wheat flour, with maximum probable daily intakes of 1.28 and 1.20 µg kg -1 b.w. day -1 for females and males, respectively. This population is at an increased risk of exposure to DON due to consumption of wheat flour-based foods in Brazil.

  5. Validation of a new mortality risk prediction model for people 65 years and older in northwest Russia: The Crystal risk score.

    PubMed

    Turusheva, Anna; Frolova, Elena; Bert, Vaes; Hegendoerfer, Eralda; Degryse, Jean-Marie

    2017-07-01

    Prediction models help to make decisions about further management in clinical practice. This study aims to develop a mortality risk score based on previously identified risk predictors and to perform internal and external validations. In a population-based prospective cohort study of 611 community-dwelling individuals aged 65+ in St. Petersburg (Russia), all-cause mortality risks over 2.5 years follow-up were determined based on the results obtained from anthropometry, medical history, physical performance tests, spirometry and laboratory tests. C-statistic, risk reclassification analysis, integrated discrimination improvement analysis, decision curves analysis, internal validation and external validation were performed. Older adults were at higher risk for mortality [HR (95%CI)=4.54 (3.73-5.52)] when two or more of the following components were present: poor physical performance, low muscle mass, poor lung function, and anemia. If anemia was combined with high C-reactive protein (CRP) and high B-type natriuretic peptide (BNP) was added the HR (95%CI) was slightly higher (5.81 (4.73-7.14)) even after adjusting for age, sex and comorbidities. Our models were validated in an external population of adults 80+. The extended model had a better predictive capacity for cardiovascular mortality [HR (95%CI)=5.05 (2.23-11.44)] compared to the baseline model [HR (95%CI)=2.17 (1.18-4.00)] in the external population. We developed and validated a new risk prediction score that may be used to identify older adults at higher risk for mortality in Russia. Additional studies need to determine which targeted interventions improve the outcomes of these at-risk individuals. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Applying Social Psychological Models to Predicting HIV-Related Sexual Risk Behaviors Among African Americans

    PubMed Central

    Cochran, Susan D.; Mays, Vickie M.

    2011-01-01

    Existing models of attitude-behavior relationships, including the Health Belief Model, the Theory of Reasoned Action, and the Self-Efficacy Theory, are increasingly being used by psychologists to predict human immunodeficiency virus (HIV)-related risk behaviors. The authors briefly highlight some of the difficulties that might arise in applying these models to predicting the risk behaviors of African Americans. These social psychological models tend to emphasize the importance of individualistic, direct control of behavioral choices and deemphasize factors, such as racism and poverty, particularly relevant to that segment of the African American population most at risk for HIV infection. Applications of these models without taking into account the unique issues associated with behavioral choices within the African American community may fail to capture the relevant determinants of risk behaviors. PMID:23529205

  7. [Severe intimate partner violence risk prediction scale-revised].

    PubMed

    Echeburúa, Enrique; Amor, Pedro Javier; Loinaz, Ismael; de Corral, Paz

    2010-11-01

    The aim of this study was to describe the psychometric properties of the Severe Intimate Partner Violence Risk Prediction Scale and to revise it in order to ponderate the 20 items according to their discriminant capacity and to solve the missing item problem. The sample for this study consisted of 450 male batterers who were reported to the police station. The victims were classified as high-risk (18.2%), moderate-risk (45.8%) and low-risk (36%), depending on the cutoff scores in the original scale. Internal consistency (Cronbach's alpha=.72) and interrater reliability (r=.73) were acceptable. The point biserial correlation coefficient between each item and the corrected total score of the 20-item scale was calculated to determine the most discriminative items, which were associated with the context of intimate partner violence in the last month, with the male batterer's profile and with the victim's vulnerability. A revised scale (EPV-R) with new cutoff scores and indications on how to deal with the missing items were proposed in accordance with these results. This easy-to-use tool appears to be suitable to the requirements of criminal justice professionals and is intended for use in safety planning. Implications of these results for further research are discussed.

  8. Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach.

    PubMed

    Fernandes, G S; Bhattacharya, A; McWilliams, D F; Ingham, S L; Doherty, M; Zhang, W

    2017-03-20

    Twenty-five percent of the British population over the age of 50 years experiences knee pain. Knee pain can limit physical ability and cause distress and bears significant socioeconomic costs. The objectives of this study were to develop and validate the first risk prediction model for incident knee pain in the Nottingham community and validate this internally within the Nottingham cohort and externally within the Osteoarthritis Initiative (OAI) cohort. A total of 1822 participants from the Nottingham community who were at risk for knee pain were followed for 12 years. Of this cohort, two-thirds (n = 1203) were used to develop the risk prediction model, and one-third (n = 619) were used to validate the model. Incident knee pain was defined as pain on most days for at least 1 month in the past 12 months. Predictors were age, sex, body mass index, pain elsewhere, prior knee injury and knee alignment. A Bayesian logistic regression model was used to determine the probability of an OR >1. The Hosmer-Lemeshow χ 2 statistic (HLS) was used for calibration, and ROC curve analysis was used for discrimination. The OAI cohort from the United States was also used to examine the performance of the model. A risk prediction model for knee pain incidence was developed using a Bayesian approach. The model had good calibration, with an HLS of 7.17 (p = 0.52) and moderate discriminative ability (ROC 0.70) in the community. Individual scenarios are given using the model. However, the model had poor calibration (HLS 5866.28, p < 0.01) and poor discriminative ability (ROC 0.54) in the OAI cohort. To our knowledge, this is the first risk prediction model for knee pain, regardless of underlying structural changes of knee osteoarthritis, in the community using a Bayesian modelling approach. The model appears to work well in a community-based population but not in individuals with a higher risk for knee osteoarthritis, and it may provide a convenient tool for use in

  9. Developing prediction equations and a mobile phone application to identify infants at risk of obesity.

    PubMed

    Santorelli, Gillian; Petherick, Emily S; Wright, John; Wilson, Brad; Samiei, Haider; Cameron, Noël; Johnson, William

    2013-01-01

    Advancements in knowledge of obesity aetiology and mobile phone technology have created the opportunity to develop an electronic tool to predict an infant's risk of childhood obesity. The study aims were to develop and validate equations for the prediction of childhood obesity and integrate them into a mobile phone application (App). Anthropometry and childhood obesity risk data were obtained for 1868 UK-born White or South Asian infants in the Born in Bradford cohort. Logistic regression was used to develop prediction equations (at 6 ± 1.5, 9 ± 1.5 and 12 ± 1.5 months) for risk of childhood obesity (BMI at 2 years >91(st) centile and weight gain from 0-2 years >1 centile band) incorporating sex, birth weight, and weight gain as predictors. The discrimination accuracy of the equations was assessed by the area under the curve (AUC); internal validity by comparing area under the curve to those obtained in bootstrapped samples; and external validity by applying the equations to an external sample. An App was built to incorporate six final equations (two at each age, one of which included maternal BMI). The equations had good discrimination (AUCs 86-91%), with the addition of maternal BMI marginally improving prediction. The AUCs in the bootstrapped and external validation samples were similar to those obtained in the development sample. The App is user-friendly, requires a minimum amount of information, and provides a risk assessment of low, medium, or high accompanied by advice and website links to government recommendations. Prediction equations for risk of childhood obesity have been developed and incorporated into a novel App, thereby providing proof of concept that childhood obesity prediction research can be integrated with advancements in technology.

  10. Automated identification and predictive tools to help identify high-risk heart failure patients: pilot evaluation.

    PubMed

    Evans, R Scott; Benuzillo, Jose; Horne, Benjamin D; Lloyd, James F; Bradshaw, Alejandra; Budge, Deborah; Rasmusson, Kismet D; Roberts, Colleen; Buckway, Jason; Geer, Norma; Garrett, Teresa; Lappé, Donald L

    2016-09-01

    Develop and evaluate an automated identification and predictive risk report for hospitalized heart failure (HF) patients. Dictated free-text reports from the previous 24 h were analyzed each day with natural language processing (NLP), to help improve the early identification of hospitalized patients with HF. A second application that uses an Intermountain Healthcare-developed predictive score to determine each HF patient's risk for 30-day hospital readmission and 30-day mortality was also developed. That information was included in an identification and predictive risk report, which was evaluated at a 354-bed hospital that treats high-risk HF patients. The addition of NLP-identified HF patients increased the identification score's sensitivity from 82.6% to 95.3% and its specificity from 82.7% to 97.5%, and the model's positive predictive value is 97.45%. Daily multidisciplinary discharge planning meetings are now based on the information provided by the HF identification and predictive report, and clinician's review of potential HF admissions takes less time compared to the previously used manual methodology (10 vs 40 min). An evaluation of the use of the HF predictive report identified a significant reduction in 30-day mortality and a significant increase in patient discharges to home care instead of to a specialized nursing facility. Using clinical decision support to help identify HF patients and automatically calculating their 30-day all-cause readmission and 30-day mortality risks, coupled with a multidisciplinary care process pathway, was found to be an effective process to improve HF patient identification, significantly reduce 30-day mortality, and significantly increase patient discharges to home care. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  11. An integrated approach to evaluating alternative risk prediction strategies: a case study comparing alternative approaches for preventing invasive fungal disease.

    PubMed

    Sadique, Z; Grieve, R; Harrison, D A; Jit, M; Allen, E; Rowan, K M

    2013-12-01

    This article proposes an integrated approach to the development, validation, and evaluation of new risk prediction models illustrated with the Fungal Infection Risk Evaluation study, which developed risk models to identify non-neutropenic, critically ill adult patients at high risk of invasive fungal disease (IFD). Our decision-analytical model compared alternative strategies for preventing IFD at up to three clinical decision time points (critical care admission, after 24 hours, and end of day 3), followed with antifungal prophylaxis for those judged "high" risk versus "no formal risk assessment." We developed prognostic models to predict the risk of IFD before critical care unit discharge, with data from 35,455 admissions to 70 UK adult, critical care units, and validated the models externally. The decision model was populated with positive predictive values and negative predictive values from the best-fitting risk models. We projected lifetime cost-effectiveness and expected value of partial perfect information for groups of parameters. The risk prediction models performed well in internal and external validation. Risk assessment and prophylaxis at the end of day 3 was the most cost-effective strategy at the 2% and 1% risk threshold. Risk assessment at each time point was the most cost-effective strategy at a 0.5% risk threshold. Expected values of partial perfect information were high for positive predictive values or negative predictive values (£11 million-£13 million) and quality-adjusted life-years (£11 million). It is cost-effective to formally assess the risk of IFD for non-neutropenic, critically ill adult patients. This integrated approach to developing and evaluating risk models is useful for informing clinical practice and future research investment. © 2013 International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Published by International Society for Pharmacoeconomics and Outcomes Research (ISPOR) All rights reserved.

  12. Multimethod Prediction of Physical Parent-Child Aggression Risk in Expectant Mothers and Fathers with Social Information Processing Theory

    PubMed Central

    Rodriguez, Christina M.; Smith, Tamika L.; Silvia, Paul J.

    2015-01-01

    The Social Information Processing (SIP) model postulates that parents undergo a series of stages in implementing physical discipline that can escalate into physical child abuse. The current study utilized a multimethod approach to investigate whether SIP factors can predict risk of parent-child aggression (PCA) in a diverse sample of expectant mothers and fathers. SIP factors of PCA attitudes, negative child attributions, reactivity, and empathy were considered as potential predictors of PCA risk; additionally, analyses considered whether personal history of PCA predicted participants’ own PCA risk through its influence on their attitudes and attributions. Findings indicate that, for both mothers and fathers, history influenced attitudes but not attributions in predicting PCA risk, and attitudes and attributions predicted PCA risk; empathy and reactivity predicted negative child attributions for expectant mothers, but only reactivity significantly predicted attributions for expectant fathers. Path models for expectant mothers and fathers were remarkably similar. Overall, the findings provide support for major aspects of the SIP model. Continued work is needed in studying the progression of these factors across time for both mothers and fathers as well as the inclusion of other relevant ecological factors to the SIP model. PMID:26631420

  13. Postpartum posttraumatic stress disorder in a fetal high-risk maternity hospital in the city of Rio de Janeiro, Brazil.

    PubMed

    Henriques, Tatiana; Moraes, Claudia Leite de; Reichenheim, Michael E; Azevedo, Gustavo Lobato de; Coutinho, Evandro Silva Freire; Figueira, Ivan Luiz de Vasconcellos

    2015-12-01

    The objectives of this study were to estimate the prevalence of postpartum posttraumatic stress disorder (PTSD) in a maternity hospital for fetal high-risk pregnancies and to identify vulnerable subgroups. This was a cross-sectional study at a fetal high-risk maternity hospital in Rio de Janeiro, Brazil, with a sample of 456 women who had given birth at this hospital. The Trauma History Questionnaire and Post-Traumatic Stress Disorder Checklist were used to screen for lifetime traumatic events and PTSD symptoms, respectively. Overall prevalence of PTSD was 9.4%. Higher PTSD prevalence was associated with three or more births, a newborn with a 1-minute Apgar score of seven or less, history of mental disorder prior to or during the index pregnancy, postpartum depression, physical or psychological intimate partner violence during the pregnancy, a history of unwanted sexual experience, and lifetime exposure to five or more traumas. Rapid diagnosis and treatment of PTSD are essential to improve the mother's quality of life and the infant's health.

  14. Predicting HIV/STD risk level and substance use disorders among incarcerated adolescents.

    PubMed

    Rowe, Cynthia L; Wang, Wei; Greenbaum, Paul; Liddle, Howard A

    2008-12-01

    Incarcerated adolescents are among the most vulnerable groups for STD infection, and substance abuse is prevalent in over half of this population. Substance abuse and HIV/STD-associated risk behaviors are closely linked among juvenile justice-involved youth, but it is unclear whether common antecedents explain these different problems. The current study examined predictors of HIV/STD risk level and substance use disorders, and investigated whether family variables added unique predictive variance for these problems among incarcerated youth. The sample included 154 substance-involved youth ages 13 to 17 recruited in detention facilities in Miami and Tampa, FL and was primarily male (82%) and African-American (58%). Using a comprehensive assessment strategy with data obtained from youth report, parent report, and laboratory confirmed STD testing, the results show that delinquency is a consistent predictor of both HIV/STD risk level and substance use disorders, and also that substance use directly predicts HIV/STD risk level among incarcerated adolescents. Consistent with previous research, family conflict is an important predictor of substance use disorders even after controlling for other factors. The results suggest the need for integrated family-based interventions addressing delinquency, substance abuse, and HIV/STD-associated risk factors with juvenile justice-involved adolescents.

  15. Risk attitudes and personality traits predict perceptions of benefits and risks for medicinal products: a field study of European medical assessors.

    PubMed

    Beyer, Andrea R; Fasolo, Barbara; de Graeff, P A; Hillege, H L

    2015-01-01

    Risk attitudes and personality traits are known predictors of decision making among laypersons, but very little is known of their influence among experts participating in organizational decision making. Seventy-five European medical assessors were assessed in a field study using the Domain Specific Risk Taking scale and the Big Five Inventory scale. Assessors rated the risks and benefits for a mock "clinical dossier" specific to their area of expertise, and ordinal regression models were used to assess the odds of risk attitude or personality traits in predicting either the benefit or the risk ratings. An increase in the "conscientiousness" score predicted an increase in the perception of the drug's benefit, and male assessors gave higher scores for the drug's benefit ratings than did female assessors. Extraverted assessors saw fewer risks, and assessors with a perceived neutral-averse or averse risk profile saw greater risks. Medical assessors perceive the benefits and risks of medicines via a complex interplay of the medical situation, their personality traits and even their gender. Further research in this area is needed to determine how these potential biases are managed within the regulatory setting. Copyright © 2015 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

  16. Comparison of time series models for predicting campylobacteriosis risk in New Zealand.

    PubMed

    Al-Sakkaf, A; Jones, G

    2014-05-01

    Predicting campylobacteriosis cases is a matter of considerable concern in New Zealand, after the number of the notified cases was the highest among the developed countries in 2006. Thus, there is a need to develop a model or a tool to predict accurately the number of campylobacteriosis cases as the Microbial Risk Assessment Model used to predict the number of campylobacteriosis cases failed to predict accurately the number of actual cases. We explore the appropriateness of classical time series modelling approaches for predicting campylobacteriosis. Finding the most appropriate time series model for New Zealand data has additional practical considerations given a possible structural change, that is, a specific and sudden change in response to the implemented interventions. A univariate methodological approach was used to predict monthly disease cases using New Zealand surveillance data of campylobacteriosis incidence from 1998 to 2009. The data from the years 1998 to 2008 were used to model the time series with the year 2009 held out of the data set for model validation. The best two models were then fitted to the full 1998-2009 data and used to predict for each month of 2010. The Holt-Winters (multiplicative) and ARIMA (additive) intervention models were considered the best models for predicting campylobacteriosis in New Zealand. It was noticed that the prediction by an additive ARIMA with intervention was slightly better than the prediction by a Holt-Winter multiplicative method for the annual total in year 2010, the former predicting only 23 cases less than the actual reported cases. It is confirmed that classical time series techniques such as ARIMA with intervention and Holt-Winters can provide a good prediction performance for campylobacteriosis risk in New Zealand. The results reported by this study are useful to the New Zealand Health and Safety Authority's efforts in addressing the problem of the campylobacteriosis epidemic. © 2013 Blackwell Verlag GmbH.

  17. How to interpret a small increase in AUC with an additional risk prediction marker: decision analysis comes through.

    PubMed

    Baker, Stuart G; Schuit, Ewoud; Steyerberg, Ewout W; Pencina, Michael J; Vickers, Andrew; Vickers, Andew; Moons, Karel G M; Mol, Ben W J; Lindeman, Karen S

    2014-09-28

    An important question in the evaluation of an additional risk prediction marker is how to interpret a small increase in the area under the receiver operating characteristic curve (AUC). Many researchers believe that a change in AUC is a poor metric because it increases only slightly with the addition of a marker with a large odds ratio. Because it is not possible on purely statistical grounds to choose between the odds ratio and AUC, we invoke decision analysis, which incorporates costs and benefits. For example, a timely estimate of the risk of later non-elective operative delivery can help a woman in labor decide if she wants an early elective cesarean section to avoid greater complications from possible later non-elective operative delivery. A basic risk prediction model for later non-elective operative delivery involves only antepartum markers. Because adding intrapartum markers to this risk prediction model increases AUC by 0.02, we questioned whether this small improvement is worthwhile. A key decision-analytic quantity is the risk threshold, here the risk of later non-elective operative delivery at which a patient would be indifferent between an early elective cesarean section and usual care. For a range of risk thresholds, we found that an increase in the net benefit of risk prediction requires collecting intrapartum marker data on 68 to 124 women for every correct prediction of later non-elective operative delivery. Because data collection is non-invasive, this test tradeoff of 68 to 124 is clinically acceptable, indicating the value of adding intrapartum markers to the risk prediction model. Copyright © 2014 John Wiley & Sons, Ltd.

  18. Use of support vector machines for disease risk prediction in genome-wide association studies: concerns and opportunities.

    PubMed

    Mittag, Florian; Büchel, Finja; Saad, Mohamad; Jahn, Andreas; Schulte, Claudia; Bochdanovits, Zoltan; Simón-Sánchez, Javier; Nalls, Mike A; Keller, Margaux; Hernandez, Dena G; Gibbs, J Raphael; Lesage, Suzanne; Brice, Alexis; Heutink, Peter; Martinez, Maria; Wood, Nicholas W; Hardy, John; Singleton, Andrew B; Zell, Andreas; Gasser, Thomas; Sharma, Manu

    2012-12-01

    The success of genome-wide association studies (GWAS) in deciphering the genetic architecture of complex diseases has fueled the expectations whether the individual risk can also be quantified based on the genetic architecture. So far, disease risk prediction based on top-validated single-nucleotide polymorphisms (SNPs) showed little predictive value. Here, we applied a support vector machine (SVM) to Parkinson disease (PD) and type 1 diabetes (T1D), to show that apart from magnitude of effect size of risk variants, heritability of the disease also plays an important role in disease risk prediction. Furthermore, we performed a simulation study to show the role of uncommon (frequency 1-5%) as well as rare variants (frequency <1%) in disease etiology of complex diseases. Using a cross-validation model, we were able to achieve predictions with an area under the receiver operating characteristic curve (AUC) of ~0.88 for T1D, highlighting the strong heritable component (∼90%). This is in contrast to PD, where we were unable to achieve a satisfactory prediction (AUC ~0.56; heritability ~38%). Our simulations showed that simultaneous inclusion of uncommon and rare variants in GWAS would eventually lead to feasible disease risk prediction for complex diseases such as PD. The used software is available at http://www.ra.cs.uni-tuebingen.de/software/MACLEAPS/. © 2012 Wiley Periodicals, Inc.

  19. Undergraduate Student Retention in Context: An Examination of First-Year Risk Prediction and Advising Practices within a College of Education

    ERIC Educational Resources Information Center

    Litchfield, Bradley C.

    2013-01-01

    This study examined the use of an institutionally-specific risk prediction model in the university's College of Education. Set in a large, urban, public university, the risk model predicted incoming students' first-semester GPAs, which, in turn, predicted the students' risk of attrition. Additionally, the study investigated advising practices…

  20. Validated Risk Score for Predicting 6-Month Mortality in Infective Endocarditis.

    PubMed

    Park, Lawrence P; Chu, Vivian H; Peterson, Gail; Skoutelis, Athanasios; Lejko-Zupa, Tatjana; Bouza, Emilio; Tattevin, Pierre; Habib, Gilbert; Tan, Ren; Gonzalez, Javier; Altclas, Javier; Edathodu, Jameela; Fortes, Claudio Querido; Siciliano, Rinaldo Focaccia; Pachirat, Orathai; Kanj, Souha; Wang, Andrew

    2016-04-18

    Host factors and complications have been associated with higher mortality in infective endocarditis (IE). We sought to develop and validate a model of clinical characteristics to predict 6-month mortality in IE. Using a large multinational prospective registry of definite IE (International Collaboration on Endocarditis [ICE]-Prospective Cohort Study [PCS], 2000-2006, n=4049), a model to predict 6-month survival was developed by Cox proportional hazards modeling with inverse probability weighting for surgery treatment and was internally validated by the bootstrapping method. This model was externally validated in an independent prospective registry (ICE-PLUS, 2008-2012, n=1197). The 6-month mortality was 971 of 4049 (24.0%) in the ICE-PCS cohort and 342 of 1197 (28.6%) in the ICE-PLUS cohort. Surgery during the index hospitalization was performed in 48.1% and 54.0% of the cohorts, respectively. In the derivation model, variables related to host factors (age, dialysis), IE characteristics (prosthetic or nosocomial IE, causative organism, left-sided valve vegetation), and IE complications (severe heart failure, stroke, paravalvular complication, and persistent bacteremia) were independently associated with 6-month mortality, and surgery was associated with a lower risk of mortality (Harrell's C statistic 0.715). In the validation model, these variables had similar hazard ratios (Harrell's C statistic 0.682), with a similar, independent benefit of surgery (hazard ratio 0.74, 95% CI 0.62-0.89). A simplified risk model was developed by weight adjustment of these variables. Six-month mortality after IE is ≈25% and is predicted by host factors, IE characteristics, and IE complications. Surgery during the index hospitalization is associated with lower mortality but is performed less frequently in the highest risk patients. A simplified risk model may be used to identify specific risk subgroups in IE. © 2016 The Authors. Published on behalf of the American Heart

  1. Development and evaluation of a composite risk score to predict kidney transplant failure.

    PubMed

    Moore, Jason; He, Xiang; Shabir, Shazia; Hanvesakul, Rajesh; Benavente, David; Cockwell, Paul; Little, Mark A; Ball, Simon; Inston, Nicholas; Johnston, Atholl; Borrows, Richard

    2011-05-01

    Although risk factors for kidney transplant failure are well described, prognostic risk scores to estimate risk in prevalent transplant recipients are limited. Development and validation of risk-prediction instruments. The development data set included 2,763 prevalent patients more than 12 months posttransplant enrolled into the LOTESS (Long Term Efficacy and Safety Surveillance) Study. The validation data set included 731 patients who underwent transplant at a single UK center. Estimated glomerular filtration rate (eGFR) and other risk factors were evaluated using Cox regression. Scores for death-censored and overall transplant failure were based on the summed hazard ratios for baseline predictor variables. Predictive performance was assessed using calibration (Hosmer-Lemeshow statistic), discrimination (C statistic), and clinical reclassification (net reclassification improvement) compared with eGFR alone. In the development data set, 196 patients died and another 225 experienced transplant failure. eGFR, recipient age, race, serum urea and albumin levels, declining eGFR, and prior acute rejection predicted death-censored transplant failure. eGFR, recipient age, sex, serum urea and albumin levels, and declining eGFR predicted overall transplant failure. In the validation data set, 44 patients died and another 101 experienced transplant failure. The weighted scores comprising these variables showed adequate discrimination and calibration for death-censored (C statistic, 0.83; 95% CI, 0.75-0.91; Hosmer-Lemeshow χ(2)P = 0.8) and overall (C statistic, 0.70; 95% CI, 0.64-0.77; Hosmer-Lemeshow χ(2)P = 0.5) transplant failure. However, the scores failed to reclassify risk compared with eGFR alone (net reclassification improvements of 7.6% [95% CI, -0.2 to 13.4; P = 0.09] and 4.3% [95% CI, -2.7 to 11.8; P = 0.3] for death-censored and overall transplant failure, respectively). Retrospective analysis of predominantly cyclosporine-treated patients; limited study size and

  2. Building a genome analysis pipeline to predict disease risk and prevent disease.

    PubMed

    Bromberg, Y

    2013-11-01

    Reduced costs and increased speed and accuracy of sequencing can bring the genome-based evaluation of individual disease risk to the bedside. While past efforts have identified a number of actionable mutations, the bulk of genetic risk remains hidden in sequence data. The biggest challenge facing genomic medicine today is the development of new techniques to predict the specifics of a given human phenome (set of all expressed phenotypes) encoded by each individual variome (full set of genome variants) in the context of the given environment. Numerous tools exist for the computational identification of the functional effects of a single variant. However, the pipelines taking advantage of full genomic, exomic, transcriptomic (and other) sequences have only recently become a reality. This review looks at the building of methodologies for predicting "variome"-defined disease risk. It also discusses some of the challenges for incorporating such a pipeline into everyday medical practice. © 2013. Published by Elsevier Ltd. All rights reserved.

  3. Predicting the risk of suicide by analyzing the text of clinical notes.

    PubMed

    Poulin, Chris; Shiner, Brian; Thompson, Paul; Vepstas, Linas; Young-Xu, Yinong; Goertzel, Benjamin; Watts, Bradley; Flashman, Laura; McAllister, Thomas

    2014-01-01

    We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients.

  4. Predicting the Risk of Suicide by Analyzing the Text of Clinical Notes

    PubMed Central

    Thompson, Paul; Vepstas, Linas; Young-Xu, Yinong; Goertzel, Benjamin; Watts, Bradley; Flashman, Laura; McAllister, Thomas

    2014-01-01

    We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients. PMID:24489669

  5. Predicting risk in patients with acetaminophen overdose

    PubMed Central

    James, Laura P.; Gill, Prit; Simpson, Pippa

    2014-01-01

    Acetaminophen (APAP) overdose is a very common cause of drug overdose and acute liver failure in the US and Europe. Mechanism-based biomarkers of APAP toxicity have the potential to improve the clinical management of patients with large dose ingestions of APAP. The current approach to the management of APAP toxicity is limited by imprecise and time-constrained risk assessments and late-stage markers of liver injury. A recent study of “low-risk” APAP overdose patients who all received treatment with N-acetylcysteine, found that cell-death biomarkers were more sensitive than alanine aminotransferase (ALT) and APAP concentrations in predicting the development of acute liver injury. The data suggest a potential role for new biomarkers to identify “low risk” patients following APAP overdose. However, a practical and ethical consideration that complicates predictive biomarker research in this area is the clinical need to deliver antidote treatment within 10 hours of APAP overdose. The treatment effect and time-dependent nature of N-acetylcysteine treatment must be considered in future “predictive” toxicology studies of APAP-induced liver injury. PMID:23984999

  6. A Risk Prediction Score for Kidney Failure or Mortality in Rhabdomyolysis

    PubMed Central

    McMahon, Gearoid M.; Zeng, Xiaoxi; Waikar, Sushrut S.

    2016-01-01

    IMPORTANCE Rhabdomyolysis ranges in severity from asymptomatic elevations in creatine phosphokinase levels to a life-threatening disorder characterized by severe acute kidney injury requiring hemodialysis or continuous renal replacement therapy (RRT). OBJECTIVE To develop a risk prediction tool to identify patients at greatest risk of RRT or in-hospital mortality. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study of 2371 patients admitted between January 1, 2000, and March 31, 2011, to 2 large teaching hospitals in Boston, Massachusetts, with creatine phosphokinase levels in excess of 5000 U/L within 3 days of admission. The derivation cohort consisted of 1397 patients from Massachusetts General Hospital, and the validation cohort comprised 974 patients from Brigham and Women’s Hospital. MAIN OUTCOMES AND MEASURES The composite of RRT or in-hospital mortality. RESULTS The causes and outcomes of rhabdomyolysis were similar between the derivation and validation cohorts. In total, the composite outcome occurred in 19.0% of patients (8.0% required RRT and 14.1% died during hospitalization). The highest rates of the composite outcome were from compartment syndrome (41.2%), sepsis (39.3%), and following cardiac arrest (58.5%). The lowest rates were from myositis (1.7%), exercise (3.2%), and seizures (6.0%). The independent predictors of the composite outcome were age, female sex, cause of rhabdomyolysis, and values of initial creatinine, creatine phosphokinase, phosphate, calcium, and bicarbonate. We developed a risk-prediction score from these variables in the derivation cohort and subsequently applied it in the validation cohort. The C statistic for the prediction model was 0.82 (95% CI, 0.80–0.85) in the derivation cohort and 0.83 (0.80–0.86) in the validation cohort. The Hosmer-Lemeshow P values were .14 and .28, respectively. In the validation cohort, among the patients with the lowest risk score (<5), 2.3% died or needed RRT. Among the patients

  7. Pitfalls and Precautions When Using Predicted Failure Data for Quantitative Analysis of Safety Risk for Human Rated Launch Vehicles

    NASA Technical Reports Server (NTRS)

    Hatfield, Glen S.; Hark, Frank; Stott, James

    2016-01-01

    Launch vehicle reliability analysis is largely dependent upon using predicted failure rates from data sources such as MIL-HDBK-217F. Reliability prediction methodologies based on component data do not take into account risks attributable to manufacturing, assembly, and process controls. These sources often dominate component level reliability or risk of failure probability. While consequences of failure is often understood in assessing risk, using predicted values in a risk model to estimate the probability of occurrence will likely underestimate the risk. Managers and decision makers often use the probability of occurrence in determining whether to accept the risk or require a design modification. Due to the absence of system level test and operational data inherent in aerospace applications, the actual risk threshold for acceptance may not be appropriately characterized for decision making purposes. This paper will establish a method and approach to identify the pitfalls and precautions of accepting risk based solely upon predicted failure data. This approach will provide a set of guidelines that may be useful to arrive at a more realistic quantification of risk prior to acceptance by a program.

  8. The predictive validity of the HERO Scorecard in determining future health care cost and risk trends.

    PubMed

    Goetzel, Ron Z; Henke, Rachel Mosher; Benevent, Richele; Tabrizi, Maryam J; Kent, Karen B; Smith, Kristyn J; Roemer, Enid Chung; Grossmeier, Jessica; Mason, Shawn T; Gold, Daniel B; Noeldner, Steven P; Anderson, David R

    2014-02-01

    To determine the ability of the Health Enhancement Research Organization (HERO) Scorecard to predict changes in health care expenditures. Individual employee health care insurance claims data for 33 organizations completing the HERO Scorecard from 2009 to 2011 were linked to employer responses to the Scorecard. Organizations were dichotomized into "high" versus "low" scoring groups and health care cost trends were compared. A secondary analysis examined the tool's ability to predict health risk trends. "High" scorers experienced significant reductions in inflation-adjusted health care costs (averaging an annual trend of -1.6% over 3 years) compared with "low" scorers whose cost trend remained stable. The risk analysis was inconclusive because of the small number of employers scoring "low." The HERO Scorecard predicts health care cost trends among employers. More research is needed to determine how well it predicts health risk trends for employees.

  9. Political consumer behaviour among university students in Brazil and Germany: The role of contextual features and core political values.

    PubMed

    Kotzur, Patrick F; Torres, Cláudio V; Kedzior, Karina K; Boehnke, Klaus

    2017-04-01

    This study investigates the relationship between political consumerism and core political values (CPVs) among university students in Brazil (N = 414) and Germany (N = 222). Despite the prerequisite to endorse values that are compatible with political consumerism, contextual features of one's immediate environment might affect overall levels of political consumerism. Our results show that political consumerism is significantly associated with higher income in Brazil (but not in Germany). After controlling for income, political consumerism was practised more frequently in Germany than in Brazil, in urban compared with rural areas, and was not dependent on gender. The urban-rural split was stronger in Brazil than in Germany. These results confirm our hypothesis that contextual features are associated with political consumerism. Furthermore, the political value Equality positively predicted political consumerism in both countries. In contrast, Traditional Morality and support of Free Enterprise negatively predicted political consumerism, although the effect sizes of these relationships were only small. These results suggest that political consumerism among university students is widespread in Germany but not in Brazil. Interestingly, regardless of its low prevalence in Brazil, political consumerism is positively associated with the CPV of Equality among university students in both countries. © 2015 International Union of Psychological Science.

  10. A Bayesian network model for predicting type 2 diabetes risk based on electronic health records

    NASA Astrophysics Data System (ADS)

    Xie, Jiang; Liu, Yan; Zeng, Xu; Zhang, Wu; Mei, Zhen

    2017-07-01

    An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.

  11. Preexposure Prophylaxis and Predicted Condom Use Among High-Risk Men Who Have Sex With Men

    PubMed Central

    Golub, Sarit A.; Kowalczyk, William; Weinberger, Corina L.; Parsons, Jeffrey T.

    2010-01-01

    Objectives Preexposure prophylaxis (PREP) is an emerging HIV prevention strategy; however, many fear it may lead to neglect of traditional risk reduction practices through behavioral disinhibition or risk compensation. Methods Participants were 180 HIV-negative high-risk men who have sex with men recruited in New York City, who completed an Audio Computer Assisted Self Interview-administered survey between September 2007 and July 2009. Bivariate and multivariate logistic regression models were used to predict intention to use PREP and perceptions that PREP would decrease condom use. Results Almost 70% (n = 124) of participants reported that they would be likely to use PREP if it were at least 80% effective in preventing HIV. Of those who would use PREP, over 35% reported that they would be likely to decrease condom use while on PREP. In multivariate analyses, arousal/pleasure barriers to condom use significantly predicted likelihood of PREP use (odds ratio = 1.71, P < 0.05) and risk perception motivations for condom use significantly predicted decreased condom use on PREP (odds ratio = 2.48, P < 0.05). Discussion These data provide support for both behavioral disinhibition and risk compensation models and underscore the importance of developing behavioral interventions to accompany any wide-scale provision of PREP to high-risk populations. PMID:20512046

  12. Controlling behaviours and technology-facilitated abuse perpetrated by men receiving substance use treatment in England and Brazil: Prevalence and risk factors.

    PubMed

    Gilchrist, Gail; Canfield, Martha; Radcliffe, Polly; D'Oliveira, Ana Flavia Pires Lucas

    2017-01-01

    men receiving substance use treatment in England and Brazil: Prevalence and risk factors. Drug Alcohol Rev 2017;36:52-63]. © 2017 The Authors. Drug and Alcohol Review published by John Wiley & Sons Australia, Ltd on behalf of Australasian Professional Society on Alcohol and other Drugs.

  13. Angiogenic factors combined with clinical risk factors to predict preterm pre-eclampsia in nulliparous women: a predictive test accuracy study.

    PubMed

    Myers, J E; Kenny, L C; McCowan, L M E; Chan, E H Y; Dekker, G A; Poston, L; Simpson, N A B; North, R A

    2013-09-01

    To assess the performance of clinical risk factors, uterine artery Doppler and angiogenic markers to predict preterm pre-eclampsia in nulliparous women. Predictive test accuracy study. Prospective multicentre cohort study Screening for Pregnancy Endpoints (SCOPE). Low-risk nulliparous women with a singleton pregnancy were recruited. Clinical risk factor data were obtained and plasma placental growth factor (PlGF), soluble endoglin and soluble fms-like tyrosine kinase-1 (sFlt-1) were measured at 14-16 weeks of gestation. Prediction models were developed using multivariable stepwise logistic regression. Preterm pre-eclampsia (delivered before 37(+0)  weeks of gestation). Of the 3529 women recruited, 187 (5.3%) developed pre-eclampsia of whom 47 (1.3%) delivered preterm. Controls (n = 188) were randomly selected from women without preterm pre-eclampsia and included women who developed other pregnancy complications. An area under a receiver operating characteristic curve (AUC) of 0.76 (95% CI 0.67-0.84) was observed using previously reported clinical risk variables. The AUC improved following the addition of PlGF measured at 14-16 weeks (0.84; 95% CI 0.77-0.91), but no further improvement was observed with the addition of uterine artery Doppler or the other angiogenic markers. A sensitivity of 45% (95% CI 0.31-0.59) (5% false-positive rate) and post-test probability of 11% (95% CI 9-13) were observed using clinical risk variables and PlGF measurement. Addition of plasma PlGF at 14-16 weeks of gestation to clinical risk assessment improved the identification of nulliparous women at increased risk of developing preterm pre-eclampsia, but the performance is not sufficient to warrant introduction as a clinical screening test. These findings are marker dependent, not assay dependent; additional markers are needed to achieve clinical utility. © 2013 The Authors BJOG An International Journal of Obstetrics and Gynaecology © 2013 RCOG.

  14. Periconceptional use of folic acid and risk of miscarriage - findings of the Oral Cleft Prevention Program in Brazil.

    PubMed

    Vila-Nova, Camila; Wehby, George L; Queirós, Fernanda C; Chakraborty, Hrishkesh; Félix, Temis M; Goco, Norman; Moore, Janet; Gewehr, Eduardo V; Lins, Lorene; Affonso, Carla M C; Murray, Jeffrey C

    2013-07-01

    We report on the risk of miscarriage with high- and low-dosage periconceptional folic acid (FA) supplementation from a double-blind randomized clinical trial for prevention of orofacial cleft recurrence in Brazil. Women at risk of recurrence of orofacial clefts in their offspring were randomized into high (4 mg/day) and low (0.4 mg/day) doses of FA supplementation. The women received the study pills before pregnancy, and supplementation continued throughout the first trimester. Miscarriage rates were compared between the two FA groups and with the population rate. A total of 268 pregnancies completed the study protocol, with 141 in the 4.0-mg group and 127 in the 0.4-mg group. The miscarriage rate was 14.2% in the low-dose FA group (0.4 mg/day) and 11.3% for the high-dose group (4 mg/day) (P=0.4877). These miscarriage rates are not significantly different from the miscarriage rate in the Brazilian population, estimated to be around 14% (P=0.311). These results indicate that high-dose FA does not increase miscarriage risk in this population and add further information to the literature on the safety of high FA supplementation for prevention of birth defect recurrence.

  15. Perturbed threat monitoring following a traumatic event predicts risk for post-traumatic stress disorder.

    PubMed

    Naim, R; Wald, I; Lior, A; Pine, D S; Fox, N A; Sheppes, G; Halpern, P; Bar-Haim, Y

    2014-07-01

    Post-traumatic stress disorder (PTSD) is a chronic and difficult to treat psychiatric disorder. Objective, performance-based diagnostic markers that uniquely index risk for PTSD above and beyond subjective self-report markers could inform attempts to improve prevention and early intervention. We evaluated the predictive value of threat-related attention bias measured immediately after a potentially traumatic event, as a risk marker for PTSD at a 3-month follow-up. We measured the predictive contribution of attentional threat bias above and beyond that of the more established marker of risk for PTSD, self-reported psychological dissociation. Dissociation symptoms and threat-related attention bias were measured in 577 motor vehicle accident (MVA) survivors (mean age = 35.02 years, 356 males) within 24 h of admission to an emergency department (ED) of a large urban hospital. PTSD symptoms were assessed at a 3-month follow-up using the Clinician-Administered PTSD Scale (CAPS). Self-reported dissociation symptoms significantly accounted for 16% of the variance in PTSD at follow-up, and attention bias toward threat significantly accounted for an additional 4% of the variance in PTSD. Threat-related attention bias can be reliably measured in the context of a hospital ED and significantly predicts risk for later PTSD. Possible mechanisms underlying the association between threat bias following a potentially traumatic event and risk for PTSD are discussed. The potential application of an attention bias modification treatment (ABMT) tailored to reduce risk for PTSD is suggested.

  16. Framingham risk score can predict cognitive decline progression in Alzheimer's disease.

    PubMed

    Viticchi, Giovanna; Falsetti, Lorenzo; Buratti, Laura; Boria, Cristiano; Luzzi, Simona; Bartolini, Marco; Provinciali, Leandro; Silvestrini, Mauro

    2015-11-01

    The role of vascular factors in influencing cognitive decline has been extensively investigated, and some difficulties in defining their weight in dementia pathogenesis have emerged. The aim of the study was to investigate the relevance of the Framingham cardiovascular risk profile (FCRP) in influencing cognitive deterioration in a population of Alzheimer's disease (AD) patients. Two hundred eighty-four consecutive AD patients were enrolled. For each patient, FCRP score was calculated. We did a 1-year follow-up to quantify the cognitive decline by recording changes in the Clinical Dementia Rating score. The FCRP score predicted cognitive deterioration with an area under the curve of 0.63 (95% confidence interval: 0.57-0.69; p < 0.0001). In the subpopulation of patients with a genetic increased predisposition to develop cognitive deterioration and with an advanced vascular impairment, the FCRP predictive value significantly increased with an area under the curve of 0.77 (95% confidence interval: 0.52-0.93; p < 0.05). Our findings show that FCRP can predict the progression of deterioration in AD patients. This was particularly evident in patients with major genetic and atherosclerotic risk factors. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. Predicting influent biochemical oxygen demand: Balancing energy demand and risk management.

    PubMed

    Zhu, Jun-Jie; Kang, Lulu; Anderson, Paul R

    2018-01-01

    Ready access to comprehensive influent information can help water reclamation plant (WRP) operators implement better real-time process controls, provide operational reliability and reduce energy consumption. The five-day biochemical oxygen demand (BOD 5 ), a critical parameter for WRP process control, is expensive and difficult to measure using hard-sensors. An alternative approach based on a soft-sensor methodology shows promise, but can be problematic when used to predict high BOD 5 values. Underestimating high BOD 5 concentrations for process control could result in an insufficient amount of aeration, increasing the risk of an effluent violation. To address this issue, we tested a hierarchical hybrid soft-sensor approach involving multiple linear regression, artificial neural networks (ANN), and compromise programming. While this hybrid approach results in a slight decrease in overall prediction accuracy relative to the approach based on ANN only, the underestimation percentage is substantially lower (37% vs. 61%) for predictions of carbonaceous BOD 5 (CBOD 5 ) concentrations higher than the long-term average value. The hybrid approach is also flexible and can be adjusted depending on the relative importance between energy savings and managing the risk of an effluent violation. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Fractionation and potential toxic risk of metals from superficial sediment in Itaipu Lake--boundary between Brazil and Paraguay.

    PubMed

    Kalwa, Miguel; Quináia, Sueli Pércio; Pletsch, Adelmo L; Techy, Laura; Felsner, Maria Lurdes

    2013-01-01

    The objective of this study was to evaluate fractions of metals (labile and pseudo-total) extracted from sediment samples collected in Itaipu Lake (boundary between Brazil and Paraguay) and to assess the dynamics and mobility of these fractions by identifying the same bioavailability and ecological risk to metals in the aquatic environment. The concentrations of metal ions were determined by flame atomic absorption spectrometry. There was a correlation between the metal ions, both in the labile and the pseudo-total, with regard to particle size. To assess metals concentrations in sediment, numerical sediment-quality guidelines were applied. The concentrations of aluminum, cadmium, iron, manganese, lead, and zinc in all sediment samples are lower than the proposed probable effects level (PEL), thus possibly indicating that there are no harmful effects from these metals. In contrast, concentrations of copper, chromium, and nickel exceeded the PEL in some samples, thus indicating that these stations are at potential risk. The level of contamination in sediments of Itaipu Lake for all metals was evaluated using contamination factor, degree of contamination, and sum-of-metals toxic unit.

  19. [Seroprevalence and risk factors for syphilis in prisoners in Goiás, Brazil].

    PubMed

    de Andrade, A L; Martelli, C M; Sousa, L C; de Sousa, M A; Zicker, F

    1989-01-01

    A cross-sectional survey was carried-out among 299 prisoners in the Penitentiary Center of Industrial Activity of Goiás (CEPAIGO), to determine the seroprevalence to T. pallidum and to identify risk factors associated to seropositivity. The seropositivity criterion was a positive VDRL test at any titer. A questionnaire was applied to evaluate the following risk factors: time of imprisonment, clinical evidence of sexually transmitted diseases (STD), history of syphilis or others STD, homo/bisexuality and number of sexual partners. The positive (PPV) and negative (NPV) predictive values of the history of syphilis were calculated. Seroprevalence of 18.4% was found and no difference was detected in the different age groups. The PPV of history of syphilis was 26% indicating that 74% of the individuals who have reported syphilis in the past presented a negative VDRL test. Among all the risk factors studied, homo/bisexuality was the only one with statistically significant association with seropositivity (relative risks 5.7-95% CL1.2-26, p = 0.03). The paper discusses the methodological problems related with the investigation.

  20. Alcohol use disorders among people living with HIV/AIDS in Southern Brazil: prevalence, risk factors and biological markers outcomes.

    PubMed

    da Silva, Cláudio Moss; Mendoza-Sassi, Raúl Andrés; da Mota, Luisa Dias; Nader, Maíba Mikhael; de Martinez, Ana Maria Barral

    2017-04-11

    Alcohol abuse is an important public health problem, frequently unrecognized among people living with HIV/AIDS (PLWHA), and requires investigation and intervention. It is usually associated with lower adherence to highly active antiretroviral therapy (HAART). It can also produce adverse clinical outcomes, such as changes in certain HIV markers, particularly CD4 cell counts and HIV viral loads (VLs). Thus, this study aimed to evaluate the prevalence of alcohol abuse among PLWHA, its associated risk factors and effects on CD4 cell counts and HIV VLs in southern Brazil. Between December 2012 and July 2013, 343 patients were interviewed at a reference hospital in southern Brazil. The instrument used was the Alcohol Use Disorder Identification Test (AUDIT), and a cutoff of eight points or more was applied. Socioeconomic, demographic, clinical and laboratory data were also collected. The statistical analysis included a Poisson regression to evaluate the factors associated with alcohol use disorder, and a linear regression was performed to assess the relationship between AUDIT scores and CD4 cell counts and HIV VLs. Alcohol abuse was present in 28.6% of the respondents, and possible dependence was present in 5%. The risk factors identified included being male, mixed or black skin color, low education and the use of intravenous or inhaled drugs. A higher AUDIT score was associated with a lower CD4 cell count but was not associated with higher HIV VL values. Our results show the importance of screening for alcohol abuse in this group. The prevalence of alcohol abuse was high, and it was associated with socioeconomic factors and the use of illicit drugs. Moreover, AUDIT score negatively affected CD4 cell counts as well.