Evaluation of Time- and Concentration-dependent Toxic Effect Models for use in Aquatic Risk Assessments, Oral Presentation

EPA Science Inventory

Various models have been proposed for describing the time- and concentration-dependence of toxic effects to aquatic organisms, which would improve characterization of risks in natural systems. Selected models were evaluated using results from a study on the lethality of copper t...

A risk-model for hospital mortality among patients with severe sepsis or septic shock based on German national administrative claims data

PubMed Central

Fleischmann-Struzek, Carolin; Rüddel, Hendrik; Reinhart, Konrad; Thomas-Rüddel, Daniel O.

2018-01-01

Background Sepsis is a major cause of preventable deaths in hospitals. Feasible and valid methods for comparing quality of sepsis care between hospitals are needed. The aim of this study was to develop a risk-adjustment model suitable for comparing sepsis-related mortality between German hospitals. Methods We developed a risk-model using national German claims data. Since these data are available with a time-lag of 1.5 years only, the stability of the model across time was investigated. The model was derived from inpatient cases with severe sepsis or septic shock treated in 2013 using logistic regression with backward selection and generalized estimating equations to correct for clustering. It was validated among cases treated in 2015. Finally, the model development was repeated in 2015. To investigate secular changes, the risk-adjusted trajectory of mortality across the years 2010–2015 was analyzed. Results The 2013 deviation sample consisted of 113,750 cases; the 2015 validation sample consisted of 134,851 cases. The model developed in 2013 showed good validity regarding discrimination (AUC = 0.74), calibration (observed mortality in 1st and 10th risk-decile: 11%-78%), and fit (R2 = 0.16). Validity remained stable when the model was applied to 2015 (AUC = 0.74, 1st and 10th risk-decile: 10%-77%, R2 = 0.17). There was no indication of overfitting of the model. The final model developed in year 2015 contained 40 risk-factors. Between 2010 and 2015 hospital mortality in sepsis decreased from 48% to 42%. Adjusted for risk-factors the trajectory of decrease was still significant. Conclusions The risk-model shows good predictive validity and stability across time. The model is suitable to be used as an external algorithm for comparing risk-adjusted sepsis mortality among German hospitals or regions based on administrative claims data, but secular changes need to be taken into account when interpreting risk-adjusted mortality. PMID:29558486

A risk-model for hospital mortality among patients with severe sepsis or septic shock based on German national administrative claims data.

PubMed

Schwarzkopf, Daniel; Fleischmann-Struzek, Carolin; Rüddel, Hendrik; Reinhart, Konrad; Thomas-Rüddel, Daniel O

2018-01-01

Sepsis is a major cause of preventable deaths in hospitals. Feasible and valid methods for comparing quality of sepsis care between hospitals are needed. The aim of this study was to develop a risk-adjustment model suitable for comparing sepsis-related mortality between German hospitals. We developed a risk-model using national German claims data. Since these data are available with a time-lag of 1.5 years only, the stability of the model across time was investigated. The model was derived from inpatient cases with severe sepsis or septic shock treated in 2013 using logistic regression with backward selection and generalized estimating equations to correct for clustering. It was validated among cases treated in 2015. Finally, the model development was repeated in 2015. To investigate secular changes, the risk-adjusted trajectory of mortality across the years 2010-2015 was analyzed. The 2013 deviation sample consisted of 113,750 cases; the 2015 validation sample consisted of 134,851 cases. The model developed in 2013 showed good validity regarding discrimination (AUC = 0.74), calibration (observed mortality in 1st and 10th risk-decile: 11%-78%), and fit (R2 = 0.16). Validity remained stable when the model was applied to 2015 (AUC = 0.74, 1st and 10th risk-decile: 10%-77%, R2 = 0.17). There was no indication of overfitting of the model. The final model developed in year 2015 contained 40 risk-factors. Between 2010 and 2015 hospital mortality in sepsis decreased from 48% to 42%. Adjusted for risk-factors the trajectory of decrease was still significant. The risk-model shows good predictive validity and stability across time. The model is suitable to be used as an external algorithm for comparing risk-adjusted sepsis mortality among German hospitals or regions based on administrative claims data, but secular changes need to be taken into account when interpreting risk-adjusted mortality.

Endogenous time-varying risk aversion and asset returns.

PubMed

Berardi, Michele

2016-01-01

Stylized facts about statistical properties for short horizon returns in financial markets have been identified in the literature, but a satisfactory understanding for their manifestation is yet to be achieved. In this work, we show that a simple asset pricing model with representative agent is able to generate time series of returns that replicate such stylized facts if the risk aversion coefficient is allowed to change endogenously over time in response to unexpected excess returns under evolutionary forces. The same model, under constant risk aversion, would instead generate returns that are essentially Gaussian. We conclude that an endogenous time-varying risk aversion represents a very parsimonious way to make the model match real data on key statistical properties, and therefore deserves careful consideration from economists and practitioners alike.

Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis

PubMed Central

Barrett, Jessica; Pennells, Lisa; Sweeting, Michael; Willeit, Peter; Di Angelantonio, Emanuele; Gudnason, Vilmundur; Nordestgaard, Børge G.; Psaty, Bruce M; Goldbourt, Uri; Best, Lyle G; Assmann, Gerd; Salonen, Jukka T; Nietert, Paul J; Verschuren, W. M. Monique; Brunner, Eric J; Kronmal, Richard A; Salomaa, Veikko; Bakker, Stephan J L; Dagenais, Gilles R; Sato, Shinichi; Jansson, Jan-Håkan; Willeit, Johann; Onat, Altan; de la Cámara, Agustin Gómez; Roussel, Ronan; Völzke, Henry; Dankner, Rachel; Tipping, Robert W; Meade, Tom W; Donfrancesco, Chiara; Kuller, Lewis H; Peters, Annette; Gallacher, John; Kromhout, Daan; Iso, Hiroyasu; Knuiman, Matthew; Casiglia, Edoardo; Kavousi, Maryam; Palmieri, Luigi; Sundström, Johan; Davis, Barry R; Njølstad, Inger; Couper, David; Danesh, John; Thompson, Simon G; Wood, Angela

2017-01-01

Abstract The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962–2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction. PMID:28549073

Risk assessment of flood disaster and forewarning model at different spatial-temporal scales

NASA Astrophysics Data System (ADS)

Zhao, Jun; Jin, Juliang; Xu, Jinchao; Guo, Qizhong; Hang, Qingfeng; Chen, Yaqian

2018-05-01

Aiming at reducing losses from flood disaster, risk assessment of flood disaster and forewarning model is studied. The model is built upon risk indices in flood disaster system, proceeding from the whole structure and its parts at different spatial-temporal scales. In this study, on the one hand, it mainly establishes the long-term forewarning model for the surface area with three levels of prediction, evaluation, and forewarning. The method of structure-adaptive back-propagation neural network on peak identification is used to simulate indices in prediction sub-model. Set pair analysis is employed to calculate the connection degrees of a single index, comprehensive index, and systematic risk through the multivariate connection number, and the comprehensive assessment is made by assessment matrixes in evaluation sub-model. The comparison judging method is adopted to divide warning degree of flood disaster on risk assessment comprehensive index with forewarning standards in forewarning sub-model and then the long-term local conditions for proposing planning schemes. On the other hand, it mainly sets up the real-time forewarning model for the spot, which introduces the real-time correction technique of Kalman filter based on hydrological model with forewarning index, and then the real-time local conditions for presenting an emergency plan. This study takes Tunxi area, Huangshan City of China, as an example. After risk assessment and forewarning model establishment and application for flood disaster at different spatial-temporal scales between the actual and simulated data from 1989 to 2008, forewarning results show that the development trend for flood disaster risk remains a decline on the whole from 2009 to 2013, despite the rise in 2011. At the macroscopic level, project and non-project measures are advanced, while at the microcosmic level, the time, place, and method are listed. It suggests that the proposed model is feasible with theory and application, thus offering a way for assessing and forewarning flood disaster risk.

Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions.

PubMed

Bae, Sangwon; Chung, Tammy; Ferreira, Denzil; Dey, Anind K; Suffoletto, Brian

2018-08-01

Real-time detection of drinking could improve timely delivery of interventions aimed at reducing alcohol consumption and alcohol-related injury, but existing detection methods are burdensome or impractical. To evaluate whether phone sensor data and machine learning models are useful to detect alcohol use events, and to discuss implications of these results for just-in-time mobile interventions. 38 non-treatment seeking young adult heavy drinkers downloaded AWARE app (which continuously collected mobile phone sensor data), and reported alcohol consumption (number of drinks, start/end time of prior day's drinking) for 28days. We tested various machine learning models using the 20 most informative sensor features to classify time periods as non-drinking, low-risk (1 to 3/4 drinks per occasion for women/men), and high-risk drinking (>4/5 drinks per occasion for women/men). Among 30 participants in the analyses, 207 non-drinking, 41 low-risk, and 45 high-risk drinking episodes were reported. A Random Forest model using 30-min windows with 1day of historical data performed best for detecting high-risk drinking, correctly classifying high-risk drinking windows 90.9% of the time. The most informative sensor features were related to time (i.e., day of week, time of day), movement (e.g., change in activities), device usage (e.g., screen duration), and communication (e.g., call duration, typing speed). Preliminary evidence suggests that sensor data captured from mobile phones of young adults is useful in building accurate models to detect periods of high-risk drinking. Interventions using mobile phone sensor features could trigger delivery of a range of interventions to potentially improve effectiveness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula

NASA Astrophysics Data System (ADS)

Sarhadi, Ali; Burn, Donald H.; Concepción Ausín, María.; Wiper, Michael P.

2016-03-01

A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments. The results demonstrate that the nature and the risk of extreme-climate multidimensional processes are changed over time under the impact of climate change, and accordingly the long-term decision making strategies should be updated based on the anomalies of the nonstationary environment.

Adolescents Exiting Homelessness over Two Years: The Risk Amplification and Abatement Model

ERIC Educational Resources Information Center

Milburn, Norweeta G.; Rice, Eric; Rotheram-Borus, Mary Jane; Mallett, Shelley; Rosenthal, Doreen; Batterham, Phillip; May, Susanne J.; Witkin, Andrea; Duan, Naihua

2009-01-01

The Risk Amplification and Abatement Model (RAAM) demonstrates that negative contact with socializing agents amplify risk, while positive contact abates risk for homeless adolescents. To test this model, the likelihood of exiting homelessness and returning to familial housing at 2 years and stably exiting over time are examined with longitudinal…

Combining operational models and data into a dynamic vessel risk assessment tool for coastal regions

NASA Astrophysics Data System (ADS)

Fernandes, R.; Braunschweig, F.; Lourenço, F.; Neves, R.

2015-07-01

The technological evolution in terms of computational capacity, data acquisition systems, numerical modelling and operational oceanography is supplying opportunities for designing and building holistic approaches and complex tools for newer and more efficient management (planning, prevention and response) of coastal water pollution risk events. A combined methodology to dynamically estimate time and space variable shoreline risk levels from ships has been developed, integrating numerical metocean forecasts and oil spill simulations with vessel tracking automatic identification systems (AIS). The risk rating combines the likelihood of an oil spill occurring from a vessel navigating in a study area - Portuguese Continental shelf - with the assessed consequences to the shoreline. The spill likelihood is based on dynamic marine weather conditions and statistical information from previous accidents. The shoreline consequences reflect the virtual spilled oil amount reaching shoreline and its environmental and socio-economic vulnerabilities. The oil reaching shoreline is quantified with an oil spill fate and behaviour model running multiple virtual spills from vessels along time. Shoreline risks can be computed in real-time or from previously obtained data. Results show the ability of the proposed methodology to estimate the risk properly sensitive to dynamic metocean conditions and to oil transport behaviour. The integration of meteo-oceanic + oil spill models with coastal vulnerability and AIS data in the quantification of risk enhances the maritime situational awareness and the decision support model, providing a more realistic approach in the assessment of shoreline impacts. The risk assessment from historical data can help finding typical risk patterns, "hot spots" or developing sensitivity analysis to specific conditions, whereas real time risk levels can be used in the prioritization of individual ships, geographical areas, strategic tug positioning and implementation of dynamic risk-based vessel traffic monitoring.

Conceptualizing a Dynamic Fall Risk Model Including Intrinsic Risks and Exposures.

PubMed

Klenk, Jochen; Becker, Clemens; Palumbo, Pierpaolo; Schwickert, Lars; Rapp, Kilan; Helbostad, Jorunn L; Todd, Chris; Lord, Stephen R; Kerse, Ngaire

2017-11-01

Falls are a major cause of injury and disability in older people, leading to serious health and social consequences including fractures, poor quality of life, loss of independence, and institutionalization. To design and provide adequate prevention measures, accurate understanding and identification of person's individual fall risk is important. However, to date, the performance of fall risk models is weak compared with models estimating, for example, cardiovascular risk. This deficiency may result from 2 factors. First, current models consider risk factors to be stable for each person and not change over time, an assumption that does not reflect real-life experience. Second, current models do not consider the interplay of individual exposure including type of activity (eg, walking, undertaking transfers) and environmental risks (eg, lighting, floor conditions) in which activity is performed. Therefore, we posit a dynamic fall risk model consisting of intrinsic risk factors that vary over time and exposure (activity in context). eHealth sensor technology (eg, smartphones) begins to enable the continuous measurement of both the above factors. We illustrate our model with examples of real-world falls from the FARSEEING database. This dynamic framework for fall risk adds important aspects that may improve understanding of fall mechanisms, fall risk models, and the development of fall prevention interventions. Copyright © 2017 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.

Dynamic Blowout Risk Analysis Using Loss Functions.

PubMed

Abimbola, Majeed; Khan, Faisal

2018-02-01

Most risk analysis approaches are static; failing to capture evolving conditions. Blowout, the most feared accident during a drilling operation, is a complex and dynamic event. The traditional risk analysis methods are useful in the early design stage of drilling operation while falling short during evolving operational decision making. A new dynamic risk analysis approach is presented to capture evolving situations through dynamic probability and consequence models. The dynamic consequence models, the focus of this study, are developed in terms of loss functions. These models are subsequently integrated with the probability to estimate operational risk, providing a real-time risk analysis. The real-time evolving situation is considered dependent on the changing bottom-hole pressure as drilling progresses. The application of the methodology and models are demonstrated with a case study of an offshore drilling operation evolving to a blowout. © 2017 Society for Risk Analysis.

Safety analytics for integrating crash frequency and real-time risk modeling for expressways.

PubMed

Wang, Ling; Abdel-Aty, Mohamed; Lee, Jaeyoung

2017-07-01

To find crash contributing factors, there have been numerous crash frequency and real-time safety studies, but such studies have been conducted independently. Until this point, no researcher has simultaneously analyzed crash frequency and real-time crash risk to test whether integrating them could better explain crash occurrence. Therefore, this study aims at integrating crash frequency and real-time safety analyses using expressway data. A Bayesian integrated model and a non-integrated model were built: the integrated model linked the crash frequency and the real-time models by adding the logarithm of the estimated expected crash frequency in the real-time model; the non-integrated model independently estimated the crash frequency and the real-time crash risk. The results showed that the integrated model outperformed the non-integrated model, as it provided much better model results for both the crash frequency and the real-time models. This result indicated that the added component, the logarithm of the expected crash frequency, successfully linked and provided useful information to the two models. This study uncovered few variables that are not typically included in the crash frequency analysis. For example, the average daily standard deviation of speed, which was aggregated based on speed at 1-min intervals, had a positive effect on crash frequency. In conclusion, this study suggested a methodology to improve the crash frequency and real-time models by integrating them, and it might inspire future researchers to understand crash mechanisms better. Copyright © 2017 Elsevier Ltd. All rights reserved.

Score tests for independence in semiparametric competing risks models.

PubMed

Saïd, Mériem; Ghazzali, Nadia; Rivest, Louis-Paul

2009-12-01

A popular model for competing risks postulates the existence of a latent unobserved failure time for each risk. Assuming that these underlying failure times are independent is attractive since it allows standard statistical tools for right-censored lifetime data to be used in the analysis. This paper proposes simple independence score tests for the validity of this assumption when the individual risks are modeled using semiparametric proportional hazards regressions. It assumes that covariates are available, making the model identifiable. The score tests are derived for alternatives that specify that copulas are responsible for a possible dependency between the competing risks. The test statistics are constructed by adding to the partial likelihoods for the individual risks an explanatory variable for the dependency between the risks. A variance estimator is derived by writing the score function and the Fisher information matrix for the marginal models as stochastic integrals. Pitman efficiencies are used to compare test statistics. A simulation study and a numerical example illustrate the methodology proposed in this paper.

Personalized long-term prediction of cognitive function: Using sequential assessments to improve model performance.

PubMed

Chi, Chih-Lin; Zeng, Wenjun; Oh, Wonsuk; Borson, Soo; Lenskaia, Tatiana; Shen, Xinpeng; Tonellato, Peter J

2017-12-01

Prediction of onset and progression of cognitive decline and dementia is important both for understanding the underlying disease processes and for planning health care for populations at risk. Predictors identified in research studies are typically accessed at one point in time. In this manuscript, we argue that an accurate model for predicting cognitive status over relatively long periods requires inclusion of time-varying components that are sequentially assessed at multiple time points (e.g., in multiple follow-up visits). We developed a pilot model to test the feasibility of using either estimated or observed risk factors to predict cognitive status. We developed two models, the first using a sequential estimation of risk factors originally obtained from 8 years prior, then improved by optimization. This model can predict how cognition will change over relatively long time periods. The second model uses observed rather than estimated time-varying risk factors and, as expected, results in better prediction. This model can predict when newly observed data are acquired in a follow-up visit. Performances of both models that are evaluated in10-fold cross-validation and various patient subgroups show supporting evidence for these pilot models. Each model consists of multiple base prediction units (BPUs), which were trained using the same set of data. The difference in usage and function between the two models is the source of input data: either estimated or observed data. In the next step of model refinement, we plan to integrate the two types of data together to flexibly predict dementia status and changes over time, when some time-varying predictors are measured only once and others are measured repeatedly. Computationally, both data provide upper and lower bounds for predictive performance. Copyright © 2017 Elsevier Inc. All rights reserved.

Time Factor in the Theory of Anthropogenic Risk Prediction in Complex Dynamic Systems

NASA Astrophysics Data System (ADS)

Ostreikovsky, V. A.; Shevchenko, Ye N.; Yurkov, N. K.; Kochegarov, I. I.; Grishko, A. K.

2018-01-01

The article overviews the anthropogenic risk models that take into consideration the development of different factors in time that influence the complex system. Three classes of mathematical models have been analyzed for the use in assessing the anthropogenic risk of complex dynamic systems. These models take into consideration time factor in determining the prospect of safety change of critical systems. The originality of the study is in the analysis of five time postulates in the theory of anthropogenic risk and the safety of highly important objects. It has to be stressed that the given postulates are still rarely used in practical assessment of equipment service life of critically important systems. That is why, the results of study presented in the article can be used in safety engineering and analysis of critically important complex technical systems.

Predicting the Risk of Attrition for Undergraduate Students with Time Based Modelling

ERIC Educational Resources Information Center

Chai, Kevin E. K.; Gibson, David

2015-01-01

Improving student retention is an important and challenging problem for universities. This paper reports on the development of a student attrition model for predicting which first year students are most at-risk of leaving at various points in time during their first semester of study. The objective of developing such a model is to assist…

Semicompeting risks in aging research: methods, issues and needs

PubMed Central

Varadhan, Ravi; Xue, Qian-Li; Bandeen-Roche, Karen

2015-01-01

A semicompeting risks problem involves two-types of events: a nonterminal and a terminal event (death). Typically, the nonterminal event is the focus of the study, but the terminal event can preclude the occurrence of the nonterminal event. Semicompeting risks are ubiquitous in studies of aging. Examples of semicompeting risk dyads include: dementia and death, frailty syndrome and death, disability and death, and nursing home placement and death. Semicompeting risk models can be divided into two broad classes: models based only on observables quantities (class O) and those based on potential (latent) failure times (class L). The classical illness-death model belongs to class O. This model is a special case of the multistate models, which has been an active area of methodology development. During the past decade and a half, there has also been a flurry of methodological activity on semicompeting risks based on latent failure times (L models). These advances notwithstanding, the semi-competing risks methodology has not penetrated biomedical research, in general, and gerontological research, in particular. Some possible reasons for this lack of uptake are: the methods are relatively new and sophisticated, conceptual problems associated with potential failure time models are difficult to overcome, paucity of expository articles aimed at educating practitioners, and non-availability of readily usable software. The main goals of this review article are: (i) to describe the major types of semicompeting risks problems arising in aging research, (ii) to provide a brief survey of the semicompeting risks methods, (iii) to suggest appropriate methods for addressing the problems in aging research, (iv) to highlight areas where more work is needed, and (v) to suggest ways to facilitate the uptake of the semicompeting risks methodology by the broader biomedical research community. PMID:24729136

A bootstrap based space-time surveillance model with an application to crime occurrences

NASA Astrophysics Data System (ADS)

Kim, Youngho; O'Kelly, Morton

2008-06-01

This study proposes a bootstrap-based space-time surveillance model. Designed to find emerging hotspots in near-real time, the bootstrap based model is characterized by its use of past occurrence information and bootstrap permutations. Many existing space-time surveillance methods, using population at risk data to generate expected values, have resulting hotspots bounded by administrative area units and are of limited use for near-real time applications because of the population data needed. However, this study generates expected values for local hotspots from past occurrences rather than population at risk. Also, bootstrap permutations of previous occurrences are used for significant tests. Consequently, the bootstrap-based model, without the requirement of population at risk data, (1) is free from administrative area restriction, (2) enables more frequent surveillance for continuously updated registry database, and (3) is readily applicable to criminology and epidemiology surveillance. The bootstrap-based model performs better for space-time surveillance than the space-time scan statistic. This is shown by means of simulations and an application to residential crime occurrences in Columbus, OH, year 2000.

Comparisons of lung tumour mortality risk in the Japanese A-bomb survivors and in the Colorado Plateau uranium miners: support for the ICRP lung model.

PubMed

Little, M P

2002-03-01

To estimate the ratio of risks for exposure to radon progeny relative to low-LET radiation based on human lung cancer data, taking account of possible time and age variations in radiation-induced lung cancer risk. Fitting two sorts of time- and age-adjusted relative risk models to a case-control dataset nested within the Colorado Plateau uranium miner cohort and to the Japanese atomic (A)-bomb survivor mortality data. If all A-bomb survivors are compared with the Colorado data, there are statistically significant (two-sided p < 0.05) differences between the two datasets in the pattern of the variation of relative risk with time after exposure, age at exposure and attained age. The excess relative risk decreases much faster with time, age at exposure and attained age in the Colorado uranium miners than in the Japanese A-bomb survivors. If only male A-bomb survivors are compared with the Colorado data, there are no longer statistically significant differences between the two datasets in the pattern of variation of relative risk with time after exposure, age at exposure or attained age. There are no statistically significant differences between the male and female A-bomb survivors in the speed of reduction of relative risk with time after exposure, age at exposure or attained age, although there are indications of rather faster reduction of relative risk with time and age among male survivors than among female survivors. The implicit risk conversion factor for exposure to radon progeny relative to the A-bomb radiation in the male survivors is 1.8 x 10(-2) Sv WLM(-1) (95% CI 6.1 x10(-3), 1.1 x 10(-1)) using a model with exponential adjustments for the effects of radiation for time since exposure and age at exposure, and 1.9 x 10(-2) Sv WLM(-1) (95% CI 6.2 x 10(-3), 1.6 x 10(-1)) using a model with adjustments for the effects of radiation proportional to powers of time since exposure and attained age. Estimates of the risk conversion factor calculated using variant assumptions as to the definition of lung cancer in the Colorado data, or by excluding miners for whom exposure estimates may be less reliable, are very similar. The absence of information on cigarette smoking in the Japanese A-bomb survivors, and the possibility that this may confound the time trends in radiation-induced lung cancer risk in that cohort, imply that these findings should be interpreted with caution. There are no statistically significant differences between the male A-bomb survivors data and the Colorado miner data in the pattern of variation of relative risk with time after exposure and age at exposure. The risk conversion factor is very close to the value suggested by the latest ICRP lung model, albeit with substantial uncertainties.

Saturation of tobacco smoking models and risk of alcohol and tobacco use among adolescents.

PubMed

Taylor, Jennifer E; Conard, Mark W; Koetting O'Byrne, Kristin; Haddock, C Keith; Poston, W S Carlos

2004-09-01

To examine how saturation of an adolescent's environment with models of cigarette smoking (e.g., parents, siblings, friends) affects the probability of tobacco and alcohol use among junior high and high school students. The Health and Smoking Questionnaire was administered to 806 adolescents (182 smokers and 624 nonsmokers; 57.2% female) average age of 15.1 years (SD = 1.6) in a mid-size Midwestern town. The questionnaire contains standardized items in five domains: demographics, smoking status and history, perceptions of risk and risk reduction, risk factors for tobacco use, and parenting style. Risk for smoking or using alcohol increased dramatically as the number of models who smoke increased in an adolescent's environment. For instance, adolescents with one significant other who smoked were nearly four times (OR = 3.76, p <.001) more likely to smoke than someone with no significant others who smoked. However, if an adolescent had four significant others who smoked, they were over 160 times more likely to smoke (OR = 161.25, p <.001). Similar results were found for alcohol use; adolescents who had one significant other who smoked were more than 2.5 (OR = 2.66, p <.001) times more likely to drink than those without smoking models. Adolescents who had four significant other smoking models were 13 times (OR = 13.08, p <.001) more likely to drink. As the number of cigarette smokers in an adolescent's environment increases, risk of tobacco and alcohol use increases substantially. These data suggest that multiple models of tobacco use will substantially increase risk for substance use in adolescents.

Spatio-temporal population estimates for risk management

NASA Astrophysics Data System (ADS)

Cockings, Samantha; Martin, David; Smith, Alan; Martin, Rebecca

2013-04-01

Accurate estimation of population at risk from hazards and effective emergency management of events require not just appropriate spatio-temporal modelling of hazards but also of population. While much recent effort has been focused on improving the modelling and predictions of hazards (both natural and anthropogenic), there has been little parallel advance in the measurement or modelling of population statistics. Different hazard types occur over diverse temporal cycles, are of varying duration and differ significantly in their spatial extent. Even events of the same hazard type, such as flood events, vary markedly in their spatial and temporal characteristics. Conceptually and pragmatically then, population estimates should also be available for similarly varying spatio-temporal scales. Routine population statistics derived from traditional censuses or surveys are usually static representations in both space and time, recording people at their place of usual residence on census/survey night and presenting data for administratively defined areas. Such representations effectively fix the scale of population estimates in both space and time, which is unhelpful for meaningful risk management. Over recent years, the Pop24/7 programme of research, based at the University of Southampton (UK), has developed a framework for spatio-temporal modelling of population, based on gridded population surfaces. Based on a data model which is fully flexible in terms of space and time, the framework allows population estimates to be produced for any time slice relevant to the data contained in the model. It is based around a set of origin and destination centroids, which have capacities, spatial extents and catchment areas, all of which can vary temporally, such as by time of day, day of week, season. A background layer, containing information on features such as transport networks and landuse, provides information on the likelihood of people being in certain places at specific times. Unusual patterns associated with special events can also be modelled and the framework is fully volume preserving. Outputs from the model are gridded population surfaces for the specified time slice, either for total population or by sub-groups (e.g. age). Software to implement the models (SurfaceBuilder247) has been developed and pre-processed layers for typical time slices for England and Wales in 2001 and 2006 are available for UK academic purposes. The outputs and modelling framework from the Pop24/7 programme provide significant opportunities for risk management applications. For estimates of mid- to long-term cumulative population exposure to hazards, such as in flood risk mapping, populations can be produced for numerous time slices and integrated with flood models. For applications in emergency response/ management, time-specific population models can be used as seeds for agent-based models or other response/behaviour models. Estimates for sub-groups of the population also permit exploration of vulnerability through space and time. This paper outlines the requirements for effective spatio-temporal population models for risk management. It then describes the Pop24/7 framework and illustrates its potential for risk management through presentation of examples from natural and anthropogenic hazard applications. The paper concludes by highlighting key challenges for future research in this area.

Evaluation of Enhanced Risk Monitors for Use on Advanced Reactors

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

Ramuhalli, Pradeep; Veeramany, Arun; Bonebrake, Christopher A.

This study provides an overview of the methodology for integrating time-dependent failure probabilities into nuclear power reactor risk monitors. This prototypic enhanced risk monitor (ERM) methodology was evaluated using a hypothetical probabilistic risk assessment (PRA) model, generated using a simplified design of a liquid-metal-cooled advanced reactor (AR). Component failure data from industry compilation of failures of components similar to those in the simplified AR model were used to initialize the PRA model. Core damage frequency (CDF) over time were computed and analyzed. In addition, a study on alternative risk metrics for ARs was conducted. Risk metrics that quantify the normalizedmore » cost of repairs, replacements, or other operations and management (O&M) actions were defined and used, along with an economic model, to compute the likely economic risk of future actions such as deferred maintenance based on the anticipated change in CDF due to current component condition and future anticipated degradation. Such integration of conventional-risk metrics with alternate-risk metrics provides a convenient mechanism for assessing the impact of O&M decisions on safety and economics of the plant. It is expected that, when integrated with supervisory control algorithms, such integrated-risk monitors will provide a mechanism for real-time control decision-making that ensure safety margins are maintained while operating the plant in an economically viable manner.« less

Combining operational models and data into a dynamic vessel risk assessment tool for coastal regions

NASA Astrophysics Data System (ADS)

Fernandes, R.; Braunschweig, F.; Lourenço, F.; Neves, R.

2016-02-01

The technological evolution in terms of computational capacity, data acquisition systems, numerical modelling and operational oceanography is supplying opportunities for designing and building holistic approaches and complex tools for newer and more efficient management (planning, prevention and response) of coastal water pollution risk events. A combined methodology to dynamically estimate time and space variable individual vessel accident risk levels and shoreline contamination risk from ships has been developed, integrating numerical metocean forecasts and oil spill simulations with vessel tracking automatic identification systems (AIS). The risk rating combines the likelihood of an oil spill occurring from a vessel navigating in a study area - the Portuguese continental shelf - with the assessed consequences to the shoreline. The spill likelihood is based on dynamic marine weather conditions and statistical information from previous accidents. The shoreline consequences reflect the virtual spilled oil amount reaching shoreline and its environmental and socio-economic vulnerabilities. The oil reaching shoreline is quantified with an oil spill fate and behaviour model running multiple virtual spills from vessels along time, or as an alternative, a correction factor based on vessel distance from coast. Shoreline risks can be computed in real time or from previously obtained data. Results show the ability of the proposed methodology to estimate the risk properly sensitive to dynamic metocean conditions and to oil transport behaviour. The integration of meteo-oceanic + oil spill models with coastal vulnerability and AIS data in the quantification of risk enhances the maritime situational awareness and the decision support model, providing a more realistic approach in the assessment of shoreline impacts. The risk assessment from historical data can help finding typical risk patterns ("hot spots") or developing sensitivity analysis to specific conditions, whereas real-time risk levels can be used in the prioritization of individual ships, geographical areas, strategic tug positioning and implementation of dynamic risk-based vessel traffic monitoring.

Risk Modelling of Agricultural Products

NASA Astrophysics Data System (ADS)

Nugrahani, E. H.

2017-03-01

In the real world market, agricultural commodity are imposed with fluctuating prices. This means that the price of agricultural products are relatively volatile, which means that agricultural business is a quite risky business for farmers. This paper presents some mathematical models to model such risks in the form of its volatility, based on certain assumptions. The proposed models are time varying volatility model, as well as time varying volatility with mean reversion and with seasonal mean equation models. Implementation on empirical data show that agricultural products are indeed risky.

[Case study on health risk assessment based on site-specific conceptual model].

PubMed

Zhong, Mao-Sheng; Jiang, Lin; Yao, Jue-Jun; Xia, Tian-Xiang; Zhu, Xiao-Ying; Han, Dan; Zhang, Li-Na

2013-02-01

Site investigation was carried out on an area to be redeveloped as a subway station, which is right downstream of the groundwater of a former chemical plant. The results indicate the subsurface soil and groundwater in the area are both polluted heavily by 1,2-dichloroethane, which was caused by the chemical plant upstream with the highest concentration was 104.08 mg.kg-1 for soil sample at 8.6 m below ground and the highest concentration was 18500 microg.L-1 for groundwater. Further, a site-specific contamination conceptual model, giving consideration to the specific structure configuration of the station, was developed, and the corresponding risk calculation equation was derived. The carcinogenic risks calculated with models developed on the generic site conceptual model and derived herein on the site-specific conceptual model were compared. Both models indicate that the carcinogenic risk is significantly higher than the acceptable level which is 1 x 10(-6). The comparison result reveals that the risk calculated with the former models for soil and groundwater are higher than the one calculated with the latter models by 2 times and 1.5 times, respectively. The finding in this paper indicates that the generic risk assessment model may underestimate the risk if specific site conditions and structure configuration are not considered.

The Analysis of Rush Orders Risk in Supply Chain: A Simulation Approach

NASA Technical Reports Server (NTRS)

Mahfouz, Amr; Arisha, Amr

2011-01-01

Satisfying customers by delivering demands at agreed time, with competitive prices, and in satisfactory quality level are crucial requirements for supply chain survival. Incidence of risks in supply chain often causes sudden disruptions in the processes and consequently leads to customers losing their trust in a company's competence. Rush orders are considered to be one of the main types of supply chain risks due to their negative impact on the overall performance, Using integrated definition modeling approaches (i.e. IDEF0 & IDEF3) and simulation modeling technique, a comprehensive integrated model has been developed to assess rush order risks and examine two risk mitigation strategies. Detailed functions sequence and objects flow were conceptually modeled to reflect on macro and micro levels of the studied supply chain. Discrete event simulation models were then developed to assess and investigate the mitigation strategies of rush order risks, the objective of this is to minimize order cycle time and cost.

Melanoma Risk Prediction Models

Cancer.gov

Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

Changes in erectile dysfunction over time in relation to Framingham cardiovascular risk in the Boston Area Community Health (BACH) Survey.

PubMed

Fang, Shona C; Rosen, Raymond C; Vita, Joseph A; Ganz, Peter; Kupelian, Varant

2015-01-01

Erectile dysfunction (ED) is associated with cardiovascular disease (CVD); however, the association between change in ED status over time and future underlying CVD risk is unclear. The aim of this study was to investigate the association between change in ED status and Framingham CVD risk, as well change in Framingham risk. We studied 965 men free of CVD in the Boston Area Community Health (BACH) Survey, a longitudinal cohort study with three assessments. ED was assessed with the five-item International Index of Erectile Function at BACH I (2002-2005) and BACH II (2007-2010) and classified as no ED/transient ED/persistent ED. CVD risk was assessed with 10-year Framingham CVD risk algorithm at BACH I and BACH III (2010-2012). Linear regression models controlled for baseline age, socio-demographic and lifestyle factors, as well as baseline Framingham risk. Models were also stratified by age (≥/< 50 years). Framingham CVD risk and change in Framingham CVD risk were the main outcome measures. Transient and persistent ED was significantly associated with increased Framingham risk and change in risk over time in univariate and age-adjusted models. In younger men, persistent ED was associated with a Framingham risk that was 1.58 percentage points higher (95% confidence interval [CI]: 0.11, 3.06) and in older men, a Framingham risk that was 2.54 percentage points higher (95% CI: -1.5, 6.59), compared with those without ED. Change in Framingham risk over time was also associated with transient and persistent ED in men <50 years, but not in older men. Data suggest that even after taking into account other CVD risk factors, transient and persistent ED is associated with Framingham CVD risk and a greater increase in Framingham risk over time, particularly in younger men. Findings further support clinical assessment of CVD risk in men presenting with ED, especially those under 50 years. © 2014 International Society for Sexual Medicine.

Challenges in risk estimation using routinely collected clinical data: The example of estimating cervical cancer risks from electronic health-records.

PubMed

Landy, Rebecca; Cheung, Li C; Schiffman, Mark; Gage, Julia C; Hyun, Noorie; Wentzensen, Nicolas; Kinney, Walter K; Castle, Philip E; Fetterman, Barbara; Poitras, Nancy E; Lorey, Thomas; Sasieni, Peter D; Katki, Hormuzd A

2018-06-01

Electronic health-records (EHR) are increasingly used by epidemiologists studying disease following surveillance testing to provide evidence for screening intervals and referral guidelines. Although cost-effective, undiagnosed prevalent disease and interval censoring (in which asymptomatic disease is only observed at the time of testing) raise substantial analytic issues when estimating risk that cannot be addressed using Kaplan-Meier methods. Based on our experience analysing EHR from cervical cancer screening, we previously proposed the logistic-Weibull model to address these issues. Here we demonstrate how the choice of statistical method can impact risk estimates. We use observed data on 41,067 women in the cervical cancer screening program at Kaiser Permanente Northern California, 2003-2013, as well as simulations to evaluate the ability of different methods (Kaplan-Meier, Turnbull, Weibull and logistic-Weibull) to accurately estimate risk within a screening program. Cumulative risk estimates from the statistical methods varied considerably, with the largest differences occurring for prevalent disease risk when baseline disease ascertainment was random but incomplete. Kaplan-Meier underestimated risk at earlier times and overestimated risk at later times in the presence of interval censoring or undiagnosed prevalent disease. Turnbull performed well, though was inefficient and not smooth. The logistic-Weibull model performed well, except when event times didn't follow a Weibull distribution. We have demonstrated that methods for right-censored data, such as Kaplan-Meier, result in biased estimates of disease risks when applied to interval-censored data, such as screening programs using EHR data. The logistic-Weibull model is attractive, but the model fit must be checked against Turnbull non-parametric risk estimates. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Multilevel joint competing risk models

NASA Astrophysics Data System (ADS)

Karunarathna, G. H. S.; Sooriyarachchi, M. R.

2017-09-01

Joint modeling approaches are often encountered for different outcomes of competing risk time to event and count in many biomedical and epidemiology studies in the presence of cluster effect. Hospital length of stay (LOS) has been the widely used outcome measure in hospital utilization due to the benchmark measurement for measuring multiple terminations such as discharge, transferred, dead and patients who have not completed the event of interest at the follow up period (censored) during hospitalizations. Competing risk models provide a method of addressing such multiple destinations since classical time to event models yield biased results when there are multiple events. In this study, the concept of joint modeling has been applied to the dengue epidemiology in Sri Lanka, 2006-2008 to assess the relationship between different outcomes of LOS and platelet count of dengue patients with the district cluster effect. Two key approaches have been applied to build up the joint scenario. In the first approach, modeling each competing risk separately using the binary logistic model, treating all other events as censored under the multilevel discrete time to event model, while the platelet counts are assumed to follow a lognormal regression model. The second approach is based on the endogeneity effect in the multilevel competing risks and count model. Model parameters were estimated using maximum likelihood based on the Laplace approximation. Moreover, the study reveals that joint modeling approach yield more precise results compared to fitting two separate univariate models, in terms of AIC (Akaike Information Criterion).

Using Simulation to Interpret a Discrete Time Survival Model in a Complex Biological System: Fertility and Lameness in Dairy Cows

PubMed Central

Hudson, Christopher D.; Huxley, Jonathan N.; Green, Martin J.

2014-01-01

The ever-growing volume of data routinely collected and stored in everyday life presents researchers with a number of opportunities to gain insight and make predictions. This study aimed to demonstrate the usefulness in a specific clinical context of a simulation-based technique called probabilistic sensitivity analysis (PSA) in interpreting the results of a discrete time survival model based on a large dataset of routinely collected dairy herd management data. Data from 12,515 dairy cows (from 39 herds) were used to construct a multilevel discrete time survival model in which the outcome was the probability of a cow becoming pregnant during a given two day period of risk, and presence or absence of a recorded lameness event during various time frames relative to the risk period amongst the potential explanatory variables. A separate simulation model was then constructed to evaluate the wider clinical implications of the model results (i.e. the potential for a herd’s incidence rate of lameness to influence its overall reproductive performance) using PSA. Although the discrete time survival analysis revealed some relatively large associations between lameness events and risk of pregnancy (for example, occurrence of a lameness case within 14 days of a risk period was associated with a 25% reduction in the risk of the cow becoming pregnant during that risk period), PSA revealed that, when viewed in the context of a realistic clinical situation, a herd’s lameness incidence rate is highly unlikely to influence its overall reproductive performance to a meaningful extent in the vast majority of situations. Construction of a simulation model within a PSA framework proved to be a very useful additional step to aid contextualisation of the results from a discrete time survival model, especially where the research is designed to guide on-farm management decisions at population (i.e. herd) rather than individual level. PMID:25101997

Using simulation to interpret a discrete time survival model in a complex biological system: fertility and lameness in dairy cows.

PubMed

Hudson, Christopher D; Huxley, Jonathan N; Green, Martin J

2014-01-01

The ever-growing volume of data routinely collected and stored in everyday life presents researchers with a number of opportunities to gain insight and make predictions. This study aimed to demonstrate the usefulness in a specific clinical context of a simulation-based technique called probabilistic sensitivity analysis (PSA) in interpreting the results of a discrete time survival model based on a large dataset of routinely collected dairy herd management data. Data from 12,515 dairy cows (from 39 herds) were used to construct a multilevel discrete time survival model in which the outcome was the probability of a cow becoming pregnant during a given two day period of risk, and presence or absence of a recorded lameness event during various time frames relative to the risk period amongst the potential explanatory variables. A separate simulation model was then constructed to evaluate the wider clinical implications of the model results (i.e. the potential for a herd's incidence rate of lameness to influence its overall reproductive performance) using PSA. Although the discrete time survival analysis revealed some relatively large associations between lameness events and risk of pregnancy (for example, occurrence of a lameness case within 14 days of a risk period was associated with a 25% reduction in the risk of the cow becoming pregnant during that risk period), PSA revealed that, when viewed in the context of a realistic clinical situation, a herd's lameness incidence rate is highly unlikely to influence its overall reproductive performance to a meaningful extent in the vast majority of situations. Construction of a simulation model within a PSA framework proved to be a very useful additional step to aid contextualisation of the results from a discrete time survival model, especially where the research is designed to guide on-farm management decisions at population (i.e. herd) rather than individual level.

Quantitative assessment of the microbial risk of leafy greens from farm to consumption: preliminary framework, data, and risk estimates.

PubMed

Danyluk, Michelle D; Schaffner, Donald W

2011-05-01

This project was undertaken to relate what is known about the behavior of Escherichia coli O157:H7 under laboratory conditions and integrate this information to what is known regarding the 2006 E. coli O157:H7 spinach outbreak in the context of a quantitative microbial risk assessment. The risk model explicitly assumes that all contamination arises from exposure in the field. Extracted data, models, and user inputs were entered into an Excel spreadsheet, and the modeling software @RISK was used to perform Monte Carlo simulations. The model predicts that cut leafy greens that are temperature abused will support the growth of E. coli O157:H7, and populations of the organism may increase by as much a 1 log CFU/day under optimal temperature conditions. When the risk model used a starting level of -1 log CFU/g, with 0.1% of incoming servings contaminated, the predicted numbers of cells per serving were within the range of best available estimates of pathogen levels during the outbreak. The model predicts that levels in the field of -1 log CFU/g and 0.1% prevalence could have resulted in an outbreak approximately the size of the 2006 E. coli O157:H7 outbreak. This quantitative microbial risk assessment model represents a preliminary framework that identifies available data and provides initial risk estimates for pathogenic E. coli in leafy greens. Data gaps include retail storage times, correlations between storage time and temperature, determining the importance of E. coli O157:H7 in leafy greens lag time models, and validation of the importance of cross-contamination during the washing process.

A quantile-based Time at Risk: A new approach for assessing risk in financial markets

NASA Astrophysics Data System (ADS)

Bolgorian, Meysam; Raei, Reza

2013-11-01

In this paper, we provide a new measure for evaluation of risk in financial markets. This measure is based on the return interval of critical events in financial markets or other investment situations. Our main goal was to devise a model like Value at Risk (VaR). As VaR, for a given financial asset, probability level and time horizon, gives a critical value such that the likelihood of loss on the asset over the time horizon exceeds this value is equal to the given probability level, our concept of Time at Risk (TaR), using a probability distribution function of return intervals, provides a critical time such that the probability that the return interval of a critical event exceeds this time equals the given probability level. As an empirical application, we applied our model to data from the Tehran Stock Exchange Price Index (TEPIX) as a financial asset (market portfolio) and reported the results.

Developmental pathways from maltreatment to risk behavior: Sexual behavior as a catalyst.

PubMed

Negriff, Sonya

2018-05-01

Although delinquency, substance use, and sexual activity are established to be highly intercorrelated, the extant research provides minimal evidence in support of one particular sequence of risk behavior or on the cascade effects from maltreatment. The present study tested a longitudinal model incorporating maltreatment, deviant peers, sexual behavior, delinquency, and substance use to elucidate the sequential pathway(s) from maltreatment to each specific risk behavior throughout adolescence. Data came from a longitudinal study on the effects of maltreatment on adolescent development (N = 454) with four study assessments from early (Time 1 M age = 10.98) to late adolescence (Time 4 M age = 18.22). Results from the cross-lagged model showed a sequence from maltreatment to sexual behavior (Time 1), to delinquency (Time 2), to sexual behavior (Time 3), to substance use and delinquency (Time 4). These findings support sexual behavior as the initial risk behavior that is the catalyst for engagement in more advanced risk behaviors across adolescence.

Prostate Cancer Risk Prediction Models

Cancer.gov

Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

Bladder Cancer Risk Prediction Models

Cancer.gov

Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

Ovarian Cancer Risk Prediction Models

Cancer.gov

Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

Pancreatic Cancer Risk Prediction Models

Cancer.gov

Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

Testicular Cancer Risk Prediction Models

Cancer.gov

Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

Breast Cancer Risk Prediction Models

Cancer.gov

Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

Esophageal Cancer Risk Prediction Models

Cancer.gov

Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

Cervical Cancer Risk Prediction Models

Cancer.gov

Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

Liver Cancer Risk Prediction Models

Cancer.gov

Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

Lung Cancer Risk Prediction Models

Cancer.gov

Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

Colorectal Cancer Risk Prediction Models

Cancer.gov

Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

Modeling hard clinical end-point data in economic analyses.

PubMed

Kansal, Anuraag R; Zheng, Ying; Palencia, Roberto; Ruffolo, Antonio; Hass, Bastian; Sorensen, Sonja V

2013-11-01

The availability of hard clinical end-point data, such as that on cardiovascular (CV) events among patients with type 2 diabetes mellitus, is increasing, and as a result there is growing interest in using hard end-point data of this type in economic analyses. This study investigated published approaches for modeling hard end-points from clinical trials and evaluated their applicability in health economic models with different disease features. A review of cost-effectiveness models of interventions in clinically significant therapeutic areas (CV diseases, cancer, and chronic lower respiratory diseases) was conducted in PubMed and Embase using a defined search strategy. Only studies integrating hard end-point data from randomized clinical trials were considered. For each study included, clinical input characteristics and modeling approach were summarized and evaluated. A total of 33 articles (23 CV, eight cancer, two respiratory) were accepted for detailed analysis. Decision trees, Markov models, discrete event simulations, and hybrids were used. Event rates were incorporated either as constant rates, time-dependent risks, or risk equations based on patient characteristics. Risks dependent on time and/or patient characteristics were used where major event rates were >1%/year in models with fewer health states (<7). Models of infrequent events or with numerous health states generally preferred constant event rates. The detailed modeling information and terminology varied, sometimes requiring interpretation. Key considerations for cost-effectiveness models incorporating hard end-point data include the frequency and characteristics of the relevant clinical events and how the trial data is reported. When event risk is low, simplification of both the model structure and event rate modeling is recommended. When event risk is common, such as in high risk populations, more detailed modeling approaches, including individual simulations or explicitly time-dependent event rates, are more appropriate to accurately reflect the trial data.

Sensitivity Analysis of Launch Vehicle Debris Risk Model

NASA Technical Reports Server (NTRS)

Gee, Ken; Lawrence, Scott L.

2010-01-01

As part of an analysis of the loss of crew risk associated with an ascent abort system for a manned launch vehicle, a model was developed to predict the impact risk of the debris resulting from an explosion of the launch vehicle on the crew module. The model consisted of a debris catalog describing the number, size and imparted velocity of each piece of debris, a method to compute the trajectories of the debris and a method to calculate the impact risk given the abort trajectory of the crew module. The model provided a point estimate of the strike probability as a function of the debris catalog, the time of abort and the delay time between the abort and destruction of the launch vehicle. A study was conducted to determine the sensitivity of the strike probability to the various model input parameters and to develop a response surface model for use in the sensitivity analysis of the overall ascent abort risk model. The results of the sensitivity analysis and the response surface model are presented in this paper.

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.

A review and critique of some models used in competing risk analysis.

PubMed

Gail, M

1975-03-01

We have introduced a notation which allows one to define competing risk models easily and to examine underlying assumptions. We have treated the actuarial model for competing risk in detail, comparing it with other models and giving useful variance formulae both for the case when times of death are available and for the case when they are not. The generality of these methods is illustrated by an example treating two dependent competing risks.

Functional correlation approach to operational risk in banking organizations

NASA Astrophysics Data System (ADS)

Kühn, Reimer; Neu, Peter

2003-05-01

A Value-at-Risk-based model is proposed to compute the adequate equity capital necessary to cover potential losses due to operational risks, such as human and system process failures, in banking organizations. Exploring the analogy to a lattice gas model from physics, correlations between sequential failures are modeled by as functionally defined, heterogeneous couplings between mutually supportive processes. In contrast to traditional risk models for market and credit risk, where correlations are described as equal-time-correlations by a covariance matrix, the dynamics of the model shows collective phenomena such as bursts and avalanches of process failures.

Suicidal Ideation and Its Recurrence in Boys and Men from Early Adolescence to Early Adulthood: An Event History Analysis

PubMed Central

Kerr, David C. R.; Owen, Lee. D.; Capaldi, Deborah M.

2008-01-01

Occurrence and recurrences of suicidal ideation (SI) were modeled among boys/men assessed annually from ages 12 to 29 years. Multiple-spell discrete-time event history analyses permitted (a) determination of whether risk for SI escalates with prior experiences of SI (Spell effects), (b) while accounting for changes in risk with time (Period effects), and (c) controlling for vulnerability factors. Self-reported SI (presence/absence in past week), depressive symptoms, alcohol/substance use, and antisocial behavior, and official arrest records were collected annually from 205 boys recruited on the basis of community risk for delinquency. Parents’ self-reported psychopathology and SES were collected in childhood. Period effects supported decreasing risk for SI over time. Spell and time-varying, 1-year lagged substance use and depressive symptoms independently predicted increased risk for SI. Models involving SI with intent were explored. Consistent with interpersonal psychological theory, risk for young men’s SI increases with past experience of SI, even with key propensities controlled; however, risk also decays over time. Targeting conditions that confer risk for SI is essential. Preventing and delaying SI occurrence and recurrence may represent independent mechanisms by which prevention efforts operate. PMID:18729614

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.

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

Early adolescence behavior problems and timing of poverty during childhood: A comparison of lifecourse models.

PubMed

Mazza, Julia Rachel S E; Lambert, Jean; Zunzunegui, Maria Victoria; Tremblay, Richard E; Boivin, Michel; Côté, Sylvana M

2017-03-01

Poverty is a well-established risk factor for the development of behavior problems, yet little is known about how timing of exposure to childhood poverty relates to behavior problems in early adolescence. To examine the differential effects of the timing of poverty between birth and late childhood on behavior problems in early adolescence by modeling lifecourse models, corresponding to sensitive periods, accumulation of risk and social mobility models. We used the Quebec Longitudinal Study of Child Development (N = 2120). Poverty was defined as living below the low-income thresholds defined by Statistics Canada and grouped into three time periods: between ages 0-3 years, 5-7 years, and 8-12 years. Main outcomes were teacher's report of hyperactivity, opposition and physical aggression at age 13 years. Structured linear regression analyses were conducted to estimate the contribution of poverty during the three selected time periods to behavior problems. Partial F-tests were used to compare nested lifecourse models to a full saturated model (all poverty main effects and possible interactions). Families who experienced poverty at all time periods were 9.3% of the original sample. Those who were poor at least one time period were 39.2%. The accumulation of risk model was the best fitting model for hyperactivity and opposition. The risk for physical aggression problems was associated only to poverty between 0 and 3 years supporting the sensitive period. Early and prolonged exposure to childhood poverty predicted higher levels of behavior problems in early adolescence. Antipoverty policies targeting the first years of life and long term support to pregnant women living in poverty are likely to reduce behavior problems in early adolescence. Copyright © 2017 Elsevier Ltd. All rights reserved.

A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models.

EPA Science Inventory

BackgroundExposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of a...

Chemotherapy appointment scheduling under uncertainty using mean-risk stochastic integer programming.

PubMed

Alvarado, Michelle; Ntaimo, Lewis

2018-03-01

Oncology clinics are often burdened with scheduling large volumes of cancer patients for chemotherapy treatments under limited resources such as the number of nurses and chairs. These cancer patients require a series of appointments over several weeks or months and the timing of these appointments is critical to the treatment's effectiveness. Additionally, the appointment duration, the acuity levels of each appointment, and the availability of clinic nurses are uncertain. The timing constraints, stochastic parameters, rising treatment costs, and increased demand of outpatient oncology clinic services motivate the need for efficient appointment schedules and clinic operations. In this paper, we develop three mean-risk stochastic integer programming (SIP) models, referred to as SIP-CHEMO, for the problem of scheduling individual chemotherapy patient appointments and resources. These mean-risk models are presented and an algorithm is devised to improve computational speed. Computational results were conducted using a simulation model and results indicate that the risk-averse SIP-CHEMO model with the expected excess mean-risk measure can decrease patient waiting times and nurse overtime when compared to deterministic scheduling algorithms by 42 % and 27 %, respectively.

Assessing patient risk of central line-associated bacteremia via machine learning.

PubMed

Beeler, Cole; Dbeibo, Lana; Kelley, Kristen; Thatcher, Levi; Webb, Douglas; Bah, Amadou; Monahan, Patrick; Fowler, Nicole R; Nicol, Spencer; Judy-Malcolm, Alisa; Azar, Jose

2018-04-13

Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors, there remains a need to accurately predict the risk of CLABSIs and, in real time, prevent them from occurring. A predictive model was developed using retrospective data from a large academic healthcare system. Models were developed with machine learning via construction of random forests using validated input variables. Fifteen variables accounted for the most significant effect on CLABSI prediction based on a retrospective study of 70,218 unique patient encounters between January 1, 2013, and May 31, 2016. The area under the receiver operating characteristic curve for the best-performing model was 0.82 in production. This model has multiple applications for resource allocation for CLABSI prevention, including serving as a tool to target patients at highest risk for potentially cost-effective but otherwise time-limited interventions. Machine learning can be used to develop accurate models to predict the risk of CLABSI in real time prior to the development of infection. Copyright © 2018 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Uncertainty in a spatial evacuation model

NASA Astrophysics Data System (ADS)

Mohd Ibrahim, Azhar; Venkat, Ibrahim; Wilde, Philippe De

2017-08-01

Pedestrian movements in crowd motion can be perceived in terms of agents who basically exhibit patient or impatient behavior. We model crowd motion subject to exit congestion under uncertainty conditions in a continuous space and compare the proposed model via simulations with the classical social force model. During a typical emergency evacuation scenario, agents might not be able to perceive with certainty the strategies of opponents (other agents) owing to the dynamic changes entailed by the neighborhood of opponents. In such uncertain scenarios, agents will try to update their strategy based on their own rules or their intrinsic behavior. We study risk seeking, risk averse and risk neutral behaviors of such agents via certain game theory notions. We found that risk averse agents tend to achieve faster evacuation time whenever the time delay in conflicts appears to be longer. The results of our simulations also comply with previous work and conform to the fact that evacuation time of agents becomes shorter once mutual cooperation among agents is achieved. Although the impatient strategy appears to be the rational strategy that might lead to faster evacuation times, our study scientifically shows that the more the agents are impatient, the slower is the egress time.

A method for mapping fire hazard and risk across multiple scales and its application in fire management

Treesearch

Robert E. Keane; Stacy A. Drury; Eva C. Karau; Paul F. Hessburg; Keith M. Reynolds

2010-01-01

This paper presents modeling methods for mapping fire hazard and fire risk using a research model called FIREHARM (FIRE Hazard and Risk Model) that computes common measures of fire behavior, fire danger, and fire effects to spatially portray fire hazard over space. FIREHARM can compute a measure of risk associated with the distribution of these measures over time using...

Sarcopenic obesity and overall mortality: Results from the application of novel models of body composition phenotypes to the National Health and Nutrition Examination Survey 1999-2004.

PubMed

Van Aller, Carla; Lara, Jose; Stephan, Blossom C M; Donini, Lorenzo Maria; Heymsfield, Steven; Katzmarzyk, Peter T; Wells, Jonathan C K; Prado, Carla M; Siervo, Mario

2018-02-15

There is no consensus on the definition of sarcopenic obesity (SO), resulting in inconsistent associations of SO with mortality risk. We aim to evaluate association of dual energy x-ray absorptiometry (DXA) SO models with mortality risk in a US adult population (≥50 years). The study population consisted of 3577 participants aged 50 years and older from the 1999-2004 National Health and Nutrition and Examination Survey with mortality follow-up data through December 31, 2011. Difference in survival time in people with and without SO defined by three body composition DXA models (Model 1: body composition phenotype model; Model 2: Truncal Fat Mass (TrFM)/Appendicular Skeletal Muscle Mass (ASM) ratio model; Model 3: Fat Mass (FM)/Fat Free Mass (FFM) ratio). The differences between the models were assessed by the acceleration failure time model, and expressed as time ratios (TR). Participants age 50-70 years with SO had a significantly decreased survival time, according to the body composition phenotype model (TR: 0.92; 95% CI: 0.87-0.97), and TrFM/ASM ratio model (TR: 0.88; 95% CI: 0.81-0.95). The FM/FFM ratio model did not detect significant differences in survival time. Participants with SO aged 70 years and older did not have a significantly decreased survival time, according to all three models. A SO phenotype increases mortality risk in people of age 50-70 years, but not in people aged 70 years and older. The application of the body composition phenotype and the TrFM/ASM ratio models may represent useful diagnostic approaches to improve the prediction of disease and mortality risk. Copyright © 2018 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Tutorial: Parallel Computing of Simulation Models for Risk Analysis.

PubMed

Reilly, Allison C; Staid, Andrea; Gao, Michael; Guikema, Seth D

2016-10-01

Simulation models are widely used in risk analysis to study the effects of uncertainties on outcomes of interest in complex problems. Often, these models are computationally complex and time consuming to run. This latter point may be at odds with time-sensitive evaluations or may limit the number of parameters that are considered. In this article, we give an introductory tutorial focused on parallelizing simulation code to better leverage modern computing hardware, enabling risk analysts to better utilize simulation-based methods for quantifying uncertainty in practice. This article is aimed primarily at risk analysts who use simulation methods but do not yet utilize parallelization to decrease the computational burden of these models. The discussion is focused on conceptual aspects of embarrassingly parallel computer code and software considerations. Two complementary examples are shown using the languages MATLAB and R. A brief discussion of hardware considerations is located in the Appendix. © 2016 Society for Risk Analysis.

Time-varying Concurrent Risk of Extreme Droughts and Heatwaves in California

NASA Astrophysics Data System (ADS)

Sarhadi, A.; Diffenbaugh, N. S.; Ausin, M. C.

2016-12-01

Anthropogenic global warming has changed the nature and the risk of extreme climate phenomena such as droughts and heatwaves. The concurrent of these nature-changing climatic extremes may result in intensifying undesirable consequences in terms of human health and destructive effects in water resources. The present study assesses the risk of concurrent extreme droughts and heatwaves under dynamic nonstationary conditions arising from climate change in California. For doing so, a generalized fully Bayesian time-varying multivariate risk framework is proposed evolving through time under dynamic human-induced environment. In this methodology, an extreme, Bayesian, dynamic copula (Gumbel) is developed to model the time-varying dependence structure between the two different climate extremes. The time-varying extreme marginals are previously modeled using a Generalized Extreme Value (GEV) distribution. Bayesian Markov Chain Monte Carlo (MCMC) inference is integrated to estimate parameters of the nonstationary marginals and copula using a Gibbs sampling method. Modelled marginals and copula are then used to develop a fully Bayesian, time-varying joint return period concept for the estimation of concurrent risk. Here we argue that climate change has increased the chance of concurrent droughts and heatwaves over decades in California. It is also demonstrated that a time-varying multivariate perspective should be incorporated to assess realistic concurrent risk of the extremes for water resources planning and management in a changing climate in this area. The proposed generalized methodology can be applied for other stochastic nature-changing compound climate extremes that are under the influence of climate change.

Predicting Time to Hospital Discharge for Extremely Preterm Infants

PubMed Central

Hintz, Susan R.; Bann, Carla M.; Ambalavanan, Namasivayam; Cotten, C. Michael; Das, Abhik; Higgins, Rosemary D.

2010-01-01

As extremely preterm infant mortality rates have decreased, concerns regarding resource utilization have intensified. Accurate models to predict time to hospital discharge could aid in resource planning, family counseling, and perhaps stimulate quality improvement initiatives. Objectives For infants <27 weeks estimated gestational age (EGA), to develop, validate and compare several models to predict time to hospital discharge based on time-dependent covariates, and based on the presence of 5 key risk factors as predictors. Patients and Methods This was a retrospective analysis of infants <27 weeks EGA, born 7/2002-12/2005 and surviving to discharge from a NICHD Neonatal Research Network site. Time to discharge was modeled as continuous (postmenstrual age at discharge, PMAD), and categorical variables (“Early” and “Late” discharge). Three linear and logistic regression models with time-dependent covariate inclusion were developed (perinatal factors only, perinatal+early neonatal factors, perinatal+early+later factors). Models for Early and Late discharge using the cumulative presence of 5 key risk factors as predictors were also evaluated. Predictive capabilities were compared using coefficient of determination (R2) for linear models, and AUC of ROC curve for logistic models. Results Data from 2254 infants were included. Prediction of PMAD was poor, with only 38% of variation explained by linear models. However, models incorporating later clinical characteristics were more accurate in predicting “Early” or “Late” discharge (full models: AUC 0.76-0.83 vs. perinatal factor models: AUC 0.56-0.69). In simplified key risk factors models, predicted probabilities for Early and Late discharge compared favorably with observed rates. Furthermore, the AUC (0.75-0.77) were similar to those of models including the full factor set. Conclusions Prediction of Early or Late discharge is poor if only perinatal factors are considered, but improves substantially with knowledge of later-occurring morbidities. Prediction using a few key risk factors is comparable to full models, and may offer a clinically applicable strategy. PMID:20008430

The role of building models in the evaluation of heat-related risks

NASA Astrophysics Data System (ADS)

Buchin, Oliver; Jänicke, Britta; Meier, Fred; Scherer, Dieter; Ziegler, Felix

2016-04-01

Hazard-risk relationships in epidemiological studies are generally based on the outdoor climate, despite the fact that most of humans' lifetime is spent indoors. By coupling indoor and outdoor climates with a building model, the risk concept developed can still be based on the outdoor conditions but also includes exposure to the indoor climate. The influence of non-linear building physics and the impact of air conditioning on heat-related risks can be assessed in a plausible manner using this risk concept. For proof of concept, the proposed risk concept is compared to a traditional risk analysis. As an example, daily and city-wide mortality data of the age group 65 and older in Berlin, Germany, for the years 2001-2010 are used. Four building models with differing complexity are applied in a time-series regression analysis. This study shows that indoor hazard better explains the variability in the risk data compared to outdoor hazard, depending on the kind of building model. Simplified parameter models include the main non-linear effects and are proposed for the time-series analysis. The concept shows that the definitions of heat events, lag days, and acclimatization in a traditional hazard-risk relationship are influenced by the characteristics of the prevailing building stock.

A simple prognostic model for overall survival in metastatic renal cell carcinoma.

PubMed

Assi, Hazem I; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis.

A simple prognostic model for overall survival in metastatic renal cell carcinoma

PubMed Central

Assi, Hazem I.; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

Introduction: The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. Methods: We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. Results: There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. Conclusions: In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis. PMID:27217858

Risk Prediction Models for Other Cancers or Multiple Sites

Cancer.gov

Developing statistical models that estimate the probability of developing other multiple cancers over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

Breakthrough seizures—Further analysis of the Standard versus New Antiepileptic Drugs (SANAD) study

PubMed Central

Powell, Graham A.; Tudur Smith, Catrin; Marson, Anthony G.

2017-01-01

Objectives To develop prognostic models for risk of a breakthrough seizure, risk of seizure recurrence after a breakthrough seizure, and likelihood of achieving 12-month remission following a breakthrough seizure. A breakthrough seizure is one that occurs following at least 12 months remission whilst on treatment. Methods We analysed data from the SANAD study. This long-term randomised trial compared treatments for participants with newly diagnosed epilepsy. Multivariable Cox models investigated how clinical factors affect the probability of each outcome. Best fitting multivariable models were produced with variable reduction by Akaike’s Information Criterion. Risks associated with combinations of risk factors were calculated from each multivariable model. Results Significant factors in the multivariable model for risk of a breakthrough seizure following 12-month remission were number of tonic-clonic seizures by achievement of 12-month remission, time taken to achieve 12-month remission, and neurological insult. Significant factors in the model for risk of seizure recurrence following a breakthrough seizure were total number of drugs attempted to achieve 12-month remission, time to achieve 12-month remission prior to breakthrough seizure, and breakthrough seizure treatment decision. Significant factors in the model for likelihood of achieving 12-month remission after a breakthrough seizure were gender, age at breakthrough seizure, time to achieve 12-month remission prior to breakthrough, and breakthrough seizure treatment decision. Conclusions This is the first analysis to consider risk of a breakthrough seizure and subsequent outcomes. The described models can be used to identify people most likely to have a breakthrough seizure, a seizure recurrence following a breakthrough seizure, and to achieve 12-month remission following a breakthrough seizure. The results suggest that focussing on achieving 12-month remission swiftly represents the best therapeutic aim to reduce the risk of a breakthrough seizure and subsequent negative outcomes. This will aid individual patient risk stratification and the design of future epilepsy trials. PMID:29267375

Medical resource inventory model for emergency preparation with uncertain demand and stochastic occurrence time under considering different risk preferences at the airport

PubMed Central

Pan, Wei; Guo, Ying; Jin, Lei; Liao, ShuJie

2017-01-01

With the high accident rate of civil aviation, medical resource inventory becomes more important for emergency management at the airport. Meanwhile, medical products usually are time-sensitive and short lifetime. Moreover, we find that the optimal medical resource inventory depends on multiple factors such as different risk preferences, the material shelf life and so on. Thus, it becomes very complex in a real-life environment. According to this situation, we construct medical resource inventory decision model for emergency preparation at the airport. Our model is formulated in such a way as to simultaneously consider uncertain demand, stochastic occurrence time and different risk preferences. For solving this problem, a new programming is developed. Finally, a numerical example is presented to illustrate the proposed method. The results show that it is effective for determining the optimal medical resource inventory for emergency preparation with uncertain demand and stochastic occurrence time under considering different risk preferences at the airport. PMID:28931007

Medical resource inventory model for emergency preparation with uncertain demand and stochastic occurrence time under considering different risk preferences at the airport.

PubMed

Pan, Wei; Guo, Ying; Jin, Lei; Liao, ShuJie

2017-01-01

With the high accident rate of civil aviation, medical resource inventory becomes more important for emergency management at the airport. Meanwhile, medical products usually are time-sensitive and short lifetime. Moreover, we find that the optimal medical resource inventory depends on multiple factors such as different risk preferences, the material shelf life and so on. Thus, it becomes very complex in a real-life environment. According to this situation, we construct medical resource inventory decision model for emergency preparation at the airport. Our model is formulated in such a way as to simultaneously consider uncertain demand, stochastic occurrence time and different risk preferences. For solving this problem, a new programming is developed. Finally, a numerical example is presented to illustrate the proposed method. The results show that it is effective for determining the optimal medical resource inventory for emergency preparation with uncertain demand and stochastic occurrence time under considering different risk preferences at the airport.

The risk typology of healthcare access and its association with unmet healthcare needs in Asian Americans.

PubMed

Jang, Yuri; Park, Nan Sook; Yoon, Hyunwoo; Huang, Ya-Ching; Rhee, Min-Kyoung; Chiriboga, David A; Kim, Miyong T

2018-01-01

Using data from the 2015 Asian American Quality of Life Survey (N = 2,609), latent profile analysis was conducted on general (health insurance, usual place for care and income) and immigrant-specific (nativity, length of stay in the U.S., English proficiency and acculturation) risk factors of healthcare access. Latent profile analysis identified a three-cluster model (low-risk, moderate-risk and high-risk groups). Compared with the low-risk group, the odds of having an unmet healthcare need was 1.52 times greater in the moderate-risk group and 2.24 times greater in the high-risk group. Challenging the myth of model minority, the present sample of Asian Americans demonstrates its vulnerability in access to healthcare. Findings also show the heterogeneity in healthcare access risk profiles. © 2017 John Wiley & Sons Ltd.

Bayesian algorithm implementation in a real time exposure assessment model on benzene with calculation of associated cancer risks.

PubMed

Sarigiannis, Dimosthenis A; Karakitsios, Spyros P; Gotti, Alberto; Papaloukas, Costas L; Kassomenos, Pavlos A; Pilidis, Georgios A

2009-01-01

The objective of the current study was the development of a reliable modeling platform to calculate in real time the personal exposure and the associated health risk for filling station employees evaluating current environmental parameters (traffic, meteorological and amount of fuel traded) determined by the appropriate sensor network. A set of Artificial Neural Networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees. Furthermore, a Physiology Based Pharmaco-Kinetic (PBPK) risk assessment model was developed in order to calculate the lifetime probability distribution of leukemia to the employees, fed by data obtained by the ANN model. Bayesian algorithm was involved in crucial points of both model sub compartments. The application was evaluated in two filling stations (one urban and one rural). Among several algorithms available for the development of the ANN exposure model, Bayesian regularization provided the best results and seemed to be a promising technique for prediction of the exposure pattern of that occupational population group. On assessing the estimated leukemia risk under the scope of providing a distribution curve based on the exposure levels and the different susceptibility of the population, the Bayesian algorithm was a prerequisite of the Monte Carlo approach, which is integrated in the PBPK-based risk model. In conclusion, the modeling system described herein is capable of exploiting the information collected by the environmental sensors in order to estimate in real time the personal exposure and the resulting health risk for employees of gasoline filling stations.

Bayesian Algorithm Implementation in a Real Time Exposure Assessment Model on Benzene with Calculation of Associated Cancer Risks

PubMed Central

Sarigiannis, Dimosthenis A.; Karakitsios, Spyros P.; Gotti, Alberto; Papaloukas, Costas L.; Kassomenos, Pavlos A.; Pilidis, Georgios A.

2009-01-01

The objective of the current study was the development of a reliable modeling platform to calculate in real time the personal exposure and the associated health risk for filling station employees evaluating current environmental parameters (traffic, meteorological and amount of fuel traded) determined by the appropriate sensor network. A set of Artificial Neural Networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees. Furthermore, a Physiology Based Pharmaco-Kinetic (PBPK) risk assessment model was developed in order to calculate the lifetime probability distribution of leukemia to the employees, fed by data obtained by the ANN model. Bayesian algorithm was involved in crucial points of both model sub compartments. The application was evaluated in two filling stations (one urban and one rural). Among several algorithms available for the development of the ANN exposure model, Bayesian regularization provided the best results and seemed to be a promising technique for prediction of the exposure pattern of that occupational population group. On assessing the estimated leukemia risk under the scope of providing a distribution curve based on the exposure levels and the different susceptibility of the population, the Bayesian algorithm was a prerequisite of the Monte Carlo approach, which is integrated in the PBPK-based risk model. In conclusion, the modeling system described herein is capable of exploiting the information collected by the environmental sensors in order to estimate in real time the personal exposure and the resulting health risk for employees of gasoline filling stations. PMID:22399936

Use of space-time models to investigate the stability of patterns of disease.

PubMed

Abellan, Juan Jose; Richardson, Sylvia; Best, Nicky

2008-08-01

The use of Bayesian hierarchical spatial models has become widespread in disease mapping and ecologic studies of health-environment associations. In this type of study, the data are typically aggregated over an extensive time period, thus neglecting the time dimension. The output of purely spatial disease mapping studies is therefore the average spatial pattern of risk over the period analyzed, but the results do not inform about, for example, whether a high average risk was sustained over time or changed over time. We investigated how including the time dimension in disease-mapping models strengthens the epidemiologic interpretation of the overall pattern of risk. We discuss a class of Bayesian hierarchical models that simultaneously characterize and estimate the stable spatial and temporal patterns as well as departures from these stable components. We show how useful rules for classifying areas as stable can be constructed based on the posterior distribution of the space-time interactions. We carry out a simulation study to investigate the sensitivity and specificity of the decision rules we propose, and we illustrate our approach in a case study of congenital anomalies in England. Our results confirm that extending hierarchical disease-mapping models to models that simultaneously consider space and time leads to a number of benefits in terms of interpretation and potential for detection of localized excesses.

Predicting Adolescent Perceptions of the Risks and Benefits of Cigarette Smoking: A Longitudinal Investigation

PubMed Central

Morrell, Holly E. R.; Song, Anna V.; Halpern-Felsher, Bonnie L.

2010-01-01

Objective To evaluate developmental changes, personal smoking experiences, and vicarious smoking experiences as predictors of adolescents’ perceptions of the risks and benefits of cigarette smoking over time, in order to identify new and effective targets for youth smoking prevention programs. Design 395 adolescents were surveyed every six months for two school years, from the beginning of 9th grade to the end of 10th grade. Main Outcome Measures Time, participant smoking, friend smoking, parental smoking, and sex were evaluated as predictors of smoking-related short-term risk perceptions, long-term risk perceptions, and benefits perceptions using multilevel modeling techniques. Results Perceptions of benefits did not change over time. Perceptions of risk decreased with time, but not after sex and parental smoking were included in the model. Adolescents with personal smoking experience reported decreasing perceptions of risk and increasing perceptions of benefits over time. Adolescents with more than 6 friends who smoked also reported increasing perceptions of benefits over time. Conclusions Changes in risk perceptions may not purely be the result of developmental processes, but may also be influenced by personal and vicarious experience with smoking. Findings highlight the importance of identifying and targeting modifiable factors that may influence perceptions. PMID:20939640

Additive mixed effect model for recurrent gap time data.

PubMed

Ding, Jieli; Sun, Liuquan

2017-04-01

Gap times between recurrent events are often of primary interest in medical and observational studies. The additive hazards model, focusing on risk differences rather than risk ratios, has been widely used in practice. However, the marginal additive hazards model does not take the dependence among gap times into account. In this paper, we propose an additive mixed effect model to analyze gap time data, and the proposed model includes a subject-specific random effect to account for the dependence among the gap times. Estimating equation approaches are developed for parameter estimation, and the asymptotic properties of the resulting estimators are established. In addition, some graphical and numerical procedures are presented for model checking. The finite sample behavior of the proposed methods is evaluated through simulation studies, and an application to a data set from a clinic study on chronic granulomatous disease is provided.

Probabilistic risk assessment for a loss of coolant accident in McMaster Nuclear Reactor and application of reliability physics model for modeling human reliability

NASA Astrophysics Data System (ADS)

Ha, Taesung

A probabilistic risk assessment (PRA) was conducted for a loss of coolant accident, (LOCA) in the McMaster Nuclear Reactor (MNR). A level 1 PRA was completed including event sequence modeling, system modeling, and quantification. To support the quantification of the accident sequence identified, data analysis using the Bayesian method and human reliability analysis (HRA) using the accident sequence evaluation procedure (ASEP) approach were performed. Since human performance in research reactors is significantly different from that in power reactors, a time-oriented HRA model (reliability physics model) was applied for the human error probability (HEP) estimation of the core relocation. This model is based on two competing random variables: phenomenological time and performance time. The response surface and direct Monte Carlo simulation with Latin Hypercube sampling were applied for estimating the phenomenological time, whereas the performance time was obtained from interviews with operators. An appropriate probability distribution for the phenomenological time was assigned by statistical goodness-of-fit tests. The human error probability (HEP) for the core relocation was estimated from these two competing quantities: phenomenological time and operators' performance time. The sensitivity of each probability distribution in human reliability estimation was investigated. In order to quantify the uncertainty in the predicted HEPs, a Bayesian approach was selected due to its capability of incorporating uncertainties in model itself and the parameters in that model. The HEP from the current time-oriented model was compared with that from the ASEP approach. Both results were used to evaluate the sensitivity of alternative huinan reliability modeling for the manual core relocation in the LOCA risk model. This exercise demonstrated the applicability of a reliability physics model supplemented with a. Bayesian approach for modeling human reliability and its potential usefulness of quantifying model uncertainty as sensitivity analysis in the PRA model.

Risk factors for accident death in the U.S. Army, 2004-2009.

PubMed

Lewandowski-Romps, Lisa; Peterson, Christopher; Berglund, Patricia A; Collins, Stacey; Cox, Kenneth; Hauret, Keith; Jones, Bruce; Kessler, Ronald C; Mitchell, Colter; Park, Nansook; Schoenbaum, Michael; Stein, Murray B; Ursano, Robert J; Heeringa, Steven G

2014-12-01

Accidents are one of the leading causes of death among U.S. active-duty Army soldiers. Evidence-based approaches to injury prevention could be strengthened by adding person-level characteristics (e.g., demographics) to risk models tested on diverse soldier samples studied over time. To identify person-level risk indicators of accident deaths in Regular Army soldiers during a time frame of intense military operations, and to discriminate risk of not-line-of-duty from line-of-duty accident deaths. Administrative data acquired from multiple Army/Department of Defense sources for active duty Army soldiers during 2004-2009 were analyzed in 2013. Logistic regression modeling was used to identify person-level sociodemographic, service-related, occupational, and mental health predictors of accident deaths. Delayed rank progression or demotion and being male, unmarried, in a combat arms specialty, and of low rank/service length increased odds of accident death for enlisted soldiers. Unique to officers was high risk associated with aviation specialties. Accident death risk decreased over time for currently deployed, enlisted soldiers and increased for those never deployed. Mental health diagnosis was associated with risk only for previous and never-deployed, enlisted soldiers. Models did not discriminate not-line-of-duty from line-of-duty accident deaths. Adding more refined person-level and situational risk indicators to current models could enhance understanding of accident death risk specific to soldier rank and deployment status. Stable predictors could help identify high risk of accident deaths in future cohorts of Regular Army soldiers. Copyright © 2014 American Journal of Preventive Medicine. All rights reserved.

Risk Factors for Accident Death in the U.S. Army, 2004–2009

PubMed Central

Lewandowski-Romps, Lisa; Peterson, Christopher; Berglund, Patricia A.; Collins, Stacey; Cox, Kenneth; Hauret, Keith; Jones, Bruce; Kessler, Ronald C.; Mitchell, Colter; Park, Nansook; Schoenbaum, Michael; Stein, Murray B.; Ursano, Robert J.; Heeringa, Steven G.

2014-01-01

Background Accidents are one of the leading causes of death among U.S. active duty Army soldiers. Evidence-based approaches to injury prevention could be strengthened by adding person-level characteristics (e.g., demographics) to risk models tested on diverse soldier samples studied over time. Purpose To identify person-level risk indicators of accident deaths in Regular Army soldiers during a time frame of intense military operations, and to discriminate risk of not-line-of-duty (NLOD) from line-of-duty (LOD) accident deaths. Methods Administrative data acquired from multiple Army/Department of Defense sources for active duty Army soldiers during 2004–2009 were analyzed in 2013. Logistic regression modeling was used to identify person-level sociodemographic, service-related, occupational, and mental health predictors of accident deaths. Results Delayed rank progression or demotion and being male, unmarried, in a combat arms specialty, and of low rank/service length increased odds of accident death for enlisted soldiers. Unique to officers was high risk associated with aviation specialties. Accident death risk decreased over time for currently deployed, enlisted soldiers while increasing for those never deployed. Mental health diagnosis was associated with risk only for previous and never-deployed, enlisted soldiers. Models did not discriminate NLOD from LOD accident deaths. Conclusions Adding more refined person-level and situational risk indicators to current models could enhance understanding of accident death risk specific to soldier rank and deployment status. Stable predictors could help identify high risk of accident deaths in future cohorts of Regular Army soldiers. PMID:25441238

Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.

PubMed

Cheung, Li C; Pan, Qing; Hyun, Noorie; Schiffman, Mark; Fetterman, Barbara; Castle, Philip E; Lorey, Thomas; Katki, Hormuzd A

2017-09-30

For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Development of a GCR Event-based Risk Model

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.; Ponomarev, Artem L.; Plante, Ianik; Carra, Claudio; Kim, Myung-Hee

2009-01-01

A goal at NASA is to develop event-based systems biology models of space radiation risks that will replace the current dose-based empirical models. Complex and varied biochemical signaling processes transmit the initial DNA and oxidative damage from space radiation into cellular and tissue responses. Mis-repaired damage or aberrant signals can lead to genomic instability, persistent oxidative stress or inflammation, which are causative of cancer and CNS risks. Protective signaling through adaptive responses or cell repopulation is also possible. We are developing a computational simulation approach to galactic cosmic ray (GCR) effects that is based on biological events rather than average quantities such as dose, fluence, or dose equivalent. The goal of the GCR Event-based Risk Model (GERMcode) is to provide a simulation tool to describe and integrate physical and biological events into stochastic models of space radiation risks. We used the quantum multiple scattering model of heavy ion fragmentation (QMSFRG) and well known energy loss processes to develop a stochastic Monte-Carlo based model of GCR transport in spacecraft shielding and tissue. We validated the accuracy of the model by comparing to physical data from the NASA Space Radiation Laboratory (NSRL). Our simulation approach allows us to time-tag each GCR proton or heavy ion interaction in tissue including correlated secondary ions often of high multiplicity. Conventional space radiation risk assessment employs average quantities, and assumes linearity and additivity of responses over the complete range of GCR charge and energies. To investigate possible deviations from these assumptions, we studied several biological response pathway models of varying induction and relaxation times including the ATM, TGF -Smad, and WNT signaling pathways. We then considered small volumes of interacting cells and the time-dependent biophysical events that the GCR would produce within these tissue volumes to estimate how GCR event rates mapped to biological signaling induction and relaxation times. We considered several hypotheses related to signaling and cancer risk, and then performed simulations for conditions where aberrant or adaptive signaling would occur on long-duration space mission. Our results do not support the conventional assumptions of dose, linearity and additivity. A discussion on how event-based systems biology models, which focus on biological signaling as the mechanism to propagate damage or adaptation, can be further developed for cancer and CNS space radiation risk projections is given.

Quantitative microbial risk assessment for Escherichia coli O157:H7, Salmonella enterica, and Listeria monocytogenes in leafy green vegetables consumed at salad bars, based on modeling supply chain logistics.

PubMed

Tromp, S O; Rijgersberg, H; Franz, E

2010-10-01

Quantitative microbial risk assessments do not usually account for the planning and ordering mechanisms (logistics) of a food supply chain. These mechanisms and consumer demand determine the storage and delay times of products. The aim of this study was to quantitatively assess the difference between simulating supply chain logistics (MOD) and assuming fixed storage times (FIX) in microbial risk estimation for the supply chain of fresh-cut leafy green vegetables destined for working-canteen salad bars. The results of the FIX model were previously published (E. Franz, S. O. Tromp, H. Rijgersberg, and H. J. van der Fels-Klerx, J. Food Prot. 73:274-285, 2010). Pathogen growth was modeled using stochastic discrete-event simulation of the applied logistics concept. The public health effects were assessed by conducting an exposure assessment and risk characterization. The relative growths of Escherichia coli O157 (17%) and Salmonella enterica (15%) were identical in the MOD and FIX models. In contrast, the relative growth of Listeria monocytogenes was considerably higher in the MOD model (1,156%) than in the FIX model (194%). The probability of L. monocytogenes infection in The Netherlands was higher in the MOD model (5.18×10(-8)) than in the FIX model (1.23×10(-8)). The risk of listeriosis-induced fetal mortality in the perinatal population increased from 1.24×10(-4) (FIX) to 1.66×10(-4) (MOD). Modeling the probabilistic nature of supply chain logistics is of additional value for microbial risk assessments regarding psychrotrophic pathogens in food products for which time and temperature are the postharvest preventive measures in guaranteeing food safety.

Persistent hemifacial spasm after microvascular decompression: a risk assessment model.

PubMed

Shah, Aalap; Horowitz, Michael

2017-06-01

Microvascular decompression (MVD) for hemifacial spasm (HFS) provides resolution of disabling symptoms such as eyelid twitching and muscle contractions of the entire hemiface. The primary aim of this study was to evaluate the predictive value of patient demographics and spasm characteristics on long-term outcomes, with or without intraoperative lateral spread response (LSR) as an additional variable in a risk assessment model. A retrospective study was undertaken to evaluate the associations of pre-operative patient characteristics, as well as intraoperative LSR and need for a staged procedure on the presence of persistent or recurrent HFS at the time of hospital discharge and at follow-up. A risk assessment model was constructed with the inclusion of six clinically or statistically significant variables from the univariate analyses. A receiving operator characteristic curve was generated, and area under the curve was calculated to determine the strength of the predictive model. A risk assessment model was first created consisting of significant pre-operative variables (Model 1) (age >50, female gender, history of botulinum toxin use, platysma muscle involvement). This model demonstrated borderline predictive value for persistent spasm at discharge (AUC .60; p=.045) and fair predictive value at follow-up (AUC .75; p=.001). Intraoperative variables (e.g. LSR persistence) demonstrated little additive value (Model 2) (AUC .67). Patients with a higher risk score (three or greater) demonstrated greater odds of persistent HFS at the time of discharge (OR 1.5 [95%CI 1.16-1.97]; p=.035), as well as greater odds of persistent or recurrent spasm at the time of follow-up (OR 3.0 [95%CI 1.52-5.95]; p=.002) Conclusions: A risk assessment model consisting of pre-operative clinical characteristics is useful in prognosticating HFS persistence at follow-up.

Estimation of value at risk and conditional value at risk using normal mixture distributions model

NASA Astrophysics Data System (ADS)

Kamaruzzaman, Zetty Ain; Isa, Zaidi

2013-04-01

Normal mixture distributions model has been successfully applied in financial time series analysis. In this paper, we estimate the return distribution, value at risk (VaR) and conditional value at risk (CVaR) for monthly and weekly rates of returns for FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI) from July 1990 until July 2010 using the two component univariate normal mixture distributions model. First, we present the application of normal mixture distributions model in empirical finance where we fit our real data. Second, we present the application of normal mixture distributions model in risk analysis where we apply the normal mixture distributions model to evaluate the value at risk (VaR) and conditional value at risk (CVaR) with model validation for both risk measures. The empirical results provide evidence that using the two components normal mixture distributions model can fit the data well and can perform better in estimating value at risk (VaR) and conditional value at risk (CVaR) where it can capture the stylized facts of non-normality and leptokurtosis in returns distribution.

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

Combined risk assessment of nonstationary monthly water quality based on Markov chain and time-varying copula.

PubMed

Shi, Wei; Xia, Jun

2017-02-01

Water quality risk management is a global hot research linkage with the sustainable water resource development. Ammonium nitrogen (NH 3 -N) and permanganate index (COD Mn ) as the focus indicators in Huai River Basin, are selected to reveal their joint transition laws based on Markov theory. The time-varying moments model with either time or land cover index as explanatory variables is applied to build the time-varying marginal distributions of water quality time series. Time-varying copula model, which takes the non-stationarity in the marginal distribution and/or the time variation in dependence structure between water quality series into consideration, is constructed to describe a bivariate frequency analysis for NH 3 -N and COD Mn series at the same monitoring gauge. The larger first-order Markov joint transition probability indicates water quality state Class V w , Class IV and Class III will occur easily in the water body of Bengbu Sluice. Both marginal distribution and copula models are nonstationary, and the explanatory variable time yields better performance than land cover index in describing the non-stationarities in the marginal distributions. In modelling the dependence structure changes, time-varying copula has a better fitting performance than the copula with the constant or the time-trend dependence parameter. The largest synchronous encounter risk probability of NH 3 -N and COD Mn simultaneously reaching Class V is 50.61%, while the asynchronous encounter risk probability is largest when NH 3 -N and COD Mn is inferior to class V and class IV water quality standards, respectively.

Comparison of three lifecourse models of poverty in predicting cardiovascular disease risk in youth.

PubMed

Kakinami, Lisa; Séguin, Louise; Lambert, Marie; Gauvin, Lise; Nikiema, Béatrice; Paradis, Gilles

2013-08-01

Childhood poverty heightens the risk of adulthood cardiovascular disease (CVD), but the underlying pathways are poorly understood. Three lifecourse models have been proposed but have never been tested among youth. We assessed the longitudinal association of childhood poverty with CVD risk factors in 10-year-old youth according to the timing, accumulation, and mobility models. The Québec Longitudinal Study of Child Development birth cohort was established in 1998 (n = 2120). Poverty was defined as annual income below the low-income thresholds defined by Statistics Canada. Multiple imputation was used for missing data. Multivariable linear regression models adjusted for gender, pubertal stage, parental education, maternal age, whether the household was a single parent household, whether the child was overweight or obese, the child's physical activity in the past week, and family history. Approximately 40% experienced poverty at least once, 16% throughout childhood, and 25% intermittently. Poverty was associated with significantly elevated triglycerides and insulin according to the timing and accumulation models, although the timing model was superior for predicting insulin and the accumulation model was superior for predicting triglycerides. Early and prolonged exposure to poverty significantly increases CVD risk among 10-year-old youth. Copyright © 2013 Elsevier Inc. All rights reserved.

Do repeated assessments of performance status improve predictions for risk of death among patients with cancer? A population-based cohort study.

PubMed

Su, Jiandong; Barbera, Lisa; Sutradhar, Rinku

2015-06-01

Prior work has utilized longitudinal information on performance status to demonstrate its association with risk of death among cancer patients; however, no study has assessed whether such longitudinal information improves the predictions for risk of death. To examine whether the use of repeated performance status assessments improve predictions for risk of death compared to using only performance status assessment at the time of cancer diagnosis. This was a population-based longitudinal study of adult outpatients who had a cancer diagnosis and had at least one assessment of performance status. To account for each patient's changing performance status over time, we implemented a Cox model with a time-varying covariate for performance status. This model was compared to a Cox model using only a time-fixed (baseline) covariate for performance status. The regression coefficients of each model were derived based on a randomly selected 60% of patients, and then, the predictive ability of each model was assessed via concordance probabilities when applied to the remaining 40% of patients. Our study consisted of 15,487 cancer patients with over 53,000 performance status assessments. The utilization of repeated performance status assessments improved predictions for risk of death compared to using only the performance status assessment taken at diagnosis. When studying the hazard of death among patients with cancer, if available, researchers should incorporate changing information on performance status scores, instead of simply baseline information on performance status. © The Author(s) 2015.

Decision making under time pressure, modeled in a prospect theory framework.

PubMed

Young, Diana L; Goodie, Adam S; Hall, Daniel B; Wu, Eric

2012-07-01

The current research examines the effects of time pressure on decision behavior based on a prospect theory framework. In Experiments 1 and 2, participants estimated certainty equivalents for binary gains-only bets in the presence or absence of time pressure. In Experiment 3, participants assessed comparable bets that were framed as losses. Data were modeled to establish psychological mechanisms underlying decision behavior. In Experiments 1 and 2, time pressure led to increased risk attractiveness, but no significant differences emerged in either probability discriminability or outcome utility. In Experiment 3, time pressure reduced probability discriminability, which was coupled with severe risk-seeking behavior for both conditions in the domain of losses. No significant effects of control over outcomes were observed. Results provide qualified support for theories that suggest increased risk-seeking for gains under time pressure.

Decision making under time pressure, modeled in a prospect theory framework

PubMed Central

Young, Diana L.; Goodie, Adam S.; Hall, Daniel B.; Wu, Eric

2012-01-01

The current research examines the effects of time pressure on decision behavior based on a prospect theory framework. In Experiments 1 and 2, participants estimated certainty equivalents for binary gains-only bets in the presence or absence of time pressure. In Experiment 3, participants assessed comparable bets that were framed as losses. Data were modeled to establish psychological mechanisms underlying decision behavior. In Experiments 1 and 2, time pressure led to increased risk attractiveness, but no significant differences emerged in either probability discriminability or outcome utility. In Experiment 3, time pressure reduced probability discriminability, which was coupled with severe risk-seeking behavior for both conditions in the domain of losses. No significant effects of control over outcomes were observed. Results provide qualified support for theories that suggest increased risk-seeking for gains under time pressure. PMID:22711977

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.

Quantile uncertainty and value-at-risk model risk.

PubMed

Alexander, Carol; Sarabia, José María

2012-08-01

This article develops a methodology for quantifying model risk in quantile risk estimates. The application of quantile estimates to risk assessment has become common practice in many disciplines, including hydrology, climate change, statistical process control, insurance and actuarial science, and the uncertainty surrounding these estimates has long been recognized. Our work is particularly important in finance, where quantile estimates (called Value-at-Risk) have been the cornerstone of banking risk management since the mid 1980s. A recent amendment to the Basel II Accord recommends additional market risk capital to cover all sources of "model risk" in the estimation of these quantiles. We provide a novel and elegant framework whereby quantile estimates are adjusted for model risk, relative to a benchmark which represents the state of knowledge of the authority that is responsible for model risk. A simulation experiment in which the degree of model risk is controlled illustrates how to quantify Value-at-Risk model risk and compute the required regulatory capital add-on for banks. An empirical example based on real data shows how the methodology can be put into practice, using only two time series (daily Value-at-Risk and daily profit and loss) from a large bank. We conclude with a discussion of potential applications to nonfinancial risks. © 2012 Society for Risk Analysis.

The effect of differences in time to detection of circulating microbubbles on the risk of decompression sickness

NASA Technical Reports Server (NTRS)

Kumar, K. V.; Gilbert, J. H.; Powell, M. R.; Waligora, J. M.

1992-01-01

Circulating microbubbles (CMB) are frequently detected prior to the appearance of symptoms of Decompression Sickness (DCS). It is difficult to analyze the effect of CMB on symptoms due to differences in the time to detection of CMB. This paper uses survival analysis models to evaluate the risk of symptoms in the presence of CMB. Methods: Information on 81 exposures to an altitude of 6,400 m (6.5 psi) for a period of three hours, with simulated extravehicular activities, was examined. The presence or absence of CMB was included as a time dependent covariate of the Cox proportional hazards regression model. Using this technique, the subgroup of exposures with CMB was analyzed further. Mean (S.D.) time in minutes to onset of CMB and symptoms were 125 (63) and 165 (33) respectively, following the three hours exposure. The risk of symptoms (17/81) increased 14 times in the presence of CMB, after controlling for variations in time to detection of CMB. Further, the risk was lower when time to detection of CMB was greater than 60 minutes (risk ratio = 0.96; 95 percent confidence intervals = 0.94 - 0.99 0.99 P less than 0.01) compared to CMB before 60 minutes at altitude. Conclusions: Survival analysis showed that individual risk of DCS changes significantly due to variations in time to detection of CMB. This information is important in evaluating the risk of DCS in the presence of CMB.

A quantitative risk assessment model for Vibrio parahaemolyticus in raw oysters in Sao Paulo State, Brazil.

PubMed

Sobrinho, Paulo de S Costa; Destro, Maria T; Franco, Bernadette D G M; Landgraf, Mariza

2014-06-16

A risk assessment of Vibrio parahaemolyticus associated with raw oysters produced and consumed in São Paulo State was developed. The model was built according to the United States Food and Drug Administration framework for risk assessment. The outcome of the exposure assessment estimated the prevalence and density of pathogenic V. parahaemolyticus in raw oysters from harvest to consumption. The result of the exposure step was combined with a Beta-Poisson dose-response model to estimate the probability of illness. The model predicted that the average risks per serving of raw oysters were 4.7×10(-4), 6.0×10(-4), 4.7×10(-4) and 3.1×10(-4) for spring, summer, fall and winter, respectively. Sensitivity analyses indicated that the most influential variables on the risk of illness were the total density of V. parahaemolyticus at harvest, transport temperature, relative prevalence of pathogenic strains and storage time at retail. Only storage time under refrigeration at retail showed negative correlation with the risk of illness. Copyright © 2014 Elsevier B.V. All rights reserved.

Flood risk in a changing world - a coupled transdisciplinary modelling framework for flood risk assessment in an Alpine study area

NASA Astrophysics Data System (ADS)

Huttenlau, Matthias; Schneeberger, Klaus; Winter, Benjamin; Pazur, Robert; Förster, Kristian; Achleitner, Stefan; Bolliger, Janine

2017-04-01

Devastating flood events have caused substantial economic damage across Europe during past decades. Flood risk management has therefore become a topic of crucial interest across state agencies, research communities and the public sector including insurances. There is consensus that mitigating flood risk relies on impact assessments which quantitatively account for a broad range of aspects in a (changing) environment. Flood risk assessments which take into account the interaction between the drivers climate change, land-use change and socio-economic change might bring new insights to the understanding of the magnitude and spatial characteristic of flood risks. Furthermore, the comparative assessment of different adaptation measures can give valuable information for decision-making. With this contribution we present an inter- and transdisciplinary research project aiming at developing and applying such an impact assessment relying on a coupled modelling framework for the Province of Vorarlberg in Austria. Stakeholder engagement ensures that the final outcomes of our study are accepted and successfully implemented in flood management practice. The study addresses three key questions: (i) What are scenarios of land- use and climate change for the study area? (ii) How will the magnitude and spatial characteristic of future flood risk change as a result of changes in climate and land use? (iii) Are there spatial planning and building-protection measures which effectively reduce future flood risk? The modelling framework has a modular structure comprising modules (i) climate change, (ii) land-use change, (iii) hydrologic modelling, (iv) flood risk analysis, and (v) adaptation measures. Meteorological time series are coupled with spatially explicit scenarios of land-use change to model runoff time series. The runoff time series are combined with impact indicators such as building damages and results are statistically assessed to analyse flood risk scenarios. Thus, the regional flood risk can be expressed in terms of expected annual damage and damages associated with a low probability of occurrence. We consider building protection measures explicitly as part of the consequence analysis of flood risk whereas spatial planning measures are already considered as explicit scenarios in the course of land-use change modelling.

Quantifying the predictive accuracy of time-to-event models in the presence of competing risks.

PubMed

Schoop, Rotraut; Beyersmann, Jan; Schumacher, Martin; Binder, Harald

2011-02-01

Prognostic models for time-to-event data play a prominent role in therapy assignment, risk stratification and inter-hospital quality assurance. The assessment of their prognostic value is vital not only for responsible resource allocation, but also for their widespread acceptance. The additional presence of competing risks to the event of interest requires proper handling not only on the model building side, but also during assessment. Research into methods for the evaluation of the prognostic potential of models accounting for competing risks is still needed, as most proposed methods measure either their discrimination or calibration, but do not examine both simultaneously. We adapt the prediction error proposal of Graf et al. (Statistics in Medicine 1999, 18, 2529–2545) and Gerds and Schumacher (Biometrical Journal 2006, 48, 1029–1040) to handle models with competing risks, i.e. more than one possible event type, and introduce a consistent estimator. A simulation study investigating the behaviour of the estimator in small sample size situations and for different levels of censoring together with a real data application follows.

Contributions of Genes and Environment to Developmental Change in Alcohol Use.

PubMed

Long, E C; Verhulst, B; Aggen, S H; Kendler, K S; Gillespie, N A

2017-09-01

The precise nature of how genetic and environmental risk factors influence changes in alcohol use (AU) over time has not yet been investigated. Therefore, the aim of the present study is to examine the nature of longitudinal changes in these risk factors to AU from mid-adolescence through young adulthood. Using a large sample of male twins, we compared five developmental models that each makes different predictions regarding the longitudinal changes in genetic and environmental risks for AU. The best-fitting model indicated that genetic influences were consistent with a gradual growth in the liability to AU, whereas unique environmental risk factors were consistent with an accumulation of risks across time. These results imply that two distinct processes influence adolescent AU between the ages of 15-25. Genetic effects influence baseline levels of AU and rates of change across time, while unique environmental effects are more cumulative.

The returns and risks of investment portfolio in stock market crashes

NASA Astrophysics Data System (ADS)

Li, Jiang-Cheng; Long, Chao; Chen, Xiao-Dan

2015-06-01

The returns and risks of investment portfolio in stock market crashes are investigated by considering a theoretical model, based on a modified Heston model with a cubic nonlinearity, proposed by Spagnolo and Valenti. Through numerically simulating probability density function of returns and the mean escape time of the model, the results indicate that: (i) the maximum stability of returns is associated with the maximum dispersion of investment portfolio and an optimal stop-loss position; (ii) the maximum risks are related with a worst dispersion of investment portfolio and the risks of investment portfolio are enhanced by increasing stop-loss position. In addition, the good agreements between the theoretical result and real market data are found in the behaviors of the probability density function and the mean escape time.

Consumers' behavior in quantitative microbial risk assessment for pathogens in raw milk: Incorporation of the likelihood of consumption as a function of storage time and temperature.

PubMed

Crotta, Matteo; Paterlini, Franco; Rizzi, Rita; Guitian, Javier

2016-02-01

Foodborne disease as a result of raw milk consumption is an increasing concern in Western countries. Quantitative microbial risk assessment models have been used to estimate the risk of illness due to different pathogens in raw milk. In these models, the duration and temperature of storage before consumption have a critical influence in the final outcome of the simulations and are usually described and modeled as independent distributions in the consumer phase module. We hypothesize that this assumption can result in the computation, during simulations, of extreme scenarios that ultimately lead to an overestimation of the risk. In this study, a sensorial analysis was conducted to replicate consumers' behavior. The results of the analysis were used to establish, by means of a logistic model, the relationship between time-temperature combinations and the probability that a serving of raw milk is actually consumed. To assess our hypothesis, 2 recently published quantitative microbial risk assessment models quantifying the risks of listeriosis and salmonellosis related to the consumption of raw milk were implemented. First, the default settings described in the publications were kept; second, the likelihood of consumption as a function of the length and temperature of storage was included. When results were compared, the density of computed extreme scenarios decreased significantly in the modified model; consequently, the probability of illness and the expected number of cases per year also decreased. Reductions of 11.6 and 12.7% in the proportion of computed scenarios in which a contaminated milk serving was consumed were observed for the first and the second study, respectively. Our results confirm that overlooking the time-temperature dependency may yield to an important overestimation of the risk. Furthermore, we provide estimates of this dependency that could easily be implemented in future quantitative microbial risk assessment models of raw milk pathogens. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Relationship between US Societal Fatality Risk per Vehicle Miles of Travel and Mass, for Individual Vehicle Models over Time (Model Year)

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

Wenzel, Tom P.

This report presents a new approach to analyze the relationship between vehicle mass and risk: tracking fatality risk by vehicle model year and mass, for individual vehicle models. This approach is appealing as it greatly minimizes the influence of driver characteristics and behavior, and crash circumstances, on fatality risk. However, only the most popular vehicle models, with the largest number of fatalities, can be analyzed in this manner. While the analysis of all vehicle models of a given type suggests that there is a relationship between increased mass and fatality risk, analysis of the ten most popular four-door car modelsmore » separately suggests that this relationship is weak: in many cases when the mass of a specific vehicle model is increased societal fatality risk is unchanged or even increases. These results suggest that increasing the mass of an individual vehicle model does not necessarily lead to decreased societal fatality risk.« less

Socio-hydrological modelling of floods: investigating community resilience, adaptation capacity and risk

NASA Astrophysics Data System (ADS)

Ciullo, Alessio; Viglione, Alberto; Castellarin, Attilio

2016-04-01

Changes in flood risk occur because of changes in climate and hydrology, and in societal exposure and vulnerability. Research on change in flood risk has demonstrated that the mutual interactions and continuous feedbacks between floods and societies has to be taken into account in flood risk management. The present work builds on an existing conceptual model of an hypothetical city located in the proximity of a river, along whose floodplains the community evolves over time. The model reproduces the dynamic co-evolution of four variables: flooding, population density of the flooplain, amount of structural protection measures and memory of floods. These variables are then combined in a way to mimic the temporal change of community resilience, defined as the (inverse of the) amount of time for the community to recover from a shock, and adaptation capacity, defined as ratio between damages due to subsequent events. Also, temporal changing exposure, vulnerability and probability of flooding are also modelled, which results in a dynamically varying flood-risk. Examples are provided that show how factors such as collective memory and risk taking attitude influence the dynamics of community resilience, adaptation capacity and risk.

Cabin Environment Physics Risk Model

NASA Technical Reports Server (NTRS)

Mattenberger, Christopher J.; Mathias, Donovan Leigh

2014-01-01

This paper presents a Cabin Environment Physics Risk (CEPR) model that predicts the time for an initial failure of Environmental Control and Life Support System (ECLSS) functionality to propagate into a hazardous environment and trigger a loss-of-crew (LOC) event. This physics-of failure model allows a probabilistic risk assessment of a crewed spacecraft to account for the cabin environment, which can serve as a buffer to protect the crew during an abort from orbit and ultimately enable a safe return. The results of the CEPR model replace the assumption that failure of the crew critical ECLSS functionality causes LOC instantly, and provide a more accurate representation of the spacecraft's risk posture. The instant-LOC assumption is shown to be excessively conservative and, moreover, can impact the relative risk drivers identified for the spacecraft. This, in turn, could lead the design team to allocate mass for equipment to reduce overly conservative risk estimates in a suboptimal configuration, which inherently increases the overall risk to the crew. For example, available mass could be poorly used to add redundant ECLSS components that have a negligible benefit but appear to make the vehicle safer due to poor assumptions about the propagation time of ECLSS failures.

Risk analysis of Listeria spp. contamination in two types of ready-to-eat chicken meat products.

PubMed

Keeratipibul, Suwimon; Lekroengsin, Sumalin

2009-01-01

This study was conducted to determine the risk of Listeria contamination in frozen ready-to-eat roasted and steamed chicken meat in a chicken plant in Thailand. Environmental surfaces were divided into three zones. Zone 1 included surfaces in direct contact with products. Zones 2 and 3 included indirect contact surfaces; zone 2 was next to zone 1, and zone 3 was located next to zone 2 and relatively far from the product. A mathematical model for the probability of product contamination after contact with contaminated zone 1 surfaces was established. This model was augmented by an already established model for the probability of Listeria contamination on zone 1 surfaces. Sensitivity analysis revealed that the prevalence of Listeria on zone 1 surfaces before cleaning and sanitizing, production time, and concentration and contact time of sanitizer were correlated with contamination of both products. Alternative risk management measures for reducing the risk of Listeria contamination were developed using sanitizer concentrations of 0.25 to 1.25% (vol/vol), sanitizer contact times of 5 to 20 min, and production times of 5 to 20 h. The plant's risk manager chose a 0.25% (vol/vol) sanitizer concentration, a contact time of 20 min, and a production time of 20 h. After implementation of the selected risk management option, the prevalence of Listeria on roasted and steamed products was reduced by 2.19 and 2.01%, respectively. The prevalence of Listeria in zones 1, 2, and 3 was also reduced by 3.13, 11.24, and 25.66%, respectively.

Fuzzy multi-objective chance-constrained programming model for hazardous materials transportation

NASA Astrophysics Data System (ADS)

Du, Jiaoman; Yu, Lean; Li, Xiang

2016-04-01

Hazardous materials transportation is an important and hot issue of public safety. Based on the shortest path model, this paper presents a fuzzy multi-objective programming model that minimizes the transportation risk to life, travel time and fuel consumption. First, we present the risk model, travel time model and fuel consumption model. Furthermore, we formulate a chance-constrained programming model within the framework of credibility theory, in which the lengths of arcs in the transportation network are assumed to be fuzzy variables. A hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is designed for finding a satisfactory solution. Finally, some numerical examples are given to demonstrate the efficiency of the proposed model and algorithm.

Lung Cancer Mortality (1950–1999) among Eldorado Uranium Workers: A Comparison of Models of Carcinogenesis and Empirical Excess Risk Models

PubMed Central

Eidemüller, Markus; Jacob, Peter; Lane, Rachel S. D.; Frost, Stanley E.; Zablotska, Lydia B.

2012-01-01

Lung cancer mortality after exposure to radon decay products (RDP) among 16,236 male Eldorado uranium workers was analyzed. Male workers from the Beaverlodge and Port Radium uranium mines and the Port Hope radium and uranium refinery and processing facility who were first employed between 1932 and 1980 were followed up from 1950 to 1999. A total of 618 lung cancer deaths were observed. The analysis compared the results of the biologically-based two-stage clonal expansion (TSCE) model to the empirical excess risk model. The spontaneous clonal expansion rate of pre-malignant cells was reduced at older ages under the assumptions of the TSCE model. Exposure to RDP was associated with increase in the clonal expansion rate during exposure but not afterwards. The increase was stronger for lower exposure rates. A radiation-induced bystander effect could be a possible explanation for such an exposure response. Results on excess risks were compared to a linear dose-response parametric excess risk model with attained age, time since exposure and dose rate as effect modifiers. In all models the excess relative risk decreased with increasing attained age, increasing time since exposure and increasing exposure rate. Large model uncertainties were found in particular for small exposure rates. PMID:22936975

Engineering Risk Assessment of Space Thruster Challenge Problem

NASA Technical Reports Server (NTRS)

Mathias, Donovan L.; Mattenberger, Christopher J.; Go, Susie

2014-01-01

The Engineering Risk Assessment (ERA) team at NASA Ames Research Center utilizes dynamic models with linked physics-of-failure analyses to produce quantitative risk assessments of space exploration missions. This paper applies the ERA approach to the baseline and extended versions of the PSAM Space Thruster Challenge Problem, which investigates mission risk for a deep space ion propulsion system with time-varying thruster requirements and operations schedules. The dynamic mission is modeled using a combination of discrete and continuous-time reliability elements within the commercially available GoldSim software. Loss-of-mission (LOM) probability results are generated via Monte Carlo sampling performed by the integrated model. Model convergence studies are presented to illustrate the sensitivity of integrated LOM results to the number of Monte Carlo trials. A deterministic risk model was also built for the three baseline and extended missions using the Ames Reliability Tool (ART), and results are compared to the simulation results to evaluate the relative importance of mission dynamics. The ART model did a reasonable job of matching the simulation models for the baseline case, while a hybrid approach using offline dynamic models was required for the extended missions. This study highlighted that state-of-the-art techniques can adequately adapt to a range of dynamic problems.

[Design of a risk matrix to assess sterile formulations at health care facilities].

PubMed

Martín de Rosales Cabrera, A M; López Cabezas, C; García Salom, P

2014-05-01

To design a matrix allowing classifying sterile formulations prepared at the hospital with different risk levels. i) Literature search and critical appraisal of the model proposed by the European Resolution CM/Res Ap(2011)1, ii) Identification of the risk associated to the elaboration process by means of the AMFE methodology (Modal Analysis of Failures and Effects), iii) estimation of the severity associated to the risks detected. After initially trying a model of numeric scoring, the classification matrix was changed to an alphabetical classification, grading each criterion from A to D.Each preparation assessed is given a 6-letter combination with three possible risk levels: low, intermediate, and high. This model was easier for risk assignment, and more reproducible. The final model designed analyzes 6 criteria: formulation process, administration route, the drug's safety profile, amount prepared, distribution, and susceptibility for microbiological contamination.The risk level obtained will condition the requirements of the formulation area, validity time, and storing conditions. The matrix model proposed may help health care institutions to better assess the risk of sterile formulations prepared,and provides information about the acceptable validity time according to the storing conditions and the manufacturing area. Its use will increase the safety level of this procedure as well as help in resources planning and distribution. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

A quantitative risk-based model for reasoning over critical system properties

NASA Technical Reports Server (NTRS)

Feather, M. S.

2002-01-01

This position paper suggests the use of a quantitative risk-based model to help support reeasoning and decision making that spans many of the critical properties such as security, safety, survivability, fault tolerance, and real-time.

Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization.

PubMed

Liang, Yong; Chai, Hua; Liu, Xiao-Ying; Xu, Zong-Ben; Zhang, Hai; Leung, Kwong-Sak

2016-03-01

One of the most important objectives of the clinical cancer research is to diagnose cancer more accurately based on the patients' gene expression profiles. Both Cox proportional hazards model (Cox) and accelerated failure time model (AFT) have been widely adopted to the high risk and low risk classification or survival time prediction for the patients' clinical treatment. Nevertheless, two main dilemmas limit the accuracy of these prediction methods. One is that the small sample size and censored data remain a bottleneck for training robust and accurate Cox classification model. In addition to that, similar phenotype tumours and prognoses are actually completely different diseases at the genotype and molecular level. Thus, the utility of the AFT model for the survival time prediction is limited when such biological differences of the diseases have not been previously identified. To try to overcome these two main dilemmas, we proposed a novel semi-supervised learning method based on the Cox and AFT models to accurately predict the treatment risk and the survival time of the patients. Moreover, we adopted the efficient L1/2 regularization approach in the semi-supervised learning method to select the relevant genes, which are significantly associated with the disease. The results of the simulation experiments show that the semi-supervised learning model can significant improve the predictive performance of Cox and AFT models in survival analysis. The proposed procedures have been successfully applied to four real microarray gene expression and artificial evaluation datasets. The advantages of our proposed semi-supervised learning method include: 1) significantly increase the available training samples from censored data; 2) high capability for identifying the survival risk classes of patient in Cox model; 3) high predictive accuracy for patients' survival time in AFT model; 4) strong capability of the relevant biomarker selection. Consequently, our proposed semi-supervised learning model is one more appropriate tool for survival analysis in clinical cancer research.

Population-based absolute risk estimation with survey data

PubMed Central

Kovalchik, Stephanie A.; Pfeiffer, Ruth M.

2013-01-01

Absolute risk is the probability that a cause-specific event occurs in a given time interval in the presence of competing events. We present methods to estimate population-based absolute risk from a complex survey cohort that can accommodate multiple exposure-specific competing risks. The hazard function for each event type consists of an individualized relative risk multiplied by a baseline hazard function, which is modeled nonparametrically or parametrically with a piecewise exponential model. An influence method is used to derive a Taylor-linearized variance estimate for the absolute risk estimates. We introduce novel measures of the cause-specific influences that can guide modeling choices for the competing event components of the model. To illustrate our methodology, we build and validate cause-specific absolute risk models for cardiovascular and cancer deaths using data from the National Health and Nutrition Examination Survey. Our applications demonstrate the usefulness of survey-based risk prediction models for predicting health outcomes and quantifying the potential impact of disease prevention programs at the population level. PMID:23686614

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 Cause). Furthermore, the proposed admission-time models already outperform the performance of LACE, which is a discharge-time model (0.61 vs. 0.57 for AMI, 0.61 vs. 0.56 for HF, 0.56 vs. 0.53 for PN, 0.60 vs. 0.55 for All Cause). Similar conclusions can be drawn from other hospitals as well. The same performance comparison also holds for precision and recall at top-decile predictions. Most of the performance improvements are statistically significant. The institution-specific readmission risk prediction framework is more flexible and more effective than the one-size-fit-all models like the LACE, sometimes twice and three-time more effective. The admission-time models are able to give early warning signs compared to the discharge-time models, and may be able to help hospital staff intervene early while the patient is still in the hospital. Copyright © 2015 Elsevier B.V. All rights reserved.

Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model.

PubMed

Sampid, Marius Galabe; Hasim, Haslifah M; Dai, Hongsheng

2018-01-01

In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian Markov-switching GJR-GARCH(1,1) model with skewed Student's-t innovation, copula functions and extreme value theory. A Bayesian Markov-switching GJR-GARCH(1,1) model that identifies non-constant volatility over time and allows the GARCH parameters to vary over time following a Markov process, is combined with copula functions and EVT to formulate the Bayesian Markov-switching GJR-GARCH(1,1) copula-EVT VaR model, which is then used to forecast the level of risk on financial asset returns. We further propose a new method for threshold selection in EVT analysis, which we term the hybrid method. Empirical and back-testing results show that the proposed VaR models capture VaR reasonably well in periods of calm and in periods of crisis.

Default risk modeling beyond the first-passage approximation: extended Black-Cox model.

PubMed

Katz, Yuri A; Shokhirev, Nikolai V

2010-07-01

We develop a generalization of the Black-Cox structural model of default risk. The extended model captures uncertainty related to firm's ability to avoid default even if company's liabilities momentarily exceeding its assets. Diffusion in a linear potential with the radiation boundary condition is used to mimic a company's default process. The exact solution of the corresponding Fokker-Planck equation allows for derivation of analytical expressions for the cumulative probability of default and the relevant hazard rate. Obtained closed formulas fit well the historical data on global corporate defaults and demonstrate the split behavior of credit spreads for bonds of companies in different categories of speculative-grade ratings with varying time to maturity. Introduction of the finite rate of default at the boundary improves valuation of credit risk for short time horizons, which is the key advantage of the proposed model. We also consider the influence of uncertainty in the initial distance to the default barrier on the outcome of the model and demonstrate that this additional source of incomplete information may be responsible for nonzero credit spreads for bonds with very short time to maturity.

Using toxicokinetic-toxicodynamic modeling as an acute risk assessment refinement approach in vertebrate ecological risk assessment.

PubMed

Ducrot, Virginie; Ashauer, Roman; Bednarska, Agnieszka J; Hinarejos, Silvia; Thorbek, Pernille; Weyman, Gabriel

2016-01-01

Recent guidance identified toxicokinetic-toxicodynamic (TK-TD) modeling as a relevant approach for risk assessment refinement. Yet, its added value compared to other refinement options is not detailed, and how to conduct the modeling appropriately is not explained. This case study addresses these issues through 2 examples of individual-level risk assessment for 2 hypothetical plant protection products: 1) evaluating the risk for small granivorous birds and small omnivorous mammals of a single application, as a seed treatment in winter cereals, and 2) evaluating the risk for fish after a pulsed treatment in the edge-of-field zone. Using acute test data, we conducted the first tier risk assessment as defined in the European Food Safety Authority (EFSA) guidance. When first tier risk assessment highlighted a concern, refinement options were discussed. Cases where the use of models should be preferred over other existing refinement approaches were highlighted. We then practically conducted the risk assessment refinement by using 2 different models as examples. In example 1, a TK model accounting for toxicokinetics and relevant feeding patterns in the skylark and in the wood mouse was used to predict internal doses of the hypothetical active ingredient in individuals, based on relevant feeding patterns in an in-crop situation, and identify the residue levels leading to mortality. In example 2, a TK-TD model accounting for toxicokinetics, toxicodynamics, and relevant exposure patterns in the fathead minnow was used to predict the time-course of fish survival for relevant FOCUS SW exposure scenarios and identify which scenarios might lead to mortality. Models were calibrated using available standard data and implemented to simulate the time-course of internal dose of active ingredient or survival for different exposure scenarios. Simulation results were discussed and used to derive the risk assessment refinement endpoints used for decision. Finally, we compared the "classical" risk assessment approach with the model-based approach. These comparisons showed that TK and TK-TD models can bring more realism to the risk assessment through the possibility to study realistic exposure scenarios and to simulate relevant mechanisms of effects (including delayed toxicity and recovery). Noticeably, using TK-TD models is currently the most relevant way to directly connect realistic exposure patterns to effects. We conclude with recommendations on how to properly use TK and TK-TD model in acute risk assessment for vertebrates. © 2015 SETAC.

Impact of Patient Navigation Interventions on Timely Diagnostic Follow Up for Abnormal Cervical Screening.

PubMed

Paskett, Electra D; Dudley, Donald; Young, Gregory S; Bernardo, Brittany M; Wells, Kristen J; Calhoun, Elizabeth A; Fiscella, Kevin; Patierno, Steven R; Warren-Mears, Victoria; Battaglia, Tracy A

2016-01-01

As part of the Patient Navigation Research Program, we examined the effect of patient navigation versus usual care on timely diagnostic follow-up, defined as clinical management for women with cervical abnormalities within accepted time frames. Participants from four Patient Navigation Research Program centers were divided into low- and high-risk abnormality groups and analyzed separately. Low-risk participants (n = 2088) were those who enrolled with an initial Pap test finding of atypical squamous cells of undetermined significance (ASCUS) with a positive high-risk human papillomavirus (HPV) serotype, atypical glandular cells, or low-grade squamous intraepithelial lesion (LGSIL). High-risk participants were those with an initial finding of high-grade squamous intraepithelial lesion (HGSIL) (n = 229). A dichotomous outcome of timely diagnostic follow-up within 180 days was used for the low-risk abnormality group and timely diagnostic follow-up within 60 days for the high-risk group, consistent with treatment guidelines. A logistic mixed-effects regression model was used to evaluate the intervention effect using a random effect for study arm within an institution. A backward selection process was used for multivariable model building, considering the impact of each predictor on the intervention effect. Low-risk women in the patient navigation arm showed an improvement in the odds of timely diagnostic follow-up across all racial groups, but statistically significant effects were only observed in non-English-speaking Hispanics (OR 5.88, 95% CI 2.81-12.29). No effect was observed among high-risk women. These results suggest that patient navigation can improve timely diagnostic follow-up among women with low-risk cervical abnormalities, particularly in non-English-speaking Hispanic women.

Validation of prediction models: examining temporal and geographic stability of baseline risk and estimated covariate effects

PubMed Central

Austin, Peter C.; van Klaveren, David; Vergouwe, Yvonne; Nieboer, Daan; Lee, Douglas S.; Steyerberg, Ewout W.

2018-01-01

Background Stability in baseline risk and estimated predictor effects both geographically and temporally is a desirable property of clinical prediction models. However, this issue has received little attention in the methodological literature. Our objective was to examine methods for assessing temporal and geographic heterogeneity in baseline risk and predictor effects in prediction models. Methods We studied 14,857 patients hospitalized with heart failure at 90 hospitals in Ontario, Canada, in two time periods. We focussed on geographic and temporal variation in baseline risk (intercept) and predictor effects (regression coefficients) of the EFFECT-HF mortality model for predicting 1-year mortality in patients hospitalized for heart failure. We used random effects logistic regression models for the 14,857 patients. Results The baseline risk of mortality displayed moderate geographic variation, with the hospital-specific probability of 1-year mortality for a reference patient lying between 0.168 and 0.290 for 95% of hospitals. Furthermore, the odds of death were 11% lower in the second period than in the first period. However, we found minimal geographic or temporal variation in predictor effects. Among 11 tests of differences in time for predictor variables, only one had a modestly significant P value (0.03). Conclusions This study illustrates how temporal and geographic heterogeneity of prediction models can be assessed in settings with a large sample of patients from a large number of centers at different time periods. PMID:29350215

How rapidly does the excess risk of lung cancer decline following quitting smoking? A quantitative review using the negative exponential model.

PubMed

Fry, John S; Lee, Peter N; Forey, Barbara A; Coombs, Katharine J

2013-10-01

The excess lung cancer risk from smoking declines with time quit, but the shape of the decline has never been precisely modelled, or meta-analyzed. From a database of studies of at least 100 cases, we extracted 106 blocks of RRs (from 85 studies) comparing current smokers, former smokers (by time quit) and never smokers. Corresponding pseudo-numbers of cases and controls (or at-risk) formed the data for fitting the negative exponential model. We estimated the half-life (H, time in years when the excess risk becomes half that for a continuing smoker) for each block, investigated model fit, and studied heterogeneity in H. We also conducted sensitivity analyses allowing for reverse causation, either ignoring short-term quitters (S1) or considering them smokers (S2). Model fit was poor ignoring reverse causation, but much improved for both sensitivity analyses. Estimates of H were similar for all three analyses. For the best-fitting analysis (S1), H was 9.93 (95% CI 9.31-10.60), but varied by sex (females 7.92, males 10.71), and age (<50years 6.98, 70+years 12.99). Given that reverse causation is taken account of, the model adequately describes the decline in excess risk. However, estimates of H may be biased by factors including misclassification of smoking status. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Financial Crisis: A New Measure for Risk of Pension Fund Portfolios

PubMed Central

Cadoni, Marinella; Melis, Roberta; Trudda, Alessandro

2015-01-01

It has been argued that pension funds should have limitations on their asset allocation, based on the risk profile of the different financial instruments available on the financial markets. This issue proves to be highly relevant at times of market crisis, when a regulation establishing limits to risk taking for pension funds could prevent defaults. In this paper we present a framework for evaluating the risk level of a single financial instrument or a portfolio. By assuming that the log asset returns can be described by a multifractional Brownian motion, we evaluate the risk using the time dependent Hurst parameter H(t) which models volatility. To provide a measure of the risk, we model the Hurst parameter with a random variable with mixture of beta distribution. We prove the efficacy of the methodology by implementing it on different risk level financial instruments and portfolios. PMID:26086529

Financial Crisis: A New Measure for Risk of Pension Fund Portfolios.

PubMed

Cadoni, Marinella; Melis, Roberta; Trudda, Alessandro

2015-01-01

It has been argued that pension funds should have limitations on their asset allocation, based on the risk profile of the different financial instruments available on the financial markets. This issue proves to be highly relevant at times of market crisis, when a regulation establishing limits to risk taking for pension funds could prevent defaults. In this paper we present a framework for evaluating the risk level of a single financial instrument or a portfolio. By assuming that the log asset returns can be described by a multifractional Brownian motion, we evaluate the risk using the time dependent Hurst parameter H(t) which models volatility. To provide a measure of the risk, we model the Hurst parameter with a random variable with mixture of beta distribution. We prove the efficacy of the methodology by implementing it on different risk level financial instruments and portfolios.

I spy with my little eye: cognitive processing of framed physical activity messages.

PubMed

Bassett-Gunter, Rebecca L; Latimer-Cheung, Amy E; Martin Ginis, Kathleen A; Castelhano, Monica

2014-01-01

The primary purpose was to examine the relative cognitive processing of gain-framed versus loss-framed physical activity messages following exposure to health risk information. Guided by the Extended Parallel Process Model, the secondary purpose was to examine the relation between dwell time, message recall, and message-relevant thoughts, as well as perceived risk, personal relevance, and fear arousal. Baseline measures of perceived risk for inactivity-related disease and health problems were administered to 77 undergraduate students. Participants read population-specific health risk information while wearing a head-mounted eye tracker, which measured dwell time on message content. Perceived risk was then reassessed. Next, participants read PA messages while the eye tracker measured dwell time on message content. Immediately following message exposure, recall, thought-listing, fear arousal, and personal relevance were measured. Dwell time on gain-framed messages was significantly greater than loss-framed messages. However, message recall and thought-listing did not differ by message frame. Dwell time was not significantly related to recall or thought-listing. Consistent with the Extended Parallel Process Model, fear arousal was significantly related to recall, thought-listing, and personal relevance. In conclusion, gain-framed messages may evoke greater dwell time than loss-famed messages. However, dwell time alone may be insufficient for evoking further cognitive processing.

Markov chains and semi-Markov models in time-to-event analysis.

PubMed

Abner, Erin L; Charnigo, Richard J; Kryscio, Richard J

2013-10-25

A variety of statistical methods are available to investigators for analysis of time-to-event data, often referred to as survival analysis. Kaplan-Meier estimation and Cox proportional hazards regression are commonly employed tools but are not appropriate for all studies, particularly in the presence of competing risks and when multiple or recurrent outcomes are of interest. Markov chain models can accommodate censored data, competing risks (informative censoring), multiple outcomes, recurrent outcomes, frailty, and non-constant survival probabilities. Markov chain models, though often overlooked by investigators in time-to-event analysis, have long been used in clinical studies and have widespread application in other fields.

Markov chains and semi-Markov models in time-to-event analysis

PubMed Central

Abner, Erin L.; Charnigo, Richard J.; Kryscio, Richard J.

2014-01-01

A variety of statistical methods are available to investigators for analysis of time-to-event data, often referred to as survival analysis. Kaplan-Meier estimation and Cox proportional hazards regression are commonly employed tools but are not appropriate for all studies, particularly in the presence of competing risks and when multiple or recurrent outcomes are of interest. Markov chain models can accommodate censored data, competing risks (informative censoring), multiple outcomes, recurrent outcomes, frailty, and non-constant survival probabilities. Markov chain models, though often overlooked by investigators in time-to-event analysis, have long been used in clinical studies and have widespread application in other fields. PMID:24818062

Utility of Risk Models in Decision Making After Radical Prostatectomy: Lessons from a Natural History Cohort of Intermediate- and High-Risk Men.

PubMed

Ross, Ashley E; Yousefi, Kasra; Davicioni, Elai; Ghadessi, Mercedeh; Johnson, Michael H; Sundi, Debasish; Tosoian, Jeffery J; Han, Misop; Humphreys, Elizabeth B; Partin, Alan W; Walsh, Patrick C; Trock, Bruce J; Schaeffer, Edward M

2016-03-01

Current guidelines suggest adjuvant radiation therapy for men with adverse pathologic features (APFs) at radical prostatectomy (RP). We examine at-risk men treated only with RP until the time of metastasis. To evaluate whether clinicopathologic risk models can help guide postoperative therapeutic decision making. Men with National Comprehensive Cancer Network intermediate- or high-risk localized prostate cancer undergoing RP in the prostate-specific antigen (PSA) era were identified (n=3089). Only men with initial undetectable PSA after surgery and who received no therapy prior to metastasis were included. APFs were defined as pT3 disease or positive surgical margins. Area under the receiver operating characteristic curve (AUC) for time to event data was used to measure the discrimination performance of the risk factors. Cumulative incidence curves were constructed using Fine and Gray competing risks analysis to estimate the risk of biochemical recurrence (BCR) or metastasis, taking censoring and death due to other causes into consideration. Overall, 43% of the cohort (n=1327) had APFs at RP. Median follow-up for censored patients was 5 yr. Cumulative incidence of metastasis was 6% at 10 yr after RP for all patients. Cumulative incidence of metastasis among men with APFs was 7.5% at 10 yr after RP. Among men with BCR, the incidence of metastasis was 38% 5 yr after BCR. At 10 yr after RP, time-dependent AUC for predicting metastasis by Cancer of the Prostate Risk Assessment Postsurgical or Eggener risk models was 0.81 (95% confidence interval [CI], 0.72-0.97) and 0.78 (95% CI, 0.67-0.97) in the APF population, respectively. At 5 yr after BCR, these values were lower (0.58 [95% CI, 0.50-0.66] and 0.70 [95% CI, 0.63-0.76]) among those who developed BCR. Use of risk model cut points could substantially reduce overtreatment while minimally increasing undertreatment (ie, use of an Eggener cut point of 2.5% for treatment of men with APFs would spare 46% from treatment while only allowing for metastatic events in 1% at 10 yr after RP). Use of risk models reduces overtreatment and should be a routine part of patient counseling when considering adjuvant therapy. Risk model performance is significantly reduced among men with BCR. Use of current risk models can help guide decision making regarding therapy after surgery and reduce overtreatment. Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Risk assessment of fungal spoilage: A case study of Aspergillus niger on yogurt.

PubMed

Gougouli, Maria; Koutsoumanis, Konstantinos P

2017-08-01

A quantitative risk assessment model of yogurt spoilage by Aspergillus niger was developed based on a stochastic modeling approach for mycelium growth by taking into account the important sources of variability such as time-temperature conditions during the different stages of chill chain and individual spore behavior. Input parameters were fitted to the appropriate distributions and A. niger colony's diameter at each stage of the chill chain was estimated using Monte Carlo simulation. By combining the output of the growth model with the fungus prevalence, that can be estimated by the industry using challenge tests, the risk of spoilage translated to number of yogurt cups in which a visible mycelium of A. niger is being formed at the time of consumption was assessed. The risk assessment output showed that for a batch of 100,000 cups in which the percentage of contaminated cups with A. niger was 1% the predicted numbers (median (5 th , 95 th percentiles)) of the cups with a visible mycelium at consumption time were 8 (5, 14). For higher percentages of 3, 5 and 10 the predicted numbers (median (5 th , 95 th percentiles)) of the spoiled cups at consumption time were estimated to be 24 (16, 35), 39 (29, 52) and 80 (64, 94), respectively. The developed model can lead to a more effective risk-based quality management of yogurt and support the decision making in yogurt production. Copyright © 2017 Elsevier Ltd. All rights reserved.

The analysis of factors of management of safety of critical information infrastructure with use of dynamic models

NASA Astrophysics Data System (ADS)

Trostyansky, S. N.; Kalach, A. V.; Lavlinsky, V. V.; Lankin, O. V.

2018-03-01

Based on the analysis of the dynamic model of panel data by region, including fire statistics for surveillance sites and statistics of a set of regional socio-economic indicators, as well as the time of rapid response of the state fire service to fires, the probability of fires in the surveillance sites and the risk of human death in The result of such fires from the values of the corresponding indicators for the previous year, a set of regional social-economics factors, as well as regional indicators time rapid response of the state fire service in the fire. The results obtained are consistent with the results of the application to the fire risks of the model of a rational offender. Estimation of the economic equivalent of human life from data on surveillance objects for Russia, calculated on the basis of the analysis of the presented dynamic model of fire risks, correctly agrees with the known literary data. The results obtained on the basis of the econometric approach to fire risks allow us to forecast fire risks at the supervisory sites in the regions of Russia and to develop management solutions to minimize such risks.

Investigating Gender Differences under Time Pressure in Financial Risk Taking.

PubMed

Xie, Zhixin; Page, Lionel; Hardy, Ben

2017-01-01

There is a significant gender imbalance on financial trading floors. This motivated us to investigate gender differences in financial risk taking under pressure. We used a well-established approach from behavior economics to analyze a series of risky monetary choices by male and female participants with and without time pressure. We also used second to fourth digit ratio (2D:4D) and face width-to-height ratio (fWHR) as correlates of pre-natal exposure to testosterone. We constructed a structural model and estimated the participants' risk attitudes and probability perceptions via maximum likelihood estimation under both expected utility (EU) and rank-dependent utility (RDU) models. In line with existing research, we found that male participants are less risk averse and that the gender gap in risk attitudes increases under moderate time pressure. We found that female participants with lower 2D:4D ratios and higher fWHR are less risk averse in RDU estimates. Males with lower 2D:4D ratios were less risk averse in EU estimations, but more risk averse using RDU estimates. We also observe that men whose ratios indicate a greater prenatal exposure to testosterone exhibit a greater optimism and overestimation of small probabilities of success.

Development, Validation and Deployment of a Real Time 30 Day Hospital Readmission Risk Assessment Tool in the Maine Healthcare Information Exchange.

PubMed

Hao, Shiying; Wang, Yue; Jin, Bo; Shin, Andrew Young; Zhu, Chunqing; Huang, Min; Zheng, Le; Luo, Jin; Hu, Zhongkai; Fu, Changlin; Dai, Dorothy; Wang, Yicheng; Culver, Devore S; Alfreds, Shaun T; Rogow, Todd; Stearns, Frank; Sylvester, Karl G; Widen, Eric; Ling, Xuefeng B

2015-01-01

Identifying patients at risk of a 30-day readmission can help providers design interventions, and provide targeted care to improve clinical effectiveness. This study developed a risk model to predict a 30-day inpatient hospital readmission for patients in Maine, across all payers, all diseases and all demographic groups. Our objective was to develop a model to determine the risk for inpatient hospital readmission within 30 days post discharge. All patients within the Maine Health Information Exchange (HIE) system were included. The model was retrospectively developed on inpatient encounters between January 1, 2012 to December 31, 2012 from 24 randomly chosen hospitals, and then prospectively validated on inpatient encounters from January 1, 2013 to December 31, 2013 using all HIE patients. A risk assessment tool partitioned the entire HIE population into subgroups that corresponded to probability of hospital readmission as determined by a corresponding positive predictive value (PPV). An overall model c-statistic of 0.72 was achieved. The total 30-day readmission rates in low (score of 0-30), intermediate (score of 30-70) and high (score of 70-100) risk groupings were 8.67%, 24.10% and 74.10%, respectively. A time to event analysis revealed the higher risk groups readmitted to a hospital earlier than the lower risk groups. Six high-risk patient subgroup patterns were revealed through unsupervised clustering. Our model was successfully integrated into the statewide HIE to identify patient readmission risk upon admission and daily during hospitalization or for 30 days subsequently, providing daily risk score updates. The risk model was validated as an effective tool for predicting 30-day readmissions for patients across all payer, disease and demographic groups within the Maine HIE. Exposing the key clinical, demographic and utilization profiles driving each patient's risk of readmission score may be useful to providers in developing individualized post discharge care plans.

SPATIAL-TEMPORAL DISTRIBUTION OF WATERBORNE INFECTIOUS DISEASE RISK USING THE HYDRAULIC MODEL AND OUTPATIENT DATA

NASA Astrophysics Data System (ADS)

Amano, Ayako; Sakuma, Taisuke; Kazama, So

This study evaluated waterborne infectious diseases risk and incidence rate around Phonm Penh in Cambodia. We use the hydraulic flood simulation, coliform bacterium diffusion model, dose-response model and outpatient data for quantitative analysis. The results obtained are as follows; 1. The incidence (incidence rate) of diarrhea as water borne diseases risk is 0.14 million people (9%) in the inundation area. 2. The residents in the inundation area are exposed up to 4 times as high risk as daily mean calculated by the integrated model combined in the regional scale. 3.The infectious disease risk due to floods and inundation indicated is effective as an element to explain the risk. The scenario explains 34% number of patient estimated by the outpatient data.

Optimal timing of vitamin K antagonist resumption after upper gastrointestinal bleeding. A risk modelling analysis.

PubMed

Majeed, Ammar; Wallvik, Niklas; Eriksson, Joakim; Höijer, Jonas; Bottai, Matteo; Holmström, Margareta; Schulman, Sam

2017-02-28

The optimal timing of vitamin K antagonists (VKAs) resumption after an upper gastrointestinal (GI) bleeding, in patients with continued indication for oral anticoagulation, is uncertain. We included consecutive cases of VKA-associated upper GI bleeding from three hospitals retrospectively. Data on the bleeding location, timing of VKA resumption, recurrent GI bleeding and thromboembolic events were collected. A model was constructed to evaluate the 'total risk', based on the sum of the cumulative rates of recurrent GI bleeding and thromboembolic events, depending on the timing of VKA resumption. A total of 121 (58 %) of 207 patients with VKA-associated upper GI bleeding were restarted on anticoagulation after a median (interquartile range) of one (0.2-3.4) week after the index bleeding. Restarting VKAs was associated with a reduced risk of thromboembolism (HR 0.19; 95 % CI, 0.07-0.55) and death (HR 0.61; 95 % CI, 0.39-0.94), but with an increased risk of recurrent GI bleeding (HR 2.5; 95 % CI, 1.4-4.5). The composite risk obtained from the combined statistical model of recurrent GI bleeding, and thromboembolism decreased if VKAs were resumed after three weeks and reached a nadir at six weeks after the index GI bleeding. On this background we will discuss how the disutility of the outcomes may influence the decision regarding timing of resumption. In conclusion, the optimal timing of VKA resumption after VKA-associated upper GI bleeding appears to be between 3-6 weeks after the index bleeding event but has to take into account the degree of thromboembolic risk, patient values and preferences.

DEVELOPMENT AND PEER REVIEW OF TIME-TO-EFFECT MODELS FOR THE ANALYSIS OF NEUROTOXICITY AND OTHER TIME DEPENDENT DATA

EPA Science Inventory

Neurobehavioral studies pose unique challenges for dose-response modeling, including small sample size and relatively large intra-subject variation, repeated measurements over time, multiple endpoints with both continuous and ordinal scales, and time dependence of risk characteri...

Predicting the risk for hospital-onset Clostridium difficile infection (HO-CDI) at the time of inpatient admission: HO-CDI risk score.

PubMed

Tabak, Ying P; Johannes, Richard S; Sun, Xiaowu; Nunez, Carlos M; McDonald, L Clifford

2015-06-01

To predict the likelihood of hospital-onset Clostridium difficile infection (HO-CDI) based on patient clinical presentations at admission Retrospective data analysis Six US acute care hospitals Adult inpatients We used clinical data collected at the time of admission in electronic health record (EHR) systems to develop and validate a HO-CDI predictive model. The outcome measure was HO-CDI cases identified by a nonduplicate positive C. difficile toxin assay result with stool specimens collected >48 hours after inpatient admission. We fit a logistic regression model to predict the risk of HO-CDI. We validated the model using 1,000 bootstrap simulations. Among 78,080 adult admissions, 323 HO-CDI cases were identified (ie, a rate of 4.1 per 1,000 admissions). The logistic regression model yielded 14 independent predictors, including hospital community onset CDI pressure, patient age ≥65, previous healthcare exposures, CDI in previous admission, admission to the intensive care unit, albumin ≤3 g/dL, creatinine >2.0 mg/dL, bands >32%, platelets ≤150 or >420 109/L, and white blood cell count >11,000 mm3. The model had a c-statistic of 0.78 (95% confidence interval [CI], 0.76-0.81) with good calibration. Among 79% of patients with risk scores of 0-7, 19 HO-CDIs occurred per 10,000 admissions; for patients with risk scores >20, 623 HO-CDIs occurred per 10,000 admissions (P<.0001). Using clinical parameters available at the time of admission, this HO-CDI model demonstrated good predictive ability, and it may have utility as an early risk identification tool for HO-CDI preventive interventions and outcome comparisons.

Estimating effectiveness in HIV prevention trials with a Bayesian hierarchical compound Poisson frailty model

PubMed Central

Coley, Rebecca Yates; Browna, Elizabeth R.

2016-01-01

Inconsistent results in recent HIV prevention trials of pre-exposure prophylactic interventions may be due to heterogeneity in risk among study participants. Intervention effectiveness is most commonly estimated with the Cox model, which compares event times between populations. When heterogeneity is present, this population-level measure underestimates intervention effectiveness for individuals who are at risk. We propose a likelihood-based Bayesian hierarchical model that estimates the individual-level effectiveness of candidate interventions by accounting for heterogeneity in risk with a compound Poisson-distributed frailty term. This model reflects the mechanisms of HIV risk and allows that some participants are not exposed to HIV and, therefore, have no risk of seroconversion during the study. We assess model performance via simulation and apply the model to data from an HIV prevention trial. PMID:26869051

A Bayesian model averaging approach for estimating the relative risk of mortality associated with heat waves in 105 U.S. cities.

PubMed

Bobb, Jennifer F; Dominici, Francesca; Peng, Roger D

2011-12-01

Estimating the risks heat waves pose to human health is a critical part of assessing the future impact of climate change. In this article, we propose a flexible class of time series models to estimate the relative risk of mortality associated with heat waves and conduct Bayesian model averaging (BMA) to account for the multiplicity of potential models. Applying these methods to data from 105 U.S. cities for the period 1987-2005, we identify those cities having a high posterior probability of increased mortality risk during heat waves, examine the heterogeneity of the posterior distributions of mortality risk across cities, assess sensitivity of the results to the selection of prior distributions, and compare our BMA results to a model selection approach. Our results show that no single model best predicts risk across the majority of cities, and that for some cities heat-wave risk estimation is sensitive to model choice. Although model averaging leads to posterior distributions with increased variance as compared to statistical inference conditional on a model obtained through model selection, we find that the posterior mean of heat wave mortality risk is robust to accounting for model uncertainty over a broad class of models. © 2011, The International Biometric Society.

A scan statistic for identifying optimal risk windows in vaccine safety studies using self-controlled case series design.

PubMed

Xu, Stanley; Hambidge, Simon J; McClure, David L; Daley, Matthew F; Glanz, Jason M

2013-08-30

In the examination of the association between vaccines and rare adverse events after vaccination in postlicensure observational studies, it is challenging to define appropriate risk windows because prelicensure RCTs provide little insight on the timing of specific adverse events. Past vaccine safety studies have often used prespecified risk windows based on prior publications, biological understanding of the vaccine, and expert opinion. Recently, a data-driven approach was developed to identify appropriate risk windows for vaccine safety studies that use the self-controlled case series design. This approach employs both the maximum incidence rate ratio and the linear relation between the estimated incidence rate ratio and the inverse of average person time at risk, given a specified risk window. In this paper, we present a scan statistic that can identify appropriate risk windows in vaccine safety studies using the self-controlled case series design while taking into account the dependence of time intervals within an individual and while adjusting for time-varying covariates such as age and seasonality. This approach uses the maximum likelihood ratio test based on fixed-effects models, which has been used for analyzing data from self-controlled case series design in addition to conditional Poisson models. Copyright © 2013 John Wiley & Sons, Ltd.

MSW Time to Tumor Model and Supporting Documentation

EPA Science Inventory

The multistage Weibull (MSW) time-to-tumor model and related documentation were developed principally (but not exclusively) for conducting time-to-tumor analyses to support risk assessments under the IRIS program. These programs and related docum...

Road Risk Modeling and Cloud-Aided Safety-Based Route Planning.

PubMed

Li, Zhaojian; Kolmanovsky, Ilya; Atkins, Ella; Lu, Jianbo; Filev, Dimitar P; Michelini, John

2016-11-01

This paper presents a safety-based route planner that exploits vehicle-to-cloud-to-vehicle (V2C2V) connectivity. Time and road risk index (RRI) are considered as metrics to be balanced based on user preference. To evaluate road segment risk, a road and accident database from the highway safety information system is mined with a hybrid neural network model to predict RRI. Real-time factors such as time of day, day of the week, and weather are included as correction factors to the static RRI prediction. With real-time RRI and expected travel time, route planning is formulated as a multiobjective network flow problem and further reduced to a mixed-integer programming problem. A V2C2V implementation of our safety-based route planning approach is proposed to facilitate access to real-time information and computing resources. A real-world case study, route planning through the city of Columbus, Ohio, is presented. Several scenarios illustrate how the "best" route can be adjusted to favor time versus safety metrics.

Architecture for Integrated Medical Model Dynamic Probabilistic Risk Assessment

NASA Technical Reports Server (NTRS)

Jaworske, D. A.; Myers, J. G.; Goodenow, D.; Young, M.; Arellano, J. D.

2016-01-01

Probabilistic Risk Assessment (PRA) is a modeling tool used to predict potential outcomes of a complex system based on a statistical understanding of many initiating events. Utilizing a Monte Carlo method, thousands of instances of the model are considered and outcomes are collected. PRA is considered static, utilizing probabilities alone to calculate outcomes. Dynamic Probabilistic Risk Assessment (dPRA) is an advanced concept where modeling predicts the outcomes of a complex system based not only on the probabilities of many initiating events, but also on a progression of dependencies brought about by progressing down a time line. Events are placed in a single time line, adding each event to a queue, as managed by a planner. Progression down the time line is guided by rules, as managed by a scheduler. The recently developed Integrated Medical Model (IMM) summarizes astronaut health as governed by the probabilities of medical events and mitigation strategies. Managing the software architecture process provides a systematic means of creating, documenting, and communicating a software design early in the development process. The software architecture process begins with establishing requirements and the design is then derived from the requirements.

MODELING APPROACHES FOR ESTIMATING THE DOSIMETRY OF INHALED TOXICANTS IN CHILDREN

EPA Science Inventory

Risk assessment of inhaled toxicants has typically focused upon adults, with modeling used to extrapolate dosimetry and risks from laboratory animals to humans. However, behavioral factors such as time spent playing outdoors can lead to more exposure to inhaled toxicants in chil...

A Strategy for Assessing the Impact of Time-Varying Family Risk Factors on High School Dropout

ERIC Educational Resources Information Center

Randolph, Karen A.; Fraser, Mark W.; Orthner, Dennis K.

2006-01-01

Human behavior is dynamic, influenced by changing situations over time. Yet the impact of the dynamic nature of important explanatory variables on outcomes has only recently begun to be estimated in developmental models. Using a risk factor perspective, this article demonstrates the potential benefits of regressing time-varying outcome measures on…

Modeling logistic performance in quantitative microbial risk assessment.

PubMed

Rijgersberg, Hajo; Tromp, Seth; Jacxsens, Liesbeth; Uyttendaele, Mieke

2010-01-01

In quantitative microbial risk assessment (QMRA), food safety in the food chain is modeled and simulated. In general, prevalences, concentrations, and numbers of microorganisms in media are investigated in the different steps from farm to fork. The underlying rates and conditions (such as storage times, temperatures, gas conditions, and their distributions) are determined. However, the logistic chain with its queues (storages, shelves) and mechanisms for ordering products is usually not taken into account. As a consequence, storage times-mutually dependent in successive steps in the chain-cannot be described adequately. This may have a great impact on the tails of risk distributions. Because food safety risks are generally very small, it is crucial to model the tails of (underlying) distributions as accurately as possible. Logistic performance can be modeled by describing the underlying planning and scheduling mechanisms in discrete-event modeling. This is common practice in operations research, specifically in supply chain management. In this article, we present the application of discrete-event modeling in the context of a QMRA for Listeria monocytogenes in fresh-cut iceberg lettuce. We show the potential value of discrete-event modeling in QMRA by calculating logistic interventions (modifications in the logistic chain) and determining their significance with respect to food safety.

Mental Models of Cause and Inheritance for Type 2 Diabetes Among Unaffected Individuals Who Have a Positive Family History.

PubMed

Daack-Hirsch, Sandra; Shah, Lisa L; Cady, Alyssa D

2018-03-01

Using the familial risk perception (FRP) model as a framework, we elicited causal and inheritance explanations for type 2 diabetes (T2D) from people who do not have T2D but have a family history for it. We identified four composite mental models for cause of T2D: (a) purely genetic; (b) purely behavioral/environmental; (c) direct multifactorial, in which risk factors interact and over time directly lead to T2D; and (d) indirect multifactorial, in which risk factors interact and over time cause a precursor health condition (such as obesity or metabolic syndrome) that leads to T2D. Interestingly, participants described specific risk factors such as genetics, food habits, lifestyle, weight, and culture as "running in the family." Our findings provide insight into lay beliefs about T2D that can be used by clinicians to anticipate or make sense of responses to questions they pose to patients about mental models for T2D.

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies.

PubMed

Goldman, Gretchen T; Mulholland, James A; Russell, Armistead G; Strickland, Matthew J; Klein, Mitchel; Waller, Lance A; Tolbert, Paige E

2011-06-22

Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta. Daily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits. Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed. For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.

Training loads and injury risk in Australian football-differing acute: chronic workload ratios influence match injury risk.

PubMed

Carey, David L; Blanch, Peter; Ong, Kok-Leong; Crossley, Kay M; Crow, Justin; Morris, Meg E

2017-08-01

(1) To investigate whether a daily acute:chronic workload ratio informs injury risk in Australian football players; (2) to identify which combination of workload variable, acute and chronic time window best explains injury likelihood. Workload and injury data were collected from 53 athletes over 2 seasons in a professional Australian football club. Acute:chronic workload ratios were calculated daily for each athlete, and modelled against non-contact injury likelihood using a quadratic relationship. 6 workload variables, 8 acute time windows (2-9 days) and 7 chronic time windows (14-35 days) were considered (336 combinations). Each parameter combination was compared for injury likelihood fit (using R 2 ). The ratio of moderate speed running workload (18-24 km/h) in the previous 3 days (acute time window) compared with the previous 21 days (chronic time window) best explained the injury likelihood in matches (R 2 =0.79) and in the immediate 2 or 5 days following matches (R 2 =0.76-0.82). The 3:21 acute:chronic workload ratio discriminated between high-risk and low-risk athletes (relative risk=1.98-2.43). Using the previous 6 days to calculate the acute workload time window yielded similar results. The choice of acute time window significantly influenced model performance and appeared to reflect the competition and training schedule. Daily workload ratios can inform injury risk in Australian football. Clinicians and conditioning coaches should consider the sport-specific schedule of competition and training when choosing acute and chronic time windows. For Australian football, the ratio of moderate speed running in a 3-day or 6-day acute time window and a 21-day chronic time window best explained injury risk. 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/.

Training loads and injury risk in Australian football—differing acute: chronic workload ratios influence match injury risk

PubMed Central

Carey, David L; Blanch, Peter; Ong, Kok-Leong; Crossley, Kay M; Crow, Justin; Morris, Meg E

2017-01-01

Aims (1) To investigate whether a daily acute:chronic workload ratio informs injury risk in Australian football players; (2) to identify which combination of workload variable, acute and chronic time window best explains injury likelihood. Methods Workload and injury data were collected from 53 athletes over 2 seasons in a professional Australian football club. Acute:chronic workload ratios were calculated daily for each athlete, and modelled against non-contact injury likelihood using a quadratic relationship. 6 workload variables, 8 acute time windows (2–9 days) and 7 chronic time windows (14–35 days) were considered (336 combinations). Each parameter combination was compared for injury likelihood fit (using R2). Results The ratio of moderate speed running workload (18–24 km/h) in the previous 3 days (acute time window) compared with the previous 21 days (chronic time window) best explained the injury likelihood in matches (R2=0.79) and in the immediate 2 or 5 days following matches (R2=0.76–0.82). The 3:21 acute:chronic workload ratio discriminated between high-risk and low-risk athletes (relative risk=1.98–2.43). Using the previous 6 days to calculate the acute workload time window yielded similar results. The choice of acute time window significantly influenced model performance and appeared to reflect the competition and training schedule. Conclusions Daily workload ratios can inform injury risk in Australian football. Clinicians and conditioning coaches should consider the sport-specific schedule of competition and training when choosing acute and chronic time windows. For Australian football, the ratio of moderate speed running in a 3-day or 6-day acute time window and a 21-day chronic time window best explained injury risk. PMID:27789430

Simulation Assisted Risk Assessment Applied to Launch Vehicle Conceptual Design

NASA Technical Reports Server (NTRS)

Mathias, Donovan L.; Go, Susie; Gee, Ken; Lawrence, Scott

2008-01-01

A simulation-based risk assessment approach is presented and is applied to the analysis of abort during the ascent phase of a space exploration mission. The approach utilizes groupings of launch vehicle failures, referred to as failure bins, which are mapped to corresponding failure environments. Physical models are used to characterize the failure environments in terms of the risk due to blast overpressure, resulting debris field, and the thermal radiation due to a fireball. The resulting risk to the crew is dynamically modeled by combining the likelihood of each failure, the severity of the failure environments as a function of initiator and time of the failure, the robustness of the crew module, and the warning time available due to early detection. The approach is shown to support the launch vehicle design process by characterizing the risk drivers and identifying regions where failure detection would significantly reduce the risk to the crew.

Cognitive and affective influences on perceived risk of ovarian cancer†

PubMed Central

Peipins, Lucy A.; McCarty, Frances; Hawkins, Nikki A.; Rodriguez, Juan L.; Scholl, Lawrence E.; Leadbetter, Steven

2015-01-01

Introduction Studies suggest that both affective and cognitive processes are involved in the perception of vulnerability to cancer and that affect has an early influence in this assessment of risk. We constructed a path model based on a conceptual framework of heuristic reasoning (affect, resemblance, and availability) coupled with cognitive processes involved in developing personal models of cancer causation. Methods From an eligible cohort of 16 700 women in a managed care organization, we randomly selected 2524 women at high, elevated, and average risk of ovarian cancer and administered a questionnaire to test our model (response rate 76.3%). Path analysis delineated the relationships between personal and cognitive characteristics (number of relatives with cancer, age, ideas about cancer causation, perceived resemblance to an affected friend or relative, and ovarian cancer knowledge) and emotional constructs (closeness to an affected relative or friend, time spent processing the cancer experience, and cancer worry) on perceived risk of ovarian cancer. Results Our final model fit the data well (root mean square error of approximation (RMSEA) = 0.028, comparative fit index (CFI) = 0.99, normed fit index (NFI) = 0.98). This final model (1) demonstrated the nature and direction of relationships between cognitive characteristics and perceived risk; (2) showed that time spent processing the cancer experience was associated with cancer worry; and (3) showed that cancer worry moderately influenced perceived risk. Discussion Our results highlight the important role that family cancer experience has on cancer worry and shows how cancer experience translates into personal risk perceptions. This understanding informs the discordance between medical or objective risk assessment and personal risk assessment. PMID:24916837

Predicting mortality over different time horizons: which data elements are needed?

PubMed

Goldstein, Benjamin A; Pencina, Michael J; Montez-Rath, Maria E; Winkelmayer, Wolfgang C

2017-01-01

Electronic health records (EHRs) are a resource for "big data" analytics, containing a variety of data elements. We investigate how different categories of information contribute to prediction of mortality over different time horizons among patients undergoing hemodialysis treatment. We derived prediction models for mortality over 7 time horizons using EHR data on older patients from a national chain of dialysis clinics linked with administrative data using LASSO (least absolute shrinkage and selection operator) regression. We assessed how different categories of information relate to risk assessment and compared discrete models to time-to-event models. The best predictors used all the available data (c-statistic ranged from 0.72-0.76), with stronger models in the near term. While different variable groups showed different utility, exclusion of any particular group did not lead to a meaningfully different risk assessment. Discrete time models performed better than time-to-event models. Different variable groups were predictive over different time horizons, with vital signs most predictive for near-term mortality and demographic and comorbidities more important in long-term mortality. © 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.

A three-gene expression signature model for risk stratification of patients with neuroblastoma.

PubMed

Garcia, Idoia; Mayol, Gemma; Ríos, José; Domenech, Gema; Cheung, Nai-Kong V; Oberthuer, André; Fischer, Matthias; Maris, John M; Brodeur, Garrett M; Hero, Barbara; Rodríguez, Eva; Suñol, Mariona; Galvan, Patricia; de Torres, Carmen; Mora, Jaume; Lavarino, Cinzia

2012-04-01

Neuroblastoma is an embryonal tumor with contrasting clinical courses. Despite elaborate stratification strategies, precise clinical risk assessment still remains a challenge. The purpose of this study was to develop a PCR-based predictor model to improve clinical risk assessment of patients with neuroblastoma. The model was developed using real-time PCR gene expression data from 96 samples and tested on separate expression data sets obtained from real-time PCR and microarray studies comprising 362 patients. On the basis of our prior study of differentially expressed genes in favorable and unfavorable neuroblastoma subgroups, we identified three genes, CHD5, PAFAH1B1, and NME1, strongly associated with patient outcome. The expression pattern of these genes was used to develop a PCR-based single-score predictor model. The model discriminated patients into two groups with significantly different clinical outcome [set 1: 5-year overall survival (OS): 0.93 ± 0.03 vs. 0.53 ± 0.06, 5-year event-free survival (EFS): 0.85 ± 0.04 vs. 0.042 ± 0.06, both P < 0.001; set 2 OS: 0.97 ± 0.02 vs. 0.61 ± 0.1, P = 0.005, EFS: 0.91 ± 0.8 vs. 0.56 ± 0.1, P = 0.005; and set 3 OS: 0.99 ± 0.01 vs. 0.56 ± 0.06, EFS: 0.96 ± 0.02 vs. 0.43 ± 0.05, both P < 0.001]. Multivariate analysis showed that the model was an independent marker for survival (P < 0.001, for all). In comparison with accepted risk stratification systems, the model robustly classified patients in the total cohort and in different clinically relevant risk subgroups. We propose for the first time in neuroblastoma, a technically simple PCR-based predictor model that could help refine current risk stratification systems. ©2012 AACR.

A Three-Gene Expression Signature Model for Risk Stratification of Patients with Neuroblastoma

PubMed Central

Garcia, Idoia; Mayol, Gemma; Ríos, José; Domenech, Gema; Cheung, Nai-Kong V.; Oberthuer, André; Fischer, Matthias; Maris, John M.; Brodeur, Garrett M.; Hero, Barbara; Rodríguez, Eva; Suñol, Mariona; Galvan, Patricia; de Torres, Carmen; Mora, Jaume; Lavarino, Cinzia

2014-01-01

Purpose Neuroblastoma is an embryonal tumor with contrasting clinical courses. Despite elaborate stratification strategies, precise clinical risk assessment still remains a challenge. The purpose of this study was to develop a PCR-based predictor model to improve clinical risk assessment of patients with neuroblastoma. Experimental Design The model was developed using real-time PCR gene expression data from 96 samples and tested on separate expression data sets obtained from real-time PCR and microarray studies comprising 362 patients. Results On the basis of our prior study of differentially expressed genes in favorable and unfavorable neuroblastoma subgroups, we identified three genes, CHD5, PAFAH1B1, and NME1, strongly associated with patient outcome. The expression pattern of these genes was used to develop a PCR-based single-score predictor model. The model discriminated patients into two groups with significantly different clinical outcome [set 1: 5-year overall survival (OS): 0.93 ± 0.03 vs. 0.53 ± 0.06, 5-year event-free survival (EFS): 0.85 ± 0.04 vs. 0.042 ± 0.06, both P < 0.001; set 2 OS: 0.97 ± 0.02 vs. 0.61 ± 0.1, P = 0.005, EFS: 0.91 ± 0.8 vs. 0.56 ± 0.1, P = 0.005; and set 3 OS: 0.99 ± 0.01 vs. 0.56 ± 0.06, EFS: 0.96 ± 0.02 vs. 0.43 ± 0.05, both P < 0.001]. Multivariate analysis showed that the model was an independent marker for survival (P < 0.001, for all). In comparison with accepted risk stratification systems, the model robustly classified patients in the total cohort and in different clinically relevant risk subgroups. Conclusion We propose for the first time in neuroblastoma, a technically simple PCR-based predictor model that could help refine current risk stratification systems. PMID:22328561

An Electronic Health Record Data-driven Model for Identifying Older Adults at Risk of Unintentional Falls

PubMed Central

Baus, Adam; Coben, Jeffrey; Zullig, Keith; Pollard, Cecil; Mullett, Charles; Taylor, Henry; Cochran, Jill; Jarrett, Traci; Long, Dustin

2017-01-01

Screening for risk of unintentional falls remains low in the primary care setting because of the time constraints of brief office visits. National studies suggest that physicians caring for older adults provide recommended fall risk screening only 30 to 37 percent of the time. Given prior success in developing methods for repurposing electronic health record data for the identification of fall risk, this study involves building a model in which electronic health record data could be applied for use in clinical decision support to bolster screening by proactively identifying patients for whom screening would be beneficial and targeting efforts specifically to those patients. The final model, consisting of priority and extended measures, demonstrates moderate discriminatory power, indicating that it could prove useful in a clinical setting for identifying patients at risk of falls. Focus group discussions reveal important contextual issues involving the use of fall-related data and provide direction for the development of health systems–level innovations for the use of electronic health record data for fall risk identification. PMID:29118679

Impacts of variability in cellulosic biomass yields on energy security.

PubMed

Mullins, Kimberley A; Matthews, H Scott; Griffin, W Michael; Anex, Robert

2014-07-01

The practice of modeling biomass yields on the basis of deterministic point values aggregated over space and time obscures important risks associated with large-scale biofuel use, particularly risks related to drought-induced yield reductions that may become increasingly frequent under a changing climate. Using switchgrass as a case study, this work quantifies the variability in expected yields over time and space through switchgrass growth modeling under historical and simulated future weather. The predicted switchgrass yields across the United States range from about 12 to 19 Mg/ha, and the 80% confidence intervals range from 20 to 60% of the mean. Average yields are predicted to decrease with increased temperatures and weather variability induced by climate change. Feedstock yield variability needs to be a central part of modeling to ensure that policy makers acknowledge risks to energy supplies and develop strategies or contingency plans that mitigate those risks.

Cheese Microbial Risk Assessments — A Review

PubMed Central

Choi, Kyoung-Hee; Lee, Heeyoung; Lee, Soomin; Kim, Sejeong; Yoon, Yohan

2016-01-01

Cheese is generally considered a safe and nutritious food, but foodborne illnesses linked to cheese consumption have occurred in many countries. Several microbial risk assessments related to Listeria monocytogenes, Staphylococcus aureus, and Escherichia coli infections, causing cheese-related foodborne illnesses, have been conducted. Although the assessments of microbial risk in soft and low moisture cheeses such as semi-hard and hard cheeses have been accomplished, it has been more focused on the correlations between pathogenic bacteria and soft cheese, because cheese-associated foodborne illnesses have been attributed to the consumption of soft cheeses. As a part of this microbial risk assessment, predictive models have been developed to describe the relationship between several factors (pH, Aw, starter culture, and time) and the fates of foodborne pathogens in cheese. Predictions from these studies have been used for microbial risk assessment as a part of exposure assessment. These microbial risk assessments have identified that risk increased in cheese with high moisture content, especially for raw milk cheese, but the risk can be reduced by preharvest and postharvest preventions. For accurate quantitative microbial risk assessment, more data including interventions such as curd cooking conditions (temperature and time) and ripening period should be available for predictive models developed with cheese, cheese consumption amounts and cheese intake frequency data as well as more dose-response models. PMID:26950859

How many or how much? Testing the relative influence of the number of social network risks versus the amount of time exposed to social network risks on post-treatment substance use.

PubMed

Eddie, David; Kelly, John F

2017-06-01

Having high-risk, substance-using friends is associated with young adult substance use disorder (SUD) relapse. It is unclear, however, whether it is the total number of high-risk friends, or the amount of time spent with high-risk friends that leads to relapse. Unclear also, is to what extent low-risk friends buffer risk. This study examined the influence of number of high-risk and low-risk friends, and the amount time spent with these friends on post-treatment percent days abstinent (PDA). Young adult inpatients (N=302) were assessed at intake, and 3, 6, and 12 months on social network measures and PDA. Mixed models tested for effects of number of high- and low-risk friends, and time spent with these friends on PDA, and for net-risk friend effects to test whether low-risk friends offset risk. Within and across assessments, number of, and time spent with high-risk friends was negatively associated with PDA, while the inverse was true for low-risk friends. Early post-treatment, time spent with friends more strongly predicted PDA than number of friends. Participants were more deleteriously affected by time with high-risk friends the longer they were out of treatment, while contemporaneously protection conferred by low-risk friends increased. This interaction effect, however, was not observed with number of high- or low-risk friends, or number of friends net-risk. Young adult SUD patients struggling to break ties with high-risk friends should be encouraged to minimize time with them. Clinicians should also encourage patients to grow their social network of low-risk friends. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting Time to Reflux of Children With Antenatal Hydronephrosis: A Competing Risks Approach.

PubMed

Nazemipour, Maryam; Kajbafzadeh, Abdol-Mohammad; Mohammad, Kazem; Rahimi Foroushani, Abbas; Mahmoudi, Mahmood

2017-07-01

The aim of this study was describing methodological aspects and applying a trivariate Weibull survival model using the competing risks concept to predict time to occurrence different types of reflux (unilateral (left, right) or bilateral) in children with antenatal hydronephrosis. Data from 333 children in Pediatric Urology Research Center of Children's Hospital Medical Center, affiliated with Tehran University of Medical Sciences was used. The effect of some demographic and clinical factors on child's reflux was studied. The assumption of independent between times of different types of reflux was evaluated. Of infants 80.5% were boy. The percentage of children experienced right, left and bilateral reflux or have been censored are 15.3%, 14.1%, 60.4% and 10.2% respectively. For the time of left reflux, variables, Week of diagnosis ANH, UC, UA, HUN, HN, APD_Right, Direction of ANH, CA19-9 baby, Urethra were significant. For the time of right reflux, variables, constipation, UC, UA, HUN, APD_Right, Direction and Severity of ANH, Bladder, and finally for the time of bilateral reflux, variables, Week of diagnosis ANH, Gender, UA, HUN, HN, APD_Left, Urethra, and Bladder were significant P<0.05. In the presence of competing risks, it is inappropriate to use the Kaplan-Meier method and standard Cox model which do not take competing risks into account. Trivariate Weibull survival model using competing risks not only is able to calculate the hazard rate of variables with different type of events but also it will be able to compare the hazard rate within the same type of event with different covariates.

Pre- to post-immigration sexual risk behaviour and alcohol use among recent Latino immigrants in Miami.

PubMed

Berger Cardoso, Jodi; Ren, Yi; Swank, Paul; Sanchez, Mariana; De La Rosa, Mario

2016-10-01

Retrospective pre-immigration data on sexual risk and alcohol use behaviours was collected from 527 recent Latino immigrants to the USA, aged 18-34. Two follow-up assessments (12 months apart) reported on post-immigration behaviours. Using a mixed model growth curve analysis, a six-level sexual risk change variable was constructed combining measures of sexual partners and condom use. The mixed model growth curve was also used to examine associations between changes in sexual risk behaviour and changes in alcohol use and for testing interaction effects of gender and documentation status. Results suggest that individuals with high sexual risk behaviour at pre-immigration converge to low/moderate risk post-immigration, and that those who were sexually inactive or had low sexual risk at pre-immigration increased their risk post-immigration. Individuals with moderately higher initial but decreasing sexual risk behaviour showed the steepest decline in alcohol use, but their drinking at Time 3 was still higher than individuals reporting low sexual risk at Time 1. On average, men drank more than women, except women in one of the highest sexual risk categories at Time 1 - who seemed to drink as much, if not more, than men. Undocumented men reported more frequent drinking than documented men. In contrast, undocumented women reported lower alcohol use than documented women.

An analysis of security price risk and return among publicly traded pharmacy corporations.

PubMed

Gilligan, Adrienne M; Skrepnek, Grant H

2013-01-01

Community pharmacies have been subject to intense and increasing competition in the past several decades. To determine the security price risk and rate of return of publicly traded pharmacy corporations present on the major U.S. stock exchanges from 1930 to 2009. The Center of Research in Security Prices (CRSP) database was used to examine monthly security-level stock market prices in this observational retrospective study. The primary outcome of interest was the equity risk premium, with analyses focusing upon financial metrics associated with risk and return based upon modern portfolio theory (MPT) including: abnormal returns (i.e., alpha), volatility (i.e., beta), and percentage of returns explained (i.e., adjusted R(2)). Three equilibrium models were estimated using random-effects generalized least squares (GLS): 1) the Capital Asset Pricing Model (CAPM); 2) Fama-French Three-Factor Model; and 3) Carhart Four-Factor Model. Seventy-five companies were examined from 1930 to 2009, with overall adjusted R(2) values ranging from 0.13 with the CAPM to 0.16 with the Four-Factor model. Alpha was not significant within any of the equilibrium models across the entire 80-year time period, though was found from 1999 to 2009 in the Three- and Four-Factor models to be associated with a large, significant, and negative risk-adjusted abnormal returns of -33.84%. Volatility varied across specific time periods based upon the financial model employed. This investigation of risk and return within publicly listed pharmacy corporations from 1930 to 2009 found that substantial losses were incurred particularly from 1999 to 2009, with risk-adjusted security valuations decreasing by one-third. Copyright © 2013 Elsevier Inc. All rights reserved.

Frailty Models for Familial Risk with Application to Breast Cancer.

PubMed

Gorfine, Malka; Hsu, Li; Parmigiani, Giovanni

2013-12-01

In evaluating familial risk for disease we have two main statistical tasks: assessing the probability of carrying an inherited genetic mutation conferring higher risk; and predicting the absolute risk of developing diseases over time, for those individuals whose mutation status is known. Despite substantial progress, much remains unknown about the role of genetic and environmental risk factors, about the sources of variation in risk among families that carry high-risk mutations, and about the sources of familial aggregation beyond major Mendelian effects. These sources of heterogeneity contribute substantial variation in risk across families. In this paper we present simple and efficient methods for accounting for this variation in familial risk assessment. Our methods are based on frailty models. We implemented them in the context of generalizing Mendelian models of cancer risk, and compared our approaches to others that do not consider heterogeneity across families. Our extensive simulation study demonstrates that when predicting the risk of developing a disease over time conditional on carrier status, accounting for heterogeneity results in a substantial improvement in the area under the curve of the receiver operating characteristic. On the other hand, the improvement for carriership probability estimation is more limited. We illustrate the utility of the proposed approach through the analysis of BRCA1 and BRCA2 mutation carriers in the Washington Ashkenazi Kin-Cohort Study of Breast Cancer.

Models of Time Use in Paid and Unpaid Work

ERIC Educational Resources Information Center

Beaujot, Roderic; Liu, Jianye

2005-01-01

Models of time use need to consider especially the reproductive and productive activities of women and men. For husband-wife families, the breadwinner, one-earner, or complementary-roles model has advantages in terms of efficiency or specialization and stability; however, it is a high-risk model for women and children. The alternate model has been…

An integrative formal model of motivation and decision making: The MGPM*.

PubMed

Ballard, Timothy; Yeo, Gillian; Loft, Shayne; Vancouver, Jeffrey B; Neal, Andrew

2016-09-01

We develop and test an integrative formal model of motivation and decision making. The model, referred to as the extended multiple-goal pursuit model (MGPM*), is an integration of the multiple-goal pursuit model (Vancouver, Weinhardt, & Schmidt, 2010) and decision field theory (Busemeyer & Townsend, 1993). Simulations of the model generated predictions regarding the effects of goal type (approach vs. avoidance), risk, and time sensitivity on prioritization. We tested these predictions in an experiment in which participants pursued different combinations of approach and avoidance goals under different levels of risk. The empirical results were consistent with the predictions of the MGPM*. Specifically, participants pursuing 1 approach and 1 avoidance goal shifted priority from the approach to the avoidance goal over time. Among participants pursuing 2 approach goals, those with low time sensitivity prioritized the goal with the larger discrepancy, whereas those with high time sensitivity prioritized the goal with the smaller discrepancy. Participants pursuing 2 avoidance goals generally prioritized the goal with the smaller discrepancy. Finally, all of these effects became weaker as the level of risk increased. We used quantitative model comparison to show that the MGPM* explained the data better than the original multiple-goal pursuit model, and that the major extensions from the original model were justified. The MGPM* represents a step forward in the development of a general theory of decision making during multiple-goal pursuit. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Proactive assessment of accident risk to improve safety on a system of freeways : [research brief].

DOT National Transportation Integrated Search

2012-05-01

As traffic safety on freeways continues to be a growing concern, much progress has been made in shifting from reactive (incident detection) to proactive (real-time crash risk assessment) traffic strategies. Reliable models that can take in real-time ...

Impact of wait times on the effectiveness of transcatheter aortic valve replacement in severe aortic valve disease: a discrete event simulation model.

PubMed

Wijeysundera, Harindra C; Wong, William W L; Bennell, Maria C; Fremes, Stephen E; Radhakrishnan, Sam; Peterson, Mark; Ko, Dennis T

2014-10-01

There is increasing demand for transcatheter aortic valve replacement (TAVR) as the primary treatment option for patients with severe aortic stenosis who are high-risk surgical candidates or inoperable. We used mathematical simulation models to estimate the hypothetical effectiveness of TAVR with increasing wait times. We applied discrete event modelling, using data from the Placement of Aortic Transcatheter Valves (PARTNER) trials. We compared TAVR with medical therapy in the inoperable cohort, and compared TAVR to conventional aortic valve surgery in the high-risk cohort. One-year mortality and wait-time deaths were calculated in different scenarios by varying TAVR wait times from 10 days to 180 days, while maintaining a constant wait time for surgery at a mean of 15.6 days. In the inoperable cohort, the 1-year mortality for medical therapy was 50%. When the TAVR wait time was 10 days, the TAVR wait-time mortality was 1.9% with a 1-year mortality of 31.5%. TAVR wait-time deaths increased to 28.9% with a 180-day wait, with a 1-year mortality of 41.4%. In the high-risk cohort, the wait-time deaths and 1-year mortality for the surgical patients were 2.5% and 27%, respectively. The TAVR wait-time deaths increased from 2.2% with a 10-day wait to 22.4% with a 180-day wait, and a corresponding increase in 1-year mortality from 24.5% to 32.6%. Mortality with TAVR exceeded surgery when TAVR wait times exceeded 60 days. Modest increases in TAVR wait times have a substantial effect on the effectiveness of TAVR in inoperable patients and high-risk surgical candidates. Copyright © 2014 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.

When Dread Risks Are More Dreadful than Continuous Risks: Comparing Cumulative Population Losses over Time.

PubMed

Bodemer, Nicolai; Ruggeri, Azzurra; Galesic, Mirta

2013-01-01

People show higher sensitivity to dread risks, rare events that kill many people at once, compared with continuous risks, relatively frequent events that kill many people over a longer period of time. The different reaction to dread risks is often considered a bias: If the continuous risk causes the same number of fatalities, it should not be perceived as less dreadful. We test the hypothesis that a dread risk may have a stronger negative impact on the cumulative population size over time in comparison with a continuous risk causing the same number of fatalities. This difference should be particularly strong when the risky event affects children and young adults who would have produced future offspring if they had survived longer. We conducted a series of simulations, with varying assumptions about population size, population growth, age group affected by risky event, and the underlying demographic model. Results show that dread risks affect the population more severely over time than continuous risks that cause the same number of fatalities, suggesting that fearing a dread risk more than a continuous risk is an ecologically rational strategy.

Transit time distributions to assess present and future contamination risk of karst aquifers over Europe and the Mediterranean

NASA Astrophysics Data System (ADS)

Hartmann, Andreas; Gleeson, Tom; Wada, Yoshihide; Wagener, Thorsten

2016-04-01

Karst develops through the dissolution of carbonate rock. Karst groundwater in Europe is a major source of fresh water contributing up to half of the total drinking water supply in some countries. Climate model projections suggest that in the next 100 years, karst regions will experience a strong increase in temperature and a serious decrease of precipitation - especially in the Mediterranean region. Previous work showed that the karstic preferential recharge processes result in enhanced recharge rates and future climate sensitivity. But as there is fast water flow form the surface to the aquifer, there is also an enhanced risk of groundwater contamination. In this study we will assess the contamination risk of karst aquifers over Europe and the Mediterranean using simulated transit time distributions. Using a new type of semi-distributed model that considers the spatial heterogeneity of the karst system by distribution functions we simulated a range of spatially variable pathways of karstic groundwater recharge. The model is driven by the bias-corrected 5 GCMs of the ISI-MIP project (RCP8.5). Transit time distributions are calculated by virtual tracer experiments. These are repeated several times in the present (1991-2010) and the future (2080-2099). We can show that regions with larger fractions of preferential recharge show higher risks of contamination and that spatial patterns of contamination risk change towards the future.

Survival analysis with error-prone time-varying covariates: a risk set calibration approach

PubMed Central

Liao, Xiaomei; Zucker, David M.; Li, Yi; Spiegelman, Donna

2010-01-01

Summary Occupational, environmental, and nutritional epidemiologists are often interested in estimating the prospective effect of time-varying exposure variables such as cumulative exposure or cumulative updated average exposure, in relation to chronic disease endpoints such as cancer incidence and mortality. From exposure validation studies, it is apparent that many of the variables of interest are measured with moderate to substantial error. Although the ordinary regression calibration approach is approximately valid and efficient for measurement error correction of relative risk estimates from the Cox model with time-independent point exposures when the disease is rare, it is not adaptable for use with time-varying exposures. By re-calibrating the measurement error model within each risk set, a risk set regression calibration method is proposed for this setting. An algorithm for a bias-corrected point estimate of the relative risk using an RRC approach is presented, followed by the derivation of an estimate of its variance, resulting in a sandwich estimator. Emphasis is on methods applicable to the main study/external validation study design, which arises in important applications. Simulation studies under several assumptions about the error model were carried out, which demonstrated the validity and efficiency of the method in finite samples. The method was applied to a study of diet and cancer from Harvard’s Health Professionals Follow-up Study (HPFS). PMID:20486928

Default risk modeling beyond the first-passage approximation: Extended Black-Cox model

NASA Astrophysics Data System (ADS)

Katz, Yuri A.; Shokhirev, Nikolai V.

2010-07-01

We develop a generalization of the Black-Cox structural model of default risk. The extended model captures uncertainty related to firm’s ability to avoid default even if company’s liabilities momentarily exceeding its assets. Diffusion in a linear potential with the radiation boundary condition is used to mimic a company’s default process. The exact solution of the corresponding Fokker-Planck equation allows for derivation of analytical expressions for the cumulative probability of default and the relevant hazard rate. Obtained closed formulas fit well the historical data on global corporate defaults and demonstrate the split behavior of credit spreads for bonds of companies in different categories of speculative-grade ratings with varying time to maturity. Introduction of the finite rate of default at the boundary improves valuation of credit risk for short time horizons, which is the key advantage of the proposed model. We also consider the influence of uncertainty in the initial distance to the default barrier on the outcome of the model and demonstrate that this additional source of incomplete information may be responsible for nonzero credit spreads for bonds with very short time to maturity.

Comparative-effectiveness research to aid population decision making by relating clinical outcomes and quality-adjusted life years.

PubMed

Campbell, Jonathan D; Zerzan, Judy; Garrison, Louis P; Libby, Anne M

2013-04-01

Comparative-effectiveness research (CER) at the population level is missing standardized approaches to quantify and weigh interventions in terms of their clinical risks, benefits, and uncertainty. We proposed an adapted CER framework for population decision making, provided example displays of the outputs, and discussed the implications for population decision makers. Building on decision-analytical modeling but excluding cost, we proposed a 2-step approach to CER that explicitly compared interventions in terms of clinical risks and benefits and linked this evidence to the quality-adjusted life year (QALY). The first step was a traditional intervention-specific evidence synthesis of risks and benefits. The second step was a decision-analytical model to simulate intervention-specific progression of disease over an appropriate time. The output was the ability to compare and quantitatively link clinical outcomes with QALYs. The outputs from these CER models include clinical risks, benefits, and QALYs over flexible and relevant time horizons. This approach yields an explicit, structured, and consistent quantitative framework to weigh all relevant clinical measures. Population decision makers can use this modeling framework and QALYs to aid in their judgment of the individual and collective risks and benefits of the alternatives over time. Future research should study effective communication of these domains for stakeholders. Copyright © 2013 Elsevier HS Journals, Inc. All rights reserved.

Statistical Models for Tornado Climatology: Long and Short-Term Views.

PubMed

Elsner, James B; Jagger, Thomas H; Fricker, Tyler

2016-01-01

This paper estimates regional tornado risk from records of past events using statistical models. First, a spatial model is fit to the tornado counts aggregated in counties with terms that control for changes in observational practices over time. Results provide a long-term view of risk that delineates the main tornado corridors in the United States where the expected annual rate exceeds two tornadoes per 10,000 square km. A few counties in the Texas Panhandle and central Kansas have annual rates that exceed four tornadoes per 10,000 square km. Refitting the model after removing the least damaging tornadoes from the data (EF0) produces a similar map but with the greatest tornado risk shifted south and eastward. Second, a space-time model is fit to the counts aggregated in raster cells with terms that control for changes in climate factors. Results provide a short-term view of risk. The short-term view identifies a shift of tornado activity away from the Ohio Valley under El Niño conditions and away from the Southeast under positive North Atlantic oscillation conditions. The combined predictor effects on the local rates is quantified by fitting the model after leaving out the year to be predicted from the data. The models provide state-of-the-art views of tornado risk that can be used by government agencies, the insurance industry, and the general public.

Statistical Models for Tornado Climatology: Long and Short-Term Views

PubMed Central

Jagger, Thomas H.; Fricker, Tyler

2016-01-01

This paper estimates regional tornado risk from records of past events using statistical models. First, a spatial model is fit to the tornado counts aggregated in counties with terms that control for changes in observational practices over time. Results provide a long-term view of risk that delineates the main tornado corridors in the United States where the expected annual rate exceeds two tornadoes per 10,000 square km. A few counties in the Texas Panhandle and central Kansas have annual rates that exceed four tornadoes per 10,000 square km. Refitting the model after removing the least damaging tornadoes from the data (EF0) produces a similar map but with the greatest tornado risk shifted south and eastward. Second, a space-time model is fit to the counts aggregated in raster cells with terms that control for changes in climate factors. Results provide a short-term view of risk. The short-term view identifies a shift of tornado activity away from the Ohio Valley under El Niño conditions and away from the Southeast under positive North Atlantic oscillation conditions. The combined predictor effects on the local rates is quantified by fitting the model after leaving out the year to be predicted from the data. The models provide state-of-the-art views of tornado risk that can be used by government agencies, the insurance industry, and the general public. PMID:27875581

Quantifying and modeling long-range cross correlations in multiple time series with applications to world stock indices

NASA Astrophysics Data System (ADS)

Wang, Duan; Podobnik, Boris; Horvatić, Davor; Stanley, H. Eugene

2011-04-01

We propose a modified time lag random matrix theory in order to study time-lag cross correlations in multiple time series. We apply the method to 48 world indices, one for each of 48 different countries. We find long-range power-law cross correlations in the absolute values of returns that quantify risk, and find that they decay much more slowly than cross correlations between the returns. The magnitude of the cross correlations constitutes “bad news” for international investment managers who may believe that risk is reduced by diversifying across countries. We find that when a market shock is transmitted around the world, the risk decays very slowly. We explain these time-lag cross correlations by introducing a global factor model (GFM) in which all index returns fluctuate in response to a single global factor. For each pair of individual time series of returns, the cross correlations between returns (or magnitudes) can be modeled with the autocorrelations of the global factor returns (or magnitudes). We estimate the global factor using principal component analysis, which minimizes the variance of the residuals after removing the global trend. Using random matrix theory, a significant fraction of the world index cross correlations can be explained by the global factor, which supports the utility of the GFM. We demonstrate applications of the GFM in forecasting risks at the world level, and in finding uncorrelated individual indices. We find ten indices that are practically uncorrelated with the global factor and with the remainder of the world indices, which is relevant information for world managers in reducing their portfolio risk. Finally, we argue that this general method can be applied to a wide range of phenomena in which time series are measured, ranging from seismology and physiology to atmospheric geophysics.

Quantifying and modeling long-range cross correlations in multiple time series with applications to world stock indices.

PubMed

Wang, Duan; Podobnik, Boris; Horvatić, Davor; Stanley, H Eugene

2011-04-01

We propose a modified time lag random matrix theory in order to study time-lag cross correlations in multiple time series. We apply the method to 48 world indices, one for each of 48 different countries. We find long-range power-law cross correlations in the absolute values of returns that quantify risk, and find that they decay much more slowly than cross correlations between the returns. The magnitude of the cross correlations constitutes "bad news" for international investment managers who may believe that risk is reduced by diversifying across countries. We find that when a market shock is transmitted around the world, the risk decays very slowly. We explain these time-lag cross correlations by introducing a global factor model (GFM) in which all index returns fluctuate in response to a single global factor. For each pair of individual time series of returns, the cross correlations between returns (or magnitudes) can be modeled with the autocorrelations of the global factor returns (or magnitudes). We estimate the global factor using principal component analysis, which minimizes the variance of the residuals after removing the global trend. Using random matrix theory, a significant fraction of the world index cross correlations can be explained by the global factor, which supports the utility of the GFM. We demonstrate applications of the GFM in forecasting risks at the world level, and in finding uncorrelated individual indices. We find ten indices that are practically uncorrelated with the global factor and with the remainder of the world indices, which is relevant information for world managers in reducing their portfolio risk. Finally, we argue that this general method can be applied to a wide range of phenomena in which time series are measured, ranging from seismology and physiology to atmospheric geophysics.

Urban-hazard risk analysis: mapping of heat-related risks in the elderly in major Italian cities.

PubMed

Morabito, Marco; Crisci, Alfonso; Gioli, Beniamino; Gualtieri, Giovanni; Toscano, Piero; Di Stefano, Valentina; Orlandini, Simone; Gensini, Gian Franco

2015-01-01

Short-term impacts of high temperatures on the elderly are well known. Even though Italy has the highest proportion of elderly citizens in Europe, there is a lack of information on spatial heat-related elderly risks. Development of high-resolution, heat-related urban risk maps regarding the elderly population (≥ 65). A long time-series (2001-2013) of remote sensing MODIS data, averaged over the summer period for eleven major Italian cities, were downscaled to obtain high spatial resolution (100 m) daytime and night-time land surface temperatures (LST). LST was estimated pixel-wise by applying two statistical model approaches: 1) the Linear Regression Model (LRM); 2) the Generalized Additive Model (GAM). Total and elderly population density data were extracted from the Joint Research Centre population grid (100 m) from the 2001 census (Eurostat source), and processed together using "Crichton's Risk Triangle" hazard-risk methodology for obtaining a Heat-related Elderly Risk Index (HERI). The GAM procedure allowed for improved daytime and night-time LST estimations compared to the LRM approach. High-resolution maps of daytime and night-time HERI levels were developed for inland and coastal cities. Urban areas with the hazardous HERI level (very high risk) were not necessarily characterized by the highest temperatures. The hazardous HERI level was generally localized to encompass the city-centre in inland cities and the inner area in coastal cities. The two most dangerous HERI levels were greater in the coastal rather than inland cities. This study shows the great potential of combining geospatial technologies and spatial demographic characteristics within a simple and flexible framework in order to provide high-resolution urban mapping of daytime and night-time HERI. In this way, potential areas for intervention are immediately identified with up-to-street level details. This information could support public health operators and facilitate coordination for heat-related emergencies.

Urban-Hazard Risk Analysis: Mapping of Heat-Related Risks in the Elderly in Major Italian Cities

PubMed Central

Morabito, Marco; Crisci, Alfonso; Gioli, Beniamino; Gualtieri, Giovanni; Toscano, Piero; Di Stefano, Valentina; Orlandini, Simone; Gensini, Gian Franco

2015-01-01

Background Short-term impacts of high temperatures on the elderly are well known. Even though Italy has the highest proportion of elderly citizens in Europe, there is a lack of information on spatial heat-related elderly risks. Objectives Development of high-resolution, heat-related urban risk maps regarding the elderly population (≥65). Methods A long time-series (2001–2013) of remote sensing MODIS data, averaged over the summer period for eleven major Italian cities, were downscaled to obtain high spatial resolution (100 m) daytime and night-time land surface temperatures (LST). LST was estimated pixel-wise by applying two statistical model approaches: 1) the Linear Regression Model (LRM); 2) the Generalized Additive Model (GAM). Total and elderly population density data were extracted from the Joint Research Centre population grid (100 m) from the 2001 census (Eurostat source), and processed together using “Crichton’s Risk Triangle” hazard-risk methodology for obtaining a Heat-related Elderly Risk Index (HERI). Results The GAM procedure allowed for improved daytime and night-time LST estimations compared to the LRM approach. High-resolution maps of daytime and night-time HERI levels were developed for inland and coastal cities. Urban areas with the hazardous HERI level (very high risk) were not necessarily characterized by the highest temperatures. The hazardous HERI level was generally localized to encompass the city-centre in inland cities and the inner area in coastal cities. The two most dangerous HERI levels were greater in the coastal rather than inland cities. Conclusions This study shows the great potential of combining geospatial technologies and spatial demographic characteristics within a simple and flexible framework in order to provide high-resolution urban mapping of daytime and night-time HERI. In this way, potential areas for intervention are immediately identified with up-to-street level details. This information could support public health operators and facilitate coordination for heat-related emergencies. PMID:25985204

A Study of the Factors Associated with Risk for Development of Pressure Ulcers: A Longitudinal Analysis.

PubMed

Thomas, Elizebeth; Vinodkumar, Sudhaya; Mathew, Silvia; Setia, Maninder Singh

2015-01-01

Pressure ulcers (PUs) are prevalent in hospitalized patients; they may cause clinical, psychological, and economic problems in these patients. Previous studies are cross-sectional, have used pooled data, or cox-regression models to assess the risk for developing PU. However, PU risk scores change over time and models that account for time varying variables are useful for cohort analysis of data. The present longitudinal study was conducted to compare the risk of PU between surgical and nonsurgical patients, and to evaluate the factors associated with the development of these ulcers over a period of time. We evaluated 290 hospitalized patients over a 4 months period. The main outcomes for our analysis were: (1) Score on the pressure risk assessment scale; and (2) the proportion of individuals who were at severe risk for developing PUs. We used random effects models for longitudinal analysis of the data. The mean PU score was significantly higher in the nonsurgical patients compared with surgical patients at baseline (15.23 [3.86] vs. 9.33 [4.57]; P < 0.01). About 7% of the total patients had a score of >20 at baseline and were considered as being at high-risk for PU; the proportion was significantly higher among the nonsurgical patients compared with the surgical patients (14% vs. 4%, P = 0.003). In the adjusted models, there was no difference for severe risk for PU between surgical and nonsurgical patients (odds ratios [ORs]: 0.37, 95% confidence interval [CI]: 0.01-12.80). An additional day in the ward was associated with a significantly higher likelihood of being at high-risk for PU (OR: 1.47, 95% CI: 1.16-1.86). There were no significant differences between patients who were admitted for surgery compared with those who were not. An additional day in the ward, however, is important for developing a high-risk score for PU on the monitoring scale, and these patients require active interventions.

Dynamic trends in cardiac surgery: why the logistic EuroSCORE is no longer suitable for contemporary cardiac surgery and implications for future risk models

PubMed Central

Hickey, Graeme L.; Grant, Stuart W.; Murphy, Gavin J.; Bhabra, Moninder; Pagano, Domenico; McAllister, Katherine; Buchan, Iain; Bridgewater, Ben

2013-01-01

OBJECTIVES Progressive loss of calibration of the original EuroSCORE models has necessitated the introduction of the EuroSCORE II model. Poor model calibration has important implications for clinical decision-making and risk adjustment of governance analyses. The objective of this study was to explore the reasons for the calibration drift of the logistic EuroSCORE. METHODS Data from the Society for Cardiothoracic Surgery in Great Britain and Ireland database were analysed for procedures performed at all National Health Service and some private hospitals in England and Wales between April 2001 and March 2011. The primary outcome was in-hospital mortality. EuroSCORE risk factors, overall model calibration and discrimination were assessed over time. RESULTS A total of 317 292 procedures were included. Over the study period, mean age at surgery increased from 64.6 to 67.2 years. The proportion of procedures that were isolated coronary artery bypass grafts decreased from 67.5 to 51.2%. In-hospital mortality fell from 4.1 to 2.8%, but the mean logistic EuroSCORE increased from 5.6 to 7.6%. The logistic EuroSCORE remained a good discriminant throughout the study period (area under the receiver-operating characteristic curve between 0.79 and 0.85), but calibration (observed-to-expected mortality ratio) fell from 0.76 to 0.37. Inadequate adjustment for decreasing baseline risk affected calibration considerably. DISCUSSIONS Patient risk factors and case-mix in adult cardiac surgery change dynamically over time. Models like the EuroSCORE that are developed using a ‘snapshot’ of data in time do not account for this and can subsequently lose calibration. It is therefore important to regularly revalidate clinical prediction models. PMID:23152436

Innovative practice model to optimize resource utilization and improve access to care for high-risk and BRCA+ patients.

PubMed

Head, Linden; Nessim, Carolyn; Usher Boyd, Kirsty

2017-02-01

Bilateral prophylactic mastectomy (BPM) has demonstrated breast cancer risk reduction in high-risk/ BRCA + patients. However, priority of active cancers coupled with inefficient use of operating room (OR) resources presents challenges in offering BPM in a timely manner. To address these challenges, a rapid access prophylactic mastectomy and immediate reconstruction (RAPMIR) program was innovated. The purpose of this study was to evaluate RAPMIR with regards to access to care and efficiency. We retrospectively reviewed the cases of all high-risk/ BRCA + patients having had BPM between September 2012 and August 2014. Patients were divided into 2 groups: those managed through the traditional model and those managed through the RAPMIR model. RAPMIR leverages 2 concurrently running ORs with surgical oncology and plastic surgery moving between rooms to complete 3 combined BPMs with immediate reconstruction in addition to 1-2 independent cases each operative day. RAPMIR eligibility criteria included high-risk/ BRCA + status; BPM with immediate, implant-based reconstruction; and day surgery candidacy. Wait times, case volumes and patient throughput were measured and compared. There were 16 traditional patients and 13 RAPMIR patients. Mean wait time (days from referral to surgery) for RAPMIR was significantly shorter than for the traditional model (165.4 v. 309.2 d, p = 0.027). Daily patient throughput (4.3 v. 2.8), plastic surgery case volume (3.7 v. 1.6) and surgical oncology case volume (3.0 v. 2.2) were significantly greater in the RAPMIR model than the traditional model ( p = 0.003, p < 0.001 and p = 0.015, respectively). A multidisciplinary model with optimized scheduling has the potential to improve access to care and optimize resource utilization.

Innovative practice model to optimize resource utilization and improve access to care for high-risk and BRCA+ patients

PubMed Central

Head, Linden; Nessim, Carolyn; Boyd, Kirsty Usher

2017-01-01

Background Bilateral prophylactic mastectomy (BPM) has shown breast cancer risk reduction in high-risk/BRCA+ patients. However, priority of active cancers coupled with inefficient use of operating room (OR) resources presents challenges in offering BPM in a timely manner. To address these challenges, a rapid access prophylactic mastectomy and immediate reconstruction (RAPMIR) program was innovated. The purpose of this study was to evaluate RAPMIR with regards to access to care and efficiency. Methods We retrospectively reviewed the cases of all high-risk/BRCA+ patients having had BPM between September 2012 and August 2014. Patients were divided into 2 groups: those managed through the traditional model and those managed through the RAPMIR model. RAPMIR leverages 2 concurrently running ORs with surgical oncology and plastic surgery moving between rooms to complete 3 combined BPMs with immediate reconstruction in addition to 1–2 independent cases each operative day. RAPMIR eligibility criteria included high-risk/BRCA+ status; BPM with immediate, implant-based reconstruction; and day surgery candidacy. Wait times, case volumes and patient throughput were measured and compared. Results There were 16 traditional patients and 13 RAPMIR patients. Mean wait time (days from referral to surgery) for RAPMIR was significantly shorter than for the traditional model (165.4 v. 309.2 d, p = 0.027). Daily patient throughput (4.3 v. 2.8), plastic surgery case volume (3.7 v. 1.6) and surgical oncology case volume (3.0 v. 2.2) were significantly greater in the RAPMIR model than the traditional model (p = 0.003, p < 0.001 and p = 0.015, respectively). Conclusion A multidisciplinary model with optimized scheduling has the potential to improve access to care and optimize resource utilization. PMID:28234588

Innovative practice model to optimize resource utilization and improve access to care for high-risk and BRCA+ patients.

PubMed

Head, Linden; Nessim, Carolyn; Usher Boyd, Kirsty

2016-12-01

Bilateral prophylactic mastectomy (BPM) has demonstrated breast cancer risk reduction in high-risk/ BRCA + patients. However, priority of active cancers coupled with inefficient use of operating room (OR) resources presents challenges in offering BPM in a timely manner. To address these challenges, a rapid access prophylactic mastectomy and immediate reconstruction (RAPMIR) program was innovated. The purpose of this study was to evaluate RAPMIR with regards to access to care and efficiency. We retrospectively reviewed the cases of all high-risk/ BRCA + patients having had BPM between September 2012 and August 2014. Patients were divided into 2 groups: those managed through the traditional model and those managed through the RAPMIR model. RAPMIR leverages 2 concurrently running ORs with surgical oncology and plastic surgery moving between rooms to complete 3 combined BPMs with immediate reconstruction in addition to 1-2 independent cases each operative day. RAPMIR eligibility criteria included high-risk/ BRCA + status; BPM with immediate, implant-based reconstruction; and day surgery candidacy. Wait times, case volumes and patient throughput were measured and compared. There were 16 traditional patients and 13 RAPMIR patients. Mean wait time (days from referral to surgery) for RAPMIR was significantly shorter than for the traditional model (165.4 v. 309.2 d, p = 0.027). Daily patient throughput (4.3 v. 2.8), plastic surgery case volume (3.7 v. 1.6) and surgical oncology case volume (3.0 v. 2.2) were significantly greater in the RAPMIR model than the traditional model ( p = 0.003, p < 0.001 and p = 0.015, respectively). A multidisciplinary model with optimized scheduling has the potential to improve access to care and optimize resource utilization.

Estimating the Value-at-Risk for some stocks at the capital market in Indonesia based on ARMA-FIGARCH models

NASA Astrophysics Data System (ADS)

Sukono; Lesmana, E.; Susanti, D.; Napitupulu, H.; Hidayat, Y.

2017-11-01

Value-at-Risk has already become a standard measurement that must be carried out by the financial institution for both internal interest and regulatory. In this paper, the estimation of Value-at-Risk of some stocks with econometric models approach is analyzed. In this research, we assume that the stock return follows the time series model. To do the estimation of mean value we are using ARMA models, while to estimate the variance value we are using FIGARCH models. Furthermore, the mean value estimator and the variance are used to estimate the Value-at-Risk. The result of the analysis shows that from five stock PRUF, BBRI, MPPA, BMRI, and INDF, the Value-at-Risk obtained are 0.01791, 0.06037, 0.02550, 0.06030, and 0.02585 respectively. Since Value-at-Risk represents the maximum risk size of each stock at a 95% level of significance, then it can be taken into consideration in determining the investment policy on stocks.

Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.

PubMed

Perotte, Adler; Ranganath, Rajesh; Hirsch, Jamie S; Blei, David; Elhadad, Noémie

2015-07-01

As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling. The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation. The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic. A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001). A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Modelling determinants, impact, and space-time risk of age-specific mortality in rural South Africa: integrating methods to enhance policy relevance.

PubMed

Sartorius, Benn

2013-01-24

There is a lack of reliable data in developing countries to inform policy and optimise resource allocation. Health and socio-demographic surveillance sites (HDSS) have the potential to address this gap. Mortality levels and trends have previously been documented in rural South Africa. However, complex space-time clustering of mortality, determinants, and their impact has not been fully examined. To integrate advanced methods enhance the understanding of the dynamics of mortality in space-time, to identify mortality risk factors and population attributable impact, to relate disparities in risk factor distributions to spatial mortality risk, and thus, to improve policy planning and resource allocation. Agincourt HDSS supplied data for the period 1992-2008. Advanced spatial techniques were used to identify significant age-specific mortality 'hotspots' in space-time. Multivariable Bayesian models were used to assess the effects of the most significant covariates on mortality. Disparities in risk factor profiles in identified hotspots were assessed. Increasing HIV-related mortality and a subsequent decrease possibly attributable to antiretroviral therapy introduction are evident in this rural population. Distinct space-time clustering and variation (even in a small geographic area) of mortality were observed. Several known and novel risk factors were identified, and population impact was quantified. Significant differences in the risk factor profiles of the identified 'hotspots' included ethnicity; maternal, partner, and household deaths; household head demographics; migrancy; education; and poverty. A complex interaction of highly attributable multilevel factors continues to demonstrate differential space-time influences on mortality risk (especially for HIV). High-risk households and villages displayed differential risk factor profiles. This integrated approach could prove valuable to decision makers. Tailored interventions for specific child and adult high-risk mortality areas are needed, such as preventing vertical transmission, ensuring maternal survival, and improving water and sanitation infrastructure. This framework can be applied in other settings within the region.

A test of an interactive model of binge eating among undergraduate men.

PubMed

Minnich, Allison M; Gordon, Kathryn H; Holm-Denoma, Jill M; Troop-Gordon, Wendy

2014-12-01

Past research has shown that a combination of high perfectionism, high body dissatisfaction, and low self-esteem is predictive of binge eating in college women (Bardone-Cone et al., 2006). In the current study, we examined whether this triple interaction model is applicable to men. Male undergraduate college students from a large Midwestern university (n=302) completed self-report measures online at two different time points, a minimum of eight weeks apart. Analyses revealed a significant interaction between the three risk factors, such that high perfectionism, high body dissatisfaction, and low self-esteem at Time 1 were associated with higher levels of Time 2 binge eating symptoms. The triple interaction model did not predict Time 2 anxiety or depressive symptoms, which suggests model specificity. These findings offer a greater understanding of the interactive nature of risk factors in predicting binge eating symptoms among men. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multifractal Value at Risk model

NASA Astrophysics Data System (ADS)

Lee, Hojin; Song, Jae Wook; Chang, Woojin

2016-06-01

In this paper new Value at Risk (VaR) model is proposed and investigated. We consider the multifractal property of financial time series and develop a multifractal Value at Risk (MFVaR). MFVaR introduced in this paper is analytically tractable and not based on simulation. Empirical study showed that MFVaR can provide the more stable and accurate forecasting performance in volatile financial markets where large loss can be incurred. This implies that our multifractal VaR works well for the risk measurement of extreme credit events.

Modelling fatigue and the use of fatigue models in work settings.

PubMed

Dawson, Drew; Ian Noy, Y; Härmä, Mikko; Akerstedt, Torbjorn; Belenky, Gregory

2011-03-01

In recent years, theoretical models of the sleep and circadian system developed in laboratory settings have been adapted to predict fatigue and, by inference, performance. This is typically done using the timing of prior sleep and waking or working hours as the primary input and the time course of the predicted variables as the primary output. The aim of these models is to provide employers, unions and regulators with quantitative information on the likely average level of fatigue, or risk, associated with a given pattern of work and sleep with the goal of better managing the risk of fatigue-related errors and accidents/incidents. The first part of this review summarises the variables known to influence workplace fatigue and draws attention to the considerable variability attributable to individual and task variables not included in current models. The second part reviews the current fatigue models described in the scientific and technical literature and classifies them according to whether they predict fatigue directly by using the timing of prior sleep and wake (one-step models) or indirectly by using work schedules to infer an average sleep-wake pattern that is then used to predict fatigue (two-step models). The third part of the review looks at the current use of fatigue models in field settings by organizations and regulators. Given their limitations it is suggested that the current generation of models may be appropriate for use as one element in a fatigue risk management system. The final section of the review looks at the future of these models and recommends a standardised approach for their use as an element of the 'defenses-in-depth' approach to fatigue risk management. Copyright © 2010 Elsevier Ltd. All rights reserved.

A Probability Model of Decompression Sickness at 4.3 Psia after Exercise Prebreathe

NASA Technical Reports Server (NTRS)

Conkin, Johnny; Gernhardt, Michael L.; Powell, Michael R.; Pollock, Neal

2004-01-01

Exercise PB can reduce the risk of decompression sickness on ascent to 4.3 psia when performed at the proper intensity and duration. Data are from seven tests. PB times ranged from 90 to 150 min. High intensity, short duration dual-cycle ergometry was done during the PB. This was done alone, or combined with intermittent low intensity exercise or periods of rest for the remaining PB. Nonambulating men and women performed light exercise from a semi-recumbent position at 4.3 psia for four hrs. The Research Model with age tested the probability that DCS increases with advancing age. The NASA Model with gender hypothesized that the probability of DCS increases if gender is female. Accounting for exercise and rest during PB with a variable half-time compartment for computed tissue N2 pressure advances our probability modeling of hypobaric DCS. Both models show that a small increase in exercise intensity during PB reduces the risk of DCS, and a larger increase in exercise intensity dramatically reduces risk. These models support the hypothesis that aerobic fitness is an important consideration for the risk of hypobaric DCS when exercise is performed during the PB.

Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models.

PubMed

Adams, Matthew D; Kanaroglou, Pavlos S

2016-03-01

Air pollution poses health concerns at the global scale. The challenge of managing air pollution is significant because of the many air pollutants, insufficient funds for monitoring and abatement programs, and political and social challenges in defining policy to limit emissions. Some governments provide citizens with air pollution health risk information to allow them to limit their exposure. However, many regions still have insufficient air pollution monitoring networks to provide real-time mapping. Where available, these risk mapping systems either provide absolute concentration data or the concentrations are used to derive an Air Quality Index, which provides the air pollution risk for a mix of air pollutants with a single value. When risk information is presented as a single value for an entire region it does not inform on the spatial variation within the region. Without an understanding of the local variation residents can only make a partially informed decision when choosing daily activities. The single value is typically provided because of a limited number of active monitoring units in the area. In our work, we overcome this issue by leveraging mobile air pollution monitoring techniques, meteorological information and land use information to map real-time air pollution health risks. We propose an approach that can provide improved health risk information to the public by applying neural network models within a framework that is inspired by land use regression. Mobile air pollution monitoring campaigns were conducted across Hamilton from 2005 to 2013. These mobile air pollution data were modelled with a number of predictor variables that included information on the surrounding land use characteristics, the meteorological conditions, air pollution concentrations from fixed location monitors, and traffic information during the time of collection. Fine particulate matter and nitrogen dioxide were both modelled. During the model fitting process we reserved twenty percent of the data to validate the predictions. The models' performances were measured with a coefficient of determination at 0.78 and 0.34 for PM2.5 and NO2, respectively. We apply a relative importance measure to identify the importance of each variable in the neural network to partially overcome the black box issues of neural network models. Copyright © 2015 Elsevier Ltd. All rights reserved.

Conservation Risks: When Will Rhinos be Extinct?

PubMed

Haas, Timothy C; Ferreira, Sam M

2016-08-01

We develop a risk intelligence system for biodiversity enterprises. Such enterprises depend on a supply of endangered species for their revenue. Many of these enterprises, however, cannot purchase a supply of this resource and are largely unable to secure the resource against theft in the form of poaching. Because replacements are not available once a species becomes extinct, insurance products are not available to reduce the risk exposure of these enterprises to an extinction event. For many species, the dynamics of anthropogenic impacts driven by economic as well as noneconomic values of associated wildlife products along with their ecological stressors can help meaningfully predict extinction risks. We develop an agent/individual-based economic-ecological model that captures these effects and apply it to the case of South African rhinos. Our model uses observed rhino dynamics and poaching statistics. It seeks to predict rhino extinction under the present scenario. This scenario has no legal horn trade, but allows live African rhino trade and legal hunting. Present rhino populations are small and threatened by a rising onslaught of poaching. This present scenario and associated dynamics predicts continued decline in rhino population size with accelerated extinction risks of rhinos by 2036. Our model supports the computation of extinction risks at any future time point. This capability can be used to evaluate the effectiveness of proposed conservation strategies at reducing a species' extinction risk. Models used to compute risk predictions, however, need to be statistically estimated. We point out that statistically fitting such models to observations will involve massive numbers of observations on consumer behavior and time-stamped location observations on thousands of animals. Finally, we propose Big Data algorithms to perform such estimates and to interpret the fitted model's output.

Development of a real-time crash risk prediction model incorporating the various crash mechanisms across different traffic states.

PubMed

Xu, Chengcheng; Wang, Wei; Liu, Pan; Zhang, Fangwei

2015-01-01

This study aimed to identify the traffic flow variables contributing to crash risks under different traffic states and to develop a real-time crash risk model incorporating the varying crash mechanisms across different traffic states. The crash, traffic, and geometric data were collected on the I-880N freeway in California in 2008 and 2009. This study considered 4 different traffic states in Wu's 4-phase traffic theory. They are free fluid traffic, bunched fluid traffic, bunched congested traffic, and standing congested traffic. Several different statistical methods were used to accomplish the research objective. The preliminary analysis showed that traffic states significantly affected crash likelihood, collision type, and injury severity. Nonlinear canonical correlation analysis (NLCCA) was conducted to identify the underlying phenomena that made certain traffic states more hazardous than others. The results suggested that different traffic states were associated with various collision types and injury severities. The matching of traffic flow characteristics and crash characteristics in NLCCA revealed how traffic states affected traffic safety. The logistic regression analyses showed that the factors contributing to crash risks were quite different across various traffic states. To incorporate the varying crash mechanisms across different traffic states, random parameters logistic regression was used to develop a real-time crash risk model. Bayesian inference based on Markov chain Monte Carlo simulations was used for model estimation. The parameters of traffic flow variables in the model were allowed to vary across different traffic states. Compared with the standard logistic regression model, the proposed model significantly improved the goodness-of-fit and predictive performance. These results can promote a better understanding of the relationship between traffic flow characteristics and crash risks, which is valuable knowledge in the pursuit of improving traffic safety on freeways through the use of dynamic safety management systems.

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.

Real time forest fire warning and forest fire risk zoning: a Vietnamese case study

NASA Astrophysics Data System (ADS)

Chu, T.; Pham, D.; Phung, T.; Ha, A.; Paschke, M.

2016-12-01

Forest fire occurs seriously in Vietnam and has been considered as one of the major causes of forest lost and degradation. Several studies of forest fire risk warning were conducted using Modified Nesterov Index (MNI) but remaining shortcomings and inaccurate predictions that needs to be urgently improved. In our study, several important topographic and social factors such as aspect, slope, elevation, distance to residential areas and road system were considered as "permanent" factors while meteorological data were updated hourly using near-real-time (NRT) remotely sensed data (i.e. MODIS Terra/Aqua and TRMM) for the prediction and warning of fire. Due to the limited number of weather stations in Vietnam, data from all active stations (i.e. 178) were used with the satellite data to calibrate and upscale meteorological variables. These data with finer resolution were then used to generate MNI. The only significant "permanent" factors were selected as input variables based on the correlation coefficients that computed from multi-variable regression among true fire-burning (collected from 1/2007) and its spatial characteristics. These coefficients also used to suggest appropriate weight for computing forest fire risk (FR) model. Forest fire risk model was calculated from the MNI and the selected factors using fuzzy regression models (FRMs) and GIS based multi-criteria analysis. By this approach, the FR was slightly modified from MNI by the integrated use of various factors in our fire warning and prediction model. Multifactor-based maps of forest fire risk zone were generated from classifying FR into three potential danger levels. Fire risk maps were displayed using webgis technology that is easy for managing data and extracting reports. Reported fire-burnings thereafter have been used as true values for validating the forest fire risk. Fire probability has strong relationship with potential danger levels (varied from 5.3% to 53.8%) indicating that the higher potential risk, the more chance of fire happen. By adding spatial factors to continuous daily updated remote sensing based meteo-data, results are valuable for both mapping forest fire risk zones in short and long-term and real time fire warning in Vietnam. Key words: Near-real-time, forest fire warning, fuzzy regression model, remote sensing.

A prediction model for colon cancer surveillance data.

PubMed

Good, Norm M; Suresh, Krithika; Young, Graeme P; Lockett, Trevor J; Macrae, Finlay A; Taylor, Jeremy M G

2015-08-15

Dynamic prediction models make use of patient-specific longitudinal data to update individualized survival probability predictions based on current and past information. Colonoscopy (COL) and fecal occult blood test (FOBT) results were collected from two Australian surveillance studies on individuals characterized as high-risk based on a personal or family history of colorectal cancer. Motivated by a Poisson process, this paper proposes a generalized nonlinear model with a complementary log-log link as a dynamic prediction tool that produces individualized probabilities for the risk of developing advanced adenoma or colorectal cancer (AAC). This model allows predicted risk to depend on a patient's baseline characteristics and time-dependent covariates. Information on the dates and results of COLs and FOBTs were incorporated using time-dependent covariates that contributed to patient risk of AAC for a specified period following the test result. These covariates serve to update a person's risk as additional COL, and FOBT test information becomes available. Model selection was conducted systematically through the comparison of Akaike information criterion. Goodness-of-fit was assessed with the use of calibration plots to compare the predicted probability of event occurrence with the proportion of events observed. Abnormal COL results were found to significantly increase risk of AAC for 1 year following the test. Positive FOBTs were found to significantly increase the risk of AAC for 3 months following the result. The covariates that incorporated the updated test results were of greater significance and had a larger effect on risk than the baseline variables. Copyright © 2015 John Wiley & Sons, Ltd.

Spatiotemporal Visualization of Tsunami Waves Using Kml on Google Earth

NASA Astrophysics Data System (ADS)

Mohammadi, H.; Delavar, M. R.; Sharifi, M. A.; Pirooz, M. D.

2017-09-01

Disaster risk is a function of hazard and vulnerability. Risk is defined as the expected losses, including lives, personal injuries, property damages, and economic disruptions, due to a particular hazard for a given area and time period. Risk assessment is one of the key elements of a natural disaster management strategy as it allows for better disaster mitigation and preparation. It provides input for informed decision making, and increases risk awareness among decision makers and other stakeholders. Virtual globes such as Google Earth can be used as a visualization tool. Proper spatiotemporal graphical representations of the concerned risk significantly reduces the amount of effort to visualize the impact of the risk and improves the efficiency of the decision-making process to mitigate the impact of the risk. The spatiotemporal visualization of tsunami waves for disaster management process is an attractive topic in geosciences to assist investigation of areas at tsunami risk. In this paper, a method for coupling virtual globes with tsunami wave arrival time models is presented. In this process we have shown 2D+Time of tsunami waves for propagation and inundation of tsunami waves, both coastal line deformation, and the flooded areas. In addition, the worst case scenario of tsunami on Chabahar port derived from tsunami modelling is also presented using KML on google earth.

Investigating Gender Differences under Time Pressure in Financial Risk Taking

PubMed Central

Xie, Zhixin; Page, Lionel; Hardy, Ben

2017-01-01

There is a significant gender imbalance on financial trading floors. This motivated us to investigate gender differences in financial risk taking under pressure. We used a well-established approach from behavior economics to analyze a series of risky monetary choices by male and female participants with and without time pressure. We also used second to fourth digit ratio (2D:4D) and face width-to-height ratio (fWHR) as correlates of pre-natal exposure to testosterone. We constructed a structural model and estimated the participants' risk attitudes and probability perceptions via maximum likelihood estimation under both expected utility (EU) and rank-dependent utility (RDU) models. In line with existing research, we found that male participants are less risk averse and that the gender gap in risk attitudes increases under moderate time pressure. We found that female participants with lower 2D:4D ratios and higher fWHR are less risk averse in RDU estimates. Males with lower 2D:4D ratios were less risk averse in EU estimations, but more risk averse using RDU estimates. We also observe that men whose ratios indicate a greater prenatal exposure to testosterone exhibit a greater optimism and overestimation of small probabilities of success. PMID:29326566

Simulating wildfire spread behavior between two NASA Active Fire data timeframes

NASA Astrophysics Data System (ADS)

Adhikari, B.; Hodza, P.; Xu, C.; Minckley, T. A.

2017-12-01

Although NASA's Active Fire dataset is considered valuable in mapping the spatial distribution and extent of wildfires across the world, the data is only available at approximately 12-hour time intervals, creating uncertainties and risks associated with fire spread and behavior between the two Visible Infrared Imaging Radiometer Satellite (VIIRS) data collection timeframes. Our study seeks to close the information gap for the United States by using the latest Active Fire data collected for instance around 0130 hours as an ignition source and critical inputs to a wildfire model by uniquely incorporating forecasted and real-time weather conditions for predicting fire perimeter at the next 12 hour reporting time (i.e. around 1330 hours). The model ingests highly dynamic variables such as fuel moisture, temperature, relative humidity, wind among others, and prompts a Monte Carlo simulation exercise that uses a varying range of possible values for evaluating all possible wildfire behaviors. The Monte Carlo simulation implemented in this model provides a measure of the relative wildfire risk levels at various locations based on the number of times those sites are intersected by simulated fire perimeters. Model calibration is achieved using data at next reporting time (i.e. after 12 hours) to enhance the predictive quality at further time steps. While initial results indicate that the calibrated model can predict the overall geometry and direction of wildland fire spread, the model seems to over-predict the sizes of most fire perimeters possibly due to unaccounted fire suppression activities. Nonetheless, the results of this study show great promise in aiding wildland fire tracking, fighting and risk management.

Syndromic surveillance system based on near real-time cattle mortality monitoring.

PubMed

Torres, G; Ciaravino, V; Ascaso, S; Flores, V; Romero, L; Simón, F

2015-05-01

Early detection of an infectious disease incursion will minimize the impact of outbreaks in livestock. Syndromic surveillance based on the analysis of readily available data can enhance traditional surveillance systems and allow veterinary authorities to react in a timely manner. This study was based on monitoring the number of cattle carcasses sent for rendering in the veterinary unit of Talavera de la Reina (Spain). The aim was to develop a system to detect deviations from expected values which would signal unexpected health events. Historical weekly collected dead cattle (WCDC) time series stabilized by the Box-Cox transformation and adjusted by the minimum least squares method were used to build the univariate cycling regression model based on a Fourier transformation. Three different models, according to type of production system, were built to estimate the baseline expected number of WCDC. Two types of risk signals were generated: point risk signals when the observed value was greater than the upper 95% confidence interval of the expected baseline, and cumulative risk signals, generated by a modified cumulative sum algorithm, when the cumulative sums of reported deaths were above the cumulative sum of expected deaths. Data from 2011 were used to prospectively validate the model generating seven risk signals. None of them were correlated to infectious disease events but some coincided, in time, with very high climatic temperatures recorded in the region. The harvest effect was also observed during the first week of the study year. Establishing appropriate risk signal thresholds is a limiting factor of predictive models; it needs to be adjusted based on experience gained during the use of the models. To increase the sensitivity and specificity of the predictions epidemiological interpretation of non-specific risk signals should be complemented by other sources of information. The methodology developed in this study can enhance other existing early detection surveillance systems. Syndromic surveillance based on mortality monitoring can reduce the detection time for certain disease outbreaks associated with mild mortality only detected at regional level. The methodology can be adapted to monitor other parameters routinely collected at farm level which can be influenced by communicable diseases. Copyright © 2015 Elsevier B.V. All rights reserved.

A Short-Term Population Model of the Suicide Risk: The Case of Spain.

PubMed

De la Poza, Elena; Jódar, Lucas

2018-06-14

A relevant proportion of deaths by suicide have been attributed to other causes that produce the number of suicides remains hidden. The existence of a hidden number of cases is explained by the nature of the problem. Problems like this involve violence, and produce fear and social shame in victims' families. The existence of violence, fear and social shame experienced by victims favours a considerable number of suicides, identified as accidents or natural deaths. This paper proposes a short time discrete compartmental mathematical model to measure the suicidal risk for the case of Spain. The compartment model classifies and quantifies the amount of the Spanish population within the age intervals (16, 78) by their degree of suicide risk and their changes over time. Intercompartmental transits are due to the combination of quantitative and qualitative factors. Results are computed and simulations are performed to analyze the sensitivity of the model under uncertain coefficients.

Home-Based Risk of Falling Assessment Test Using a Closed-Loop Balance Model.

PubMed

Ayena, Johannes C; Zaibi, Helmi; Otis, Martin J-D; Menelas, Bob-Antoine J

2016-12-01

The aim of this study is to improve and facilitate the methods used to assess risk of falling at home among older people through the computation of a risk of falling in real time in daily activities. In order to increase a real time computation of the risk of falling, a closed-loop balance model is proposed and compared with One-Leg Standing Test (OLST). This balance model allows studying the postural response of a person having an unpredictable perturbation. Twenty-nine volunteers participated in this study for evaluating the effectiveness of the proposed system which includes seventeen elder participants: ten healthy elderly ( 68.4 ±5.5 years), seven Parkinson's disease (PD) subjects ( 66.28 ±8.9 years), and twelve healthy young adults ( 28.27 ±3.74 years). Our work suggests that there is a relationship between OLST score and the risk of falling based on center of pressure measurement with four low cost force sensors located inside an instrumented insole, which could be predicted using our suggested closed-loop balance model. For long term monitoring at home, this system could be included in a medical electronic record and could be useful as a diagnostic aid tool.

Occupational injury among full-time, part-time and casual health care workers.

PubMed

Alamgir, Hasanat; Yu, Shicheng; Chavoshi, Negar; Ngan, Karen

2008-08-01

Previous epidemiological studies have conflicting suggestions on the association of occupational injury risks with employment category across industries. This specific issue has not been examined for direct patient care occupations in the health care sector. To investigate whether work-related injury rates differ by employment category (part time, full time or casual) for registered nurses (RNs) in acute care and care aides (CAs) in long-term facilities. Incidents of occupational injury resulting in compensated time loss from work, over a 1-year period within three health regions in British Columbia (BC), Canada, were extracted from a standardized operational database. Detailed analysis was conducted using Poisson regression modeling. Among 8640 RNs in acute care, 37% worked full time, 24% part time and 25% casual. The overall rates of injuries were 7.4, 5.3 and 5.5 per 100 person-years, respectively. Among the 2967 CAs in long-term care, 30% worked full time, 20% part time and 40% casual. The overall rates of injuries were 25.8, 22.9 and 18.1 per 100 person-years, respectively. In multivariate models, having adjusted for age, gender, facility and health region, full-time RNs had significantly higher risk of sustaining injuries compared to part-time and casual workers. For CAs, full-time workers had significantly higher risk of sustaining injuries compared to casual workers. Full-time direct patient care occupations have greater risk of injury compared to part-time and casual workers within the health care sector.

Accounting for Time-Varying Confounding in the Relationship Between Obesity and Coronary Heart Disease: Analysis With G-Estimation: The ARIC Study.

PubMed

Shakiba, Maryam; Mansournia, Mohammad Ali; Salari, Arsalan; Soori, Hamid; Mansournia, Nasrin; Kaufman, Jay S

2018-06-01

In longitudinal studies, standard analysis may yield biased estimates of exposure effect in the presence of time-varying confounders that are also intermediate variables. We aimed to quantify the relationship between obesity and coronary heart disease (CHD) by appropriately adjusting for time-varying confounders. This study was performed in a subset of participants from the Atherosclerosis Risk in Communities (ARIC) Study (1987-2010), a US study designed to investigate risk factors for atherosclerosis. General obesity was defined as body mass index (weight (kg)/height (m)2) ≥30, and abdominal obesity (AOB) was defined according to either waist circumference (≥102 cm in men and ≥88 cm in women) or waist:hip ratio (≥0.9 in men and ≥0.85 in women). The association of obesity with CHD was estimated by G-estimation and compared with results from accelerated failure-time models using 3 specifications. The first model, which adjusted for baseline covariates, excluding metabolic mediators of obesity, showed increased risk of CHD for all obesity measures. Further adjustment for metabolic mediators in the second model and time-varying variables in the third model produced negligible changes in the hazard ratios. The hazard ratios estimated by G-estimation were 1.15 (95% confidence interval (CI): 0.83, 1.47) for general obesity, 1.65 (95% CI: 1.35, 1.92) for AOB based on waist circumference, and 1.38 (95% CI: 1.13, 1.99) for AOB based on waist:hip ratio, suggesting that AOB increased the risk of CHD. The G-estimated hazard ratios for both measures were further from the null than those derived from standard models.

Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks.

PubMed

Blanche, Paul; Proust-Lima, Cécile; Loubère, Lucie; Berr, Claudine; Dartigues, Jean-François; Jacqmin-Gadda, Hélène

2015-03-01

Thanks to the growing interest in personalized medicine, joint modeling of longitudinal marker and time-to-event data has recently started to be used to derive dynamic individual risk predictions. Individual predictions are called dynamic because they are updated when information on the subject's health profile grows with time. We focus in this work on statistical methods for quantifying and comparing dynamic predictive accuracy of this kind of prognostic models, accounting for right censoring and possibly competing events. Dynamic area under the ROC curve (AUC) and Brier Score (BS) are used to quantify predictive accuracy. Nonparametric inverse probability of censoring weighting is used to estimate dynamic curves of AUC and BS as functions of the time at which predictions are made. Asymptotic results are established and both pointwise confidence intervals and simultaneous confidence bands are derived. Tests are also proposed to compare the dynamic prediction accuracy curves of two prognostic models. The finite sample behavior of the inference procedures is assessed via simulations. We apply the proposed methodology to compare various prediction models using repeated measures of two psychometric tests to predict dementia in the elderly, accounting for the competing risk of death. Models are estimated on the French Paquid cohort and predictive accuracies are evaluated and compared on the French Three-City cohort. © 2014, The International Biometric Society.

A Hybrid Methodology for Modeling Risk of Adverse Events in Complex Health-Care Settings.

PubMed

Kazemi, Reza; Mosleh, Ali; Dierks, Meghan

2017-03-01

In spite of increased attention to quality and efforts to provide safe medical care, adverse events (AEs) are still frequent in clinical practice. Reports from various sources indicate that a substantial number of hospitalized patients suffer treatment-caused injuries while in the hospital. While risk cannot be entirely eliminated from health-care activities, an important goal is to develop effective and durable mitigation strategies to render the system "safer." In order to do this, though, we must develop models that comprehensively and realistically characterize the risk. In the health-care domain, this can be extremely challenging due to the wide variability in the way that health-care processes and interventions are executed and also due to the dynamic nature of risk in this particular domain. In this study, we have developed a generic methodology for evaluating dynamic changes in AE risk in acute care hospitals as a function of organizational and nonorganizational factors, using a combination of modeling formalisms. First, a system dynamics (SD) framework is used to demonstrate how organizational-level and policy-level contributions to risk evolve over time, and how policies and decisions may affect the general system-level contribution to AE risk. It also captures the feedback of organizational factors and decisions over time and the nonlinearities in these feedback effects. SD is a popular approach to understanding the behavior of complex social and economic systems. It is a simulation-based, differential equation modeling tool that is widely used in situations where the formal model is complex and an analytical solution is very difficult to obtain. Second, a Bayesian belief network (BBN) framework is used to represent patient-level factors and also physician-level decisions and factors in the management of an individual patient, which contribute to the risk of hospital-acquired AE. BBNs are networks of probabilities that can capture probabilistic relations between variables and contain historical information about their relationship, and are powerful tools for modeling causes and effects in many domains. The model is intended to support hospital decisions with regard to staffing, length of stay, and investments in safety, which evolve dynamically over time. The methodology has been applied in modeling the two types of common AEs: pressure ulcers and vascular-catheter-associated infection, and the models have been validated with eight years of clinical data and use of expert opinion. © 2017 Society for Risk Analysis.

Drawing the line on the sand

NASA Astrophysics Data System (ADS)

Ranasinghe, R.; Jongejan, R.; Wainwright, D.; Callaghan, D. P.

2016-02-01

Up to 70% of the world's sandy coastlines are eroding, resulting in gradual and continuous coastline recession. The rate of coastline recession is likely to increase due to the projected impacts of climate change on mean sea levels, offshore wave climate and storm surges. At the same time, rapid development in the world's coastal zones continues to increase potential damages, while often reducing the resilience of coastal systems. The risks associated with coastline recession are thus likely to increase over the coming decades, unless effective risk management plans are put in place. Land-use restrictions are a key component of coastal zone risk management plans. These involve the use of coastal setback lines which are mainly established by linearly adding the impacts of storms, recession due to sea level rise, and ambient long term trends in shoreline evolution. This approach does not differentiate between uncertainties that develop differently over time, nor takes into account the value and lifetime of property developments. Both shortcomings could entail considerable social cost. For balancing risk and reward, probabilistic estimates of coastline recession are a pre-requisite. Yet the presently adopted deterministic methods for establishing setback lines are unable to provide such estimates. Here, we present a quantitative risk analysis (QRA) model, underpinned by a multi-scale, physics based coastal recession model capable of providing time-dependent risk estimates. The modelling approach presented enables the determination of setback lines in terms of exceedance probabilities, a quantity that directly feeds into risk evaluations and economic optimizations. As a demonstration, the risk-informed approach is applied to Narrabeen beach, Sydney, Australia.

Identification of key outcome measures when using the instrumented timed up and go and/or posturography for fall screening.

PubMed

Sample, Renee Beach; Kinney, Allison L; Jackson, Kurt; Diestelkamp, Wiebke; Bigelow, Kimberly Edginton

2017-09-01

The Timed Up and Go (TUG) has been commonly used for fall risk assessment. The instrumented Timed Up and Go (iTUG) adds wearable sensors to capture sub-movements and may be more sensitive. Posturography assessments have also been used for determining fall risk. This study used stepwise logistic regression models to identify key outcome measures for the iTUG and posturography protocols. The effectiveness of the models containing these measures in differentiating fallers from non-fallers were then compared for each: iTUG total time duration only, iTUG, posturography, and combined iTUG and posturography assessments. One hundred and fifty older adults participated in this study. The iTUG measures were calculated utilizing APDM Inc.'s Mobility Lab software. Traditional and non-linear posturography measures were calculated from center of pressure during quiet-standing. The key outcome measures incorporated in the iTUG assessment model (sit-to-stand lean angle and height) resulted in a model sensitivity of 48.1% and max re-scaled R 2 value of 0.19. This was a higher sensitivity, indicating better differentiation, compared to the model only including total time duration (outcome of the traditional TUG), which had a sensitivity of 18.2%. When the key outcome measures of the iTUG and the posturography assessments were combined into a single model, the sensitivity was approximately the same as the iTUG model alone. Overall the findings of this study support that the iTUG demonstrates greater sensitivity than the total time duration, but that carrying out both iTUG and posturography does not greatly improve sensitivity when used as a fall risk screening tool. Copyright © 2017 Elsevier B.V. All rights reserved.

Air pollution and health risks due to vehicle traffic.

PubMed

Zhang, Kai; Batterman, Stuart

2013-04-15

Traffic congestion increases vehicle emissions and degrades ambient air quality, and recent studies have shown excess morbidity and mortality for drivers, commuters and individuals living near major roadways. Presently, our understanding of the air pollution impacts from congestion on roads is very limited. This study demonstrates an approach to characterize risks of traffic for on- and near-road populations. Simulation modeling was used to estimate on- and near-road NO2 concentrations and health risks for freeway and arterial scenarios attributable to traffic for different traffic volumes during rush hour periods. The modeling used emission factors from two different models (Comprehensive Modal Emissions Model and Motor Vehicle Emissions Factor Model version 6.2), an empirical traffic speed-volume relationship, the California Line Source Dispersion Model, an empirical NO2-NOx relationship, estimated travel time changes during congestion, and concentration-response relationships from the literature, which give emergency doctor visits, hospital admissions and mortality attributed to NO2 exposure. An incremental analysis, which expresses the change in health risks for small increases in traffic volume, showed non-linear effects. For a freeway, "U" shaped trends of incremental risks were predicted for on-road populations, and incremental risks are flat at low traffic volumes for near-road populations. For an arterial road, incremental risks increased sharply for both on- and near-road populations as traffic increased. These patterns result from changes in emission factors, the NO2-NOx relationship, the travel delay for the on-road population, and the extended duration of rush hour for the near-road population. This study suggests that health risks from congestion are potentially significant, and that additional traffic can significantly increase risks, depending on the type of road and other factors. Further, evaluations of risk associated with congestion must consider travel time, the duration of rush-hour, congestion-specific emission estimates, and uncertainties. Copyright © 2013 Elsevier B.V. All rights reserved.

Air pollution and health risks due to vehicle traffic

PubMed Central

Zhang, Kai; Batterman, Stuart

2014-01-01

Traffic congestion increases vehicle emissions and degrades ambient air quality, and recent studies have shown excess morbidity and mortality for drivers, commuters and individuals living near major roadways. Presently, our understanding of the air pollution impacts from congestion on roads is very limited. This study demonstrates an approach to characterize risks of traffic for on- and near-road populations. Simulation modeling was used to estimate on- and near-road NO2 concentrations and health risks for freeway and arterial scenarios attributable to traffic for different traffic volumes during rush hour periods. The modeling used emission factors from two different models (Comprehensive Modal Emissions Model and Motor Vehicle Emissions Factor Model version 6.2), an empirical traffic speed–volume relationship, the California Line Source Dispersion Model, an empirical NO2–NOx relationship, estimated travel time changes during congestion, and concentration–response relationships from the literature, which give emergency doctor visits, hospital admissions and mortality attributed to NO2 exposure. An incremental analysis, which expresses the change in health risks for small increases in traffic volume, showed non-linear effects. For a freeway, “U” shaped trends of incremental risks were predicted for on-road populations, and incremental risks are flat at low traffic volumes for near-road populations. For an arterial road, incremental risks increased sharply for both on- and near-road populations as traffic increased. These patterns result from changes in emission factors, the NO2–NOx relationship, the travel delay for the on-road population, and the extended duration of rush hour for the near-road population. This study suggests that health risks from congestion are potentially significant, and that additional traffic can significantly increase risks, depending on the type of road and other factors. Further, evaluations of risk associated with congestion must consider travel time, the duration of rush-hour, congestion-specific emission estimates, and uncertainties. PMID:23500830

Risk-adjusted monitoring of survival times

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

Sego, Landon H.; Reynolds, Marion R.; Woodall, William H.

2009-02-26

We consider the monitoring of clinical outcomes, where each patient has a di®erent risk of death prior to undergoing a health care procedure.We propose a risk-adjusted survival time CUSUM chart (RAST CUSUM) for monitoring clinical outcomes where the primary endpoint is a continuous, time-to-event variable that may be right censored. Risk adjustment is accomplished using accelerated failure time regression models. We compare the average run length performance of the RAST CUSUM chart to the risk-adjusted Bernoulli CUSUM chart, using data from cardiac surgeries to motivate the details of the comparison. The comparisons show that the RAST CUSUM chart is moremore » efficient at detecting a sudden decrease in the odds of death than the risk-adjusted Bernoulli CUSUM chart, especially when the fraction of censored observations is not too high. We also discuss the implementation of a prospective monitoring scheme using the RAST CUSUM chart.« less

A joint model for longitudinal and time-to-event data to better assess the specific role of donor and recipient factors on long-term kidney transplantation outcomes.

PubMed

Fournier, Marie-Cécile; Foucher, Yohann; Blanche, Paul; Buron, Fanny; Giral, Magali; Dantan, Etienne

2016-05-01

In renal transplantation, serum creatinine (SCr) is the main biomarker routinely measured to assess patient's health, with chronic increases being strongly associated with long-term graft failure risk (death with a functioning graft or return to dialysis). Joint modeling may be useful to identify the specific role of risk factors on chronic evolution of kidney transplant recipients: some can be related to the SCr evolution, finally leading to graft failure, whereas others can be associated with graft failure without any modification of SCr. Sample data for 2749 patients transplanted between 2000 and 2013 with a functioning kidney at 1-year post-transplantation were obtained from the DIVAT cohort. A shared random effect joint model for longitudinal SCr values and time to graft failure was performed. We show that graft failure risk depended on both the current value and slope of the SCr. Deceased donor graft patient seemed to have a higher SCr increase, similar to patient with diabetes history, while no significant association of these two features with graft failure risk was found. Patient with a second graft was at higher risk of graft failure, independent of changes in SCr values. Anti-HLA immunization was associated with both processes simultaneously. Joint models for repeated and time-to-event data bring new opportunities to improve the epidemiological knowledge of chronic diseases. For instance in renal transplantation, several features should receive additional attention as we demonstrated their correlation with graft failure risk was independent of the SCr evolution.

Pathways from childhood intelligence and socioeconomic status to late-life cardiovascular disease risk.

PubMed

Hagger-Johnson, Gareth; Mõttus, René; Craig, Leone C A; Starr, John M; Deary, Ian J

2012-07-01

C-reactive protein (CRP) is an acute-phase marker of systemic inflammation and considered an established risk marker for cardiovascular disease (CVD) in old age. Previous studies have suggested that low childhood intelligence, lower socioeconomic status (SES) in childhood or in later life, unhealthy behaviors, poor wellbeing, and high body mass index (BMI) are associated with inflammation. Life course models that simultaneously incorporate all these risk factors can explain how CVD risks accumulate over time, from childhood to old age. Using the data from 1,091 Scottish adults (Lothian Birth Cohort Study, 1936), a path model was constructed to predict CRP at age 70 from concurrent health behaviors, self-perceived quality of life, and BMI and adulthood SES as mediating variables, and from parental SES and childhood intelligence as distal risk factors. A well-fitting path model (CFI = .92, SRMR = .05) demonstrated significant indirect effects from childhood intelligence and parental social class to inflammation via BMI, health behaviors and quality of life (all ps < .05). Low childhood intelligence, unhealthy behaviors, and higher BMI were also direct predictors of CRP. The life course model illustrated how CVD risks may accumulate over time, beginning in childhood and being both direct and transmitted indirectly via low adult SES, unhealthy behaviors, impaired quality of life, and high BMI. Knowledge on the childhood risk factors and their pathways to poor health can be used to identify high-risk individuals for more intensive and tailored behavior change interventions, and to develop effective public health policies.

Associations between air emissions from sour gas processing plants and indices of cow retainment and survival in dairy herds in Alberta

PubMed Central

Scott, H. Morgan; Soskolne, Colin L.; Lissemore, Kerry D.; Martin, S. Wayne; Shoukri, Mohamed M.; Coppock, Robert W.; Guidotti, Tee L.

2003-01-01

This paper describes the results of an investigation into the effects of air emissions from sour gas processing plants on indices of retainment or survival of adult female dairy cattle on farms in Alberta; namely, the productive lifespan of individual animals, and annual herd-level risks for culling and mortality. Using a geographical information system, 2 dispersion models — 1 simple and 1 complex — were used to assess historical exposures to sour gas emissions at 1382 dairy farm sites from 1985 through to 1994. Multivariable survival models, adjusting for the dependence of survival responses within a herd over time, as well as potential confounding variables, were utilized to determine associations between sour gas exposure estimates and the time from the first calving date to either death or culling of 150 210 dairy cows. Generalized linear models were used to model the relationship between herd-level risks for culling and mortality and levels of sour gas exposure. No significant (P < 0.05) associations were found with the time to culling (n = 70 052). However, both dispersion model exposure estimates were significantly associated (P < 0.05) with a decreased hazard for mortality; that is, in cases where cattle had died on-farm (n = 8743). There were no significant associations (P > 0.05) between herd culling risks and the 2 dispersion model exposure estimates. There was no measurable impact of plant emissions on the annual herd risk of mortality. PMID:12528823

Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I Follow-up Study cohort

PubMed Central

Gaziano, Thomas A; Young, Cynthia R; Fitzmaurice, Garrett; Atwood, Sidney; Gaziano, J Michael

2008-01-01

Summary Background Around 80% of all cardiovascular deaths occur in developing countries. Assessment of those patients at high risk is an important strategy for prevention. Since developing countries have limited resources for prevention strategies that require laboratory testing, we assessed if a risk prediction method that did not require any laboratory tests could be as accurate as one requiring laboratory information. Methods The National Health and Nutrition Examination Survey (NHANES) was a prospective cohort study of 14 407 US participants aged between 25–74 years at the time they were first examined (between 1971 and 1975). Our follow-up study population included participants with complete information on these surveys who did not report a history of cardiovascular disease (myocardial infarction, heart failure, stroke, angina) or cancer, yielding an analysis dataset N=6186. We compared how well either method could predict first-time fatal and non-fatal cardiovascular disease events in this cohort. For the laboratory-based model, which required blood testing, we used standard risk factors to assess risk of cardiovascular disease: age, systolic blood pressure, smoking status, total cholesterol, reported diabetes status, and current treatment for hypertension. For the non-laboratory-based model, we substituted body-mass index for cholesterol. Findings In the cohort of 6186, there were 1529 first-time cardiovascular events and 578 (38%) deaths due to cardiovascular disease over 21 years. In women, the laboratory-based model was useful for predicting events, with a c statistic of 0·829. The c statistic of the non-laboratory-based model was 0·831. In men, the results were similar (0·784 for the laboratory-based model and 0·783 for the non-laboratory-based model). Results were similar between the laboratory-based and non-laboratory-based models in both men and women when restricted to fatal events only. Interpretation A method that uses non-laboratory-based risk factors predicted cardiovascular events as accurately as one that relied on laboratory-based values. This approach could simplify risk assessment in situations where laboratory testing is inconvenient or unavailable. PMID:18342687

Marriage is a dependent risk factor for mortality of colon adenocarcinoma without a time-varying effect

PubMed Central

Yu, Wei; Chen, Jie; Xiong, Weibin; Chen, Shuang; Yu, Li

2017-01-01

Background It has been well recognized that the effects of many prognostic factors could change during long-term follow-up. Although marriage has been proven to be a significant prognostic factor for the survival of colon cancer, whether the effect of marriage is constant with time remain unknown. This study analyzed the impact of marital status on the mortality of colon cancer patients with an extended Cox model that allowed for time-varying effects. Methods We identified 71,955 patients who underwent colectomy between 2004 and 2009 to treat colon adenocarcinoma from the Surveilance, Epidemiology and End Results Database. The multivariate extended Cox model was used to evaluate the effect of marital status on all-cause mortality, while the Fine-Gray competing risks model was used for colon cancer-specific mortality, with death from other causes as the competing risk. Results The unmarried patients carried a 1.37-fold increased risk of all-cause mortality compared with the married patients (95%CI: 1.33-1.40; p<0.001), and the hazard ratio remained constant over time. Being unmarried was at a higher risk of death from colon adenocarcinoma as well as death from other causes. Four variables including tumor site, tumor grade, sex and TNM stage were proved to have time-varying effects on survival. Conclusions Marriage is a dependent prognosis factor for survival of surgically treated colon adenocarcinoma patients. Psychological interventions are suggested to improve receipt of treatment among unmarried patients, as their poor survival may be due to the inefficient treatment. PMID:28423614

Marriage is a dependent risk factor for mortality of colon adenocarcinoma without a time-varying effect.

PubMed

Liu, Minling; Li, Lixian; Yu, Wei; Chen, Jie; Xiong, Weibin; Chen, Shuang; Yu, Li

2017-03-21

It has been well recognized that the effects of many prognostic factors could change during long-term follow-up. Although marriage has been proven to be a significant prognostic factor for the survival of colon cancer, whether the effect of marriage is constant with time remain unknown. This study analyzed the impact of marital status on the mortality of colon cancer patients with an extended Cox model that allowed for time-varying effects. We identified 71,955 patients who underwent colectomy between 2004 and 2009 to treat colon adenocarcinoma from the Surveilance, Epidemiology and End Results Database. The multivariate extended Cox model was used to evaluate the effect of marital status on all-cause mortality, while the Fine-Gray competing risks model was used for colon cancer-specific mortality, with death from other causes as the competing risk. The unmarried patients carried a 1.37-fold increased risk of all-cause mortality compared with the married patients (95%CI: 1.33-1.40; p<0.001), and the hazard ratio remained constant over time. Being unmarried was at a higher risk of death from colon adenocarcinoma as well as death from other causes. Four variables including tumor site, tumor grade, sex and TNM stage were proved to have time-varying effects on survival. Marriage is a dependent prognosis factor for survival of surgically treated colon adenocarcinoma patients. Psychological interventions are suggested to improve receipt of treatment among unmarried patients, as their poor survival may be due to the inefficient treatment.

A new time-series methodology for estimating relationships between elderly frailty, remaining life expectancy, and ambient air quality.

PubMed

Murray, Christian J; Lipfert, Frederick W

2012-01-01

Many publications estimate short-term air pollution-mortality risks, but few estimate the associated changes in life-expectancies. We present a new methodology for analyzing time series of health effects, in which prior frailty is assumed to precede short-term elderly nontraumatic mortality. The model is based on a subpopulation of frail individuals whose entries and exits (deaths) are functions of daily and lagged environmental conditions: ambient temperature/season, airborne particles, and ozone. This frail susceptible population is unknown; its fluctuations cannot be observed but are estimated using maximum-likelihood methods with the Kalman filter. We used an existing 14-y set of daily data to illustrate the model and then tested the assumption of prior frailty with a new generalized model that estimates the portion of the daily death count allocated to nonfrail individuals. In this demonstration dataset, new entries into the high-risk pool are associated with lower ambient temperatures and higher concentrations of particulate matter and ozone. Accounting for these effects on antecedent frailty reduces this at-risk population, yielding frail life expectancies of 5-7 days. Associations between environmental factors and entries to the at-risk pool are about twice as strong as for mortality. Nonfrail elderly deaths are seen to make only small contributions. This new model predicts a small short-lived frail population-at-risk that is stable over a wide range of environmental conditions. The predicted effects of pollution on new entries and deaths are robust and consistent with conventional morbidity/mortality times-series studies. We recommend model verification using other suitable datasets.

Trimming a hazard logic tree with a new model-order-reduction technique

USGS Publications Warehouse

Porter, Keith; Field, Edward; Milner, Kevin R

2017-01-01

The size of the logic tree within the Uniform California Earthquake Rupture Forecast Version 3, Time-Dependent (UCERF3-TD) model can challenge risk analyses of large portfolios. An insurer or catastrophe risk modeler concerned with losses to a California portfolio might have to evaluate a portfolio 57,600 times to estimate risk in light of the hazard possibility space. Which branches of the logic tree matter most, and which can one ignore? We employed two model-order-reduction techniques to simplify the model. We sought a subset of parameters that must vary, and the specific fixed values for the remaining parameters, to produce approximately the same loss distribution as the original model. The techniques are (1) a tornado-diagram approach we employed previously for UCERF2, and (2) an apparently novel probabilistic sensitivity approach that seems better suited to functions of nominal random variables. The new approach produces a reduced-order model with only 60 of the original 57,600 leaves. One can use the results to reduce computational effort in loss analyses by orders of magnitude.

Predator identity and time of day interact to shape the risk-reward trade-off for herbivorous coral reef fishes.

PubMed

Catano, Laura B; Barton, Mark B; Boswell, Kevin M; Burkepile, Deron E

2017-03-01

Non-consumptive effects (NCEs) of predators occur as prey alters their habitat use and foraging decisions to avoid predation. Although NCEs are recognized as being important across disparate ecosystems, the factors influencing their strength and importance remain poorly understood. Ecological context, such as time of day, predator identity, and prey condition, may modify how prey species perceive and respond to risk, thereby altering NCEs. To investigate how predator identity affects foraging of herbivorous coral reef fishes, we simulated predation risk using fiberglass models of two predator species (grouper Mycteroperca bonaci and barracuda Sphyraena barracuda) with different hunting modes. We quantified how predation risk alters herbivory rates across space (distance from predator) and time (dawn, mid-day, and dusk) to examine how prey reconciles the conflicting demands of avoiding predation vs. foraging. When we averaged the effect of both predators across space and time, they suppressed herbivory similarly. Yet, they altered feeding differently depending on time of day and distance from the model. Although feeding increased strongly with increasing distance from the predators particularly during dawn, we found that the barracuda model suppressed herbivory more strongly than the grouper model during mid-day. We suggest that prey hunger level and differences in predator hunting modes could influence these patterns. Understanding how context mediates NCEs provides insight into the emergent effects of predator-prey interactions on food webs. These insights have broad implications for understanding how anthropogenic alterations to predator abundances can affect the spatial and temporal dynamics of important ecosystem processes.

Sensitivity Analysis of Median Lifetime on Radiation Risks Estimates for Cancer and Circulatory Disease amongst Never-Smokers

NASA Technical Reports Server (NTRS)

Chappell, Lori J.; Cucinotta, Francis A.

2011-01-01

Radiation risks are estimated in a competing risk formalism where age or time after exposure estimates of increased risks for cancer and circulatory diseases are folded with a probability to survive to a given age. The survival function, also called the life-table, changes with calendar year, gender, smoking status and other demographic variables. An outstanding problem in risk estimation is the method of risk transfer between exposed populations and a second population where risks are to be estimated. Approaches used to transfer risks are based on: 1) Multiplicative risk transfer models -proportional to background disease rates. 2) Additive risk transfer model -risks independent of background rates. In addition, a Mixture model is often considered where the multiplicative and additive transfer assumptions are given weighted contributions. We studied the influence of the survival probability on the risk of exposure induced cancer and circulatory disease morbidity and mortality in the Multiplicative transfer model and the Mixture model. Risks for never-smokers (NS) compared to the average U.S. population are estimated to be reduced between 30% and 60% dependent on model assumptions. Lung cancer is the major contributor to the reduction for NS, with additional contributions from circulatory diseases and cancers of the stomach, liver, bladder, oral cavity, esophagus, colon, a portion of the solid cancer remainder, and leukemia. Greater improvements in risk estimates for NS s are possible, and would be dependent on improved understanding of risk transfer models, and elucidating the role of space radiation on the various stages of disease formation (e.g. initiation, promotion, and progression).

Tsunami evacuation modelling as a tool for risk reduction: application to the coastal area of El Salvador

NASA Astrophysics Data System (ADS)

González-Riancho, P.; Aguirre-Ayerbe, I.; Aniel-Quiroga, I.; Abad, S.; González, M.; Larreynaga, J.; Gavidia, F.; Gutiérrez, O. Q.; Álvarez-Gómez, J. A.; Medina, R.

2013-12-01

Advances in the understanding and prediction of tsunami impacts allow the development of risk reduction strategies for tsunami-prone areas. This paper presents an integral framework for the formulation of tsunami evacuation plans based on tsunami vulnerability assessment and evacuation modelling. This framework considers (i) the hazard aspects (tsunami flooding characteristics and arrival time), (ii) the characteristics of the exposed area (people, shelters and road network), (iii) the current tsunami warning procedures and timing, (iv) the time needed to evacuate the population, and (v) the identification of measures to improve the evacuation process. The proposed methodological framework aims to bridge between risk assessment and risk management in terms of tsunami evacuation, as it allows for an estimation of the degree of evacuation success of specific management options, as well as for the classification and prioritization of the gathered information, in order to formulate an optimal evacuation plan. The framework has been applied to the El Salvador case study, demonstrating its applicability to site-specific response times and population characteristics.

PBPK Models, BBDR Models, and Virtual Tissues: How Will They Contribute to the Use of Toxicity Pathways in Risk Assessment?

EPA Science Inventory

Accuracy in risk assessment, which is desirable in order to ensure protection of the public health while avoiding over-regulation of economically-important substances, requires quantitatively accurate, in vivo descriptions of dose-response and time-course behaviors. This level of...

Dynamic drought risk assessment using crop model and remote sensing techniques

NASA Astrophysics Data System (ADS)

Sun, H.; Su, Z.; Lv, J.; Li, L.; Wang, Y.

2017-02-01

Drought risk assessment is of great significance to reduce the loss of agricultural drought and ensure food security. The normally drought risk assessment method is to evaluate its exposure to the hazard and the vulnerability to extended periods of water shortage for a specific region, which is a static evaluation method. The Dynamic Drought Risk Assessment (DDRA) is to estimate the drought risk according to the crop growth and water stress conditions in real time. In this study, a DDRA method using crop model and remote sensing techniques was proposed. The crop model we employed is DeNitrification and DeComposition (DNDC) model. The drought risk was quantified by the yield losses predicted by the crop model in a scenario-based method. The crop model was re-calibrated to improve the performance by the Leaf Area Index (LAI) retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) data. And the in-situ station-based crop model was extended to assess the regional drought risk by integrating crop planted mapping. The crop planted area was extracted with extended CPPI method from MODIS data. This study was implemented and validated on maize crop in Liaoning province, China.

Road safety forecasts in five European countries using structural time series models.

PubMed

Antoniou, Constantinos; Papadimitriou, Eleonora; Yannis, George

2014-01-01

Modeling road safety development is a complex task and needs to consider both the quantifiable impact of specific parameters as well as the underlying trends that cannot always be measured or observed. The objective of this research is to apply structural time series models for obtaining reliable medium- to long-term forecasts of road traffic fatality risk using data from 5 countries with different characteristics from all over Europe (Cyprus, Greece, Hungary, Norway, and Switzerland). Two structural time series models are considered: (1) the local linear trend model and the (2) latent risk time series model. Furthermore, a structured decision tree for the selection of the applicable model for each situation (developed within the Road Safety Data, Collection, Transfer and Analysis [DaCoTA] research project, cofunded by the European Commission) is outlined. First, the fatality and exposure data that are used for the development of the models are presented and explored. Then, the modeling process is presented, including the model selection process, introduction of intervention variables, and development of mobility scenarios. The forecasts using the developed models appear to be realistic and within acceptable confidence intervals. The proposed methodology is proved to be very efficient for handling different cases of data availability and quality, providing an appropriate alternative from the family of structural time series models in each country. A concluding section providing perspectives and directions for future research is presented.

An empirical assessment of driver motivation and emotional states in perceived safety margins under varied driving conditions.

PubMed

Zhang, Yu; Kaber, David B

2013-01-01

Motivation models in driving behaviour postulate that driver motives and emotional states dictate risk tolerance under various traffic conditions. The present study used time and driver performance-based payment systems to manipulate motivation and risk-taking behaviour. Ten participants drove to a predefined location in a simulated driving environment. Traffic patterns (density and velocity) were manipulated to cause driver behaviour adjustments due to the need to conform with the social norms of the roadway. The driving environment complexity was investigated as a mediating factor in risk tolerance. Results revealed the performance-based payment system to closely relate to risk-taking behaviour as compared with the time-based payment system. Drivers conformed with social norms associated with specific traffic patterns. Higher roadway complexity led to a more conservative safety margins and speeds. This research contributes to the further development of motivational models of driver behaviour. This study provides empirical justification for two motivation factors in driver risk-taking decisions, including compliance with social norm and emotions triggered by incentives. Environment complexity was identified as a mediating factor in motivational behaviour model. This study also recommended safety margin measures sensitive to changes in driver risk tolerance.

Evaluation of Cox's model and logistic regression for matched case-control data with time-dependent covariates: a simulation study.

PubMed

Leffondré, Karen; Abrahamowicz, Michal; Siemiatycki, Jack

2003-12-30

Case-control studies are typically analysed using the conventional logistic model, which does not directly account for changes in the covariate values over time. Yet, many exposures may vary over time. The most natural alternative to handle such exposures would be to use the Cox model with time-dependent covariates. However, its application to case-control data opens the question of how to manipulate the risk sets. Through a simulation study, we investigate how the accuracy of the estimates of Cox's model depends on the operational definition of risk sets and/or on some aspects of the time-varying exposure. We also assess the estimates obtained from conventional logistic regression. The lifetime experience of a hypothetical population is first generated, and a matched case-control study is then simulated from this population. We control the frequency, the age at initiation, and the total duration of exposure, as well as the strengths of their effects. All models considered include a fixed-in-time covariate and one or two time-dependent covariate(s): the indicator of current exposure and/or the exposure duration. Simulation results show that none of the models always performs well. The discrepancies between the odds ratios yielded by logistic regression and the 'true' hazard ratio depend on both the type of the covariate and the strength of its effect. In addition, it seems that logistic regression has difficulty separating the effects of inter-correlated time-dependent covariates. By contrast, each of the two versions of Cox's model systematically induces either a serious under-estimation or a moderate over-estimation bias. The magnitude of the latter bias is proportional to the true effect, suggesting that an improved manipulation of the risk sets may eliminate, or at least reduce, the bias. Copyright 2003 JohnWiley & Sons, Ltd.

Assessment of credit risk based on fuzzy relations

NASA Astrophysics Data System (ADS)

Tsabadze, Teimuraz

2017-06-01

The purpose of this paper is to develop a new approach for an assessment of the credit risk to corporate borrowers. There are different models for borrowers' risk assessment. These models are divided into two groups: statistical and theoretical. When assessing the credit risk for corporate borrowers, statistical model is unacceptable due to the lack of sufficiently large history of defaults. At the same time, we cannot use some theoretical models due to the lack of stock exchange. In those cases, when studying a particular borrower given that statistical base does not exist, the decision-making process is always of expert nature. The paper describes a new approach that may be used in group decision-making. An example of the application of the proposed approach is given.

A mediator model to predict workplace influenza vaccination behaviour--an application of the health action process approach.

PubMed

Ernsting, Anna; Gellert, Paul; Schneider, Michael; Lippke, Sonia

2013-01-01

Applying the health action process approach (HAPA) to vaccination behaviour as a single-event health behaviour to study vaccination adherence and its predictors in a worksite flu vaccination programme. A total of N = 823 employees participated in a longitudinal survey. Predictors (risk perception, self-efficacy, positive and negative outcome expectancies, intention and planning) were assessed at Time 1, and behaviour was assessed five months later at Time 2. Intention and planning were specified as mediators in a path analytical logistic regression model. Risk perception, self-efficacy and positive as well as negative outcome expectancies predicted intention (R² = .76). Intention and planning predicted subsequent behaviour, and planning mediated the relation between intention and vaccination behaviour (R² = .67). In addition, results suggested the adjustment of the theoretical model: risk perception and negative outcome expectancies showed direct effects on behaviour resulting in a significantly better model fit. Findings support the general applicability of the HAPA to vaccination behaviour and the importance of planning for translating intentions into behaviour. However, the adjusted model was superior and underlined the particular role of risk perception and negative outcome expectancies for vaccination behaviour to explain underlying mechanisms in vaccination behaviour.

Proton pump inhibitor use and risk of adverse cardiovascular events in aspirin treated patients with first time myocardial infarction: nationwide propensity score matched study

PubMed Central

Grove, Erik L; Hansen, Peter Riis; Olesen, Jonas B; Ahlehoff, Ole; Selmer, Christian; Lindhardsen, Jesper; Madsen, Jan Kyst; Køber, Lars; Torp-Pedersen, Christian; Gislason, Gunnar H

2011-01-01

Objective To examine the effect of proton pump inhibitors on adverse cardiovascular events in aspirin treated patients with first time myocardial infarction. Design Retrospective nationwide propensity score matched study based on administrative data. Setting All hospitals in Denmark. Participants All aspirin treated patients surviving 30 days after a first myocardial infarction from 1997 to 2006, with follow-up for one year. Patients treated with clopidogrel were excluded. Main outcome measures The risk of the combined end point of cardiovascular death, myocardial infarction, or stroke associated with use of proton pump inhibitors was analysed using Kaplan-Meier analysis, Cox proportional hazard models, and propensity score matched Cox proportional hazard models. Results 3366 of 19 925 (16.9%) aspirin treated patients experienced recurrent myocardial infarction, stroke, or cardiovascular death. The hazard ratio for the combined end point in patients receiving proton pump inhibitors based on the time dependent Cox proportional hazard model was 1.46 (1.33 to 1.61; P<0.001) and for the propensity score matched model based on 8318 patients it was 1.61 (1.45 to 1.79; P<0.001). A sensitivity analysis showed no increase in risk related to use of H2 receptor blockers (1.04, 0.79 to 1.38; P=0.78). Conclusion In aspirin treated patients with first time myocardial infarction, treatment with proton pump inhibitors was associated with an increased risk of adverse cardiovascular events. PMID:21562004

Evaluation of risk scores for risk stratification of acute coronary syndromes in the Myocardial Infarction National Audit Project (MINAP) database.

PubMed

Gale, C P; Manda, S O M; Weston, C F; Birkhead, J S; Batin, P D; Hall, A S

2009-03-01

To compare the discriminative performance of the PURSUIT, GUSTO-1, GRACE, SRI and EMMACE risk models, assess their performance among risk supergroups and evaluate the EMMACE risk model over the wider spectrum of acute coronary syndrome (ACS). Observational study of a national registry. All acute hospitals in England and Wales. 100 686 cases of ACS between 2003 and 2005. Model performance (C-index) in predicting the likelihood of death over the time period for which they were designed. The C-index, or area under the receiver-operating curve, range 0-1, is a measure of the discriminative performance of a model. The C-indexes were: PURSUIT C-index 0.79 (95% confidence interval 0.78 to 0.80); GUSTO-1 0.80 (0.79 to 0.81); GRACE in-hospital 0.80 (0.80 to 0.81); GRACE 6-month 0.80 (0.79 to 0.80); SRI 0.79 (0.78 to 0.80); and EMMACE 0.78 (0.77 to 0.78). EMMACE maintained its ability to discriminate 30-day mortality throughout different ACS diagnoses. Recalibration of the model offered no notable improvement in performance over the original risk equation. For all models the discriminative performance was reduced in patients with diabetes, chronic renal failure or angina. The five ACS risk models maintained their discriminative performance in a large unselected English and Welsh ACS population, but performed less well in higher-risk supergroups. Simpler risk models had comparable performance to more complex risk models. The EMMACE risk score performed well across the wider spectrum of ACS diagnoses.

Mixtures of beta distributions in models of the duration of a project affected by risk

NASA Astrophysics Data System (ADS)

Gładysz, Barbara; Kuchta, Dorota

2017-07-01

This article presents a method for timetabling a project affected by risk. The times required to carry out tasks are modelled using mixtures of beta distributions. The parameters of these beta distributions are given by experts: one corresponding to the duration of a task in stable conditions, with no risks materializing, and the other corresponding to the duration of a task in the case when risks do occur. Finally, a case study will be presented and analysed: the project of constructing a shopping mall in Poland.

Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

NASA Astrophysics Data System (ADS)

Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

2015-10-01

Reliable river flow forecasts play a key role in flood risk mitigation. Among different approaches of river flow forecasting, data driven approaches have become increasingly popular in recent years due to their minimum information requirements and ability to simulate nonlinear and non-stationary characteristics of hydrological processes. In this study, attempts are made to apply four different types of data driven approaches, namely traditional artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), wavelet neural networks (WNN), and, hybrid ANFIS with multi resolution analysis using wavelets (WNF). Developed models applied for real time flood forecasting at Casino station on Richmond River, Australia which is highly prone to flooding. Hourly rainfall and runoff data were used to drive the models which have been used for forecasting with 1, 6, 12, 24, 36 and 48 h lead-time. The performance of models further improved by adding an upstream river flow data (Wiangaree station), as another effective input. All models perform satisfactorily up to 12 h lead-time. However, the hybrid wavelet-based models significantly outperforming the ANFIS and ANN models in the longer lead-time forecasting. The results confirm the robustness of the proposed structure of the hybrid models for real time runoff forecasting in the study area.

Lung cancer risk prediction to select smokers for screening CT--a model based on the Italian COSMOS trial.

PubMed

Maisonneuve, Patrick; Bagnardi, Vincenzo; Bellomi, Massimo; Spaggiari, Lorenzo; Pelosi, Giuseppe; Rampinelli, Cristiano; Bertolotti, Raffaella; Rotmensz, Nicole; Field, John K; Decensi, Andrea; Veronesi, Giulia

2011-11-01

Screening with low-dose helical computed tomography (CT) has been shown to significantly reduce lung cancer mortality but the optimal target population and time interval to subsequent screening are yet to be defined. We developed two models to stratify individual smokers according to risk of developing lung cancer. We first used the number of lung cancers detected at baseline screening CT in the 5,203 asymptomatic participants of the COSMOS trial to recalibrate the Bach model, which we propose using to select smokers for screening. Next, we incorporated lung nodule characteristics and presence of emphysema identified at baseline CT into the Bach model and proposed the resulting multivariable model to predict lung cancer risk in screened smokers after baseline CT. Age and smoking exposure were the main determinants of lung cancer risk. The recalibrated Bach model accurately predicted lung cancers detected during the first year of screening. Presence of nonsolid nodules (RR = 10.1, 95% CI = 5.57-18.5), nodule size more than 8 mm (RR = 9.89, 95% CI = 5.84-16.8), and emphysema (RR = 2.36, 95% CI = 1.59-3.49) at baseline CT were all significant predictors of subsequent lung cancers. Incorporation of these variables into the Bach model increased the predictive value of the multivariable model (c-index = 0.759, internal validation). The recalibrated Bach model seems suitable for selecting the higher risk population for recruitment for large-scale CT screening. The Bach model incorporating CT findings at baseline screening could help defining the time interval to subsequent screening in individual participants. Further studies are necessary to validate these models.

Implementation of coordinated global serotype 2 oral poliovirus vaccine cessation: risks of potential non-synchronous cessation.

PubMed

Duintjer Tebbens, Radboud J; Hampton, Lee M; Thompson, Kimberly M

2016-05-26

The endgame for polio eradication involves coordinated global cessation of oral poliovirus vaccine (OPV) with cessation of serotype 2 OPV (OPV2 cessation) implemented in late April and early May 2016 and cessation of serotypes 1 and 3 OPV (OPV13 cessation) currently planned for after 2018. The logistics associated with globally switching all use of trivalent OPV (tOPV) to bivalent OPV (bOPV) represent a significant undertaking, which may cause some complications, including delays that lead to different timing of the switch across shared borders. Building on an integrated global model for long-term poliovirus risk management, we consider the expected vulnerability of different populations to transmission of OPV2-related polioviruses as a function of time following the switch. We explore the relationship between the net reproduction number (Rn) of OPV2 at the time of the switch and the time until OPV2-related viruses imported from countries still using OPV2 can establish transmission. We also analyze some specific situations modeled after populations at high potential risk of circulating serotype 2 vaccine-derived poliovirus (cVDPV2) outbreaks in the event of a non-synchronous switch. Well-implemented tOPV immunization activities prior to the tOPV to bOPV switch (i.e., tOPV intensification sufficient to prevent the creation of indigenous cVDPV2 outbreaks) lead to sufficient population immunity to transmission to cause die-out of any imported OPV2-related viruses for over 6 months after the switch in all populations in the global model. Higher Rn of OPV2 at the time of the switch reduces the time until imported OPV2-related viruses can establish transmission and increases the time during which indigenous OPV2-related viruses circulate. Modeling specific connected populations suggests a relatively low vulnerability to importations of OPV2-related viruses that could establish transmission in the context of a non-synchronous switch from tOPV to bOPV, unless the gap between switch times becomes very long (>6 months) or a high risk of indigenous cVDPV2s already exists in the importing and/or the exporting population. Short national discrepancies in the timing of the tOPV to bOPV switch will likely not significantly increase cVDPV2 risks due to the insurance provided by tOPV intensification efforts, although the goal to coordinate national switches within the globally agreed April 17-May 1, 2016 time window minimized the risks associated with cross-border importations.

An integrated epidemiological and neural net model of the warfarin effect in managed care patients.

PubMed

Jacobs, David M; Stefanovic, Filip; Wilton, Greg; Gomez-Caminero, Andres; Schentag, Jerome J

2017-01-01

Risk assessment tools are utilized to estimate the risk for stroke and need of anticoagulation therapy for patients with atrial fibrillation (AF). These risk stratification scores are limited by the information inputted into them and a reliance on time-independent variables. The objective of this study was to develop a time-dependent neural net model to identify AF populations at high risk of poor clinical outcomes and evaluate the discriminatory ability of the model in a managed care population. We performed a longitudinal, cohort study within a health-maintenance organization from 1997 to 2008. Participants were identified with incident AF irrespective of warfarin status and followed through their duration within the database. Three clinical outcome measures were evaluated including stroke, myocardial infarction, and hemorrhage. A neural net model was developed to identify patients at high risk of clinical events and defined to be an "enriched" patient. The model defines the enrichment based on the top 10 minimum mean square error output parameters that describe the three clinical outcomes. Cox proportional hazard models were utilized to evaluate the outcome measures. Among 285 patients, the mean age was 74±12 years with a mean follow-up of 4.3±2.6 years, and 154 (54%) were treated with warfarin. After propensity score adjustment, warfarin use was associated with a slightly increased risk of adverse outcomes (including stroke, myocardial infarction, and hemorrhage), though it did not attain statistical significance (adjusted hazard ratio [aHR] =1.22; 95% confidence interval [CI] 0.75-1.97; p =0.42). Within the neural net model, subjects at high risk of adverse outcomes were identified and labeled as "enriched." Following propensity score adjustment, enriched subjects were associated with an 81% higher risk of adverse outcomes as compared to nonenriched subjects (aHR=1.81; 95% CI, 1.15-2.88; p =0.01). Enrichment methodology improves the statistical discrimination of meaningful endpoints when used in a health records-based analysis.

A real-time heat strain risk classifier using heart rate and skin temperature.

PubMed

Buller, Mark J; Latzka, William A; Yokota, Miyo; Tharion, William J; Moran, Daniel S

2008-12-01

Heat injury is a real concern to workers engaged in physically demanding tasks in high heat strain environments. Several real-time physiological monitoring systems exist that can provide indices of heat strain, e.g. physiological strain index (PSI), and provide alerts to medical personnel. However, these systems depend on core temperature measurement using expensive, ingestible thermometer pills. Seeking a better solution, we suggest the use of a model which can identify the probability that individuals are 'at risk' from heat injury using non-invasive measures. The intent is for the system to identify individuals who need monitoring more closely or who should apply heat strain mitigation strategies. We generated a model that can identify 'at risk' (PSI 7.5) workers from measures of heart rate and chest skin temperature. The model was built using data from six previously published exercise studies in which some subjects wore chemical protective equipment. The model has an overall classification error rate of 10% with one false negative error (2.7%), and outperforms an earlier model and a least squares regression model with classification errors of 21% and 14%, respectively. Additionally, the model allows the classification criteria to be adjusted based on the task and acceptable level of risk. We conclude that the model could be a valuable part of a multi-faceted heat strain management system.

The c-index is not proper for the evaluation of $t$-year predicted risks.

PubMed

Blanche, Paul; Kattan, Michael W; Gerds, Thomas A

2018-02-16

We show that the widely used concordance index for time to event outcome is not proper when interest is in predicting a $t$-year risk of an event, for example 10-year mortality. In the situation with a fixed prediction horizon, the concordance index can be higher for a misspecified model than for a correctly specified model. Impropriety happens because the concordance index assesses the order of the event times and not the order of the event status at the prediction horizon. The time-dependent area under the receiver operating characteristic curve does not have this problem and is proper in this context.

STakeholder-Objective Risk Model (STORM): Determining the aggregated risk of multiple contaminant hazards in groundwater well catchments

NASA Astrophysics Data System (ADS)

Enzenhoefer, R.; Binning, P. J.; Nowak, W.

2015-09-01

Risk is often defined as the product of probability, vulnerability and value. Drinking water supply from groundwater abstraction is often at risk due to multiple hazardous land use activities in the well catchment. Each hazard might or might not introduce contaminants into the subsurface at any point in time, which then affects the pumped quality upon transport through the aquifer. In such situations, estimating the overall risk is not trivial, and three key questions emerge: (1) How to aggregate the impacts from different contaminants and spill locations to an overall, cumulative impact on the value at risk? (2) How to properly account for the stochastic nature of spill events when converting the aggregated impact to a risk estimate? (3) How will the overall risk and subsequent decision making depend on stakeholder objectives, where stakeholder objectives refer to the values at risk, risk attitudes and risk metrics that can vary between stakeholders. In this study, we provide a STakeholder-Objective Risk Model (STORM) for assessing the total aggregated risk. Or concept is a quantitative, probabilistic and modular framework for simulation-based risk estimation. It rests on the source-pathway-receptor concept, mass-discharge-based aggregation of stochastically occuring spill events, accounts for uncertainties in the involved flow and transport models through Monte Carlo simulation, and can address different stakeholder objectives. We illustrate the application of STORM in a numerical test case inspired by a German drinking water catchment. As one may expect, the results depend strongly on the chosen stakeholder objectives, but they are equally sensitive to different approaches for risk aggregation across different hazards, contaminant types, and over time.

Changing Susceptibility to Non-Optimum Temperatures in Japan, 1972-2012: The Role of Climate, Demographic, and Socioeconomic Factors.

PubMed

Chung, Yeonseung; Yang, Daewon; Gasparrini, Antonio; Vicedo-Cabrera, Ana M; Fook Sheng Ng, Chris; Kim, Yoonhee; Honda, Yasushi; Hashizume, Masahiro

2018-05-02

Previous studies have shown that population susceptibility to non-optimum temperatures has changed over time, but little is known about the related time-varying factors that underlie the changes. Our objective was to investigate the changing population susceptibility to non-optimum temperatures in 47 prefectures of Japan over four decades from 1972 to 2012, addressing three aspects: minimum mortality temperature (MMT) and heat- and cold-related mortality risks. In addition, we aimed to examine how these aspects of susceptibility were associated with climate, demographic, and socioeconomic variables. We first used a two-stage time-series design with a time-varying distributed lag nonlinear model and multivariate meta-analysis to estimate the time-varying MMT, heat- and cold-related mortality risks. We then applied linear mixed effects models to investigate the association between each of the three time-varying aspects of susceptibility and various time-varying factors. MMT increased from 23.2 [95% confidence interval (CI): 23, 23.6] to 28.7 (27.0, 29.7) °C. Heat-related mortality risk [relative risk (RR) for the 99th percentile of temperature vs. the MMT] decreased from 1.18 (1.15, 1.21) to 1.01 (0.98, 1.04). Cold-related mortality risk (RR for the first percentile vs. the MMT) generally decreased from 1.48 (1.41, 1.54) to 1.35 (1.32, 1.40), with the exception of a few eastern prefectures that showed increased risk. The changing patterns in all three aspects differed by region, sex, and causes of death. Higher mean temperature was associated ( p <0.01) with lower heat risk, whereas higher humidity was associated with higher cold risk. A higher percentage of elderly people was associated with a higher cold risk, whereas higher economic strength of the prefecture was related to lower cold risk. Population susceptibility to heat has decreased over the last four decades in Japan. Susceptibility to cold has decreased overall except for several eastern prefectures where it has either increased or remained unchanged. Certain climate, demographic, and socioeconomic factors explored in the current study might underlie this changing susceptibility. https://doi.org/10.1289/EHP2546.

A model for estimating pathogen variability in shellfish and predicting minimum depuration times.

PubMed

McMenemy, Paul; Kleczkowski, Adam; Lees, David N; Lowther, James; Taylor, Nick

2018-01-01

Norovirus is a major cause of viral gastroenteritis, with shellfish consumption being identified as one potential norovirus entry point into the human population. Minimising shellfish norovirus levels is therefore important for both the consumer's protection and the shellfish industry's reputation. One method used to reduce microbiological risks in shellfish is depuration; however, this process also presents additional costs to industry. Providing a mechanism to estimate norovirus levels during depuration would therefore be useful to stakeholders. This paper presents a mathematical model of the depuration process and its impact on norovirus levels found in shellfish. Two fundamental stages of norovirus depuration are considered: (i) the initial distribution of norovirus loads within a shellfish population and (ii) the way in which the initial norovirus loads evolve during depuration. Realistic assumptions are made about the dynamics of norovirus during depuration, and mathematical descriptions of both stages are derived and combined into a single model. Parameters to describe the depuration effect and norovirus load values are derived from existing norovirus data obtained from U.K. harvest sites. However, obtaining population estimates of norovirus variability is time-consuming and expensive; this model addresses the issue by assuming a 'worst case scenario' for variability of pathogens, which is independent of mean pathogen levels. The model is then used to predict minimum depuration times required to achieve norovirus levels which fall within possible risk management levels, as well as predictions of minimum depuration times for other water-borne pathogens found in shellfish. Times for Escherichia coli predicted by the model all fall within the minimum 42 hours required for class B harvest sites, whereas minimum depuration times for norovirus and FRNA+ bacteriophage are substantially longer. Thus this study provides relevant information and tools to assist norovirus risk managers with future control strategies.

Fall risk as a function of time after admission to sub-acute geriatric hospital units.

PubMed

Rapp, Kilian; Ravindren, Johannes; Becker, Clemens; Lindemann, Ulrich; Jaensch, Andrea; Klenk, Jochen

2016-10-07

There is evidence about time-dependent fracture rates in different settings and situations. Lacking are data about underlying time-dependent fall risk patterns. The objective of the study was to analyse fall rates as a function of time after admission to sub-acute hospital units and to evaluate the time-dependent impact of clinical factors at baseline on fall risk. This retrospective cohort study used data of 5,255 patients admitted to sub-acute units in a geriatric rehabilitation clinic in Germany between 2010 and 2014. Falls, personal characteristics and functional status at admission were extracted from the hospital information system. The rehabilitation stay was divided in 3-day time-intervals. The fall rate was calculated for each time-interval in all patients combined and in subgroups of patients. To analyse the influence of covariates on fall risk over time multivariate negative binomial regression models were applied for each of 5 time-intervals. The overall fall rate was 10.2 falls/1,000 person-days with highest fall risks during the first week and decreasing risks within the following weeks. A particularly pronounced risk pattern with high fall risks during the first days and decreasing risks thereafter was observed in men, disoriented people, and people with a low functional status or impaired cognition. In disoriented patients, for example, the fall rate decreased from 24.6 falls/1,000 person-days in day 2-4 to about 13 falls/1,000 person-days 2 weeks later. The incidence rate ratio of baseline characteristics changed also over time. Fall risk differs considerably over time during sub-acute hospitalisation. The strongest association between time and fall risk was observed in functionally limited patients with high risks during the first days after admission and declining risks thereafter. This should be considered in the planning and application of fall prevention measures.

Optimal security investments and extreme risk.

PubMed

Mohtadi, Hamid; Agiwal, Swati

2012-08-01

In the aftermath of 9/11, concern over security increased dramatically in both the public and the private sector. Yet, no clear algorithm exists to inform firms on the amount and the timing of security investments to mitigate the impact of catastrophic risks. The goal of this article is to devise an optimum investment strategy for firms to mitigate exposure to catastrophic risks, focusing on how much to invest and when to invest. The latter question addresses the issue of whether postponing a risk mitigating decision is an optimal strategy or not. Accordingly, we develop and estimate both a one-period model and a multiperiod model within the framework of extreme value theory (EVT). We calibrate these models using probability measures for catastrophic terrorism risks associated with attacks on the food sector. We then compare our findings with the purchase of catastrophic risk insurance. © 2012 Society for Risk Analysis.

Risk selection into consumer-directed health plans: an analysis of family choices within large employers.

PubMed

McDevitt, Roland D; Haviland, Amelia M; Lore, Ryan; Laudenberger, Laura; Eisenberg, Matthew; Sood, Neeraj

2014-04-01

To identify the degree of selection into consumer-directed health plans (CDHPs) versus traditional plans over time, and factors that influence choice and temper risk selection. Sixteen large employers offering both CDHP and traditional plans during the 2004–2007 period, more than 200,000 families. We model CDHP choice with logistic regression; predictors include risk scores, in addition to family, choice setting, and plan characteristics. Additional models stratify by account type or single enrollee versus family. Risk scores, family characteristics, and enrollment decisions are derived from medical claims and enrollment files. Interviews with human resources executives provide additional data. CDHP risk scores were 74 percent of traditional plan scores in the first year, and this difference declined over time. Employer contributions to accounts and employee premium savings fostered CDHP enrollment and reduced risk selection. Having to make an active choice of plan increased CDHP enrollment but also increased risk selection. Risk selection was greater for singles than families and did not differ between HRA and HSA-based CDHPs. Risk selection was not severe and it was well managed. Employers have effective methods to encourage CDHP enrollment and temper selection against traditional plans.

Failure time analysis with unobserved heterogeneity: Earthquake duration time of Turkey

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

Ata, Nihal, E-mail: nihalata@hacettepe.edu.tr; Kadilar, Gamze Özel, E-mail: gamzeozl@hacettepe.edu.tr

Failure time models assume that all units are subject to same risks embodied in the hazard functions. In this paper, unobserved sources of heterogeneity that are not captured by covariates are included into the failure time models. Destructive earthquakes in Turkey since 1900 are used to illustrate the models and inter-event time between two consecutive earthquakes are defined as the failure time. The paper demonstrates how seismicity and tectonics/physics parameters that can potentially influence the spatio-temporal variability of earthquakes and presents several advantages compared to more traditional approaches.

iCARE

Cancer.gov

The iCARE R Package allows researchers to quickly build models for absolute risk, and apply them to estimate an individual's risk of developing disease during a specifed time interval, based on a set of user defined input parameters.

Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view.

PubMed

Li, Xiaohong; Blount, Patricia L; Vaughan, Thomas L; Reid, Brian J

2011-02-01

Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic "biomarkers" have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time.

Quantifying Systemic Risk by Solutions of the Mean-Variance Risk Model.

PubMed

Jurczyk, Jan; Eckrot, Alexander; Morgenstern, Ingo

2016-01-01

The world is still recovering from the financial crisis peaking in September 2008. The triggering event was the bankruptcy of Lehman Brothers. To detect such turmoils, one can investigate the time-dependent behaviour of correlations between assets or indices. These cross-correlations have been connected to the systemic risks within markets by several studies in the aftermath of this crisis. We study 37 different US indices which cover almost all aspects of the US economy and show that monitoring an average investor's behaviour can be used to quantify times of increased risk. In this paper the overall investing strategy is approximated by the ground-states of the mean-variance model along the efficient frontier bound to real world constraints. Changes in the behaviour of the average investor is utlilized as a early warning sign.

Quantifying Systemic Risk by Solutions of the Mean-Variance Risk Model

PubMed Central

Morgenstern, Ingo

2016-01-01

The world is still recovering from the financial crisis peaking in September 2008. The triggering event was the bankruptcy of Lehman Brothers. To detect such turmoils, one can investigate the time-dependent behaviour of correlations between assets or indices. These cross-correlations have been connected to the systemic risks within markets by several studies in the aftermath of this crisis. We study 37 different US indices which cover almost all aspects of the US economy and show that monitoring an average investor’s behaviour can be used to quantify times of increased risk. In this paper the overall investing strategy is approximated by the ground-states of the mean-variance model along the efficient frontier bound to real world constraints. Changes in the behaviour of the average investor is utlilized as a early warning sign. PMID:27351482

A Simplified Approach to Risk Assessment Based on System Dynamics: An Industrial Case Study.

PubMed

Garbolino, Emmanuel; Chery, Jean-Pierre; Guarnieri, Franck

2016-01-01

Seveso plants are complex sociotechnical systems, which makes it appropriate to support any risk assessment with a model of the system. However, more often than not, this step is only partially addressed, simplified, or avoided in safety reports. At the same time, investigations have shown that the complexity of industrial systems is frequently a factor in accidents, due to interactions between their technical, human, and organizational dimensions. In order to handle both this complexity and changes in the system over time, this article proposes an original and simplified qualitative risk evaluation method based on the system dynamics theory developed by Forrester in the early 1960s. The methodology supports the development of a dynamic risk assessment framework dedicated to industrial activities. It consists of 10 complementary steps grouped into two main activities: system dynamics modeling of the sociotechnical system and risk analysis. This system dynamics risk analysis is applied to a case study of a chemical plant and provides a way to assess the technological and organizational components of safety. © 2016 Society for Risk Analysis.

Application of wildfire simulation models for risk analysis

Treesearch

Alan A. Ager; Mark A. Finney

2009-01-01

Wildfire simulation models are being widely used by fire and fuels specialists in the U.S. to support tactical and strategic decisions related to the mitigation of wildfire risk. Much of this application has resulted from the development of a minimum travel time (MTT) fire spread algorithm (M. Finney) that makes it computationally feasible to simulate thousands of...

Low dose radiation risks for women surviving the a-bombs in Japan: generalized additive model.

PubMed

Dropkin, Greg

2016-11-24

Analyses of cancer mortality and incidence in Japanese A-bomb survivors have been used to estimate radiation risks, which are generally higher for women. Relative Risk (RR) is usually modelled as a linear function of dose. Extrapolation from data including high doses predicts small risks at low doses. Generalized Additive Models (GAMs) are flexible methods for modelling non-linear behaviour. GAMs are applied to cancer incidence in female low dose subcohorts, using anonymous public data for the 1958 - 1998 Life Span Study, to test for linearity, explore interactions, adjust for the skewed dose distribution, examine significance below 100 mGy, and estimate risks at 10 mGy. For all solid cancer incidence, RR estimated from 0 - 100 mGy and 0 - 20 mGy subcohorts is significantly raised. The response tapers above 150 mGy. At low doses, RR increases with age-at-exposure and decreases with time-since-exposure, the preferred covariate. Using the empirical cumulative distribution of dose improves model fit, and capacity to detect non-linear responses. RR is elevated over wide ranges of covariate values. Results are stable under simulation, or when removing exceptional data cells, or adjusting neutron RBE. Estimates of Excess RR at 10 mGy using the cumulative dose distribution are 10 - 45 times higher than extrapolations from a linear model fitted to the full cohort. Below 100 mGy, quasipoisson models find significant effects for all solid, squamous, uterus, corpus, and thyroid cancers, and for respiratory cancers when age-at-exposure > 35 yrs. Results for the thyroid are compatible with studies of children treated for tinea capitis, and Chernobyl survivors. Results for the uterus are compatible with studies of UK nuclear workers and the Techa River cohort. Non-linear models find large, significant cancer risks for Japanese women exposed to low dose radiation from the atomic bombings. The risks should be reflected in protection standards.

Predicting timing of clinical outcomes in patients with chronic kidney disease and severely decreased glomerular filtration rate.

PubMed

Grams, Morgan E; Sang, Yingying; Ballew, Shoshana H; Carrero, Juan Jesus; Djurdjev, Ognjenka; Heerspink, Hiddo J L; Ho, Kevin; Ito, Sadayoshi; Marks, Angharad; Naimark, David; Nash, Danielle M; Navaneethan, Sankar D; Sarnak, Mark; Stengel, Benedicte; Visseren, Frank L J; Wang, Angela Yee-Moon; Köttgen, Anna; Levey, Andrew S; Woodward, Mark; Eckardt, Kai-Uwe; Hemmelgarn, Brenda; Coresh, Josef

2018-06-01

Patients with chronic kidney disease and severely decreased glomerular filtration rate (GFR) are at high risk for kidney failure, cardiovascular disease (CVD) and death. Accurate estimates of risk and timing of these clinical outcomes could guide patient counseling and therapy. Therefore, we developed models using data of 264,296 individuals in 30 countries participating in the international Chronic Kidney Disease Prognosis Consortium with estimated GFR (eGFR)s under 30 ml/min/1.73m 2 . Median participant eGFR and urine albumin-to-creatinine ratio were 24 ml/min/1.73m 2 and 168 mg/g, respectively. Using competing-risk regression, random-effect meta-analysis, and Markov processes with Monte Carlo simulations, we developed two- and four-year models of the probability and timing of kidney failure requiring kidney replacement therapy (KRT), a non-fatal CVD event, and death according to age, sex, race, eGFR, albumin-to-creatinine ratio, systolic blood pressure, smoking status, diabetes mellitus, and history of CVD. Hypothetically applied to a 60-year-old white male with a history of CVD, a systolic blood pressure of 140 mmHg, an eGFR of 25 ml/min/1.73m 2 and a urine albumin-to-creatinine ratio of 1000 mg/g, the four-year model predicted a 17% chance of survival after KRT, a 17% chance of survival after a CVD event, a 4% chance of survival after both, and a 28% chance of death (9% as a first event, and 19% after another CVD event or KRT). Risk predictions for KRT showed good overall agreement with the published kidney failure risk equation, and both models were well calibrated with observed risk. Thus, commonly-measured clinical characteristics can predict the timing and occurrence of clinical outcomes in patients with severely decreased GFR. Copyright © 2018 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

Nonstandard working schedules and health: the systematic search for a comprehensive model.

PubMed

Merkus, Suzanne L; Holte, Kari Anne; Huysmans, Maaike A; van Mechelen, Willem; van der Beek, Allard J

2015-10-23

Theoretical models on shift work fall short of describing relevant health-related pathways associated with the broader concept of nonstandard working schedules. Shift work models neither combine relevant working time characteristics applicable to nonstandard schedules nor include the role of rest periods and recovery in the development of health complaints. Therefore, this paper aimed to develop a comprehensive model on nonstandard working schedules to address these shortcomings. A literature review was conducted using a systematic search and selection process. Two searches were performed: one associating the working time characteristics time-of-day and working time duration with health and one associating recovery after work with health. Data extracted from the models were used to develop a comprehensive model on nonstandard working schedules and health. For models on the working time characteristics, the search strategy yielded 3044 references, of which 26 met the inclusion criteria that contained 22 distinctive models. For models on recovery after work, the search strategy yielded 896 references, of which seven met the inclusion criteria containing seven distinctive models. Of the models on the working time characteristics, three combined time-of-day with working time duration, 18 were on time-of-day (i.e. shift work), and one was on working time duration. The model developed in the paper has a comprehensive approach to working hours and other work-related risk factors and proposes that they should be balanced by positive non-work factors to maintain health. Physiological processes leading to health complaints are circadian disruption, sleep deprivation, and activation that should be counterbalanced by (re-)entrainment, restorative sleep, and recovery, respectively, to maintain health. A comprehensive model on nonstandard working schedules and health was developed. The model proposes that work and non-work as well as their associated physiological processes need to be balanced to maintain good health. The model gives researchers a useful overview over the various risk factors and pathways associated with health that should be considered when studying any form of nonstandard working schedule.

Mechanistic modeling of insecticide risks to breeding birds in ...

EPA Pesticide Factsheets

Insecticide usage in the United States is ubiquitous in urban, suburban, and rural environments. In evaluating data for an insecticide registration application and for registration review, scientists at the United States Environmental Protection Agency (USEPA) assess the fate of the insecticide and the risk the insecticide poses to the environment and non-target wildlife. At the present time, current USEPA risk assessments do not include population-level endpoints. In this paper, we present a new mechanistic model, which allows risk assessors to estimate the effects of insecticide exposure on the survival and seasonal productivity of birds known to use agricultural fields during their breeding season. The new model was created from two existing USEPA avian risk assessment models, the Terrestrial Investigation Model (TIM v.3.0) and the Markov Chain Nest Productivity model (MCnest). The integrated TIM/MCnest model has been applied to assess the relative risk of 12 insecticides used to control corn pests on a suite of 31 avian species known to use cornfields in midwestern agroecosystems. The 12 insecticides that were assessed in this study are all used to treat major pests of corn (corn root worm borer, cutworm, and armyworm). After running the integrated TIM/MCnest model, we found extensive differences in risk to birds among insecticides, with chlorpyrifos and malathion (organophosphates) generally posing the greatest risk, and bifenthrin and ë-cyhalothrin (

Optimal replenishment and credit policy in supply chain inventory model under two levels of trade credit with time- and credit-sensitive demand involving default risk

NASA Astrophysics Data System (ADS)

Mahata, Puspita; Mahata, Gour Chandra; Kumar De, Sujit

2018-03-01

Traditional supply chain inventory modes with trade credit usually only assumed that the up-stream suppliers offered the down-stream retailers a fixed credit period. However, in practice the retailers will also provide a credit period to customers to promote the market competition. In this paper, we formulate an optimal supply chain inventory model under two levels of trade credit policy with default risk consideration. Here, the demand is assumed to be credit-sensitive and increasing function of time. The major objective is to determine the retailer's optimal credit period and cycle time such that the total profit per unit time is maximized. The existence and uniqueness of the optimal solution to the presented model are examined, and an easy method is also shown to find the optimal inventory policies of the considered problem. Finally, numerical examples and sensitive analysis are presented to illustrate the developed model and to provide some managerial insights.

Racial/ethnic and gender differences in individual workplace injury risk trajectories: 1988-1998.

PubMed

Berdahl, Terceira A

2008-12-01

I examined workplace injury risk over time and across racial/ethnic and gender groups to observe patterns of change and to understand how occupational characteristics and job mobility influence these changes. I used hierarchical generalized linear models to estimate individual workplace injury and illness risk over time ("trajectories") for a cohort of American workers who participated in the National Longitudinal Survey of Youth (1988-1998). Significant temporal variation in injury risk was observed across racial/ethnic and gender groups. At baseline, White men had a high risk of injury relative to the other groups and experienced the greatest decline over time. Latino men demonstrated a pattern of lower injury risk across time compared with White men. Among both Latinos and non-Latino Whites, women had lower odds of injury than did men. Non-Latino Black women's injury risk was similar to Black men's and greater than that for both Latino and non-Latino White women. Occupational characteristics and job mobility partly explained these differences. Disparities between racial/ethnic and gender groups were dynamic and changed over time. Workplace injury risk was associated with job dimensions such as work schedule, union representation, health insurance, job hours, occupational racial segregation, and occupational environmental hazards.

An Online Risk Monitor System (ORMS) to Increase Safety and Security Levels in Industry

NASA Astrophysics Data System (ADS)

Zubair, M.; Rahman, Khalil Ur; Hassan, Mehmood Ul

2013-12-01

The main idea of this research is to develop an Online Risk Monitor System (ORMS) based on Living Probabilistic Safety Assessment (LPSA). The article highlights the essential features and functions of ORMS. The basic models and modules such as, Reliability Data Update Model (RDUM), running time update, redundant system unavailability update, Engineered Safety Features (ESF) unavailability update and general system update have been described in this study. ORMS not only provides quantitative analysis but also highlights qualitative aspects of risk measures. ORMS is capable of automatically updating the online risk models and reliability parameters of equipment. ORMS can support in the decision making process of operators and managers in Nuclear Power Plants.

Stochastic landslide vulnerability modeling in space and time in a part of the northern Himalayas, India.

PubMed

Das, Iswar; Kumar, Gaurav; Stein, Alfred; Bagchi, Arunabha; Dadhwal, Vinay K

2011-07-01

Little is known about the quantitative vulnerability analysis to landslides as not many attempts have been made to assess it comprehensively. This study assesses the spatio-temporal vulnerability of elements at risk to landslides in a stochastic framework. The study includes buildings, persons inside buildings, and traffic as elements at risk to landslides. Building vulnerability is the expected damage and depends on the position of a building with respect to the landslide hazard at a given time. Population and vehicle vulnerability are the expected death toll in a building and vehicle damage in space and time respectively. The study was carried out in a road corridor in the Indian Himalayas that is highly susceptible to landslides. Results showed that 26% of the buildings fall in the high and very high vulnerability categories. Population vulnerability inside buildings showed a value >0.75 during 0800 to 1000 hours and 1600 to 1800 hours in more buildings that other times of the day. It was also observed in the study region that the vulnerability of vehicle is above 0.6 in half of the road stretches during 0800 hours to 1000 hours and 1600 to 1800 hours due to high traffic density on the road section. From this study, we conclude that the vulnerability of an element at risk to landslide is a space and time event, and can be quantified using stochastic modeling. Therefore, the stochastic vulnerability modeling forms the basis for a quantitative landslide risk analysis and assessment.

Falls classification using tri-axial accelerometers during the five-times-sit-to-stand test.

PubMed

Doheny, Emer P; Walsh, Cathal; Foran, Timothy; Greene, Barry R; Fan, Chie Wei; Cunningham, Clodagh; Kenny, Rose Anne

2013-09-01

The five-times-sit-to-stand test (FTSS) is an established assessment of lower limb strength, balance dysfunction and falls risk. Clinically, the time taken to complete the task is recorded with longer times indicating increased falls risk. Quantifying the movement using tri-axial accelerometers may provide a more objective and potentially more accurate falls risk estimate. 39 older adults, 19 with a history of falls, performed four repetitions of the FTSS in their homes. A tri-axial accelerometer was attached to the lateral thigh and used to identify each sit-stand-sit phase and sit-stand and stand-sit transitions. A second tri-axial accelerometer, attached to the sternum, captured torso acceleration. The mean and variation of the root-mean-squared amplitude, jerk and spectral edge frequency of the acceleration during each section of the assessment were examined. The test-retest reliability of each feature was examined using intra-class correlation analysis, ICC(2,k). A model was developed to classify participants according to falls status. Only features with ICC>0.7 were considered during feature selection. Sequential forward feature selection within leave-one-out cross-validation resulted in a model including four reliable accelerometer-derived features, providing 74.4% classification accuracy, 80.0% specificity and 68.7% sensitivity. An alternative model using FTSS time alone resulted in significantly reduced classification performance. Results suggest that the described methodology could provide a robust and accurate falls risk assessment. Copyright © 2013 Elsevier B.V. All rights reserved.

Stress and sleep reactivity: a prospective investigation of the stress-diathesis model of insomnia.

PubMed

Drake, Christopher L; Pillai, Vivek; Roth, Thomas

2014-08-01

To prospectively assess sleep reactivity as a diathesis of insomnia, and to delineate the interaction between this diathesis and naturalistic stress in the development of insomnia among normal sleepers. Longitudinal. Community-based. 2,316 adults from the Evolution of Pathways to Insomnia Cohort (EPIC) with no history of insomnia or depression (46.8 ± 13.2 y; 60% female). None. Participants reported the number of stressful events they encountered at baseline (Time 1), as well as the level of cognitive intrusion they experienced in response to each stressor. Stressful events (OR = 1.13; P < 0.01) and stress-induced cognitive intrusion (OR = 1.61; P < 0.01) were significant predictors of risk for insomnia one year hence (Time 2). Intrusion mediated the effects of stressful events on risk for insomnia (P < 0.05). Trait sleep reactivity significantly increased risk for insomnia (OR = 1.78; P < 0.01). Further, sleep reactivity moderated the effects of stress-induced intrusion (P < 0.05), such that the risk for insomnia as a function of intrusion was significantly higher in individuals with high sleep reactivity. Trait sleep reactivity also constituted a significant risk for depression (OR = 1.67; P < 0.01) two years later (Time 3). Insomnia at Time 2 significantly mediated this effect (P < 0.05). This study suggests that premorbid sleep reactivity is a significant risk factor for incident insomnia, and that it triggers insomnia by exacerbating the effects of stress-induced intrusion. Sleep reactivity is also a precipitant of depression, as mediated by insomnia. These findings support the stress-diathesis model of insomnia, while highlighting sleep reactivity as an important diathesis. Drake CL, Pillai V, Roth T. Stress and sleep reactivity: a prospective investigation of the stress-diathesis model of insomnia.

Estimating the decline in excess risk of chronic obstructive pulmonary disease following quitting smoking - a systematic review based on the negative exponential model.

PubMed

Lee, Peter N; Fry, John S; Forey, Barbara A

2014-03-01

We quantified the decline in COPD risk following quitting using the negative exponential model, as previously carried out for other smoking-related diseases. We identified 14 blocks of RRs (from 11 studies) comparing current smokers, former smokers (by time quit) and never smokers, some studies providing sex-specific blocks. Corresponding pseudo-numbers of cases and controls/at risk formed the data for model-fitting. We estimated the half-life (H, time since quit when the excess risk becomes half that for a continuing smoker) for each block, except for one where no decline with quitting was evident, and H was not estimable. For the remaining 13 blocks, goodness-of-fit to the model was generally adequate, the combined estimate of H being 13.32 (95% CI 11.86-14.96) years. There was no heterogeneity in H, overall or by various studied sources. Sensitivity analyses allowing for reverse causation or different assumed times for the final quitting period little affected the results. The model summarizes quitting data well. The estimate of 13.32years is substantially larger than recent estimates of 4.40years for ischaemic heart disease and 4.78years for stroke, and also larger than the 9.93years for lung cancer. Heterogeneity was unimportant for COPD, unlike for the other three diseases. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Estimating the decline in excess risk of cerebrovascular disease following quitting smoking--a systematic review based on the negative exponential model.

PubMed

Lee, Peter N; Fry, John S; Thornton, Alison J

2014-02-01

We attempted to quantify the decline in stroke risk following quitting using the negative exponential model, with methodology previously employed for IHD. We identified 22 blocks of RRs (from 13 studies) comparing current smokers, former smokers (by time quit) and never smokers. Corresponding pseudo-numbers of cases and controls/at risk formed the data for model-fitting. We tried to estimate the half-life (H, time since quit when the excess risk becomes half that for a continuing smoker) for each block. The method failed to converge or produced very variable estimates of H in nine blocks with a current smoker RR <1.40. Rejecting these, and combining blocks by amount smoked in one study where problems arose in model-fitting, the final analyses used 11 blocks. Goodness-of-fit was adequate for each block, the combined estimate of H being 4.78(95%CI 2.17-10.50) years. However, considerable heterogeneity existed, unexplained by any factor studied, with the random-effects estimate 3.08(1.32-7.16). Sensitivity analyses allowing for reverse causation or differing assumed times for the final quitting period gave similar results. The estimates of H are similar for stroke and IHD, and the individual estimates similarly heterogeneous. Fitting the model is harder for stroke, due to its weaker association with smoking. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

[Survival analysis with competing risks: estimating failure probability].

PubMed

Llorca, Javier; Delgado-Rodríguez, Miguel

2004-01-01

To show the impact of competing risks of death on survival analysis. We provide an example of survival time without chronic rejection after heart transplantation, where death before rejection acts as a competing risk. Using a computer simulation, we compare the Kaplan-Meier estimator and the multiple decrement model. The Kaplan-Meier method overestimated the probability of rejection. Next, we illustrate the use of the multiple decrement model to analyze secondary end points (in our example: death after rejection). Finally, we discuss Kaplan-Meier assumptions and why they fail in the presence of competing risks. Survival analysis should be adjusted for competing risks of death to avoid overestimation of the risk of rejection produced with the Kaplan-Meier method.

A Study of the Factors Associated with Risk for Development of Pressure Ulcers: A Longitudinal Analysis

PubMed Central

Thomas, Elizebeth; Vinodkumar, Sudhaya; Mathew, Silvia; Setia, Maninder Singh

2015-01-01

Background: Pressure ulcers (PUs) are prevalent in hospitalized patients; they may cause clinical, psychological, and economic problems in these patients. Previous studies are cross-sectional, have used pooled data, or cox-regression models to assess the risk for developing PU. However, PU risk scores change over time and models that account for time varying variables are useful for cohort analysis of data. Aims and Objectives: The present longitudinal study was conducted to compare the risk of PU between surgical and nonsurgical patients, and to evaluate the factors associated with the development of these ulcers over a period of time. Materials and Methods: We evaluated 290 hospitalized patients over a 4 months period. The main outcomes for our analysis were: (1) Score on the pressure risk assessment scale; and (2) the proportion of individuals who were at severe risk for developing PUs. We used random effects models for longitudinal analysis of the data. Results: The mean PU score was significantly higher in the nonsurgical patients compared with surgical patients at baseline (15.23 [3.86] vs. 9.33 [4.57]; P < 0.01). About 7% of the total patients had a score of >20 at baseline and were considered as being at high-risk for PU; the proportion was significantly higher among the nonsurgical patients compared with the surgical patients (14% vs. 4%, P = 0.003). In the adjusted models, there was no difference for severe risk for PU between surgical and nonsurgical patients (odds ratios [ORs]: 0.37, 95% confidence interval [CI]: 0.01–12.80). An additional day in the ward was associated with a significantly higher likelihood of being at high-risk for PU (OR: 1.47, 95% CI: 1.16–1.86). Conclusion: There were no significant differences between patients who were admitted for surgery compared with those who were not. An additional day in the ward, however, is important for developing a high-risk score for PU on the monitoring scale, and these patients require active interventions. PMID:26677269

Separating spatial search and efficiency rates as components of predation risk

PubMed Central

DeCesare, Nicholas J.

2012-01-01

Predation risk is an important driver of ecosystems, and local spatial variation in risk can have population-level consequences by affecting multiple components of the predation process. I use resource selection and proportional hazard time-to-event modelling to assess the spatial drivers of two key components of risk—the search rate (i.e. aggregative response) and predation efficiency rate (i.e. functional response)—imposed by wolves (Canis lupus) in a multi-prey system. In my study area, both components of risk increased according to topographic variation, but anthropogenic features affected only the search rate. Predicted models of the cumulative hazard, or risk of a kill, underlying wolf search paths validated well with broad-scale variation in kill rates, suggesting that spatial hazard models provide a means of scaling up from local heterogeneity in predation risk to population-level dynamics in predator–prey systems. Additionally, I estimated an integrated model of relative spatial predation risk as the product of the search and efficiency rates, combining the distinct contributions of spatial heterogeneity to each component of risk. PMID:22977145

Ambient temperature and coronary heart disease mortality in Beijing, China: a time series study

PubMed Central

2012-01-01

Background Many studies have examined the association between ambient temperature and mortality. However, less evidence is available on the temperature effects on coronary heart disease (CHD) mortality, especially in China. In this study, we examined the relationship between ambient temperature and CHD mortality in Beijing, China during 2000 to 2011. In addition, we compared time series and time-stratified case-crossover models for the non-linear effects of temperature. Methods We examined the effects of temperature on CHD mortality using both time series and time-stratified case-crossover models. We also assessed the effects of temperature on CHD mortality by subgroups: gender (female and male) and age (age > =65 and age < 65). We used a distributed lag non-linear model to examine the non-linear effects of temperature on CHD mortality up to 15 lag days. We used Akaike information criterion to assess the model fit for the two designs. Results The time series models had a better model fit than time-stratified case-crossover models. Both designs showed that the relationships between temperature and group-specific CHD mortality were non-linear. Extreme cold and hot temperatures significantly increased the risk of CHD mortality. Hot effects were acute and short-term, while cold effects were delayed by two days and lasted for five days. The old people and women were more sensitive to extreme cold and hot temperatures than young and men. Conclusions This study suggests that time series models performed better than time-stratified case-crossover models according to the model fit, even though they produced similar non-linear effects of temperature on CHD mortality. In addition, our findings indicate that extreme cold and hot temperatures increase the risk of CHD mortality in Beijing, China, particularly for women and old people. PMID:22909034

Stochastic modeling of hourly rainfall times series in Campania (Italy)

NASA Astrophysics Data System (ADS)

Giorgio, M.; Greco, R.

2009-04-01

Occurrence of flowslides and floods in small catchments is uneasy to predict, since it is affected by a number of variables, such as mechanical and hydraulic soil properties, slope morphology, vegetation coverage, rainfall spatial and temporal variability. Consequently, landslide risk assessment procedures and early warning systems still rely on simple empirical models based on correlation between recorded rainfall data and observed landslides and/or river discharges. Effectiveness of such systems could be improved by reliable quantitative rainfall prediction, which can allow gaining larger lead-times. Analysis of on-site recorded rainfall height time series represents the most effective approach for a reliable prediction of local temporal evolution of rainfall. Hydrological time series analysis is a widely studied field in hydrology, often carried out by means of autoregressive models, such as AR, ARMA, ARX, ARMAX (e.g. Salas [1992]). Such models gave the best results when applied to the analysis of autocorrelated hydrological time series, like river flow or level time series. Conversely, they are not able to model the behaviour of intermittent time series, like point rainfall height series usually are, especially when recorded with short sampling time intervals. More useful for this issue are the so-called DRIP (Disaggregated Rectangular Intensity Pulse) and NSRP (Neymann-Scott Rectangular Pulse) model [Heneker et al., 2001; Cowpertwait et al., 2002], usually adopted to generate synthetic point rainfall series. In this paper, the DRIP model approach is adopted, in which the sequence of rain storms and dry intervals constituting the structure of rainfall time series is modeled as an alternating renewal process. Final aim of the study is to provide a useful tool to implement an early warning system for hydrogeological risk management. Model calibration has been carried out with hourly rainfall hieght data provided by the rain gauges of Campania Region civil protection agency meteorological warning network. ACKNOWLEDGEMENTS The research was co-financed by the Italian Ministry of University, by means of the PRIN 2006 PRIN program, within the research project entitled ‘Definition of critical rainfall thresholds for destructive landslides for civil protection purposes'. REFERENCES Cowpertwait, P.S.P., Kilsby, C.G. and O'Connell, P.E., 2002. A space-time Neyman-Scott model of rainfall: Empirical analysis of extremes, Water Resources Research, 38(8):1-14. Salas, J.D., 1992. Analysis and modeling of hydrological time series, in D.R. Maidment, ed., Handbook of Hydrology, McGraw-Hill, New York. Heneker, T.M., Lambert, M.F. and Kuczera G., 2001. A point rainfall model for risk-based design, Journal of Hydrology, 247(1-2):54-71.

Validation of a model for ranking aquaculture facilities for risk-based disease surveillance.

PubMed

Diserens, Nicolas; Falzon, Laura Cristina; von Siebenthal, Beat; Schüpbach-Regula, Gertraud; Wahli, Thomas

2017-09-15

A semi-quantitative model for risk ranking of aquaculture facilities in Switzerland with regard to the introduction and spread of Viral Haemorrhagic Septicaemia (VHS) and Infectious Haematopoietic Necrosis (IHN) was developed in a previous study (Diserens et al., 2013). The objective of the present study was to validate this model using data collected during field visits on aquaculture sites in four Swiss cantons compared to data collected through a questionnaire in the previous study. A discrepancy between the values obtained with the two different methods was found in 32.8% of the parameters, resulting in a significant difference (p<0.001) in the risk classification of the facilities. As data gathered exclusively by means of a questionnaire are not of sufficient quality to perform a risk-based surveillance of aquaculture facilities a combination of questionnaires and farm inspections is proposed. A web-based reporting system could be advantageous for the factors which were identified as being more likely to vary over time, in particular for factors considering fish movements, which showed a marginally significant difference in their risk scores (p≥0.1) within a six- month period. Nevertheless, the model proved to be stable over the considered period of time as no substantial fluctuations in the risk categorisation were observed (Kappa agreement of 0.77).Finally, the model proved to be suitable to deliver a reliable risk ranking of Swiss aquaculture facilities according to their risk of getting infected with or spreading of VHS and IHN, as the five facilities that tested positive for these diseases in the last ten years were ranked as medium or high risk. Moreover, because the seven fish farms that were infected with Infectious Pancreatic Necrosis (IPN) during the same period also belonged to the risk categories medium and high, the classification appeared to correlate with the occurrence of this third viral fish disease. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling Day-to-day Flow Dynamics on Degradable Transport Network

PubMed Central

Gao, Bo; Zhang, Ronghui; Lou, Xiaoming

2016-01-01

Stochastic link capacity degradations are common phenomena in transport network which can cause travel time variations and further can affect travelers’ daily route choice behaviors. This paper formulates a deterministic dynamic model, to capture the day-to-day (DTD) flow evolution process in the presence of degraded link capacity degradations. The aggregated network flow dynamics are driven by travelers’ study of uncertain travel time and their choice of risky routes. This paper applies the exponential-smoothing filter to describe travelers’ study of travel time variations, and meanwhile formulates risk attitude parameter updating equation to reflect travelers’ endogenous risk attitude evolution schema. In addition, this paper conducts theoretical analyses to investigate several significant mathematical characteristics implied in the proposed DTD model, including fixed point existence, uniqueness, stability and irreversibility. Numerical experiments are used to demonstrate the effectiveness of the DTD model and verify some important dynamic system properties. PMID:27959903

Using the negative exponential distribution to quantitatively review the evidence on how rapidly the excess risk of ischaemic heart disease declines following quitting smoking.

PubMed

Lee, Peter N; Fry, John S; Hamling, Jan S

2012-10-01

No previous review has formally modelled the decline in IHD risk following quitting smoking. From PubMed searches and other sources we identified 15 prospective and eight case-control studies that compared IHD risk in current smokers, never smokers, and quitters by time period of quit, some studies providing separate blocks of results by sex, age or amount smoked. For each of 41 independent blocks, we estimated, using the negative exponential model, the time, H, when the excess risk reduced to half that caused by smoking. Goodness-of-fit to the model was adequate for 35 blocks, others showing a non-monotonic pattern of decline following quitting, with a variable pattern of misfit. After omitting one block with a current smoker RR 1.0, the combined H estimate was 4.40 (95% CI 3.26-5.95) years. There was considerable heterogeneity, H being <2years for 10 blocks and >10years for 12. H increased (p<0.001) with mean age at study start, but not clearly with other factors. Sensitivity analyses allowing for reverse causation, or varying assumed midpoint times for the final open-ended quitting period little affected goodness-of-fit of the combined estimate. The US Surgeon-General's view that excess risk approximately halves after a year's abstinence seems over-optimistic. Copyright © 2012 Elsevier Inc. All rights reserved.

Forecasting the Risks of Pollution from Ships along the Portuguese Coast

NASA Astrophysics Data System (ADS)

Fernandes, Rodrigo; Neves, Ramiro; Lourenço, Filipe; Braunschweig, Frank

2013-04-01

Pollution risks in coastal and marine environments are in general based in a static approach, considering historical data, reference situations, and typical scenarios. This approach is quite important in a planning stage. However, an alternative approach can be studied, due to the latest implementation of several different real-time monitoring tools as well as faster performances in the generation of numerical forecasts for metocean properties and trajectories of pollutants spilt at sea or costal zones. These developments provide the possibility of developing an integrated support system for better decision-making in emergency or planning issues associated to pollution risks. An innovative methodology to dynamically produce quantified risks in real-time, integrating best available information from numerical forecasts and the existing monitoring tools, has been developed and applied to the Portuguese Coast. The developed system provides coastal pollution risk levels associated to potential (or real) oil spill incidents from ship collision, grounding or foundering, taking into account regional statistic information on vessel accidents and coastal sensitivity indexes, real-time vessel information (positioning, cargo type, speed and vessel type) obtained from AIS, best-available metocean numerical forecasts (hydrodynamics, meteorology - including visibility, wave conditions) and simulated scenarios by the oil spill fate and behaviour component of MOHID Water Modelling System. Different spill fate and behaviour simulations are continuously generated and processed in background (assuming hypothetical spills from vessels), based on variable vessel information and metocean conditions. Results from these simulations are used in the quantification of consequences of potential spills. All historic information is continuously stored in a database (for risk analysis at a later stage). This dynamic approach improves the accuracy in quantification of consequences to the shoreline, as well as the decision support model, allowing a more effective prioritization of individual ships and geographical areas. This system was initially implemented in Portugal for oil spills. The implementation in other Atlantic Regions (starting in Galician Coast, Spain) is being executed in the scope of ARCOPOL+ project (2011-1/150), as well as other relevant updates. The system is being adapted to include risk modelling of chemical spills, as well as fire & explosion accidents and operational illegal discharges. Also the integration of EMSA's THETIS "ship risk profile" (according to Annex 7 from Paris Memorandum of Understanding) in the risk model is being tested. Finally, a new component is being developed to compute the risk for specific time periods, taking advantage of the information previously stored in the database on the positioning of vessels and / or results of numerical models. This component provides the possibility of obtaining a support tool for detailed characterization of risk profiles in certain periods or a sensitivity analysis on different parameters.

Risk factors for the development of heterotopic ossification in seriously burned adults: A National Institute on Disability, Independent Living and Rehabilitation Research burn model system database analysis.

PubMed

Levi, Benjamin; Jayakumar, Prakash; Giladi, Avi; Jupiter, Jesse B; Ring, David C; Kowalske, Karen; Gibran, Nicole S; Herndon, David; Schneider, Jeffrey C; Ryan, Colleen M

2015-11-01

Heterotopic ossification (HO) is a debilitating complication of burn injury; however, incidence and risk factors are poorly understood. In this study, we use a multicenter database of adults with burn injuries to identify and analyze clinical factors that predict HO formation. Data from six high-volume burn centers, in the Burn Injury Model System Database, were analyzed. Univariate logistic regression models were used for model selection. Cluster-adjusted multivariate logistic regression was then used to evaluate the relationship between clinical and demographic data and the development of HO. Of 2,979 patients in the database with information on HO that addressed risk factors for development of HO, 98 (3.5%) developed HO. Of these 98 patients, 97 had arm burns, and 96 had arm grafts. When controlling for age and sex in a multivariate model, patients with greater than 30% total body surface area burn had 11.5 times higher odds of developing HO (p < 0.001), and those with arm burns that required skin grafting had 96.4 times higher odds of developing HO (p = 0.04). For each additional time a patient went to the operating room, odds of HO increased by 30% (odds ratio, 1.32; p < 0.001), and each additional ventilator day increased odds by 3.5% (odds ratio, 1.035; p < 0.001). Joint contracture, inhalation injury, and bone exposure did not significantly increase odds of HO. Risk factors for HO development include greater than 30% total body surface area burn, arm burns, arm grafts, ventilator days, and number of trips to the operating room. Future studies can use these results to identify highest-risk patients to guide deployment of prophylactic and experimental treatments. Prognostic study, level III.

Modeling Freedom From Progression for Standard-Risk Medulloblastoma: A Mathematical Tumor Control Model With Multiple Modes of Failure

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

Brodin, N. Patrik, E-mail: nils.patrik.brodin@rh.dk; Niels Bohr Institute, University of Copenhagen, Copenhagen; Vogelius, Ivan R.

2013-10-01

Purpose: As pediatric medulloblastoma (MB) is a relatively rare disease, it is important to extract the maximum information from trials and cohort studies. Here, a framework was developed for modeling tumor control with multiple modes of failure and time-to-progression for standard-risk MB, using published pattern of failure data. Methods and Materials: Outcome data for standard-risk MB published after 1990 with pattern of relapse information were used to fit a tumor control dose-response model addressing failures in both the high-dose boost volume and the elective craniospinal volume. Estimates of 5-year event-free survival from 2 large randomized MB trials were used tomore » model the time-to-progression distribution. Uncertainty in freedom from progression (FFP) was estimated by Monte Carlo sampling over the statistical uncertainty in input data. Results: The estimated 5-year FFP (95% confidence intervals [CI]) for craniospinal doses of 15, 18, 24, and 36 Gy while maintaining 54 Gy to the posterior fossa was 77% (95% CI, 70%-81%), 78% (95% CI, 73%-81%), 79% (95% CI, 76%-82%), and 80% (95% CI, 77%-84%) respectively. The uncertainty in FFP was considerably larger for craniospinal doses below 18 Gy, reflecting the lack of data in the lower dose range. Conclusions: Estimates of tumor control and time-to-progression for standard-risk MB provides a data-driven setting for hypothesis generation or power calculations for prospective trials, taking the uncertainties into account. The presented methods can also be applied to incorporate further risk-stratification for example based on molecular biomarkers, when the necessary data become available.« less

Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model

PubMed Central

Xu, Jun-Fang; Xu, Jing; Li, Shi-Zhu; Jia, Tia-Wu; Huang, Xi-Bao; Zhang, Hua-Ming; Chen, Mei; Yang, Guo-Jing; Gao, Shu-Jing; Wang, Qing-Yun; Zhou, Xiao-Nong

2013-01-01

Background The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. Methodology/Principal Findings We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected. Conclusion/Significance Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control. PMID:23556015

Does Age of Entrance Affect Community College Completion Probabilities? Evidence from a Discrete-Time Hazard Model

ERIC Educational Resources Information Center

Calcagno, Juan Carlos; Crosta, Peter; Bailey, Thomas; Jenkins, Davis

2007-01-01

Research has consistently shown that older students--those who enter college for the first time at age 25 or older--are less likely to complete a degree or certificate. The authors estimate a single-risk discrete-time hazard model using transcript data on a cohort of first-time community college students in Florida to compare the educational…

Evaluation of the impacts of cooperative adaptive cruise control on reducing rear-end collision risks on freeways.

PubMed

Li, Ye; Wang, Hao; Wang, Wei; Xing, Lu; Liu, Shanwen; Wei, Xueyan

2017-01-01

Although plenty of studies have been conducted recently about the impacts of cooperative adaptive cruise control (CACC) system on traffic efficiency, there are few researches analyzing the safety effects of this advanced driving-assistant system. Thus, the primary objective of this study is to evaluate the impacts of the CACC system on reducing rear-end collision risks on freeways. The CACC model is firstly developed, which is based on the Intelligent Driver Model (IDM). Then, two surrogated safety measures, derived from the time-to-collision (TTC), denoting time exposed time-to-collision (TET) and time integrated time-to-collision (TIT), are introduced for quantifying the collision risks. And the safety effects are analyzed both theoretically and experimentally, by the linear stability analysis and simulations. The theoretical and simulation results conformably indicate that the CACC system brings dramatic benefits for reducing rear-end collision risks (TET and TIT are reduced more than 90%, respectively), when the desired time headway and time delay are set properly. The sensitivity analysis indicates there are few differences among different values of the threshold of TTC and the length of a CACC platoon. The results also show that the safety improvements weaken with the decrease of the penetration rates of CACC on the market and the increase of time delay between platoons. We also evaluate the traffic efficiency of the CACC system with different desired time headway. Copyright © 2016 Elsevier Ltd. All rights reserved.

Risk Prediction Models of Locoregional Failure After Radical Cystectomy for Urothelial Carcinoma: External Validation in a Cohort of Korean Patients

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

Ku, Ja Hyeon; Kim, Myong; Jeong, Chang Wook

2014-08-01

Purpose: To evaluate the predictive accuracy and general applicability of the locoregional failure model in a different cohort of patients treated with radical cystectomy. Methods and Materials: A total of 398 patients were included in the analysis. Death and isolated distant metastasis were considered competing events, and patients without any events were censored at the time of last follow-up. The model included the 3 variables pT classification, the number of lymph nodes identified, and margin status, as follows: low risk (≤pT2), intermediate risk (≥pT3 with ≥10 nodes removed and negative margins), and high risk (≥pT3 with <10 nodes removed ormore » positive margins). Results: The bootstrap-corrected concordance index of the model 5 years after radical cystectomy was 66.2%. When the risk stratification was applied to the validation cohort, the 5-year locoregional failure estimates were 8.3%, 21.2%, and 46.3% for the low-risk, intermediate-risk, and high-risk groups, respectively. The risk of locoregional failure differed significantly between the low-risk and intermediate-risk groups (subhazard ratio [SHR], 2.63; 95% confidence interval [CI], 1.35-5.11; P<.001) and between the low-risk and high-risk groups (SHR, 4.28; 95% CI, 2.17-8.45; P<.001). Although decision curves were appropriately affected by the incidence of the competing risk, decisions about the value of the models are not likely to be affected because the model remains of value over a wide range of threshold probabilities. Conclusions: The model is not completely accurate, but it demonstrates a modest level of discrimination, adequate calibration, and meaningful net benefit gain for prediction of locoregional failure after radical cystectomy.« less

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.

Modelling the impact of new patient visits on risk adjusted access at 2 clinics.

PubMed

Kolber, Michael A; Rueda, Germán; Sory, John B

2018-06-01

To evaluate the effect new outpatient clinic visits has on the availability of follow-up visits for established patients when patient visit frequency is risk adjusted. Diagnosis codes for patients from 2 Internal Medicine Clinics were extracted through billing data. The HHS-HCC risk adjusted scores for each clinic were determined based upon the average of all clinic practitioners' profiles. These scores were then used to project encounter frequencies for established patients, and for new patients entering the clinic based on risk and time of entry into the clinics. A distinct mean risk frequency distribution for physicians in each clinic could be defined providing model parameters. Within the model, follow-up visit utilization at the highest risk adjusted visit frequencies would require more follow-up slots than currently available when new patient no-show rates and annual patient loss are included. Patients seen at an intermediate or lower visit risk adjusted frequency could be accommodated when new patient no-show rates and annual patient clinic loss are considered. Value-based care is driven by control of cost while maintaining quality of care. In order to control cost, there has been a drive to increase visit frequency in primary care for those patients at increased risk. Adding new patients to primary care clinics limits the availability of follow-up slots that accrue over time for those at highest risk, thereby limiting disease and, potentially, cost control. If frequency of established care visits can be reduced by improved disease control, closing the practice to new patients, hiring health care extenders, or providing non-face to face care models then quality and cost of care may be improved. © 2018 John Wiley & Sons, Ltd.

SIS and SIR epidemic models under virtual dispersal

PubMed Central

Bichara, Derdei; Kang, Yun; Castillo-Chavez, Carlos; Horan, Richard; Perrings, Charles

2015-01-01

We develop a multi-group epidemic framework via virtual dispersal where the risk of infection is a function of the residence time and local environmental risk. This novel approach eliminates the need to define and measure contact rates that are used in the traditional multi-group epidemic models with heterogeneous mixing. We apply this approach to a general n-patch SIS model whose basic reproduction number R0 is computed as a function of a patch residence-times matrix ℙ. Our analysis implies that the resulting n-patch SIS model has robust dynamics when patches are strongly connected: there is a unique globally stable endemic equilibrium when R0 > 1 while the disease free equilibrium is globally stable when R0 ≤ 1. Our further analysis indicates that the dispersal behavior described by the residence-times matrix ℙ has profound effects on the disease dynamics at the single patch level with consequences that proper dispersal behavior along with the local environmental risk can either promote or eliminate the endemic in particular patches. Our work highlights the impact of residence times matrix if the patches are not strongly connected. Our framework can be generalized in other endemic and disease outbreak models. As an illustration, we apply our framework to a two-patch SIR single outbreak epidemic model where the process of disease invasion is connected to the final epidemic size relationship. We also explore the impact of disease prevalence driven decision using a phenomenological modeling approach in order to contrast the role of constant versus state dependent ℙ on disease dynamics. PMID:26489419

Growth Models of Dyadic Synchrony and Mother-Child Vagal Tone in the Context of Parenting At-Risk

PubMed Central

Giuliano, Ryan J.; Skowron, Elizabeth A.; Berkman, Elliot T.

2015-01-01

We used multilevel modeling to examine dynamic changes in respiratory sinus arrhythmia (RSA) and observer-coded interactive synchrony for mother-child dyads engaged in a laboratory interaction, to characterize parenting-at-risk. Seventy-nine preschooler-mother dyads including a subset with documented child maltreatment (CM; n=43) were observed completing a joint puzzle task while physiological measures were recorded. Dyads led by CM mothers showed decreases in positive synchrony over time, whereas no variation was observed in non-CM dyads. Growth models of maternal RSA indicated that mothers who maintained high levels of positive interactive synchrony with their child evidenced greater RSA reactivity, characterized by an initial withdrawal followed by augmentation as the task progressed, after accounting for CM group status. These results help to clarify patterns of RSA responding in the context of caregiver-child interactions, and demonstrate the importance of modeling dynamic changes in physiology over time in order to better understanding biological correlates of parenting-at-risk. PMID:25542759

Rollover risk prediction of heavy vehicles by reliability index and empirical modelling

NASA Astrophysics Data System (ADS)

Sellami, Yamine; Imine, Hocine; Boubezoul, Abderrahmane; Cadiou, Jean-Charles

2018-03-01

This paper focuses on a combination of a reliability-based approach and an empirical modelling approach for rollover risk assessment of heavy vehicles. A reliability-based warning system is developed to alert the driver to a potential rollover before entering into a bend. The idea behind the proposed methodology is to estimate the rollover risk by the probability that the vehicle load transfer ratio (LTR) exceeds a critical threshold. Accordingly, a so-called reliability index may be used as a measure to assess the vehicle safe functioning. In the reliability method, computing the maximum of LTR requires to predict the vehicle dynamics over the bend which can be in some cases an intractable problem or time-consuming. With the aim of improving the reliability computation time, an empirical model is developed to substitute the vehicle dynamics and rollover models. This is done by using the SVM (Support Vector Machines) algorithm. The preliminary obtained results demonstrate the effectiveness of the proposed approach.

Understanding the effects of different HIV transmission models in individual-based microsimulation of HIV epidemic dynamics in people who inject drugs

PubMed Central

MONTEIRO, J.F.G.; ESCUDERO, D.J.; WEINREB, C.; FLANIGAN, T.; GALEA, S.; FRIEDMAN, S.R.; MARSHALL, B.D.L.

2017-01-01

SUMMARY We investigated how different models of HIV transmission, and assumptions regarding the distribution of unprotected sex and syringe-sharing events (‘risk acts’), affect quantitative understanding of HIV transmission process in people who inject drugs (PWID). The individual-based model simulated HIV transmission in a dynamic sexual and injecting network representing New York City. We constructed four HIV transmission models: model 1, constant probabilities; model 2, random number of sexual and parenteral acts; model 3, viral load individual assigned; and model 4, two groups of partnerships (low and high risk). Overall, models with less heterogeneity were more sensitive to changes in numbers risk acts, producing HIV incidence up to four times higher than that empirically observed. Although all models overestimated HIV incidence, micro-simulations with greater heterogeneity in the HIV transmission modelling process produced more robust results and better reproduced empirical epidemic dynamics. PMID:26753627

[Occupational stress in assembly line workers in electronics manufacturing service and related influencing factors].

PubMed

Ji, Y Q; Li, S; Wang, C; Wang, J; Liu, X M

2016-10-20

Objective: To investigate occupational stress in assembly line workers in electronics manu-facturing service (EMS) and related influencing factors. Methods: From June to October, 2015, a cross-sectional survey was performed for 5 944 assembly line workers in EMS (observation group) and 6 270 workers from other posts (non-assembly line workers and management personnel; control group) using the self-made questionnaire for basic information, job demand-control (JDC) model questionnaire, and effort-reward imbalance (ERI) model questionnaire to collect respondents' basic information and occupational stress. Results: The observation group had significantly lower work autonomy, social support, and work reward scores than the control group (2.72 ± 0.63/3.64 ± 0.68/4.06 ± 0.80 vs 3.00 ± 0.67/3.83 ± 0.68/4.24 ± 0.75, t =23.53, 15.41, and 12.70, all P <0.05) , as well as significantly higher work effort and job involvement scores than the control group (2.34±0.78/2.48±0.78 vs 2.21±0.80/2.33±0.77, t =-9.08 and-10.90, both P <0.05). The observation group had significantly higher proportions of workers with occupational stress determined by JDC and ERI models than the control group (64.5%/12.7% vs 52.6%/9.9%, χ 2 =182.26 and 23.41, both P <0.05). Female sex, migrant workers, working time >60 hours/week, and sleeping time <7 hours/day were major risk factors for occupational stress in JDC model; education background of Bachelor's degree or above, working time >60 hours/week, and sleeping time<7 hours/day were major risk factors for occupational stress in ERI model, while female sex and a high monthly income reduced the risk of occupational stress in ERI model. Conclusion: Assembly line workers in EMS are a relatively vulnerable group and have a high degree of occupational stress. Working time >60 hours/week and sleeping time <7 hours/day are major risk factors for occupational stress.

A Risk and Maintenance Model for Bulimia Nervosa: From Impulsive Action to Compulsive Behavior

PubMed Central

Pearson, Carolyn M.; Wonderlich, Stephen A.; Smith, Gregory T.

2015-01-01

This paper offers a new model for bulimia nervosa (BN) that explains both the initial impulsive nature of binge eating and purging as well as the compulsive quality of the fully developed disorder. The model is based on a review of advances in research on BN and advances in relevant basic psychological science. It integrates transdiagnostic personality risk, eating disorder specific risk, reinforcement theory, cognitive neuroscience, and theory drawn from the drug addiction literature. We identify both a state-based and a trait-based risk pathway, and we then propose possible state-by-trait interaction risk processes. The state-based pathway emphasizes depletion of self-control. The trait-based pathway emphasizes transactions between the trait of negative urgency (the tendency to act rashly when distressed) and high-risk psychosocial learning. We then describe a process by which initially impulsive BN behaviors become compulsive over time, and we consider the clinical implications of our model. PMID:25961467

Modeling a 15-min extravehicular activity prebreathe protocol using NASA's exploration atmosphere (56.5 kPa/34% O2)

NASA Astrophysics Data System (ADS)

Abercromby, Andrew F. J.; Conkin, Johnny; Gernhardt, Michael L.

2015-04-01

NASA's plans for future human exploration missions utilize a new atmosphere of 56.5 kPa (8.2 psia), 34% O2, 66% N2 to enable rapid extravehicular activity (EVA) capability with minimal gas losses; however, existing EVA prebreathe protocols to mitigate risk of decompression sickness (DCS) are not applicable to the new exploration atmosphere. We provide preliminary analysis of a 15-min prebreathe protocol and examine the potential benefits of intermittent recompression (IR) and an abbreviated N2 purge on crew time and gas consumables usage. A probabilistic model of decompression stress based on an established biophysical model of DCS risk was developed, providing significant (p<0.0001) prediction and goodness-of-fit with 84 cases of DCS in 668 human altitude exposures including a variety of pressure profiles. DCS risk for a 15-min prebreathe protocol was then estimated under different exploration EVA scenarios. Estimated DCS risk for all EVA scenarios modeled using the 15-min prebreathe protocol ranged between 6.1% and 12.1%. Supersaturation in neurological tissues (5- and 10-min half-time compartments) is prevented and tissue tensions in faster half-time compartments (≤40 min), where the majority of whole-body N2 is located, are reduced to about the levels (30.0 vs. 27.6 kPa) achieved during a standard Shuttle prebreathe protocol. IR reduced estimated DCS risk from 9.7% to 7.9% (1.8% reduction) and from 8.4% to 6.1% (2.3% reduction) for the scenarios modeled; the penalty of N2 reuptake during IR may be outweighed by the benefit of decreased bubble size. Savings of 75% of purge gas and time (0.22 kg gas and 6 min of crew time per person per EVA) are achievable by abbreviating the EVA suit purge to 20% N2 vs. 5% N2 at the expense of an increase in estimated DCS risk from 9.7% to 12.1% (2.4% increase). A 15-min prebreathe protocol appears feasible using the new exploration atmosphere. IR between EVAs may enable reductions in suit purge and prebreathe requirements, decompression stress, and/or suit operating pressures. Ground trial validation is required before operational implementation.

Improving the use of crop models for risk assessment and climate change adaptation.

PubMed

Challinor, Andrew J; Müller, Christoph; Asseng, Senthold; Deva, Chetan; Nicklin, Kathryn Jane; Wallach, Daniel; Vanuytrecht, Eline; Whitfield, Stephen; Ramirez-Villegas, Julian; Koehler, Ann-Kristin

2018-01-01

Crop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1.Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk?2.Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output.3.Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper.

Patients with Testicular Cancer Undergoing CT Surveillance Demonstrate a Pitfall of Radiation-induced Cancer Risk Estimates: The Timing Paradox

PubMed Central

Eisenberg, Jonathan D.; Lee, Richard J.; Gilmore, Michael E.; Turan, Ekin A.; Singh, Sarabjeet; Kalra, Mannudeep K.; Liu, Bob; Kong, Chung Yin; Gazelle, G. Scott

2013-01-01

Purpose: To demonstrate a limitation of lifetime radiation-induced cancer risk metrics in the setting of testicular cancer surveillance—in particular, their failure to capture the delayed timing of radiation-induced cancers over the course of a patient’s lifetime. Materials and Methods: Institutional review board approval was obtained for the use of computed tomographic (CT) dosimetry data in this study. Informed consent was waived. This study was HIPAA compliant. A Markov model was developed to project outcomes in patients with testicular cancer who were undergoing CT surveillance in the decade after orchiectomy. To quantify effects of early versus delayed risks, life expectancy losses and lifetime mortality risks due to testicular cancer were compared with life expectancy losses and lifetime mortality risks due to radiation-induced cancers from CT. Projections of life expectancy loss, unlike lifetime risk estimates, account for the timing of risks over the course of a lifetime, which enabled evaluation of the described limitation of lifetime risk estimates. Markov chain Monte Carlo methods were used to estimate the uncertainty of the results. Results: As an example of evidence yielded, 33-year-old men with stage I seminoma who were undergoing CT surveillance were projected to incur a slightly higher lifetime mortality risk from testicular cancer (598 per 100 000; 95% uncertainty interval [UI]: 302, 894) than from radiation-induced cancers (505 per 100 000; 95% UI: 280, 730). However, life expectancy loss attributable to testicular cancer (83 days; 95% UI: 42, 124) was more than three times greater than life expectancy loss attributable to radiation-induced cancers (24 days; 95% UI: 13, 35). Trends were consistent across modeled scenarios. Conclusion: Lifetime radiation risk estimates, when used for decision making, may overemphasize radiation-induced cancer risks relative to short-term health risks. © RSNA, 2012 Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12121015/-/DC1 PMID:23249573

Recharge heterogeneity and high intensity rainfall events increase contamination risk for Mediterranean groundwater resources

NASA Astrophysics Data System (ADS)

Hartmann, Andreas; Jasechko, Scott; Gleeson, Tom; Wada, Yoshihide; Andreo, Bartolomé; Barberá, Juan Antonio; Brielmann, Heike; Charlier, Jean-Baptiste; Darling, George; Filippini, Maria; Garvelmann, Jakob; Goldscheider, Nico; Kralik, Martin; Kunstmann, Harald; Ladouche, Bernard; Lange, Jens; Mudarra, Matías; Francisco Martín, José; Rimmer, Alon; Sanchez, Damián; Stumpp, Christine; Wagener, Thorsten

2017-04-01

Karst develops through the dissolution of carbonate rock and results in pronounced spatiotemporal heterogeneity of hydrological processes. Karst groundwater in Europe is a major source of fresh water contributing up to half of the total drinking water supply in some countries like Austria or Slovenia. Previous work showed that karstic recharge processes enhance and alter the sensitivity of recharge to climate variability. The enhanced preferential flow from the surface to the aquifer may be followed by enhanced risk of groundwater contamination. In this study we assess the contamination risk of karst aquifers over Europe and the Mediterranean using simulated transit time distributions. Using a new type of semi-distributed model that considers the spatial heterogeneity of karst hydraulic properties, we were able to simulate karstic groundwater recharge including its heterogeneous spatiotemporal dynamics. The model is driven by gridded daily climate data from the Global Land Data Assimilation System (GLDAS). Transit time distributions are calculated using virtual tracer experiments. We evaluated our simulations by independent information on transit times derived from observed time series of water isotopes of >70 karst springs over Europe. The simulations indicate that, compared to humid, mountain and desert regions, the Mediterranean region shows a stronger risk of contamination in Europe because preferential flow processes are most pronounced given thin soil layers and the seasonal abundance of high intensity rainfall events in autumn and winter. Our modelling approach includes strong simplifications and its results cannot easily be generalized but it still highlights that the combined effects of variable climate and heterogeneous catchment properties constitute a strong risk on water quality.

Modeling individual movement decisions of brown hare (Lepus europaeus) as a key concept for realistic spatial behavior and exposure: A population model for landscape-level risk assessment.

PubMed

Kleinmann, Joachim U; Wang, Magnus

2017-09-01

Spatial behavior is of crucial importance for the risk assessment of pesticides and for the assessment of effects of agricultural practice or multiple stressors, because it determines field use, exposition, and recovery. Recently, population models have increasingly been used to understand the mechanisms driving risk and recovery or to conduct landscape-level risk assessments. To include spatial behavior appropriately in population models for use in risk assessments, a new method, "probabilistic walk," was developed, which simulates the detailed daily movement of individuals by taking into account food resources, vegetation cover, and the presence of conspecifics. At each movement step, animals decide where to move next based on probabilities being determined from this information. The model was parameterized to simulate populations of brown hares (Lepus europaeus). A detailed validation of the model demonstrated that it can realistically reproduce various natural patterns of brown hare ecology and behavior. Simulated proportions of time animals spent in fields (PT values) were also comparable to field observations. It is shown that these important parameters for the risk assessment may, however, vary in different landscapes. The results demonstrate the value of using population models to reduce uncertainties in risk assessment and to better understand which factors determine risk in a landscape context. Environ Toxicol Chem 2017;36:2299-2307. © 2017 SETAC. © 2017 SETAC.

Frequency, Type, and Volume of Leisure-Time Physical Activity and Risk of Coronary Heart Disease in Young Women.

PubMed

Chomistek, Andrea K; Henschel, Beate; Eliassen, A Heather; Mukamal, Kenneth J; Rimm, Eric B

2016-07-26

The inverse association between physical activity and coronary heart disease (CHD) risk has primarily been shown in studies of middle-aged and older adults. Evidence for the benefits of frequency, type, and volume of leisure-time physical activity in young women is limited. We conducted a prospective analysis among 97 230 women aged 27 to 44 years at baseline in 1991. Leisure-time physical activity was assessed biennially by questionnaire. Cox proportional hazards models were used to examine the associations between physical activity frequency, type, and volume, and CHD risk. During 20 years of follow-up, we documented 544 incident CHD cases. In multivariable-adjusted models, the hazard ratio (95% confidence interval) of CHD comparing ≥30 with <1 metabolic equivalent of task-hours/wk of physical activity was 0.75 (0.57-0.99) (P, trend=0.01). Brisk walking alone was also associated with significantly lower CHD risk. Physical activity frequency was not associated with CHD risk when models also included overall activity volume. Finally, the association was not modified by body mass index (kg/m(2)) (P, interaction=0.70). Active women (≥30 metabolic equivalent of task-hours/wk) with body mass index<25 kg/m(2) had 0.52 (95% confidence interval, 0.35-0.78) times the rate of CHD in comparison with women who were obese (body mass index≥30 kg/m(2)) and inactive (physical activity <1 metabolic equivalent of task-hours/wk). These prospective data suggest that total volume of leisure-time physical activity is associated with lower risk of incident CHD among young women. In addition, this association was not modified by weight, emphasizing that it is important for normal weight, overweight, and obese women to be physically active. © 2016 American Heart Association, Inc.

How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.

PubMed

Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall

2016-01-01

Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

Building Assets Reducing Risks: Academic Success for All Students through Positive Relationships and Use of Real-Time Data

ERIC Educational Resources Information Center

Corsello, Maryann; Sharma, Anu; Jerabek, Angela

2015-01-01

Building Assets Reducing Risks (BARR) is a social emotional model that achieves academic outcomes through combining use of real-time student data with proven relationship-building strategies and intensive teacher collaboration to prevent course failure. BARR is a recipient of US Department of Education "Investing in Innovation (i3)"…

Statistical correlations and risk analyses techniques for a diving dual phase bubble model and data bank using massively parallel supercomputers.

PubMed

Wienke, B R; O'Leary, T R

2008-05-01

Linking model and data, we detail the LANL diving reduced gradient bubble model (RGBM), dynamical principles, and correlation with data in the LANL Data Bank. Table, profile, and meter risks are obtained from likelihood analysis and quoted for air, nitrox, helitrox no-decompression time limits, repetitive dive tables, and selected mixed gas and repetitive profiles. Application analyses include the EXPLORER decompression meter algorithm, NAUI tables, University of Wisconsin Seafood Diver tables, comparative NAUI, PADI, Oceanic NDLs and repetitive dives, comparative nitrogen and helium mixed gas risks, USS Perry deep rebreather (RB) exploration dive,world record open circuit (OC) dive, and Woodville Karst Plain Project (WKPP) extreme cave exploration profiles. The algorithm has seen extensive and utilitarian application in mixed gas diving, both in recreational and technical sectors, and forms the bases forreleased tables and decompression meters used by scientific, commercial, and research divers. The LANL Data Bank is described, and the methods used to deduce risk are detailed. Risk functions for dissolved gas and bubbles are summarized. Parameters that can be used to estimate profile risk are tallied. To fit data, a modified Levenberg-Marquardt routine is employed with L2 error norm. Appendices sketch the numerical methods, and list reports from field testing for (real) mixed gas diving. A Monte Carlo-like sampling scheme for fast numerical analysis of the data is also detailed, as a coupled variance reduction technique and additional check on the canonical approach to estimating diving risk. The method suggests alternatives to the canonical approach. This work represents a first time correlation effort linking a dynamical bubble model with deep stop data. Supercomputing resources are requisite to connect model and data in application.

Alcohol Use and Sexual Risk Behaviors in a Migrant Worker Community.

PubMed

McCoy, H Virginia; Shehadeh, Nancy; Rubens, Muni

2016-06-01

There are not many studies exploring the association between alcohol use and risky sexual behaviors among migrant workers. This study analyzed how changes in alcohol use was associated with changes in risky sexual behavior and psychosocial variables. Data for this study was drawn from an HIV risk reduction project. Repeated measures ANOVA and Linear mixed model statistical method was conducted to find changes and association between alcohol use, sexual risk and psychosocial variables over time. The sample (n = 203) was composed of African Americans (33.0 %) and Hispanics (77.0 %) men. Both groups, over time, showed reduction in sexual risk in accordance with reduction in alcohol use. Changes in alcohol use and psychosocial variables showed significant association with sexual risk changes over time. Psychological strategies like building social support should be considered for HIV risk reduction intervention directed towards high alcohol consuming migrant workers.

Musculoskeletal Modeling Component of the NASA Digital Astronaut Project

NASA Technical Reports Server (NTRS)

Lewandowski, B. E.; Pennline, J. A.; Stalker, A. R.; Mulugeta, L.; Myers, J. G.

2011-01-01

The NASA Digital Astronaut Project s (DAP) objective is to provide computational tools that support research of the physiological response to low gravity environments and analyses of how changes cause health and safety risks to the astronauts and to the success of the mission. The spaceflight risk associated with muscle atrophy is impaired performance due to reduced muscle mass, strength and endurance. Risks of early onset of osteoporosis and bone fracture are among the spaceflight risks associated with loss of bone mineral density. METHODS: Tools under development include a neuromuscular model, a biomechanical model and a bone remodeling model. The neuromuscular model will include models of neuromuscular drive, muscle atrophy, fiber morphology and metabolic processes as a function of time in space. Human movement will be modeled with the biomechanical model, using muscle and bone model parameters at various states. The bone remodeling model will allow analysis of bone turnover, loss and adaptation. A comprehensive trade study was completed to identify the current state of the art in musculoskeletal modeling. The DAP musculoskeletal models will be developed using a combination of existing commercial software and academic research codes identified in the study, which will be modified for use in human spaceflight research. These individual models are highly dependent upon each other and will be integrated together once they reach sufficient levels of maturity. ANALYSES: The analyses performed with these models will include comparison of different countermeasure exercises for optimizing effectiveness and comparison of task requirements and the state of strength and endurance of a crew member at a particular time in a mission. DISCUSSION: The DAP musculoskeletal model has the potential to complement research conducted on spaceflight induced changes to the musculoskeletal system. It can help with hypothesis formation, identification of causative mechanisms and supplementing small data samples.

A statistical regression model for the estimation of acrylamide concentrations in French fries for excess lifetime cancer risk assessment.

PubMed

Chen, Ming-Jen; Hsu, Hui-Tsung; Lin, Cheng-Li; Ju, Wei-Yuan

2012-10-01

Human exposure to acrylamide (AA) through consumption of French fries and other foods has been recognized as a potential health concern. Here, we used a statistical non-linear regression model, based on the two most influential factors, cooking temperature and time, to estimate AA concentrations in French fries. The R(2) of the predictive model is 0.83, suggesting the developed model was significant and valid. Based on French fry intake survey data conducted in this study and eight frying temperature-time schemes which can produce tasty and visually appealing French fries, the Monte Carlo simulation results showed that if AA concentration is higher than 168 ppb, the estimated cancer risk for adolescents aged 13-18 years in Taichung City would be already higher than the target excess lifetime cancer risk (ELCR), and that by taking into account this limited life span only. In order to reduce the cancer risk associated with AA intake, the AA levels in French fries might have to be reduced even further if the epidemiological observations are valid. Our mathematical model can serve as basis for further investigations on ELCR including different life stages and behavior and population groups. Copyright © 2012 Elsevier Ltd. All rights reserved.

Using multiple decrement models to estimate risk and morbidity from specific AIDS illnesses. Multicenter AIDS Cohort Study (MACS).

PubMed

Hoover, D R; Peng, Y; Saah, A J; Detels, R R; Day, R S; Phair, J P

A simple non-parametric approach is developed to simultaneously estimate net incidence and morbidity time from specific AIDS illnesses in populations at high risk for death from these illnesses and other causes. The disease-death process has four-stages that can be recast as two sandwiching three-state multiple decrement processes. Non-parametric estimation of net incidence and morbidity time with error bounds are achieved from these sandwiching models through modification of methods from Aalen and Greenwood, and bootstrapping. An application to immunosuppressed HIV-1 infected homosexual men reveals that cytomegalovirus disease, Kaposi's sarcoma and Pneumocystis pneumonia are likely to occur and cause significant morbidity time.

A System Dynamics Model for Planning Cardiovascular Disease Interventions

PubMed Central

Homer, Jack; Evans, Elizabeth; Zielinski, Ann

2010-01-01

Planning programs for the prevention and treatment of cardiovascular disease (CVD) is a challenge to every community that wants to make the best use of its limited resources. Selecting programs that provide the greatest impact is difficult because of the complex set of causal pathways and delays that link risk factors to CVD. We describe a system dynamics simulation model developed for a county health department that incorporates and tracks the effects of those risk factors over time on both first-time and recurrent events. We also describe how the model was used to evaluate the potential impacts of various intervention strategies for reducing the county's CVD burden and present the results of those policy tests. PMID:20167899

A Hydrological Modeling Framework for Flood Risk Assessment for Japan

NASA Astrophysics Data System (ADS)

Ashouri, H.; Chinnayakanahalli, K.; Chowdhary, H.; Sen Gupta, A.

2016-12-01

Flooding has been the most frequent natural disaster that claims lives and imposes significant economic losses to human societies worldwide. Japan, with an annual rainfall of up to approximately 4000 mm is extremely vulnerable to flooding. The focus of this research is to develop a macroscale hydrologic model for simulating flooding toward an improved understanding and assessment of flood risk across Japan. The framework employs a conceptual hydrological model, known as the Probability Distributed Model (PDM), as well as the Muskingum-Cunge flood routing procedure for simulating streamflow. In addition, a Temperature-Index model is incorporated to account for snowmelt and its contribution to streamflow. For an efficient calibration of the model, in terms of computational timing and convergence of the parameters, a set of A Priori parameters is obtained based on the relationships between the model parameters and the physical properties of watersheds. In this regard, we have implemented a particle tracking algorithm and a statistical model which use high resolution Digital Terrain Models to estimate different time related parameters of the model such as time to peak of the unit hydrograph. In addition, global soil moisture and depth data are used to generate A Priori estimation of maximum soil moisture capacity, an important parameter of the PDM model. Once the model is calibrated, its performance is examined during the Typhoon Nabi which struck Japan in September 2005 and caused severe flooding throughout the country. The model is also validated for the extreme precipitation event in 2012 which affected Kyushu. In both cases, quantitative measures show that simulated streamflow depicts good agreement with gauge-based observations. The model is employed to simulate thousands of possible flood events for the entire Japan which makes a basis for a comprehensive flood risk assessment and loss estimation for the flood insurance industry.

[LaboRisCh: an algorithm for assessment of health risks due to chemicals in research laboratories and similar workplaces].

PubMed

Strafella, Elisabetta; Bracci, M; Calisti, R; Governa, M; Santarelli, Lory

2008-01-01

Chemical risk assessment in research laboratories is complicated by factors such as the large number of agents to be considered, each present in small quantities, and the very short and erratic periods of exposure, all of which make reliable environmental and biological monitoring particularly difficult and at times impossible. In such environments, a preliminary evaluation procedure based on algorithms would be useful to establish the hazard potential of a given situation and to guide the appropriate intervention. The LaboRisCh model was expressly designed to assess the health risk due to chemicals in research laboratories and similar workplaces. The model is based on the calculation of the value of a synthetic single risk index for each substance and compound found in a laboratory and, subsequently, of a further synthetic single risk index for the whole laboratory or, where required, a section thereof. This makes LaboRisCh a compromise between need for information, ease of use, and resources required for the assessment. The risk index includes several items, chiefly the physical and chemical properties, intrinsic hazard potential, amount, dilution, and time of exposure to each agent; waste management; possible interactions; presence and efficiency of collective and individual protection devices, and staff training in good laboratory practices. The value of the synthetic single index corresponds to one of three areas: no risk (green), possible risk (yellow), and certain risk (red). Preliminary data confirm the model. LaboRisCh appears to be a reliable method for chemical risk assessment in research laboratories and similar workplaces.

Spatially varying density dependence drives a shifting mosaic of survival in a recovering apex predator (Canis lupus).

PubMed

O'Neil, Shawn T; Bump, Joseph K; Beyer, Dean E

2017-11-01

Understanding landscape patterns in mortality risk is crucial for promoting recovery of threatened and endangered species. Humans affect mortality risk in large carnivores such as wolves ( Canis lupus ), but spatiotemporally varying density dependence can significantly influence the landscape of survival. This potentially occurs when density varies spatially and risk is unevenly distributed. We quantified spatiotemporal sources of variation in survival rates of gray wolves ( C. lupus ) during a 21-year period of population recovery in the Upper Peninsula of Michigan, USA. We focused on mapping risk across time using Cox Proportional Hazards (CPH) models with time-dependent covariates, thus exploring a shifting mosaic of survival. Extended CPH models and time-dependent covariates revealed influences of seasonality, density dependence and experience, as well as individual-level factors and landscape predictors of risk. We used results to predict the shifting landscape of risk at the beginning, middle, and end of the wolf recovery time series. Survival rates varied spatially and declined over time. Long-term change was density-dependent, with landscape predictors such as agricultural land cover and edge densities contributing negatively to survival. Survival also varied seasonally and depended on individual experience, sex, and resident versus transient status. The shifting landscape of survival suggested that increasing density contributed to greater potential for human conflict and wolf mortality risk. Long-term spatial variation in key population vital rates is largely unquantified in many threatened, endangered, and recovering species. Variation in risk may indicate potential for source-sink population dynamics, especially where individuals preemptively occupy suitable territories, which forces new individuals into riskier habitat types as density increases. We encourage managers to explore relationships between adult survival and localized changes in population density. Density-dependent risk maps can identify increasing conflict areas or potential habitat sinks which may persist due to high recruitment in adjacent habitats.

The role of time and risk preferences in adherence to physician advice on health behavior change.

PubMed

van der Pol, Marjon; Hennessy, Deirdre; Manns, Braden

2017-04-01

Changing physical activity and dietary behavior in chronic disease patients is associated with significant health benefits but is difficult to achieve. An often-used strategy is for the physician or other health professional to encourage behavior changes by providing advice on the health consequences of such behaviors. However, adherence to advice on health behavior change varies across individuals. This paper uses data from a population-based cross-sectional survey of 1849 individuals with chronic disease to explore whether differences in individuals' time and risk preferences can help explain differences in adherence. Health behaviors are viewed as investments in health capital within the Grossman model. Physician advice plays a role in the model in that it improves the understanding of the future health consequences of investments. It can be hypothesized that the effect of advice on health behavior will depend on an individuals' time and risk preference. Within the survey, which measured a variety of health-related behaviors and outcomes, including receipt and compliance with advice on dietary and physical activity changes, time preferences were measured using financial planning horizon, and risk preferences were measured through a commonly used question which asked respondents to indicate their willingness to take risks on a ten-point scale. Results suggest that time preferences play a role in adherence to physical activity advice. While time preferences also play a role in adherence to dietary advice, this effect is only apparent for males. Risk preferences do not seem to be associated with adherence. The results suggest that increasing the salience of more immediate benefits of health behavior change may improve adherence.

Risk is still relevant: Time-varying associations between perceived risk and marijuana use among US 12th grade students from 1991 to 2016.

PubMed

Terry-McElrath, Yvonne M; O'Malley, Patrick M; Patrick, Megan E; Miech, Richard A

2017-11-01

Perceived risk of harm has long been a key preventive factor for adolescent marijuana use. However, in recent years, perceived risk has decreased markedly and marijuana use has increased only slightly, leading to new questions about their association. This study investigates the magnitude and stability of the US adolescent marijuana risk/use association from 1991 to 2016, overall and by gender and race/ethnicity. Self-reported data on past 12-month marijuana use, perceived risk of regular marijuana use, gender, and race/ethnicity were obtained from 275,768 US 12th grade students participating in the nationally representative Monitoring the Future study. Time-varying effect modeling (TVEM) was used to examine the marijuana risk/use association over time. Both before and after controlling for gender and race/ethnicity, perceived risk was a strong protective factor against adolescent marijuana use. The magnitude of the great risk/use association strengthened for Hispanic students; remained generally stable over time for 12th graders overall, males, females, and White students; and weakened for Black students. The magnitude of the moderate risk/use association strengthened for 12th graders overall, males, females, White and Hispanic students, but did not continue to strengthen for Black students from 2005 onwards. In general, marijuana use prevalence decreased over time within all levels of perceived risk. Perceived risk remains a strong protective factor for adolescent marijuana use, and the protective association for moderate risk (vs. no/slight risk) is actually increasing over time. Results suggest that accurate and credible information on the risks associated with marijuana use should remain a key component of prevention efforts. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cognitive and affective influences on perceived risk of ovarian cancer.

PubMed

Peipins, Lucy A; McCarty, Frances; Hawkins, Nikki A; Rodriguez, Juan L; Scholl, Lawrence E; Leadbetter, Steven

2015-03-01

Studies suggest that both affective and cognitive processes are involved in the perception of vulnerability to cancer and that affect has an early influence in this assessment of risk. We constructed a path model based on a conceptual framework of heuristic reasoning (affect, resemblance, and availability) coupled with cognitive processes involved in developing personal models of cancer causation. From an eligible cohort of 16 700 women in a managed care organization, we randomly selected 2524 women at high, elevated, and average risk of ovarian cancer and administered a questionnaire to test our model (response rate 76.3%). Path analysis delineated the relationships between personal and cognitive characteristics (number of relatives with cancer, age, ideas about cancer causation, perceived resemblance to an affected friend or relative, and ovarian cancer knowledge) and emotional constructs (closeness to an affected relative or friend, time spent processing the cancer experience, and cancer worry) on perceived risk of ovarian cancer. Our final model fit the data well (root mean square error of approximation (RMSEA) = 0.028, comparative fit index (CFI) = 0.99, normed fit index (NFI) = 0.98). This final model (1) demonstrated the nature and direction of relationships between cognitive characteristics and perceived risk; (2) showed that time spent processing the cancer experience was associated with cancer worry; and (3) showed that cancer worry moderately influenced perceived risk. Our results highlight the important role that family cancer experience has on cancer worry and shows how cancer experience translates into personal risk perceptions. This understanding informs the discordance between medical or objective risk assessment and personal risk assessment. Published in 2014. This article is a U.S. Government work and is in the public domain in the USA. Published in 2014. This article is a U.S. Government work and is in the public domain in the USA.

Tsunami evacuation analysis, modelling and planning: application to the coastal area of El Salvador

NASA Astrophysics Data System (ADS)

Gonzalez-Riancho, Pino; Aguirre-Ayerbe, Ignacio; Aniel-Quiroga, Iñigo; Abad Herrero, Sheila; González Rodriguez, Mauricio; Larreynaga, Jeniffer; Gavidia, Francisco; Quetzalcoalt Gutiérrez, Omar; Álvarez-Gómez, Jose Antonio; Medina Santamaría, Raúl

2014-05-01

Advances in the understanding and prediction of tsunami impacts allow the development of risk reduction strategies for tsunami-prone areas. Conducting adequate tsunami risk assessments is essential, as the hazard, vulnerability and risk assessment results allow the identification of adequate, site-specific and vulnerability-oriented risk management options, with the formulation of a tsunami evacuation plan being one of the main expected results. An evacuation plan requires the analysis of the territory and an evaluation of the relevant elements (hazard, population, evacuation routes, and shelters), the modelling of the evacuation, and the proposal of alternatives for those communities located in areas with limited opportunities for evacuation. Evacuation plans, which are developed by the responsible authorities and decision makers, would benefit from a clear and straightforward connection between the scientific and technical information from tsunami risk assessments and the subsequent risk reduction options. Scientifically-based evacuation plans would translate into benefits for the society in terms of mortality reduction. This work presents a comprehensive framework for the formulation of tsunami evacuation plans based on tsunami vulnerability assessment and evacuation modelling. This framework considers (i) the hazard aspects (tsunami flooding characteristics and arrival time), (ii) the characteristics of the exposed area (people, shelters and road network), (iii) the current tsunami warning procedures and timing, (iv) the time needed to evacuate the population, and (v) the identification of measures to improve the evacuation process, such as the potential location for vertical evacuation shelters and alternative routes. The proposed methodological framework aims to bridge the gap between risk assessment and risk management in terms of tsunami evacuation, as it allows for an estimation of the degree of evacuation success of specific management options, as well as for the classification and prioritization of the gathered information, in order to formulate an optimal evacuation plan. The framework has been applied to the El Salvador case study through the project "Tsunami Hazard and Risk Assessment in El Salvador", funded by AECID during the period 2009-12, demonstrating its applicability to site-specific response times and population characteristics.

Application of tissue time course data to elucidate mechanistic details of carbon tetrachloride (CC14) transport using an updated physiologically based pharmacokinetic (PBPK) model in rats

EPA Science Inventory

CCl4 is a common environmental contaminant in water and superfund sites, and a model liver toxicant. One application of PBPK models used in risk assessment is simulation of internal dose for the metric involved with toxicity, particularly for different routes of exposure. Time-co...

D-dimer levels over time and the risk of recurrent venous thromboembolism: an update of the Vienna prediction model.

PubMed

Eichinger, Sabine; Heinze, Georg; Kyrle, Paul A

2014-01-02

Patients with unprovoked venous thromboembolism (VTE) can be stratified according to their recurrence risk based on their sex, the VTE location, and D-dimer measured 3 weeks after anticoagulation by the Vienna Prediction Model. We aimed to expand the model to also assess the recurrence risk from later points on. Five hundred and fifty-three patients with a first VTE were followed for a median of 68 months. We excluded patients with VTE provoked by a transient risk factor or female hormone intake, with a natural inhibitor deficiency, the lupus anticoagulant, or cancer. The study end point was recurrent VTE, which occurred in 150 patients. D-dimer levels did not substantially increase over time. Subdistribution hazard ratios (95% confidence intervals) dynamically changed from 2.43 (1.57 to 3.77) at 3 weeks to 2.27 (1.48 to 3.48), 1.98 (1.30 to 3.02) , and 1.73 (1.11 to 2.69) at 3, 9, and 15 months in men versus women, from 1.84 (1.00 to 3.43) to 1.68 (0.91 to 3.10), 1.49 (0.79 to 2.81) , and 1.44 (0.76 to 2.72) in patients with proximal deep vein thrombosis or pulmonary embolism compared with calf vein thrombosis, and from 1.30 (1.07 to 1.58) to 1.27 (1.06 to 1.51), 1.20 (1.02 to 1.41), and 1.13 (0.95 to 1.36) per doubling D-dimer. Using a dynamic landmark competing risks regression approach, we generated nomograms and a web-based calculator to calculate risk scores and recurrence rates from multiple times after anticoagulation. Risk of recurrent VTE after discontinuation of anticoagulation can be predicted from multiple random time points by integrating the patient's sex, location of first VTE, and serial D-dimer measurements.

Human immunodeficiency virus prevalence, incidence, and residual transmission risk in first-time and repeat blood donations in Zimbabwe: implications on blood safety.

PubMed

Mapako, Tonderai; Mvere, David A; Chitiyo, McLeod E; Rusakaniko, Simbarashe; Postma, Maarten J; van Hulst, Marinus

2013-10-01

National Blood Service Zimbabwe human immunodeficiency virus (HIV) risk management strategy includes screening and discarding of first-time donations, which are collected in blood packs without an anticoagulant (dry pack). To evaluate the impact of discarding first-time donations on blood safety the HIV prevalence, incidence, and residual risk in first-time and repeat donations (wet packs) were compared. Donor data from 2002 to 2010 were retrieved from a centralized national electronic donor database and retrospectively analyzed. Chi-square test was used to compare HIV prevalence with relative risk (RR), and the RR point estimates and 95% confidence interval (CI) are reported. Trend analysis was done using Cochran-Armitage trend test. HIV residual risk estimates were determined using published residual risk estimation models. Over the 9 years the overall HIV prevalence estimates are 1.29% (n = 116,058) and 0.42% (n = 434,695) for first-time and repeat donations, respectively. The overall RR was 3.1 (95% CI, 2.9-3.3; p < 0.0001). The overall mean residual transmission risk of HIV window phase donations in first-time was 1:7384 (range, 1:11,308-1:5356) and in repeat donors it was 1:5496 (range, 1:9943-1:3347). The significantly high HIV prevalence estimates recorded in first-time over repeat donations is indicative of the effectiveness of the HIV risk management strategy. However, comparable residual transmission risk estimates in first-time and repeat donors point to the need to further review the risk management strategies. Given the potential wastage of valuable resources, future studies should focus on the cost-effectiveness and utility of screening and discarding first-time donations. © 2013 American Association of Blood Banks.

Risk of Incident Diabetes Mellitus Associated With the Dosage and Duration of Oral Glucocorticoid Therapy in Patients With Rheumatoid Arthritis

PubMed Central

Movahedi, Mohammad; Beauchamp, Marie‐Eve; Abrahamowicz, Michal; Ray, David W.; Michaud, Kaleb; Pedro, Sofia

2016-01-01

Objective To quantify the risk of incident diabetes mellitus (DM) associated with the dosage, duration, and timing of glucocorticoid (GC) use in patients with rheumatoid arthritis (RA). Methods We undertook a cohort study using 2 databases: a UK primary care database (the Clinical Practice Research Datalink [CPRD]) including 21,962 RA patients (1992–2009) and the US National Data Bank for Rheumatic Diseases (NDB) including 12,657 RA patients (1998–2013). Information on the dosage and timing of GC use was extracted. DM in the CPRD was defined using Read codes, at least 2 prescriptions for oral antidiabetic medication, or abnormal blood test results. DM in the NDB was defined through patient self‐reports. Data were analyzed using time‐dependent Cox models and a novel weighted cumulative dose (WCD) model that accounts for dosage, duration, and timing of treatment. Results The hazard ratio (HR) was 1.30 (95% confidence interval [95% CI] 1.17–1.45) and 1.61 (95% CI 1.37–1.89) in current GC users compared to nonusers in the CPRD and the NDB, respectively. A range of conventional statistical models consistently confirmed increases in risk with the GC dosage and duration. The WCD model showed that recent GC use contributed the most to the current risk of DM, while doses taken >6 months previously did not influence current risk. In the CPRD, 5 mg of prednisolone equivalent dose for the last 1, 3, and 6 months was significantly associated with HRs of 1.20, 1.43, and 1.48, respectively, compared to nonusers. Conclusion GC use is a clinically important and quantifiable risk factor for DM. Risk is influenced by the dosage and treatment duration, although only for GC use within the last 6 months. PMID:26663814

A Framework for Widespread Replication of a Highly Spatially Resolved Childhood Lead Exposure Risk Model

PubMed Central

Kim, Dohyeong; Galeano, M. Alicia Overstreet; Hull, Andrew; Miranda, Marie Lynn

2008-01-01

Background Preventive approaches to childhood lead poisoning are critical for addressing this longstanding environmental health concern. Moreover, increasing evidence of cognitive effects of blood lead levels < 10 μg/dL highlights the need for improved exposure prevention interventions. Objectives Geographic information system–based childhood lead exposure risk models, especially if executed at highly resolved spatial scales, can help identify children most at risk of lead exposure, as well as prioritize and direct housing and health-protective intervention programs. However, developing highly resolved spatial data requires labor-and time-intensive geocoding and analytical processes. In this study we evaluated the benefit of increased effort spent geocoding in terms of improved performance of lead exposure risk models. Methods We constructed three childhood lead exposure risk models based on established methods but using different levels of geocoded data from blood lead surveillance, county tax assessors, and the 2000 U.S. Census for 18 counties in North Carolina. We used the results to predict lead exposure risk levels mapped at the individual tax parcel unit. Results The models performed well enough to identify high-risk areas for targeted intervention, even with a relatively low level of effort on geocoding. Conclusions This study demonstrates the feasibility of widespread replication of highly spatially resolved childhood lead exposure risk models. The models guide resource-constrained local health and housing departments and community-based organizations on how best to expend their efforts in preventing and mitigating lead exposure risk in their communities. PMID:19079729

A time series model of the occurrence of gastric dilatation-volvulus in a population of dogs

PubMed Central

Levine, Michael; Moore, George E

2009-01-01

Background Gastric dilatation-volvulus (GDV) is a life-threatening condition of mammals, with increased risk in large breed dogs. The study of its etiological factors is difficult due to the variety of possible living conditions. The association between meteorological events and the occurrence of GDV has been postulated but remains unclear. This study introduces the binary time series approach to the investigation of the possible meteorological risk factors for GDV. The data collected in a population of high-risk working dogs in Texas was used. Results Minimum and maximum daily atmospheric pressure on the day of GDV event and the maximum daily atmospheric pressure on the day before the GDV event were positively associated with the probability of GDV. All of the odds/multiplicative factors of a day being GDV day were interpreted conditionally on the past GDV occurrences. There was minimal difference between the binary and Poisson general linear models. Conclusion Time series modeling provided a novel method for evaluating the association between meteorological variables and GDV in a large population of dogs. Appropriate application of this method was enhanced by a common environment for the dogs and availability of meteorological data. The potential interaction between weather changes and patient risk factors for GDV deserves further investigation. PMID:19368730

A time series model of the occurrence of gastric dilatation-volvulus in a population of dogs.

PubMed

Levine, Michael; Moore, George E

2009-04-15

Gastric dilatation-volvulus (GDV) is a life-threatening condition of mammals, with increased risk in large breed dogs. The study of its etiological factors is difficult due to the variety of possible living conditions. The association between meteorological events and the occurrence of GDV has been postulated but remains unclear. This study introduces the binary time series approach to the investigation of the possible meteorological risk factors for GDV. The data collected in a population of high-risk working dogs in Texas was used. Minimum and maximum daily atmospheric pressure on the day of GDV event and the maximum daily atmospheric pressure on the day before the GDV event were positively associated with the probability of GDV. All of the odds/multiplicative factors of a day being GDV day were interpreted conditionally on the past GDV occurrences. There was minimal difference between the binary and Poisson general linear models. Time series modeling provided a novel method for evaluating the association between meteorological variables and GDV in a large population of dogs. Appropriate application of this method was enhanced by a common environment for the dogs and availability of meteorological data. The potential interaction between weather changes and patient risk factors for GDV deserves further investigation.

Model-based risk assessment and public health analysis to prevent Lyme disease

PubMed Central

Sabounchi, Nasim S.; Roome, Amanda; Spathis, Rita; Garruto, Ralph M.

2017-01-01

The number of Lyme disease (LD) cases in the northeastern United States has been dramatically increasing with over 300 000 new cases each year. This is due to numerous factors interacting over time including low public awareness of LD, risk behaviours and clothing choices, ecological and climatic factors, an increase in rodents within ecologically fragmented peri-urban built environments and an increase in tick density and infectivity in such environments. We have used a system dynamics (SD) approach to develop a simulation tool to evaluate the significance of risk factors in replicating historical trends of LD cases, and to investigate the influence of different interventions, such as increasing awareness, controlling clothing risk and reducing mouse populations, in reducing LD risk. The model accurately replicates historical trends of LD cases. Among several interventions tested using the simulation model, increasing public awareness most significantly reduces the number of LD cases. This model provides recommendations for LD prevention, including further educational programmes to raise awareness and control behavioural risk. This model has the potential to be used by the public health community to assess the risk of exposure to LD. PMID:29291075

Transitional Instability, Psychological Health, and Sexual Risk Taking among College Students

ERIC Educational Resources Information Center

Bowers, Jill R.; Segrin, Chris

2017-01-01

This study examined the effects of transitional instability on college students' (n = 402) psychological distress and sexual risk taking at two different time points over one year. Tested through structural equation models, the data revealed transitional instability had significant positive effects on psychological distress and sexual risk taking…

Bridging the etiologic and prognostic outlooks in individualized assessment of absolute risk of an illness: application in lung cancer.

PubMed

Karp, Igor; Sylvestre, Marie-Pierre; Abrahamowicz, Michal; Leffondré, Karen; Siemiatycki, Jack

2016-11-01

Assessment of individual risk of illness is an important activity in preventive medicine. Development of risk-assessment models has heretofore relied predominantly on studies involving follow-up of cohort-type populations, while case-control studies have generally been considered unfit for this purpose. To present a method for individualized assessment of absolute risk of an illness (as illustrated by lung cancer) based on data from a 'non-nested' case-control study. We used data from a case-control study conducted in Montreal, Canada in 1996-2001. Individuals diagnosed with lung cancer (n = 920) and age- and sex-matched lung-cancer-free subjects (n = 1288) completed questionnaires documenting life-time cigarette-smoking history and occupational, medical, and family history. Unweighted and weighted logistic models were fitted. Model overfitting was assessed using bootstrap-based cross-validation and 'shrinkage.' The discriminating ability was assessed by the c-statistic, and the risk-stratifying performance was assessed by examination of the variability in risk estimates over hypothetical risk-profiles. In the logistic models, the logarithm of incidence-density of lung cancer was expressed as a function of age, sex, cigarette-smoking history, history of respiratory conditions and exposure to occupational carcinogens, and family history of lung cancer. The models entailed a minimal degree of overfitting ('shrinkage' factor: 0.97 for both unweighted and weighted models) and moderately high discriminating ability (c-statistic: 0.82 for the unweighted model and 0.66 for the weighted model). The method's risk-stratifying performance was quite high. The presented method allows for individualized assessment of risk of lung cancer and can be used for development of risk-assessment models for other illnesses.

Sources of uncertanity as a basis to fill the information gap in a response to flood

NASA Astrophysics Data System (ADS)

Kekez, Toni; Knezic, Snjezana

2016-04-01

Taking into account uncertainties in flood risk management remains a challenge due to difficulties in choosing adequate structural and/or non-structural risk management options. Despite stated measures wrong decisions are often being made when flood occurs. Parameter and structural uncertainties which include model and observation errors as well as lack of knowledge about system characteristics are the main considerations. Real time flood risk assessment methods are predominantly based on measured water level values and vulnerability as well as other relevant characteristics of flood affected area. The goal of this research is to identify sources of uncertainties and to minimize information gap between the point where the water level is measured and the affected area, taking into consideration main uncertainties that can affect risk value at the observed point or section of the river. Sources of uncertainties are identified and determined using system analysis approach and relevant uncertainties are included in the risk assessment model. With such methodological approach it is possible to increase response time with more effective risk assessment which includes uncertainty propagation model. Response phase could be better planned with adequate early warning systems resulting in more time and less costs to help affected areas and save human lives. Reliable and precise information is necessary to raise emergency operability level in order to enhance safety of citizens and reducing possible damage. The results of the EPISECC (EU funded FP7) project are used to validate potential benefits of this research in order to improve flood risk management and response methods. EPISECC aims at developing a concept of a common European Information Space for disaster response which, among other disasters, considers the floods.

Short Operative Duration and Surgical Site Infection Risk in Hip and Knee Arthroplasty Procedures

PubMed Central

Dicks, Kristen V.; Baker, Arthur W.; Durkin, Michael J.; Anderson, Deverick J.; Moehring, Rebekah W.; Chen, Luke F.; Sexton, Daniel J.; Weber, David J.; Lewis, Sarah S.

2016-01-01

OBJECTIVE To determine the association (1) between shorter operative duration and surgical site infection (SSI) and (2) between surgeon median operative duration and SSI risk among first-time hip and knee arthroplasties. DESIGN Retrospective cohort study SETTING A total of 43 community hospitals located in the southeastern United States. PATIENTS Adults who developed SSIs according to National Healthcare Safety Network criteria within 365 days of first-time knee or hip arthroplasties performed between January 1, 2008 and December 31, 2012. METHODS Log-binomial regression models estimated the association (1) between operative duration and SSI outcome and (2) between surgeon median operative duration and SSI outcome. Hip and knee arthroplasties were evaluated in separate models. Each model was adjusted for American Society of Anesthesiology score and patient age. RESULTS A total of 25,531 hip arthroplasties and 42,187 knee arthroplasties were included in the study. The risk of SSI in knee arthroplasties with an operative duration shorter than the 25th percentile was 0.40 times the risk of SSI in knee arthroplasties with an operative duration between the 25th and 75th percentile (risk ratio [RR], 0.40; 95% confidence interval [CI], 0.38–0.56; P <.01). Short operative duration did not demonstrate significant association with SSI for hip arthroplasties (RR, 1.04; 95% CI, 0.79–1.37; P =.36). Knee arthroplasty surgeons with shorter median operative durations had a lower risk of SSI than surgeons with typical median operative durations (RR, 0.52; 95% CI, 0.43–0.64; P <.01). CONCLUSIONS Short operative durations were not associated with a higher SSI risk for knee or hip arthroplasty procedures in our analysis. PMID:26391277

Adolescents Exiting Homelessness Over Two Years: The Risk Amplification and Abatement Model

PubMed Central

Milburn, Norweeta G.; Rice, Eric; Rotheram-Borus, Mary Jane; Mallett, Shelley; Rosenthal, Doreen; Batterham, Phillip; May, Susanne J.; Witkin, Andrea; Duan, Naihua

2014-01-01

The Risk Amplification and Abatement Model (RAAM), demonstrates that negative contact with socializing agents amplify risk, while positive contact abates risk for homeless adolescents. To test this model, the likelihood of exiting homelessness and returning to familial housing at 2 years and stably exiting over time are examined with longitudinal data collected from 183 newly homeless adolescents followed over 2 years in Los Angeles, CA. In support of RAAM, unadjusted odds of exiting at 2 years and stably exiting over2 years revealed that engagement with pro-social peers, maternal social support, and continued school attendance all promoted exiting behaviors. Simultaneously, exposure to family violence and reliance on shelter services discouraged stably exiting behaviors. Implications for family-based interventions are proposed. PMID:25067896

Application of discriminant analysis-based model for prediction of risk of low back disorders due to workplace design in industrial jobs.

PubMed

Ganga, G M D; Esposto, K F; Braatz, D

2012-01-01

The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.

Hepatitis B vaccination and changes in sexual risk behaviour among men who have sex with men in Amsterdam.

PubMed

Xiridou, M; Wallinga, J; Dukers-Muijers, N; Coutinho, R

2009-04-01

The impact of hepatitis B vaccination in men having sex with men in Amsterdam has been marginal until now, possibly because of increases in sexual risk behaviour counterbalancing the effect of vaccination. A mathematical model is used to describe the hepatitis B epidemic. The model shows that, with the current vaccination coverage, the decrease in incidence is small in the beginning. However, the number of infections prevented per vaccine administered rises over time. Nevertheless, increased risk behaviour reduces the benefit of vaccination. Targeting high-risk men is more successful in reducing and containing the epidemic than targeting low-risk men. In conclusion, the vaccination campaign is effective and should be intensified. High-risk men should be targeted for vaccination and for risk reduction.

Multi-state model for studying an intermediate event using time-dependent covariates: application to breast cancer.

PubMed

Meier-Hirmer, Carolina; Schumacher, Martin

2013-06-20

The aim of this article is to propose several methods that allow to investigate how and whether the shape of the hazard ratio after an intermediate event depends on the waiting time to occurrence of this event and/or the sojourn time in this state. A simple multi-state model, the illness-death model, is used as a framework to investigate the occurrence of this intermediate event. Several approaches are shown and their advantages and disadvantages are discussed. All these approaches are based on Cox regression. As different time-scales are used, these models go beyond Markov models. Different estimation methods for the transition hazards are presented. Additionally, time-varying covariates are included into the model using an approach based on fractional polynomials. The different methods of this article are then applied to a dataset consisting of four studies conducted by the German Breast Cancer Study Group (GBSG). The occurrence of the first isolated locoregional recurrence (ILRR) is studied. The results contribute to the debate on the role of the ILRR with respect to the course of the breast cancer disease and the resulting prognosis. We have investigated different modelling strategies for the transition hazard after ILRR or in general after an intermediate event. Including time-dependent structures altered the resulting hazard functions considerably and it was shown that this time-dependent structure has to be taken into account in the case of our breast cancer dataset. The results indicate that an early recurrence increases the risk of death. A late ILRR increases the hazard function much less and after the successful removal of the second tumour the risk of death is almost the same as before the recurrence. With respect to distant disease, the appearance of the ILRR only slightly increases the risk of death if the recurrence was treated successfully. It is important to realize that there are several modelling strategies for the intermediate event and that each of these strategies has restrictions and may lead to different results. Especially in the medical literature considering breast cancer development, the time-dependency is often neglected in the statistical analyses. We show that the time-varying variables cannot be neglected in the case of ILRR and that fractional polynomials are a useful tool for finding the functional form of these time-varying variables.

On cancer risk estimation of urban air pollution.

PubMed Central

Törnqvist, M; Ehrenberg, L

1994-01-01

The usefulness of data from various sources for a cancer risk estimation of urban air pollution is discussed. Considering the irreversibility of initiations, a multiplicative model is preferred for solid tumors. As has been concluded for exposure to ionizing radiation, the multiplicative model, in comparison with the additive model, predicts a relatively larger number of cases at high ages, with enhanced underestimation of risks by short follow-up times in disease-epidemiological studies. For related reasons, the extrapolation of risk from animal tests on the basis of daily absorbed dose per kilogram body weight or per square meter surface area without considering differences in life span may lead to an underestimation, and agreements with epidemiologically determined values may be fortuitous. Considering these possibilities, the most likely lifetime risks of cancer death at the average exposure levels in Sweden were estimated for certain pollution fractions or indicator compounds in urban air. The risks amount to approximately 50 deaths per 100,000 for inhaled particulate organic material (POM), with a contribution from ingested POM about three times larger, and alkenes, and butadiene cause 20 deaths, respectively, per 100,000 individuals. Also, benzene and formaldehyde are expected to be associated with considerable risk increments. Comparative potency methods were applied for POM and alkenes. Due to incompleteness of the list of compounds considered and the uncertainties of the above estimates, the total risk calculation from urban air has not been attempted here. PMID:7821292

Dose-Response Association Between Physical Activity and Incident Hypertension: A Systematic Review and Meta-Analysis of Cohort Studies.

PubMed

Liu, Xuejiao; Zhang, Dongdong; Liu, Yu; Sun, Xizhuo; Han, Chengyi; Wang, Bingyuan; Ren, Yongcheng; Zhou, Junmei; Zhao, Yang; Shi, Yuanyuan; Hu, Dongsheng; Zhang, Ming

2017-05-01

Despite the inverse association between physical activity (PA) and incident hypertension, a comprehensive assessment of the quantitative dose-response association between PA and hypertension has not been reported. We performed a meta-analysis, including dose-response analysis, to quantitatively evaluate this association. We searched PubMed and Embase databases for articles published up to November 1, 2016. Random effects generalized least squares regression models were used to assess the quantitative association between PA and hypertension risk across studies. Restricted cubic splines were used to model the dose-response association. We identified 22 articles (29 studies) investigating the risk of hypertension with leisure-time PA or total PA, including 330 222 individuals and 67 698 incident cases of hypertension. The risk of hypertension was reduced by 6% (relative risk, 0.94; 95% confidence interval, 0.92-0.96) with each 10 metabolic equivalent of task h/wk increment of leisure-time PA. We found no evidence of a nonlinear dose-response association of PA and hypertension ( P nonlinearity =0.094 for leisure-time PA and 0.771 for total PA). With the linear cubic spline model, when compared with inactive individuals, for those who met the guidelines recommended minimum level of moderate PA (10 metabolic equivalent of task h/wk), the risk of hypertension was reduced by 6% (relative risk, 0.94; 95% confidence interval, 0.92-0.97). This meta-analysis suggests that additional benefits for hypertension prevention occur as the amount of PA increases. © 2017 American Heart Association, Inc.

Monitoring risk-adjusted medical outcomes allowing for changes over time.

PubMed

Steiner, Stefan H; Mackay, R Jock

2014-10-01

We consider the problem of monitoring and comparing medical outcomes, such as surgical performance, over time. Performance is subject to change due to a variety of reasons including patient heterogeneity, learning, deteriorating skills due to aging, etc. For instance, we expect inexperienced surgeons to improve their skills with practice. We propose a graphical method to monitor surgical performance that incorporates risk adjustment to account for patient heterogeneity. The procedure gives more weight to recent outcomes and down-weights the influence of outcomes further in the past. The chart is clinically interpretable as it plots an estimate of the failure rate for a "standard" patient. The chart also includes a measure of uncertainty in this estimate. We can implement the method using historical data or start from scratch. As the monitoring proceeds, we can base the estimated failure rate on a known risk model or use the observed outcomes to update the risk model as time passes. We illustrate the proposed method with an example from cardiac surgery. © The Author 2013. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Residual lifetime and 10 year absolute risks of osteoporotic fractures in Chinese men and women.

PubMed

Si, Lei; Winzenberg, Tania M; Chen, Mingsheng; Jiang, Qicheng; Palmer, Andrew J

2015-06-01

To determine the residual lifetime and 10 year absolute risks of osteoporotic fractures in Chinese men and women. A validated state-transition microsimulation model was used. Microsimulation and probabilistic sensitivity analyses were performed to address the uncertainties in the model. All parameters including fracture incidence rates and mortality rates were retrieved from published literature. Simulated subjects were run through the model until they died to estimate the residual lifetime fracture risks. A 10 year time horizon was used to determine the 10 year fracture risks. We estimated the risk of only the first osteoporotic fracture during the simulation time horizon. The residual lifetime and 10 year risks of having the first osteoporotic (hip, clinical vertebral or wrist) fracture for Chinese women aged 50 years were 40.9% (95% CI: 38.3-44.0%) and 8.2% (95% CI: 6.8-9.3%) respectively. For men, the residual lifetime and 10 year fracture risks were 8.7% (95% CI: 7.5-9.8%) and 1.2% (95% CI: 0.8-1.7%) respectively. The residual lifetime fracture risks declined with age, whilst the 10 year fracture risks increased with age until the short-term mortality risks outstripped the fracture risks. Residual lifetime and 10 year clinical vertebral fracture risks were higher than those of hip and wrist fractures in both sexes. More than one third of the Chinese women and approximately one tenth of the Chinese men aged 50 years are expected to sustain a major osteoporotic fracture in their remaining lifetimes. Due to increased fracture risks and a rapidly ageing population, osteoporosis will present a great challenge to the Chinese healthcare system. While national data was used wherever possible, regional Chinese hip and clinical vertebral fracture incidence rates were used, wrist fracture rates were taken from a Norwegian study and calibrated to the Chinese population. Other fracture sites like tibia, humerus, ribs and pelvis were not included in the analysis, thus these risks are likely to be underestimates. Fracture risk factors other than age and sex were not included in the model. Point estimates were used for fracture incidence rates, osteoporosis prevalence and mortality rates for the general population.

A methodology for overall consequence modeling in chemical industry.

PubMed

Arunraj, N S; Maiti, J

2009-09-30

Risk assessment in chemical process industry is a very important issue for safeguarding human and the ecosystem from damages caused to them. Consequence assessment is an integral part of risk assessment. However, the commonly used consequence estimation methods involve time-consuming complex mathematical models and simple assimilation of losses without considering all the consequence factors. This lead to the deterioration of quality of estimated risk value. So, the consequence modeling has to be performed in detail considering all major losses with optimal time to improve the decisive value of risk. The losses can be broadly categorized into production loss, assets loss, human health and safety loss, and environment loss. In this paper, a conceptual framework is developed to assess the overall consequence considering all the important components of major losses. Secondly, a methodology is developed for the calculation of all the major losses, which are normalized to yield the overall consequence. Finally, as an illustration, the proposed methodology is applied to a case study plant involving benzene extraction. The case study result using the proposed consequence assessment scheme is compared with that from the existing methodologies.

Assessing predation risk: optimal behaviour and rules of thumb.

PubMed

Welton, Nicky J; McNamara, John M; Houston, Alasdair I

2003-12-01

We look at a simple model in which an animal makes behavioural decisions over time in an environment in which all parameters are known to the animal except predation risk. In the model there is a trade-off between gaining information about predation risk and anti-predator behaviour. All predator attacks lead to death for the prey, so that the prey learns about predation risk by virtue of the fact that it is still alive. We show that it is not usually optimal to behave as if the current unbiased estimate of the predation risk is its true value. We consider two different ways to model reproduction; in the first scenario the animal reproduces throughout its life until it dies, and in the second scenario expected reproductive success depends on the level of energy reserves the animal has gained by some point in time. For both of these scenarios we find results on the form of the optimal strategy and give numerical examples which compare optimal behaviour with behaviour under simple rules of thumb. The numerical examples suggest that the value of the optimal strategy over the rules of thumb is greatest when there is little current information about predation risk, learning is not too costly in terms of predation, and it is energetically advantageous to learn about predation. We find that for the model and parameters investigated, a very simple rule of thumb such as 'use the best constant control' performs well.

Risk Selection into Consumer-Directed Health Plans: An Analysis of Family Choices within Large Employers

PubMed Central

McDevitt, Roland D; Haviland, Amelia M; Lore, Ryan; Laudenberger, Laura; Eisenberg, Matthew; Sood, Neeraj

2014-01-01

Objective To identify the degree of selection into consumer-directed health plans (CDHPs) versus traditional plans over time, and factors that influence choice and temper risk selection. Data Sources/Study Setting Sixteen large employers offering both CDHP and traditional plans during the 2004–2007 period, more than 200,000 families. Study Design We model CDHP choice with logistic regression; predictors include risk scores, in addition to family, choice setting, and plan characteristics. Additional models stratify by account type or single enrollee versus family. Data Collection/Extraction Methods Risk scores, family characteristics, and enrollment decisions are derived from medical claims and enrollment files. Interviews with human resources executives provide additional data. Principal Findings CDHP risk scores were 74 percent of traditional plan scores in the first year, and this difference declined over time. Employer contributions to accounts and employee premium savings fostered CDHP enrollment and reduced risk selection. Having to make an active choice of plan increased CDHP enrollment but also increased risk selection. Risk selection was greater for singles than families and did not differ between HRA and HSA-based CDHPs. Conclusions Risk selection was not severe and it was well managed. Employers have effective methods to encourage CDHP enrollment and temper selection against traditional plans. PMID:24800305

Survivorship analysis when cure is a possibility: a Monte Carlo study.

PubMed

Goldman, A I

1984-01-01

Parametric survivorship analyses of clinical trials commonly involves the assumption of a hazard function constant with time. When the empirical curve obviously levels off, one can modify the hazard function model by use of a Gompertz or Weibull distribution with hazard decreasing over time. Some cancer treatments are thought to cure some patients within a short time of initiation. Then, instead of all patients having the same hazard, decreasing over time, a biologically more appropriate model assumes that an unknown proportion (1 - pi) have constant high risk whereas the remaining proportion (pi) have essentially no risk. This paper discusses the maximum likelihood estimation of pi and the power curves of the likelihood ratio test. Monte Carlo studies provide results for a variety of simulated trials; empirical data illustrate the methods.

Application of a degree-day model of West Nile virus transmission risk to the East Coast of the United States of America.

PubMed

Konrad, Sarah K; Miller, Scott N

2012-11-01

A geographical information systems model that identifies regions of the United States of America (USA) susceptible to West Nile virus (WNV) transmission risk is presented. This system has previously been calibrated and tested in the western USA; in this paper we use datasets of WNV-killed birds from South Carolina and Connecticut to test the model in the eastern USA. Because their response to WNV infection is highly predictable, American crows were chosen as the primary source for model calibration and testing. Where crow data are absent, other birds are shown to be an effective substitute. Model results show that the same calibrated model demonstrated to work in the western USA has the same predictive ability in the eastern USA, allowing for a continental-scale evaluation of the transmission risk of WNV at a daily time step. The calibrated model is independent of mosquito species and requires inputs of only local maximum and minimum temperatures. Of benefit to the general public and vector control districts, the model predicts the onset of seasonal transmission risk, although it is less effective at identifying the end of the transmission risk season.

A Biopsychosocial Model of the Development of Chronic Conduct Problems in Adolescence

PubMed Central

Dodge, Kenneth A.; Pettit, Gregory S.

2009-01-01

A biopsychosocial model of the development of adolescent chronic conduct problems is presented and supported through a review of empirical findings. This model posits that biological dispositions and sociocultural contexts place certain children at risk in early life but that life experiences with parents, peers, and social institutions increment and mediate this risk. A transactional developmental model is best equipped to describe the emergence of chronic antisocial behavior across time. Reciprocal influences among dispositions, contexts, and life experiences lead to recursive iterations across time that exacerbate or diminish antisocial development. Cognitive and emotional processes within the child, including the acquisition of knowledge and social-information-processing patterns, mediate the relation between life experiences and conduct problem outcomes. Implications for prevention research and public policy are noted. PMID:12661890

Using Models to Inform Policy: Insights from Modeling the Complexities of Global Polio Eradication

NASA Astrophysics Data System (ADS)

Thompson, Kimberly M.

Drawing on over 20 years of experience modeling risks in complex systems, this talk will challenge SBP participants to develop models that provide timely and useful answers to critical policy questions when decision makers need them. The talk will include reflections on the opportunities and challenges associated with developing integrated models for complex problems and communicating their results effectively. Dr. Thompson will focus the talk largely on collaborative modeling related to global polio eradication and the application of system dynamics tools. After successful global eradication of wild polioviruses, live polioviruses will still present risks that could potentially lead to paralytic polio cases. This talk will present the insights of efforts to use integrated dynamic, probabilistic risk, decision, and economic models to address critical policy questions related to managing global polio risks. Using a dynamic disease transmission model combined with probabilistic model inputs that characterize uncertainty for a stratified world to account for variability, we find that global health leaders will face some difficult choices, but that they can take actions that will manage the risks effectively. The talk will emphasize the need for true collaboration between modelers and subject matter experts, and the importance of working with decision makers as partners to ensure the development of useful models that actually get used.

Human Dose-Response Data for Francisella tularensis and a Dose- and Time-Dependent Mathematical Model of Early-Phase Fever Associated with Tularemia After Inhalation Exposure.

PubMed

McClellan, Gene; Coleman, Margaret; Crary, David; Thurman, Alec; Thran, Brandolyn

2018-04-25

Military health risk assessors, medical planners, operational planners, and defense system developers require knowledge of human responses to doses of biothreat agents to support force health protection and chemical, biological, radiological, nuclear (CBRN) defense missions. This article reviews extensive data from 118 human volunteers administered aerosols of the bacterial agent Francisella tularensis, strain Schu S4, which causes tularemia. The data set includes incidence of early-phase febrile illness following administration of well-characterized inhaled doses of F. tularensis. Supplemental data on human body temperature profiles over time available from de-identified case reports is also presented. A unified, logically consistent model of early-phase febrile illness is described as a lognormal dose-response function for febrile illness linked with a stochastic time profile of fever. Three parameters are estimated from the human data to describe the time profile: incubation period or onset time for fever; rise time of fever; and near-maximum body temperature. Inhaled dose-dependence and variability are characterized for each of the three parameters. These parameters enable a stochastic model for the response of an exposed population through incorporation of individual-by-individual variability by drawing random samples from the statistical distributions of these three parameters for each individual. This model provides risk assessors and medical decisionmakers reliable representations of the predicted health impacts of early-phase febrile illness for as long as one week after aerosol exposures of human populations to F. tularensis. © 2018 Society for Risk Analysis.

Childhood asthma and return to school in Sydney, Australia.

PubMed

Lincoln, D; Morgan, G; Sheppeard, V; Jalaludin, B; Corbett, S; Beard, J

2006-09-01

To describe the seasonal pattern of hospital admissions for childhood asthma in Sydney, Australia and investigate the relationship between these admissions and time of return to school. Time-series analysis of daily hospital admissions for childhood asthma in Sydney from 1994 to 2000. We defined the time series of all asthma-related hospital admissions in Sydney between 1994 and 2000 for age groups 1-4 and 5-14 years. We analysed the time series for each age group using a generalized additive model with a log-link function, an offset term and quasi-likelihood estimation. Daily admissions were modelled using penalised regression splines adjusting for long term trends, school terms and holidays, weekday and influenza epidemics. After adjusting for potential confounding, the risk of asthma admission increased to a peak between 2 and 4 weeks after the first day of school in each term and varied between 1.5 and 3 times the risk prior to return to school for both age groups. The largest increase in asthma risk occurring in term one after the long summer holiday. The increase in admission risk began soon after the first day of school of each term for school age children 5-14 years, but not in pre-school age children 1-4 years. Returning to school after term holidays is strongly associated with increased risk of hospital admissions for asthma in children, especially following the long summer holiday. Preventive measures focused on return to school have the potential to substantially decrease admissions for asthma in children.

Track inspection planning and risk measurement analysis.

DOT National Transportation Integrated Search

2014-11-01

This project models track inspection operations on a railroad network and discusses how the inspection results can : be used to measure the risk of failure on the tracks. In particular, the inspection times of the tracks, inspection frequency of the ...

Spatiotemporal Modeling for Fine-Scale Maps of Regional Malaria Endemicity and Its Implications for Transitional Complexities in a Routine Surveillance Network in Western Cambodia

PubMed Central

Okami, Suguru; Kohtake, Naohiko

2017-01-01

Due to the associated and substantial efforts of many stakeholders involved in malaria containment, the disease burden of malaria has dramatically decreased in many malaria-endemic countries in recent years. Some decades after the past efforts of the global malaria eradication program, malaria elimination has again featured on the global health agenda. While risk distribution modeling and a mapping approach are effective tools to assist with the efficient allocation of limited health-care resources, these methods need some adjustment and reexamination in accordance with changes occurring in relation to malaria elimination. Limited available data, fine-scale data inaccessibility (for example, household or individual case data), and the lack of reliable data due to inefficiencies within the routine surveillance system, make it difficult to create reliable risk maps for decision-makers or health-care practitioners in the field. Furthermore, the risk of malaria may dynamically change due to various factors such as the progress of containment interventions and environmental changes. To address the complex and dynamic nature of situations in low-to-moderate malaria transmission settings, we built a spatiotemporal model of a standardized morbidity ratio (SMR) of malaria incidence, calculated through annual parasite incidence, using routinely reported surveillance data in combination with environmental indices such as remote sensing data, and the non-environmental regional containment status, to create fine-scale risk maps. A hierarchical Bayesian frame was employed to fit the transitioning malaria risk data onto the map. The model was set to estimate the SMRs of every study location at specific time intervals within its uncertainty range. Using the spatial interpolation of estimated SMRs at village level, we created fine-scale maps of two provinces in western Cambodia at specific time intervals. The maps presented different patterns of malaria risk distribution at specific time intervals. Moreover, the visualized weights estimated using the risk model, and the structure of the routine surveillance network, represent the transitional complexities emerging from ever-changing regional endemic situations. PMID:29034229

Crystalline silica exposure and lung cancer mortality in diatomaceous earth industry workers: a quantitative risk assessment.

PubMed

Rice, F L; Park, R; Stayner, L; Smith, R; Gilbert, S; Checkoway, H

2001-01-01

To use various exposure-response models to estimate the risk of mortality from lung cancer due to occupational exposure to respirable crystalline silica dust. Data from a cohort mortality study of 2342 white male California diatomaceous earth mining and processing workers exposed to crystalline silica dust (mainly cristobalite) were reanalyzed with Poisson regression and Cox's proportional hazards models. Internal and external adjustments were used to control for potential confounding from the effects of time since first observation, calendar time, age, and Hispanic ethnicity. Cubic smoothing spline models were used to assess the fit of the models. Exposures were lagged by 10 years. Evaluations of the fit of the models were performed by comparing their deviances. Lifetime risks of lung cancer were estimated up to age 85 with an actuarial approach that accounted for competing causes of death. Exposure to respirable crystalline silica dust was a significant predictor (p<0.05) in nearly all of the models evaluated and the linear relative rate model with a 10 year exposure lag seemed to give the best fit in the Poisson regression analysis. For those who died of lung cancer the linear relative rate model predicted rate ratios for mortality from lung cancer of about 1.6 for the mean cumulative exposure to respirable silica compared with no exposure. The excess lifetime risk (to age 85) of mortality from lung cancer for white men exposed for 45 years and with a 10 year lag period at the current Occupational Safety and Health Administration (OSHA) standard of about 0.05 mg/m(3) for respirable cristobalite dust is 19/1000 (95% confidence interval (95% CI) 5/1000 to 46/1000). There was a significant risk of mortality from lung cancer that increased with cumulative exposure to respirable crystalline silica dust. The predicted number of deaths from lung cancer suggests that current occupational health standards may not be adequately protecting workers from the risk of lung cancer.

Long-Term Disease-Free Survival of Non-Metastatic Breast Cancer Patients in Iran: A Survival Model with Competing Risks Taking Cure Fraction and Frailty into Account

PubMed

Ghavami, Vahid; Mahmoudi, Mahmood; Rahimi Foroushani, Abbas; Baghishani, Hossein; Homaei Shandiz, Fatemeh; Yaseri, Mehdi

2017-10-26

Introduction: Survival modeling is a very important tool to detect risk factors and provide a basis for health care planning. However, cancer data may have properties leading to distorted results with routine methods. Therefore, this study aimed to cover specific factors (competing risk, cure fraction and heterogeneity) with a real dataset of Iranian breast cancer patients using a competing risk-cure-frailty model. Materials and methods: For this historical cohort study, information for 550 Iranian breast cancer patients who underwent surgery for tumor removal from 2001 to 2007 and were followed up to March 2017, was analyzed using R 3.2 software. Results: In contrast to T-stage and N-stage, hormone receptor status did not have any significant effect on the cure fraction (long-term disease-free survival). However, T-stage, N-stage and hormone receptor status all had a significant effect on short-term disease-free survival so that the hazard of loco-regional relapse or distant metastasis in cases positive for a hormone receptor was only 0.3 times that for their negative hormone receptor counterparts. The likelihood of locoregional relapse in the first quartile of follow up was nearly twice that of other quartiles. The least cumulative incidence of time to locoregional relapse was for cases with a positive hormone receptor, low N stage and low T stage. The effect of frailty term was significant in this study and a model with frailty appeared more appropriate than a model without, based on the Akaike information criterion (AIC); values for the frailty model and one without the frailty parameter were 1370.39 and 1381.46, respectively. Conclusions: The data from this study indicate ae necessity to consider competing risk, cure fraction and heterogeneity in survival modeling. The competing risk-cure-frailty model can cover complex situations with survival data. Creative Commons Attribution License

A Predictive Model for Assessing Surgery-Related Acute Kidney Injury Risk in Hypertensive Patients: A Retrospective Cohort Study

PubMed Central

Liu, Xing; Ye, Yongkai; Mi, Qi; Huang, Wei; He, Ting; Huang, Pin; Xu, Nana; Wu, Qiaoyu; Wang, Anli; Li, Ying; Yuan, Hong

2016-01-01

Background Acute kidney injury (AKI) is a serious post-surgery complication; however, few preoperative risk models for AKI have been developed for hypertensive patients undergoing general surgery. Thus, in this study involving a large Chinese cohort, we developed and validated a risk model for surgery-related AKI using preoperative risk factors. Methods and Findings This retrospective cohort study included 24,451 hypertensive patients aged ≥18 years who underwent general surgery between 2007 and 2015. The endpoints for AKI classification utilized by the KDIGO (Kidney Disease: Improving Global Outcomes) system were assessed. The most discriminative predictor was selected using Fisher scores and was subsequently used to construct a stepwise multivariate logistic regression model, whose performance was evaluated via comparisons with models used in other published works using the net reclassification index (NRI) and integrated discrimination improvement (IDI) index. Results Surgery-related AKI developed in 1994 hospitalized patients (8.2%). The predictors identified by our Xiang-ya Model were age, gender, eGFR, NLR, pulmonary infection, prothrombin time, thrombin time, hemoglobin, uric acid, serum potassium, serum albumin, total cholesterol, and aspartate amino transferase. The area under the receiver-operating characteristic curve (AUC) for the validation set and cross validation set were 0.87 (95% CI 0.86–0.89) and (0.89; 95% CI 0.88–0.90), respectively, and was therefore similar to the AUC for the training set (0.89; 95% CI 0.88–0.90). The optimal cutoff value was 0.09. Our model outperformed that developed by Kate et al., which exhibited an NRI of 31.38% (95% CI 25.7%-37.1%) and an IDI of 8% (95% CI 5.52%-10.50%) for patients who underwent cardiac surgery (n = 2101). Conclusions/Significance We developed an AKI risk model based on preoperative risk factors and biomarkers that demonstrated good performance when predicting events in a large cohort of hypertensive patients who underwent general surgery. PMID:27802302

Risk Preferences and the Timing of Marriage and Childbearing

PubMed Central

SCHMIDT, LUCIE

2008-01-01

The existing literature on marriage and fertility decisions pays little attention to the roles played by risk preferences and uncertainty. However, given uncertainty regarding the availability of suitable marriage partners, the ability to contracept, and the ability to conceive, women’s risk preferences might be expected to play an important role in marriage and fertility timing decisions. By using data from the Panel Study of Income Dynamics (PSID), I find that measured risk preferences have a significant effect on the timing of both marriage and fertility. Highly risk-tolerant women are more likely to delay marriage, consistent with either a search model of marriage or a risk-pooling explanation. In addition, risk preferences affect fertility timing in a way that differs by marital status and education, and that varies over the life cycle. Greater tolerance for risk leads to earlier births at young ages, consistent with these women being less likely to contracept effectively. In addition, as the subgroup of college-educated, unmarried women nears the end of their fertile periods, highly risk-tolerant women are likely to delay childbearing relative to their more risk-averse counterparts and are therefore less likely to become mothers. These findings may have broader implications for both individual and societal well-being. PMID:18613489

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.

Infectious disease at gluten introduction and risk of childhood diabetes mellitus.

PubMed

Welander, Adina; Montgomery, Scott M; Ludvigsson, Johnny; Ludvigsson, Jonas F

2014-08-01

To investigate the risk of future diabetes mellitus type 1 (T1D) in children who suffered from infection at time of gluten introduction. Population-based prospective study. Parents filled out a diary at home. We hereby obtained data on date of gluten introduction, breastfeeding duration, and infections in 9414 children born in the southeast of Sweden from October 1, 1997, through October 1, 1999 (the All Babies in Southeast Sweden cohort). The Cox proportional hazards model was used to investigate the risk of future T1D until February 1, 2012, among children with infection at time of gluten introduction. Forty-six children (0.5%) developed T1D and were compared with 9368 reference children from the general population. Some 10 of 46 children with later T1D had an infection at time of gluten introduction (22%) compared with 2520 reference children (27%, P=.43). Later T1D was not associated with age at end of breastfeeding, age at any infection, or age at gluten introduction. Breastfeeding at time of gluten introduction was not protective against future T1D (hazard ratio 1.2; 95% CI, 0.5-2.7). In our final model, when we adjusted for age at gluten introduction, age at infection, and breastfeeding duration, infection at time of gluten introduction did not influence the risk of future T1D (hazard ratio 0.8; 95% CI, 0.3-1.6). Infection at time of gluten introduction is not a major risk factor for future T1D in nonselected children. Copyright © 2014 Elsevier Inc. All rights reserved.

Risk taking and refusal assertiveness in a longitudinal model of alcohol use among inner-city adolescents.

PubMed

Epstein, J A; Griffin, K W; Botvin, G J

2001-09-01

Risk taking and refusal assertiveness have been shown to be important determinants of adolescent alcohol use. However, it remains unclear whether youth predisposed to risk taking would be less likely to assertively refuse. This study examined the relationships among risk taking, refusal assertiveness, and alcohol use in a sample of inner-city minority students (N = 1,459), using a cross-lagged longitudinal structural equation model. Data collectors administered the questionnaire to students following a standardized protocol during a 40-min class period. Based on the tested model, risk taking was more stable over time than refusal assertiveness. Furthermore, high risk takers reported less frequent subsequent refusal assertiveness, and less frequent refusal assertiveness predicted greater drinking. A predisposition toward risk taking appears to be an enduring characteristic that is associated with low refusal assertiveness and increased alcohol use. These findings suggest that alcohol prevention programs that emphasize refusal skills training may be less effective for high risk takers. But programs that focus on enhancing competence or reducing normative expectations for peer alcohol use might be more effective for high risk-taking youth.

Capacity planning for electronic waste management facilities under uncertainty: multi-objective multi-time-step model development.

PubMed

Poonam Khanijo Ahluwalia; Nema, Arvind K

2011-07-01

Selection of optimum locations for locating new facilities and decision regarding capacities at the proposed facilities is a major concern for municipal authorities/managers. The decision as to whether a single facility is preferred over multiple facilities of smaller capacities would vary with varying priorities to cost and associated risks such as environmental or health risk or risk perceived by the society. Currently management of waste streams such as that of computer waste is being done using rudimentary practices and is flourishing as an unorganized sector, mainly as backyard workshops in many cities of developing nations such as India. Uncertainty in the quantification of computer waste generation is another major concern due to the informal setup of present computer waste management scenario. Hence, there is a need to simultaneously address uncertainty in waste generation quantities while analyzing the tradeoffs between cost and associated risks. The present study aimed to address the above-mentioned issues in a multi-time-step, multi-objective decision-support model, which can address multiple objectives of cost, environmental risk, socially perceived risk and health risk, while selecting the optimum configuration of existing and proposed facilities (location and capacities).

Risk models of dating aggression across different adolescent relationships: a developmental psychopathology approach.

PubMed

Williams, Tricia S; Connolly, Jennifer; Pepler, Debra; Craig, Wendy; Laporte, Lise

2008-08-01

The present study examined physical dating aggression in different adolescent relationships and assessed linear, threshold, and moderator risk models for recurrent aggressive relationships. The 621 participants (59% girls, 41% boys) were drawn from a 1-year longitudinal survey of Canadian high school youths ranging from Grade 9 through Grade 12. Approximately 13% of participants reported recurrent dating aggression across 2 different relationships. Using peer and dyadic risk factors from Time 1 of the study, the authors confirmed a linear risk model, such that adolescents in 2 different violent relationships had significantly more contextual risk factors than did adolescents in 1 or no violent relationship. Further, structural equation modeling assessing moderation of contextual risk factors indicated that, for adolescents with high acceptance of dating aggression, peer aggression and delinquency significantly predicted recurrent aggression in a new relationship. In comparison, for adolescents with low acceptance of dating aggression, negative relationship characteristics significantly predicted recurrent aggression. Acceptance did not moderate concurrent associations between risk factors and aggression in 1 relationship. Results support a developmental psychopathological approach to the understanding of recurrent aggression and its associated risk factors. Copyright 2008 APA, all rights reserved.

Impact of model-based risk analysis for liver surgery planning.

PubMed

Hansen, C; Zidowitz, S; Preim, B; Stavrou, G; Oldhafer, K J; Hahn, H K

2014-05-01

A model-based risk analysis for oncologic liver surgery was described in previous work (Preim et al. in Proceedings of international symposium on computer assisted radiology and surgery (CARS), Elsevier, Amsterdam, pp. 353–358, 2002; Hansen et al. Int I Comput Assist Radiol Surg 4(5):469–474, 2009). In this paper, we present an evaluation of this method. To prove whether and how the risk analysis facilitates the process of liver surgery planning, an explorative user study with 10 liver experts was conducted. The purpose was to compare and analyze their decision-making. The results of the study show that model-based risk analysis enhances the awareness of surgical risk in the planning stage. Participants preferred smaller resection volumes and agreed more on the safety margins’ width in case the risk analysis was available. In addition, time to complete the planning task and confidence of participants were not increased when using the risk analysis. This work shows that the applied model-based risk analysis may influence important planning decisions in liver surgery. It lays a basis for further clinical evaluations and points out important fields for future research.

Juvenile Offenders' Alcohol and Marijuana Trajectories: Risk and Protective Factor Effects in the Context of Time in a Supervised Facility

ERIC Educational Resources Information Center

Mauricio, Anne M.; Little, Michelle; Chassin, Laurie; Knight, George P.; Piquero, Alex R.; Losoya, Sandra H.; Vargas-Chanes, Delfino

2009-01-01

The current study modeled trajectories of substance use from ages 15 to 20 among 1,095 male serious juvenile offenders (M age = 16.54; 42% African-American, 34% Latino, 20% European-American, and 4% other ethnic/racial backgrounds) and prospectively predicted trajectories from risk and protective factors before and after controlling for time spent…

Don't put all your eggs in one nest: spread them and cut time at risk.

PubMed

Andersson, Malte; Åhlund, Matti

2012-09-01

In many egg-laying animals, some females spread their clutch among several nests. The fitness effects of this reproductive tactic are obscure. Using mathematical modeling and field observations, we analyze an unexplored benefit of egg spreading in brood parasitic and other breeding systems: reduced time at risk for offspring. If a clutch takes many days to lay until incubation and embryo development starts after the last egg, by spreading her eggs a parasitic female can reduce offspring time in the vulnerable nest at risk of predation or other destruction. The model suggests that she can achieve much of this benefit by spreading her eggs among a few nests, even if her total clutch is large. Field data from goldeneye ducks Bucephala clangula show that egg spreading enables a fecund female to lay a clutch that is much larger than average without increasing offspring time at risk in a nest. This advantage increases with female condition (fecundity) and can markedly raise female reproductive success. These results help explain the puzzle of nesting parasites in some precocial birds, which lay eggs in the nests of other females before laying eggs in their own nest. Risk reduction by egg spreading may also play a role in the evolution of other breeding systems and taxa-for instance, polyandry with male parental care in some birds and fishes.

Environmental pollution and deaths due to stroke in a city with low levels of air pollution: ecological time series study.

PubMed

Amancio, Camila Trolez; Nascimento, Luiz Fernando

2014-12-01

Little has been discussed about the increased risk of stroke after exposure to air pollutants, particularly in Brazil. The mechanisms through which air pollution can influence occurrences of vascular events such as stroke are still poorly understood. The aim of this study was to estimate the association between exposure to some air pollutants and risk of death due to stroke. Ecological time series study with data from São José dos Campos, Brazil. Data on deaths due to stroke among individuals of all ages living in São José dos Campos and on particulate matter, sulfur dioxide and ozone were used. Statistical analysis was performed using a generalized additive model of Poisson regression with the Statistica software, in unipollutant and multipollutant models. The percentage increase in the risk of increased interquartile difference was calculated. There were 1,032 deaths due to stroke, ranging from 0 to 5 per day. The statistical significance of the exposure to particulate matter was ascertained in the unipollutant model and the importance of particulate matter and sulfur dioxide, in the multipollutant model. The increases in risk were 10% and 7%, for particulate matter and sulfur dioxide, respectively. It was possible to identify exposure to air pollutants as a risk factor for death due to stroke, even in a city with low levels of air pollution.

Propagating uncertainty from hydrology into human health risk assessment

NASA Astrophysics Data System (ADS)

Siirila, E. R.; Maxwell, R. M.

2013-12-01

Hydro-geologic modeling and uncertainty assessment of flow and transport parameters can be incorporated into human health risk (both cancer and non-cancer) assessment to better understand the associated uncertainties. This interdisciplinary approach is needed now more than ever as societal problems concerning water quality are increasingly interdisciplinary as well. For example, uncertainty can originate from environmental conditions such as a lack of information or measurement error, or can manifest as variability, such as differences in physiological and exposure parameters between individuals. To complicate the matter, traditional risk assessment methodologies are independent of time, virtually neglecting any temporal dependence. Here we present not only how uncertainty and variability can be incorporated into a risk assessment, but also how time dependent risk assessment (TDRA) allows for the calculation of risk as a function of time. The development of TDRA and the inclusion of quantitative risk analysis in this research provide a means to inform decision makers faced with water quality issues and challenges. The stochastic nature of this work also provides a means to address the question of uncertainty in management decisions, a component that is frequently difficult to quantify. To illustrate this new formulation and to investigate hydraulic mechanisms for sensitivity, an example of varying environmental concentration signals resulting from rate dependencies in geochemical reactions is used. Cancer risk is computed and compared using environmental concentration ensembles modeled with sorption as 1) a linear equilibrium assumption and 2) first order kinetics. Results show that the up scaling of these small-scale processes controls the distribution, magnitude, and associated uncertainty of cancer risk.

An Integrated Probabilistic-Fuzzy Assessment of Uncertainty Associated with Human Health Risk to MSW Landfill Leachate Contamination

NASA Astrophysics Data System (ADS)

Mishra, H.; Karmakar, S.; Kumar, R.

2016-12-01

Risk assessment will not remain simple when it involves multiple uncertain variables. Uncertainties in risk assessment majorly results from (1) the lack of knowledge of input variable (mostly random), and (2) data obtained from expert judgment or subjective interpretation of available information (non-random). An integrated probabilistic-fuzzy health risk approach has been proposed for simultaneous treatment of random and non-random uncertainties associated with input parameters of health risk model. The LandSim 2.5, a landfill simulator, has been used to simulate the Turbhe landfill (Navi Mumbai, India) activities for various time horizons. Further the LandSim simulated six heavy metals concentration in ground water have been used in the health risk model. The water intake, exposure duration, exposure frequency, bioavailability and average time are treated as fuzzy variables, while the heavy metals concentration and body weight are considered as probabilistic variables. Identical alpha-cut and reliability level are considered for fuzzy and probabilistic variables respectively and further, uncertainty in non-carcinogenic human health risk is estimated using ten thousand Monte-Carlo simulations (MCS). This is the first effort in which all the health risk variables have been considered as non-deterministic for the estimation of uncertainty in risk output. The non-exceedance probability of Hazard Index (HI), summation of hazard quotients, of heavy metals of Co, Cu, Mn, Ni, Zn and Fe for male and female population have been quantified and found to be high (HI>1) for all the considered time horizon, which evidently shows possibility of adverse health effects on the population residing near Turbhe landfill.

Common carotid intima-media thickness does not add to Framingham risk score in individuals with diabetes mellitus: the USE-IMT initiative.

PubMed

den Ruijter, H M; Peters, S A E; Groenewegen, K A; Anderson, T J; Britton, A R; Dekker, J M; Engström, G; Eijkemans, M J; Evans, G W; de Graaf, J; Grobbee, D E; Hedblad, B; Hofman, A; Holewijn, S; Ikeda, A; Kavousi, M; Kitagawa, K; Kitamura, A; Koffijberg, H; Ikram, M A; Lonn, E M; Lorenz, M W; Mathiesen, E B; Nijpels, G; Okazaki, S; O'Leary, D H; Polak, J F; Price, J F; Robertson, C; Rembold, C M; Rosvall, M; Rundek, T; Salonen, J T; Sitzer, M; Stehouwer, C D A; Witteman, J C; Moons, K G; Bots, M L

2013-07-01

The aim of this work was to investigate whether measurement of the mean common carotid intima-media thickness (CIMT) improves cardiovascular risk prediction in individuals with diabetes. We performed a subanalysis among 4,220 individuals with diabetes in a large ongoing individual participant data meta-analysis involving 56,194 subjects from 17 population-based cohorts worldwide. We first refitted the risk factors of the Framingham heart risk score on the individuals without previous cardiovascular disease (baseline model) and then expanded this model with the mean common CIMT (CIMT model). The absolute 10 year risk for developing a myocardial infarction or stroke was estimated from both models. In individuals with diabetes we compared discrimination and calibration of the two models. Reclassification of individuals with diabetes was based on allocation to another cardiovascular risk category when mean common CIMT was added. During a median follow-up of 8.7 years, 684 first-time cardiovascular events occurred among the population with diabetes. The C statistic was 0.67 for the Framingham model and 0.68 for the CIMT model. The absolute 10 year risk for developing a myocardial infarction or stroke was 16% in both models. There was no net reclassification improvement with the addition of mean common CIMT (1.7%; 95% CI -1.8, 3.8). There were no differences in the results between men and women. There is no improvement in risk prediction in individuals with diabetes when measurement of the mean common CIMT is added to the Framingham risk score. Therefore, this measurement is not recommended for improving individual cardiovascular risk stratification in individuals with diabetes.

A Model for Generating Multi-hazard Scenarios

NASA Astrophysics Data System (ADS)

Lo Jacomo, A.; Han, D.; Champneys, A.

2017-12-01

Communities in mountain areas are often subject to risk from multiple hazards, such as earthquakes, landslides, and floods. Each hazard has its own different rate of onset, duration, and return period. Multiple hazards tend to complicate the combined risk due to their interactions. Prioritising interventions for minimising risk in this context is challenging. We developed a probabilistic multi-hazard model to help inform decision making in multi-hazard areas. The model is applied to a case study region in the Sichuan province in China, using information from satellite imagery and in-situ data. The model is not intended as a predictive model, but rather as a tool which takes stakeholder input and can be used to explore plausible hazard scenarios over time. By using a Monte Carlo framework and varrying uncertain parameters for each of the hazards, the model can be used to explore the effect of different mitigation interventions aimed at reducing the disaster risk within an uncertain hazard context.

Predicting invasive fungal disease due to Candida species in non-neutropenic, critically ill, adult patients in United Kingdom critical care units.

PubMed

Shahin, Jason; Allen, Elizabeth J; Patel, Krishna; Muskett, Hannah; Harvey, Sheila E; Edgeworth, Jonathan; Kibbler, Christopher C; Barnes, Rosemary A; Biswas, Sharmistha; Soni, Neil; Rowan, Kathryn M; Harrison, David A

2016-09-09

Given the predominance of invasive fungal disease (IFD) amongst the non-immunocompromised adult critically ill population, the potential benefit of antifungal prophylaxis and the lack of generalisable tools to identify high risk patients, the aim of the current study was to describe the epidemiology of IFD in UK critical care units, and to develop and validate a clinical risk prediction tool to identify non-neutropenic, critically ill adult patients at high risk of IFD who would benefit from antifungal prophylaxis. Data on risk factors for, and outcomes from, IFD were collected for consecutive admissions to adult, general critical care units in the UK participating in the Fungal Infection Risk Evaluation (FIRE) Study. Three risk prediction models were developed to model the risk of subsequent Candida IFD based on information available at three time points: admission to the critical care unit, at the end of 24 h and at the end of calendar day 3 of the critical care unit stay. The final model at each time point was evaluated in the three external validation samples. Between July 2009 and April 2011, 60,778 admissions from 96 critical care units were recruited. In total, 359 admissions (0.6 %) were admitted with, or developed, Candida IFD (66 % Candida albicans). At the rate of candidaemia of 3.3 per 1000 admissions, blood was the most common Candida IFD infection site. Of the initial 46 potential variables, the final admission model and the 24-h model both contained seven variables while the end of calendar day 3 model contained five variables. The end of calendar day 3 model performed the best with a c index of 0.709 in the full validation sample. Incidence of Candida IFD in UK critical care units in this study was consistent with reports from other European epidemiological studies, but lower than that suggested by previous hospital-wide surveillance in the UK during the 1990s. Risk modeling using classical statistical methods produced relatively simple risk models, and associated clinical decision rules, that provided acceptable discrimination for identifying patients at 'high risk' of Candida IFD. The FIRE Study was reviewed and approved by the Bolton NHS Research Ethics Committee (reference: 08/H1009/85), the Scotland A Research Ethics Committee (reference: 09/MRE00/76) and the National Information Governance Board (approval number: PIAG 2-10(f)/2005).

Using animal models to study post-partum psychiatric disorders.

PubMed

Perani, C V; Slattery, D A

2014-10-01

The post-partum period represents a time during which all maternal organisms undergo substantial plasticity in a wide variety of systems in order to ensure the well-being of the offspring. Although this time is generally associated with increased calmness and decreased stress responses, for a substantial subset of mothers, this period represents a time of particular risk for the onset of psychiatric disorders. Thus, post-partum anxiety, depression and, to a lesser extent, psychosis may develop, and not only affect the well-being of the mother but also place at risk the long-term health of the infant. Although the risk factors for these disorders, as well as normal peripartum-associated adaptations, are well known, the underlying aetiology of post-partum psychiatric disorders remains poorly understood. However, there have been a number of attempts to model these disorders in basic research, which aim to reveal their underlying mechanisms. In the following review, we first discuss known peripartum adaptations and then describe post-partum mood and anxiety disorders, including their risk factors, prevalence and symptoms. Thereafter, we discuss the animal models that have been designed in order to study them and what they have revealed about their aetiology to date. Overall, these studies show that it is feasible to study such complex disorders in animal models, but that more needs to be done in order to increase our knowledge of these severe and debilitating mood and anxiety disorders. © 2014 The British Pharmacological Society.

Assessing the polycyclic aromatic hydrocarbon (PAH) pollution of urban stormwater runoff: a dynamic modeling approach.

PubMed

Zheng, Yi; Lin, Zhongrong; Li, Hao; Ge, Yan; Zhang, Wei; Ye, Youbin; Wang, Xuejun

2014-05-15

Urban stormwater runoff delivers a significant amount of polycyclic aromatic hydrocarbons (PAHs), mostly of atmospheric origin, to receiving water bodies. The PAH pollution of urban stormwater runoff poses serious risk to aquatic life and human health, but has been overlooked by environmental modeling and management. This study proposed a dynamic modeling approach for assessing the PAH pollution and its associated environmental risk. A variable time-step model was developed to simulate the continuous cycles of pollutant buildup and washoff. To reflect the complex interaction among different environmental media (i.e. atmosphere, dust and stormwater), the dependence of the pollution level on antecedent weather conditions was investigated and embodied in the model. Long-term simulations of the model can be efficiently performed, and probabilistic features of the pollution level and its risk can be easily determined. The applicability of this approach and its value to environmental management was demonstrated by a case study in Beijing, China. The results showed that Beijing's PAH pollution of road runoff is relatively severe, and its associated risk exhibits notable seasonal variation. The current sweeping practice is effective in mitigating the pollution, but the effectiveness is both weather-dependent and compound-dependent. The proposed modeling approach can help identify critical timing and major pollutants for monitoring, assessing and controlling efforts to be focused on. The approach is extendable to other urban areas, as well as to other contaminants with similar fate and transport as PAHs. Copyright © 2014 Elsevier B.V. All rights reserved.

Novel method of vulnerability assessment of simple landfills area using the multimedia, multipathway and multireceptor risk assessment (3MRA) model, China.

PubMed

Yuan, Ying; He, Xiao-Song; Xi, Bei-Dou; Wei, Zi-Min; Tan, Wen-Bing; Gao, Ru-Tai

2016-11-01

Vulnerability assessment of simple landfills was conducted using the multimedia, multipathway and multireceptor risk assessment (3MRA) model for the first time in China. The minimum safe threshold of six contaminants (benzene, arsenic (As), cadmium (Cd), hexavalent chromium [Cr(VI)], divalent mercury [Hg(II)] and divalent nickel [Ni(II)]) in landfill and waste pile models were calculated by the 3MRA model. Furthermore, the vulnerability indexes of the six contaminants were predicted based on the model calculation. The results showed that the order of health risk vulnerability index was As > Hg(II) > Cr(VI) > benzene > Cd > Ni(II) in the landfill model, whereas the ecology risk vulnerability index was in the order of As > Hg(II) > Cr(VI) > Cd > benzene > Ni(II). In the waste pile model, the order of health risk vulnerability index was benzene > Hg(II) > Cr(VI) > As > Cd and Ni(II), whereas the ecology risk vulnerability index was in the order of Hg(II) > Cd > Cr(VI) > As > benzene > Ni(II). These results indicated that As, Hg(II) and Cr(VI) were the high risk contaminants for the case of a simple landfill in China; the concentration of these in soil and groundwater around the simple landfill should be strictly monitored, and proper mediation is also recommended for simple landfills with a high concentration of contaminants. © The Author(s) 2016.

Time-lapse seismic waveform modelling and attribute analysis using hydromechanical models for a deep reservoir undergoing depletion

NASA Astrophysics Data System (ADS)

He, Y.-X.; Angus, D. A.; Blanchard, T. D.; Wang, G.-L.; Yuan, S.-Y.; Garcia, A.

2016-04-01

Extraction of fluids from subsurface reservoirs induces changes in pore pressure, leading not only to geomechanical changes, but also perturbations in seismic velocities and hence observable seismic attributes. Time-lapse seismic analysis can be used to estimate changes in subsurface hydromechanical properties and thus act as a monitoring tool for geological reservoirs. The ability to observe and quantify changes in fluid, stress and strain using seismic techniques has important implications for monitoring risk not only for petroleum applications but also for geological storage of CO2 and nuclear waste scenarios. In this paper, we integrate hydromechanical simulation results with rock physics models and full-waveform seismic modelling to assess time-lapse seismic attribute resolution for dynamic reservoir characterization and hydromechanical model calibration. The time-lapse seismic simulations use a dynamic elastic reservoir model based on a North Sea deep reservoir undergoing large pressure changes. The time-lapse seismic traveltime shifts and time strains calculated from the modelled and processed synthetic data sets (i.e. pre-stack and post-stack data) are in a reasonable agreement with the true earth models, indicating the feasibility of using 1-D strain rock physics transform and time-lapse seismic processing methodology. Estimated vertical traveltime shifts for the overburden and the majority of the reservoir are within ±1 ms of the true earth model values, indicating that the time-lapse technique is sufficiently accurate for predicting overburden velocity changes and hence geomechanical effects. Characterization of deeper structure below the overburden becomes less accurate, where more advanced time-lapse seismic processing and migration is needed to handle the complex geometry and strong lateral induced velocity changes. Nevertheless, both migrated full-offset pre-stack and near-offset post-stack data image the general features of both the overburden and reservoir units. More importantly, the results from this study indicate that integrated seismic and hydromechanical modelling can help constrain time-lapse uncertainty and hence reduce risk due to fluid extraction and injection.

Time-based collision risk modeling for air traffic management

NASA Astrophysics Data System (ADS)

Bell, Alan E.

Since the emergence of commercial aviation in the early part of last century, economic forces have driven a steadily increasing demand for air transportation. Increasing density of aircraft operating in a finite volume of airspace is accompanied by a corresponding increase in the risk of collision, and in response to a growing number of incidents and accidents involving collisions between aircraft, governments worldwide have developed air traffic control systems and procedures to mitigate this risk. The objective of any collision risk management system is to project conflicts and provide operators with sufficient opportunity to recognize potential collisions and take necessary actions to avoid them. It is therefore the assertion of this research that the currency of collision risk management is time. Future Air Traffic Management Systems are being designed around the foundational principle of four dimensional trajectory based operations, a method that replaces legacy first-come, first-served sequencing priorities with time-based reservations throughout the airspace system. This research will demonstrate that if aircraft are to be sequenced in four dimensions, they must also be separated in four dimensions. In order to separate aircraft in four dimensions, time must emerge as the primary tool by which air traffic is managed. A functional relationship exists between the time-based performance of aircraft, the interval between aircraft scheduled to cross some three dimensional point in space, and the risk of collision. This research models that relationship and presents two key findings. First, a method is developed by which the ability of an aircraft to meet a required time of arrival may be expressed as a robust standard for both industry and operations. Second, a method by which airspace system capacity may be increased while maintaining an acceptable level of collision risk is presented and demonstrated for the purpose of formulating recommendations for procedures regulating air traffic management methods and industry standards governing performance requirements for avionics designed to support trajectory based operations.

Differences in incidence of suicide attempts between bipolar I and II disorders and major depressive disorder.

PubMed

Holma, K Mikael; Haukka, Jari; Suominen, Kirsi; Valtonen, Hanna M; Mantere, Outi; Melartin, Tarja K; Sokero, T Petteri; Oquendo, Maria A; Isometsä, Erkki T

2014-09-01

Whether risk of suicide attempts (SAs) differs between patients with bipolar disorder (BD) and patients with major depressive disorder (MDD) is unclear. We investigated whether cumulative risk differences are due to dissimilarities in time spent in high-risk states, incidence per unit time in high-risk states, or both. Incidence rates for SAs during various illness phases, based on prospective life charts, were compared between patients from the Jorvi Bipolar Study (n = 176; 18 months) and the Vantaa Depression Study (n = 249; five years). Risk factors and their interactions with diagnosis were investigated with Cox proportional hazards models. By 18 months, 19.9% of patients with BD versus 9.5% of patients with MDD had attempted suicide. However, patients with BD spent 4.6% of the time in mixed episodes, and more time in major depressive episodes (MDEs) (35% versus 21%, respectively) and in subthreshold depression (39% versus 31%, respectively) than those with MDD. Compared with full remission, the combined incidence rates of SAs were 5-, 25-, and 65-fold in subthreshold depression, MDEs, and BD mixed states, respectively. Between cohorts, incidence of attempts was not different during comparable symptom states. In Cox models, hazard was elevated during MDEs and subthreshold depression, and among patients with preceding SAs, female patients, those with poor social support, and those aged < 40 years, but was unrelated to BD diagnosis. The observed higher cumulative incidence of SAs among patients with BD than among those with MDD is mostly due to patients with BD spending more time in high-risk illness phases, not to differences in incidence during these phases, or to bipolarity itself. BD mixed phases contribute to differences involving very high incidence, but short duration. Diminishing the time spent in high-risk phases is crucial for prevention. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Evolution of natural risk: research framework and perspectives

NASA Astrophysics Data System (ADS)

Hufschmidt, G.; Crozier, M.; Glade, T.

2005-05-01

This study presents a conceptual framework for addressing temporal variation in natural risk. Numerous former natural risk analyses and investigations have demonstrated that time and related changes have a crucial influence on risk. For natural hazards, time becomes a factor for a number of reasons. Using the example of landslides to illustrate this point, it is shown that: 1. landslide history is important in determining probability of occurrence, 2. the significance of catchment variables in explaining landslide susceptibility is dependent on the time scale chosen, 3. the observer's perception of the geosystem's state changes with different time spans, and 4. the system's sensitivity varies with time. Natural hazards are not isolated events but complex features that are connected with the social system. Similarly, elements at risk and their vulnerability are highly dynamic through time, an aspect that is not sufficiently acknowledged in research. Since natural risk is an amalgam of hazard and vulnerability, its temporal behaviour has to be considered as well. Identifying these changes and their underlying processes contributes to a better understanding of natural risk today and in the future. However, no dynamic models for natural risks are currently available. Dynamic behaviour of factors affecting risk is likely to create increasing connectivity and complexity. This demands a broad approach to natural risk, since the concept of risk encapsulates aspects of many disciplines and has suffered from single-discipline approaches in the past. In New Zealand, dramatic environmental and social change has occurred in a relatively short period of time, graphically demonstrating the temporal variability of the geosystem and the social system. To understand these changes and subsequent interactions between both systems, a holistic perspective is needed. This contribution reviews available frameworks, demonstrates the need for further concepts, and gives research perspectives on a New Zealand example.

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.

Physical activity and risk of alcohol use disorders: results from a prospective cohort study.

PubMed

Ejsing, Louise Kristiansen; Becker, Ulrik; Tolstrup, Janne S; Flensborg-Madsen, Trine

2015-03-01

To examine the effect of physical activity on risk of developing alcohol use disorders in a large prospective cohort study with focus on leisure-time physical activity. Data came from the four examinations of the Copenhagen City Heart Study (CCHS), performed in 1976-1978, 1981-1983, 1991-1994 and 2001-2003. Information on physical activity (classified as Moderate/high, low or sedentary) and covariates was obtained through self-administered questionnaires, and information on alcohol use disorders was obtained from the Danish Hospital Discharge Register, the Danish Psychiatric Central Research Register and the Winalco database. In total, 18,359 people participated in the study, a mean follow-up time of 20.9 years. Cox proportional hazards model with delayed entry was used. Models were adjusted for available covariates (age, smoking habits, alcohol intake, education, income and cohabitation status) including updated time-dependent variables whenever possible. A low or moderate/high leisure-time physical activity was associated with almost half the risk of developing alcohol use disorder compared with a sedentary leisure-time physical activity. This translates into a 1.5- to 2-fold increased risk of developing alcohol use disorder (Hazard ratios for men 1.64; 95% CI 1.29-2.10 and women 1.45; 1.01-2.09) in individuals with a sedentary leisure-time physical activity, compared with a moderate to high level. However, when stratifying by presence of other psychiatric disorders, no association was observed in women with psychiatric comorbidity. Residual confounding may have been present in this study, especially according to rough measures of income and education. In both men and women, being sedentary in leisure time was a risk factor for developing an alcohol use disorder. © The Author 2014. Medical Council on Alcohol and Oxford University Press. All rights reserved.

Immunohistochemical and molecular imaging biomarker signature for the prediction of failure site after chemoradiation for head and neck squamous cell carcinoma.

PubMed

Rasmussen, Gregers Brünnich; Håkansson, Katrin E; Vogelius, Ivan R; Rasmussen, Jacob H; Friborg, Jeppe T; Fischer, Barbara M; Schumaker, Lisa; Cullen, Kevin; Therkildsen, Marianne H; Bentzen, Søren M; Specht, Lena

2017-11-01

To identify a failure site-specific prognostic model by combining immunohistochemistry (IHC) and molecular imaging information to predict long-term failure type in squamous cell carcinoma of the head and neck. Tissue microarray blocks of 196 head and neck squamous cell carcinoma cases were stained for a panel of biomarkers using IHC. Gross tumor volume (GTV) from the PET/CT radiation treatment planning CT scan, maximal Standard Uptake Value (SUVmax) of fludeoxyglucose (FDG) and clinical information were included in the model building using Cox proportional hazards models, stratified for p16 status in oropharyngeal carcinomas. Separate models were built for time to locoregional failure and time to distant metastasis. Higher than median p53 expression on IHC tended toward a risk factor for locoregional failure but was protective for distant metastasis, χ 2 for difference p = .003. The final model for locoregional failure included p53 (HR: 1.9; p: .055), concomitant cisplatin (HR: 0.41; p: .008), β-tubulin-1 (HR: 1.8; p: .08), β-tubulin-2 (HR: 0.49; p: .057) and SUVmax (HR: 2.1; p: .046). The final model for distant metastasis included p53 (HR: 0.23; p: .025), Bcl-2 (HR: 2.6; p: .08), SUVmax (HR: 3.5; p: .095) and GTV (HR: 1.7; p: .063). The models successfully distinguished between risk of locoregional failure and risk of distant metastasis, which is important information for clinical decision-making. High p53 expression has opposite prognostic effects for the two endpoints; increasing risk of locoregional failure, but decreasing the risk of metastatic failure, but external validation of this finding is needed.

Left ventricular energy model predicts adverse events in women with suspected myocardial ischemia: results from the NHLBI-sponsored women’s ischemia syndrome evaluation (WISE) study

PubMed Central

Weinberg, Nicole; Pohost, Gerald M.; Bairey Merz, C. Noel; Shaw, Leslee J.; Sopko, George; Fuisz, Anthon; Rogers, William J.; Walsh, Edward G.; Johnson, B. Delia; Sharaf, Barry L.; Pepine, Carl J.; Mankad, Sunil; Reis, Steven E.; Rayarao, Geetha; Vido, Diane A.; Bittner, Vera; Tauxe, Lindsey; Olson, Marian B.; Kelsey, Sheryl F.; Biederman, Robert WW

2013-01-01

Objectives To assess the prognostic value of a left ventricular energy-model in women with suspected myocardial ischemia. Background The prognostic value of internal energy utilization (IEU) of the left ventricle in women with suspected myocardial ischemia is unknown. Methods Women [n=227, mean age 59±12 years (range, 31-86 years)], with symptoms of myocardial ischemia, underwent myocardial perfusion imaging (MPI) assessment for regional perfusion defects along with measurement of ventricular volumes separately by gated Single Photon Emission Computed Tomography (SPECT) (n=207) and magnetic resonance imaging (MRI) (n=203). During follow-up (40±17 months), time to first major adverse cardiovascular event (MACE, death, myocardial infarction or hospitalization for congestive heart failure) was analyzed using MRI and gated SPECT variables. Results Adverse events occurred in 31 (14%). Multivariable Cox models were formed for each modality: IEU and wall thickness by MRI (Chi-squared 34, P<0.005) and IEU and systolic blood pressure by gated SEPCT (Chi-squared 34, P<0.005). The models remained predictive after adjustment for age, disease history and Framingham risk score. For each Cox model, patients were categorized as high-risk if the model hazard was positive and not high-risk otherwise. Kaplan-Meier analysis of time to MACE was performed for high-risk vs. not high-risk for MR (log rank 25.3, P<0.001) and gated SEPCT (log rank 18.2, P<0.001) models. Conclusions Among women with suspected myocardial ischemia a high internal energy utilization has higher prognostic value than either a low EF or the presence of a myocardial perfusion defect assessed using two independent modalities of MR or gated SPECT. PMID:24015377

A multiphase non-linear mixed effects model: An application to spirometry after lung transplantation.

PubMed

Rajeswaran, Jeevanantham; Blackstone, Eugene H

2017-02-01

In medical sciences, we often encounter longitudinal temporal relationships that are non-linear in nature. The influence of risk factors may also change across longitudinal follow-up. A system of multiphase non-linear mixed effects model is presented to model temporal patterns of longitudinal continuous measurements, with temporal decomposition to identify the phases and risk factors within each phase. Application of this model is illustrated using spirometry data after lung transplantation using readily available statistical software. This application illustrates the usefulness of our flexible model when dealing with complex non-linear patterns and time-varying coefficients.

[Joint application of mathematic models in assessing the residual risk of hepatitis C virus transmitted through blood transfusion].

PubMed

Wang, Xun; Jia, Yao; Xie, Yun-zheng; Li, Xiu-mei; Liu, Xiao-ying; Wu, Xiao-fei

2011-09-01

The practicable and effective methods for residual risk assessment on transfusion-transmitted disease was to establish the mathematic models. Based on the characteristics of the repeat donors which donated their blood on a regular base, a model of sero-conversion during the interval of donations was established to assess the incidence of the repeat donors. Based on the characteristics of the prevalence in the population, a model of 'prevalence increased with the age of the donor' was established to assess the incidence of those first-time donors. And based on the impact of the windows period through blood screening program, a model of residual risk associated with the incidence and the length of the windows period was established to assess the residual risk of blood transfusion. In this paper, above said 3 kinds of mathematic models were jointly applied to assess the residual risk of hepatitis C virus (HCV) which was transmitted through blood transfusion in Shanghai, based on data from the routine blood collection and screening program. All the anti-HCV unqualified blood donations were confirmed before assessment. Results showed that the residual risk of HCV transmitted through blood transfusion during Jan. 1(st), 2007 to Dec. 31(st), 2008 in Shanghai was 1:101 000. Data showed that the results of residual risk assessment with mathematic models was valuable. The residual risk of transfusion-transmitted HCV in Shanghai was at a safe level, according to the results in this paper.

Real-Time Optimal Flood Control Decision Making and Risk Propagation Under Multiple Uncertainties

NASA Astrophysics Data System (ADS)

Zhu, Feilin; Zhong, Ping-An; Sun, Yimeng; Yeh, William W.-G.

2017-12-01

Multiple uncertainties exist in the optimal flood control decision-making process, presenting risks involving flood control decisions. This paper defines the main steps in optimal flood control decision making that constitute the Forecast-Optimization-Decision Making (FODM) chain. We propose a framework for supporting optimal flood control decision making under multiple uncertainties and evaluate risk propagation along the FODM chain from a holistic perspective. To deal with uncertainties, we employ stochastic models at each link of the FODM chain. We generate synthetic ensemble flood forecasts via the martingale model of forecast evolution. We then establish a multiobjective stochastic programming with recourse model for optimal flood control operation. The Pareto front under uncertainty is derived via the constraint method coupled with a two-step process. We propose a novel SMAA-TOPSIS model for stochastic multicriteria decision making. Then we propose the risk assessment model, the risk of decision-making errors and rank uncertainty degree to quantify the risk propagation process along the FODM chain. We conduct numerical experiments to investigate the effects of flood forecast uncertainty on optimal flood control decision making and risk propagation. We apply the proposed methodology to a flood control system in the Daduhe River basin in China. The results indicate that the proposed method can provide valuable risk information in each link of the FODM chain and enable risk-informed decisions with higher reliability.

Quantifying Risk Over the Life Course – Latency, Age-Related Susceptibility, and Other Time-Varying Exposure Metrics

PubMed Central

Wang, Molin; Liao, Xiaomei; Laden, Francine; Spiegelman, Donna

2016-01-01

Identification of the latency period and age-related susceptibility, if any, is an important aspect of assessing risks of environmental, nutritional and occupational exposures. We consider estimation and inference for latency and age-related susceptibility in relative risk and excess risk models. We focus on likelihood-based methods for point and interval estimation of the latency period and age-related windows of susceptibility coupled with several commonly considered exposure metrics. The method is illustrated in a study of the timing of the effects of constituents of air pollution on mortality in the Nurses’ Health Study. PMID:26750582

Economic evaluation of strategies for restarting anticoagulation therapy after a first event of unprovoked venous thromboembolism.

PubMed

Monahan, M; Ensor, J; Moore, D; Fitzmaurice, D; Jowett, S

2017-08-01

Essentials Correct duration of treatment after a first unprovoked venous thromboembolism (VTE) is unknown. We assessed when restarting anticoagulation was worthwhile based on patient risk of recurrent VTE. When the risk over a one-year period is 17.5%, restarting is cost-effective. However, sensitivity analyses indicate large uncertainty in the estimates. Background Following at least 3 months of anticoagulation therapy after a first unprovoked venous thromboembolism (VTE), there is uncertainty about the duration of therapy. Further anticoagulation therapy reduces the risk of having a potentially fatal recurrent VTE but at the expense of a higher risk of bleeding, which can also be fatal. Objective An economic evaluation sought to estimate the long-term cost-effectiveness of using a decision rule for restarting anticoagulation therapy vs. no extension of therapy in patients based on their risk of a further unprovoked VTE. Methods A Markov patient-level simulation model was developed, which adopted a lifetime time horizon with monthly time cycles and was from a UK National Health Service (NHS)/Personal Social Services (PSS) perspective. Results Base-case model results suggest that treating patients with a predicted 1 year VTE risk of 17.5% or higher may be cost-effective if decision makers are willing to pay up to £20 000 per quality adjusted life year (QALY) gained. However, probabilistic sensitivity analysis shows that the model was highly sensitive to overall parameter uncertainty and caution is warranted in selecting the optimal decision rule on cost-effectiveness grounds. Univariate sensitivity analyses indicate variables such as anticoagulation therapy disutility and mortality risks were very influential in driving model results. Conclusion This represents the first economic model to consider the use of a decision rule for restarting therapy for unprovoked VTE patients. Better data are required to predict long-term bleeding risks during therapy in this patient group. © 2017 International Society on Thrombosis and Haemostasis.

Possibility of dying as a unified explanation of why we discount the future, get weaker with age, and display risk-aversion.

PubMed

Chowdhry, Bhagwan

2011-01-01

I formulate a simple and parsimonious evolutionary model that shows that because most species face a possibility of dying because of external factors, called extrinsic mortality in the biology literature, it can simultaneously explain (a) why we discount the future, (b) get weaker with age, and (c) display risk-aversion. The paper suggests that testable restrictions—across species, across time, or across genders—among time preference, aging, and risk-aversion could be analyzed in a simple framework .

Prevalence Incidence Mixture Models

Cancer.gov

The R package and webtool fits Prevalence Incidence Mixture models to left-censored and irregularly interval-censored time to event data that is commonly found in screening cohorts assembled from electronic health records. Absolute and relative risk can be estimated for simple random sampling, and stratified sampling (the two approaches of superpopulation and a finite population are supported for target populations). Non-parametric (absolute risks only), semi-parametric, weakly-parametric (using B-splines), and some fully parametric (such as the logistic-Weibull) models are supported.

Multiple, but not traditional risk factors predict mortality in older people: the Concord Health and Ageing in Men Project.

PubMed

Hirani, Vasant; Naganathan, Vasi; Blyth, Fiona; Le Couteur, David G; Gnjidic, Danijela; Stanaway, Fiona F; Seibel, Markus J; Waite, Louise M; Handelsman, David J; Cumming, Robert G

2014-01-01

This study aims to identify the common risk factors for mortality in community-dwelling older men. A prospective population-based study was conducted with a median of 6.7 years of follow-up. Participants included 1705 men aged ≥70 years at baseline (2005-2007) living in the community in Sydney, Australia. Demographic information, lifestyle factors, health status, self-reported history of diseases, physical performance measures, blood pressure, height and weight, disability (activities of daily living (ADL) and instrumental ADLs, instrumental ADLs (IADLs)), cognitive status, depressive symptoms and blood analyte measures were considered. Cox regression analyses were conducted to model predictors delete time until of mortality. During follow-up, 461 men (27 %) died. Using Cox proportional hazards model, significant predictors of delete time to time to mortality included in the final model (p < 0.05) were older age, body mass index < 20 kg m(2), high white cell count, anaemia, low albumin, current smoking, history of cancer, history of myocardial infarction, history of congestive heart failure, depressive symptoms and ADL and IADL disability and impaired chair stands. We found that overweight and obesity and/or being a lifelong non-drinker of alcohol were protective against mortality. Compared to men with less than or equal to one risk factor, the hazard ratio in men with three risk factors was 2.5; with four risk factors, it was 4.0; with five risk factors, it was 4.9; and for six or more risk factors, it was 11.4, respectively. We have identified common risk factors that predict mortality that may be useful in making clinical decisions among older people living in the community. Our findings suggest that, in primary care, screening and management of multiple risk factors are important to consider for extending survival, rather than simply considering individual risk factors in isolation. Some of the "traditional" risk factors for mortality in a younger population, including high blood pressure, hypercholesterolaemia, overweight and obesity and diabetes, were not independent predictors of mortality in this population of older men.

The risks and returns of stock investment in a financial market

NASA Astrophysics Data System (ADS)

Li, Jiang-Cheng; Mei, Dong-Cheng

2013-03-01

The risks and returns of stock investment are discussed via numerically simulating the mean escape time and the probability density function of stock price returns in the modified Heston model with time delay. Through analyzing the effects of delay time and initial position on the risks and returns of stock investment, the results indicate that: (i) There is an optimal delay time matching minimal risks of stock investment, maximal average stock price returns and strongest stability of stock price returns for strong elasticity of demand of stocks (EDS), but the opposite results for weak EDS; (ii) The increment of initial position recedes the risks of stock investment, strengthens the average stock price returns and enhances stability of stock price returns. Finally, the probability density function of stock price returns and the probability density function of volatility and the correlation function of stock price returns are compared with other literatures. In addition, good agreements are found between them.

Reinforcement Sensitivity and Risk for Psychopathology Following Exposure to Violence: A Vulnerability-Specificity Model in Latino Youth

PubMed Central

Gudiño, Omar G.; Nadeem, Erum; Kataoka, Sheryl H.; Lau, Anna S.

2013-01-01

Urban Latino youth are exposed to high rates of violence, which increases risk for diverse forms of psychopathology. To current study aims to increase specificity in predicting responses by testing the hypothesis that youths’ reinforcement sensitivity–behavioral inhibition (BIS) and behavioral approach (BAS)–is associated with specific clinical outcomes and increases risk for the development of such problems following exposure to violence. Utilizing a short-term longitudinal design, Latino youth (N=168) provided reports of BIS/BAS and emotional/behavioral problems at Time 1, exposure to violence between Time 1 and Time 2, and clinical symptoms at Time 2. Results suggested that reinforcement sensitivity moderated the relation between violence exposure and psychopathology, such that increasing levels of BIS were associated with elevated risk for internalizing and posttraumatic stress symptoms following exposure to violence whereas BAS increased risk for externalizing problems. The importance of building on existing knowledge to understand minority youth psychopathology is discussed. PMID:22080366

The risk of establishment of aquatic invasive species: joining invasibility and propagule pressure

PubMed Central

Leung, Brian; Mandrak, Nicholas E

2007-01-01

Invasive species are increasingly becoming a policy priority. This has spurred researchers and managers to try to estimate the risk of invasion. Conceptually, invasions are dependent both on the receiving environment (invasibility) and on the ability to reach these new areas (propagule pressure). However, analyses of risk typically examine only one or the other. Here, we develop and apply a joint model of invasion risk that simultaneously incorporates invasibility and propagule pressure. We present arguments that the behaviour of these two elements of risk differs substantially—propagule pressure is a function of time, whereas invasibility is not—and therefore have different management implications. Further, we use the well-studied zebra mussel (Dreissena polymorpha) to contrast predictions made using the joint model to those made by separate invasibility and propagule pressure models. We show that predictions of invasion progress as well as of the long-term invasion pattern are strongly affected by using a joint model. PMID:17711834

The risk of establishment of aquatic invasive species: joining invasibility and propagule pressure.

PubMed

Leung, Brian; Mandrak, Nicholas E

2007-10-22

Invasive species are increasingly becoming a policy priority. This has spurred researchers and managers to try to estimate the risk of invasion. Conceptually, invasions are dependent both on the receiving environment (invasibility) and on the ability to reach these new areas (propagule pressure). However, analyses of risk typically examine only one or the other. Here, we develop and apply a joint model of invasion risk that simultaneously incorporates invasibility and propagule pressure. We present arguments that the behaviour of these two elements of risk differs substantially--propagule pressure is a function of time, whereas invasibility is not--and therefore have different management implications. Further, we use the well-studied zebra mussel (Dreissena polymorpha) to contrast predictions made using the joint model to those made by separate invasibility and propagule pressure models. We show that predictions of invasion progress as well as of the long-term invasion pattern are strongly affected by using a joint model.

Grading the probabilities of credit default risk for Malaysian listed companies by using the KMV-Merton model

NASA Astrophysics Data System (ADS)

Anuwar, Muhammad Hafidz; Jaffar, Maheran Mohd

2017-08-01

This paper provides an overview for the assessment of credit risk specific to the banks. In finance, risk is a term to reflect the potential of financial loss. The risk of default on loan may increase when a company does not make a payment on that loan when the time comes. Hence, this framework analyses the KMV-Merton model to estimate the probabilities of default for Malaysian listed companies. In this way, banks can verify the ability of companies to meet their loan commitments in order to overcome bad investments and financial losses. This model has been applied to all Malaysian listed companies in Bursa Malaysia for estimating the credit default probabilities of companies and compare with the rating given by the rating agency, which is RAM Holdings Berhad to conform to reality. Then, the significance of this study is a credit risk grade is proposed by using the KMV-Merton model for the Malaysian listed companies.

A New View of Radiation-Induced Cancer: Integrating Short- and Long-Term Processes. Part II: Second Cancer Risk Estimation

NASA Technical Reports Server (NTRS)

Shuryak, Igor; Brenner, David J.; Hahnfeldt, Philip; Hlatky, Lynn; Sachs, Rainer K.

2009-01-01

As the number of cancer survivors grows, prediction of radiotherapy-induced second cancer risks becomes increasingly important. Because the latency period for solid tumors is long, the risks of recently introduced radiotherapy protocols are not yet directly measurable. In the accompanying article, we presented a new biologically based mathematical model, which, in principle, can estimate second cancer risks for any protocol. The novelty of the model is that it integrates, into a single formalism, mechanistic analyses of pre-malignant cell dynamics on two different time scales: short-term during radiotherapy and recovery; long-term during the entire life span. Here, we apply the model to nine solid cancer types (stomach, lung, colon, rectal, pancreatic, bladder, breast, central nervous system, and thyroid) using data on radiotherapy-induced second malignancies, on Japanese atomic bomb survivors, and on background US cancer incidence. Potentially, the model can be incorporated into radiotherapy treatment planning algorithms, adding second cancer risk as an optimization criterion.

A new view of radiation-induced cancer: integrating short- and long-term processes. Part II: second cancer risk estimation.

PubMed

Shuryak, Igor; Hahnfeldt, Philip; Hlatky, Lynn; Sachs, Rainer K; Brenner, David J

2009-08-01

As the number of cancer survivors grows, prediction of radiotherapy-induced second cancer risks becomes increasingly important. Because the latency period for solid tumors is long, the risks of recently introduced radiotherapy protocols are not yet directly measurable. In the accompanying article, we presented a new biologically based mathematical model, which, in principle, can estimate second cancer risks for any protocol. The novelty of the model is that it integrates, into a single formalism, mechanistic analyses of pre-malignant cell dynamics on two different time scales: short-term during radiotherapy and recovery; long-term during the entire life span. Here, we apply the model to nine solid cancer types (stomach, lung, colon, rectal, pancreatic, bladder, breast, central nervous system, and thyroid) using data on radiotherapy-induced second malignancies, on Japanese atomic bomb survivors, and on background US cancer incidence. Potentially, the model can be incorporated into radiotherapy treatment planning algorithms, adding second cancer risk as an optimization criterion.

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.

Introduction of a new laboratory test: an econometric approach with the use of neural network analysis.

PubMed

Jabor, A; Vlk, T; Boril, P

1996-04-15

We designed a simulation model for the assessment of the financial risks involved when a new diagnostic test is introduced in the laboratory. The model is based on a neural network consisting of ten neurons and assumes that input entities can have assigned appropriate uncertainty. Simulations are done on a 1-day interval basis. Risk analysis completes the model and the financial effects are evaluated for a selected time period. The basic output of the simulation consists of total expenses and income during the simulation time, net present value of the project at the end of simulation, total number of control samples during simulation, total number of patients evaluated and total number of used kits.

Assessing the Effect of Spaceflight on the Propensity for Astronauts to Develop Disk Herniation

NASA Technical Reports Server (NTRS)

Feiveson, A. H.; Mendez, C. M.; Somers, J. T.

2014-01-01

BACKGROUND: A previous study [1] reported that the instantaneous risk of developing a Herniated Nucleus Pulposus (HNP) was higher in astronauts who had flown at least one mission, as compared with those in the corps who had not yet flown. However, the study only analyzed time to HNP after the first mission (if any) and did not account for the possible effects of multiple missions. While many HNP's occurred well into astronauts' careers or in some cases years after retirement, the higher incidence of HNPs relatively soon after completion of space missions appears to indicate that spaceflight may lead to an increased risk of HNP. The purpose of this study was to support the Human System Risk Board assessment of back pain, evaluate the risk of injury due to dynamic loads, and update the previous dataset which contained events up to December 31, 2006. METHODS: Data was queried from the electronic medical record and provided by the Lifetime Surveillance of Astronaut Health. The data included all 330 United States astronauts from 1959 through February 2014. Cases were confirmed by Magnetic Resonance Imaging, Computerized Tomography, Myelography, operative findings, or through clinical confirmation with a neurologist or neurosurgeon. In this analysis, astronauts who had an HNP at selection into the corps or had an HNP diagnosis prior to their first flight were excluded. The statistical challenges in using the available data to separate effects of spaceflight from those associated with general astronaut training and lifestyle on propensity to develop HNPs are many. The primary outcome is reported date of first HNP (if any), which at best is only an approximation to the actual time of occurrence. To properly analyze this data with a survival analysis model, one must also know the "exposure" time - i.e. how long each astronaut has been at risk for developing an HNP. If an HNP is reported soon after a mission, is it mission caused or general? If the former, exposure time should be counted from the time of landing (assuming the risk of HNP occurring during a mission is zero). If the latter, exposure time should be counted from the time of selection; however we can't directly know which one to use. In our analysis we take both of these possibilities into account with a competing risks model, wherein two distinct stochastic processes are going on: TG = time to HNP (general) and TS = time to HNP (spaceflight). Under this type of model, whichever of these occurs first is what we observe; in other words we don't observe TG or TS, only min(TG, TS). Here, we parameterized the model in terms of separate Weibull hazard functions for each process and estimated all parameters using maximum likelihood. In addition, we allowed for a "cured fraction" - i.e. the possibility that some astronauts may never develop an HNP. RESULTS: Results will include a depiction of the competing hazard functions as well as a probability curve for the relative likelihood that an HNP reported at a given time after a mission is actually mission caused. Other factors, such as dwell time in microgravity and vehicle landing environment will be explored. An overall assessment as to whether spaceflight truly exacerbates HNP risk will be made.

A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling.

PubMed

Leger, Stefan; Zwanenburg, Alex; Pilz, Karoline; Lohaus, Fabian; Linge, Annett; Zöphel, Klaus; Kotzerke, Jörg; Schreiber, Andreas; Tinhofer, Inge; Budach, Volker; Sak, Ali; Stuschke, Martin; Balermpas, Panagiotis; Rödel, Claus; Ganswindt, Ute; Belka, Claus; Pigorsch, Steffi; Combs, Stephanie E; Mönnich, David; Zips, Daniel; Krause, Mechthild; Baumann, Michael; Troost, Esther G C; Löck, Steffen; Richter, Christian

2017-10-16

Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco-regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g. C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints.

Modeling methylene chloride exposure-reduction options for home paint-stripper users.

PubMed

Riley, D M; Small, M J; Fischhoff, B

2000-01-01

Home improvement is a popular activity, but one that can also involve exposure to hazardous substances. Paint stripping is of particular concern because of the high potential exposures to methylene chloride, a solvent that is a potential human carcinogen and neurotoxicant. This article presents a general methodology for evaluating the effectiveness of behavioral interventions for reducing these risks. It doubles as a model that assesses exposure patterns, incorporating user time-activity patterns and risk-mitigation strategies. The model draws upon recent innovations in indoor air-quality modeling to estimate exposure through inhalation and dermal pathways to paint-stripper users. It is designed to use data gathered from home paint-stripper users about room characteristics, amount of stripper used, time-activity patterns and exposure-reduction strategies (e.g., increased ventilation and modification in the timing of stripper application, scraping, and breaks). Results indicate that the effectiveness of behavioral interventions depends strongly on characteristics of the room (e.g., size, number and size of doors and windows, base air-exchange rates). The greatest simple reduction in exposure is achieved by using an exhaust fan in addition to opening windows and doors. These results can help identify the most important information for product labels and other risk-communication materials.

From alcohol initiation to tolerance to problems: Discordant twin modeling of a developmental process

PubMed Central

Deutsch, Arielle R.; Slutske, Wendy S.; Lynskey, Michael T.; Bucholz, Kathleen K.; Madden, Pamela A. F.; Heath, Andrew C.; Martin, Nicholas G.

2017-01-01

The current study examined a stage-based alcohol use trajectory model to test for potential causal effects of earlier drinking milestones on later drinking milestones in a combined sample of two cohorts of Australian monozygotic and same-sex dizygotic twins (N = 7,398, age M = 30.46, SD = 2.61, 61% mal 56% monozygotic twins). Ages of drinking, drunkenness, regular drinking, tolerance, first nontolerance alcohol use disorder symptom, and alcohol use disorder symptom onsets were assessed retrospectively. Ages of milestone attainment (i.e., age-of-onset) and time between milestones (i.e., time-to-even were examined via frailty models within a multilevel discordant twin design. For age-of-onset models, earlier ages of onset of antecedent drinking milestones increased hazards for earlier ages of onset for more proximal subsequent drinking milestones. For the time-to-event models, however, earlier ag of onset for the “starting” milestone decreased risk for a shorter time period between the starting and the “ending” milestone. Earlier age of onset of intermediate milestones between starting and ending drinking milestones had the opposite effect, increasing risk for a shorter time period between the starti and ending milestones. These results are consistent with a causal effect of an earlier age of drinking milestone onset on temporally proximal subsequent drinking milestones. PMID:27417028

Time-varying mixed logit model for vehicle merging behavior in work zone merging areas.

PubMed

Weng, Jinxian; Du, Gang; Li, Dan; Yu, Yao

2018-08-01

This study aims to develop a time-varying mixed logit model for the vehicle merging behavior in work zone merging areas during the merging implementation period from the time of starting a merging maneuver to that of completing the maneuver. From the safety perspective, vehicle crash probability and severity between the merging vehicle and its surrounding vehicles are regarded as major factors influencing vehicle merging decisions. Model results show that the model with the use of vehicle crash risk probability and severity could provide higher prediction accuracy than previous models with the use of vehicle speeds and gap sizes. It is found that lead vehicle type, through lead vehicle type, through lag vehicle type, crash probability of the merging vehicle with respect to the through lag vehicle, crash severities of the merging vehicle with respect to the through lead and lag vehicles could exhibit time-varying effects on the merging behavior. One important finding is that the merging vehicle could become more and more aggressive in order to complete the merging maneuver as quickly as possible over the elapsed time, even if it has high vehicle crash risk with respect to the through lead and lag vehicles. Copyright © 2018 Elsevier Ltd. All rights reserved.

Mapping and Modelling Malaria Risk Areas Using Climate, Socio-Demographic and Clinical Variables in Chimoio, Mozambique.

PubMed

Ferrao, Joao L; Niquisse, Sergio; Mendes, Jorge M; Painho, Marco

2018-04-19

Background : Malaria continues to be a major public health concern in Africa. Approximately 3.2 billion people worldwide are still at risk of contracting malaria, and 80% of deaths caused by malaria are concentrated in only 15 countries, most of which are in Africa. These high-burden countries have achieved a lower than average reduction of malaria incidence and mortality, and Mozambique is among these countries. Malaria eradication is therefore one of Mozambique’s main priorities. Few studies on malaria have been carried out in Chimoio, and there is no malaria map risk of the area. This map is important to identify areas at risk for application of Public Precision Health approaches. By using GIS-based spatial modelling techniques, the research goal of this article was to map and model malaria risk areas using climate, socio-demographic and clinical variables in Chimoio, Mozambique. Methods : A 30 m × 30 m Landsat image, ArcGIS 10.2 and BioclimData were used. A conceptual model for spatial problems was used to create the final risk map. The risks factors used were: the mean temperature, precipitation, altitude, slope, distance to water bodies, distance to roads, NDVI, land use and land cover, malaria prevalence and population density. Layers were created in a raster dataset. For class value comparisons between layers, numeric values were assigned to classes within each map layer, giving them the same importance. The input dataset were ranked, with different weights according to their suitability. The reclassified outputs of the data were combined. Results : Chimoio presented 96% moderate risk and 4% high-risk areas. The map showed that the central and south-west “Residential areas”, namely, Centro Hipico, Trangapsso, Bairro 5 and 1° de Maio, had a high risk of malaria, while the rest of the residential areas had a moderate risk. Conclusions : The entire Chimoio population is at risk of contracting malaria, and the precise estimation of malaria risk, therefore, has important precision public health implications and for the planning of effective control measures, such as the proper time and place to spray to combat vectors, distribution of bed nets and other control measures.

Non-Targeted Effects Models Predict Significantly Higher Mars Mission Cancer Risk than Targeted Effects Models

DOE PAGES

Cucinotta, Francis A.; Cacao, Eliedonna

2017-05-12

Cancer risk is an important concern for galactic cosmic ray (GCR) exposures, which consist of a wide-energy range of protons, heavy ions and secondary radiation produced in shielding and tissues. Relative biological effectiveness (RBE) factors for surrogate cancer endpoints in cell culture models and tumor induction in mice vary considerable, including significant variations for different tissues and mouse strains. Many studies suggest non-targeted effects (NTE) occur for low doses of high linear energy transfer (LET) radiation, leading to deviation from the linear dose response model used in radiation protection. Using the mouse Harderian gland tumor experiment, the only extensive data-setmore » for dose response modelling with a variety of particle types (>4), for the first-time a particle track structure model of tumor prevalence is used to investigate the effects of NTEs in predictions of chronic GCR exposure risk. The NTE model led to a predicted risk 2-fold higher compared to a targeted effects model. The scarcity of data with animal models for tissues that dominate human radiation cancer risk, including lung, colon, breast, liver, and stomach, suggest that studies of NTEs in other tissues are urgently needed prior to long-term space missions outside the protection of the Earth’s geomagnetic sphere.« less

Non-Targeted Effects Models Predict Significantly Higher Mars Mission Cancer Risk than Targeted Effects Models

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

Cucinotta, Francis A.; Cacao, Eliedonna

Cancer risk is an important concern for galactic cosmic ray (GCR) exposures, which consist of a wide-energy range of protons, heavy ions and secondary radiation produced in shielding and tissues. Relative biological effectiveness (RBE) factors for surrogate cancer endpoints in cell culture models and tumor induction in mice vary considerable, including significant variations for different tissues and mouse strains. Many studies suggest non-targeted effects (NTE) occur for low doses of high linear energy transfer (LET) radiation, leading to deviation from the linear dose response model used in radiation protection. Using the mouse Harderian gland tumor experiment, the only extensive data-setmore » for dose response modelling with a variety of particle types (>4), for the first-time a particle track structure model of tumor prevalence is used to investigate the effects of NTEs in predictions of chronic GCR exposure risk. The NTE model led to a predicted risk 2-fold higher compared to a targeted effects model. The scarcity of data with animal models for tissues that dominate human radiation cancer risk, including lung, colon, breast, liver, and stomach, suggest that studies of NTEs in other tissues are urgently needed prior to long-term space missions outside the protection of the Earth’s geomagnetic sphere.« less

Predictive Modeling of Risk Associated with Temperature Extremes over Continental US

NASA Astrophysics Data System (ADS)

Kravtsov, S.; Roebber, P.; Brazauskas, V.

2016-12-01

We build an extremely statistically accurate, essentially bias-free empirical emulator of atmospheric surface temperature and apply it for meteorological risk assessment over the domain of continental US. The resulting prediction scheme achieves an order-of-magnitude or larger gain of numerical efficiency compared with the schemes based on high-resolution dynamical atmospheric models, leading to unprecedented accuracy of the estimated risk distributions. The empirical model construction methodology is based on our earlier work, but is further modified to account for the influence of large-scale, global climate change on regional US weather and climate. The resulting estimates of the time-dependent, spatially extended probability of temperature extremes over the simulation period can be used as a risk management tool by insurance companies and regulatory governmental agencies.

"An integrative formal model of motivation and decision making: The MGPM*": Correction to Ballard et al. (2016).

PubMed

2017-02-01

Reports an error in "An integrative formal model of motivation and decision making: The MGPM*" by Timothy Ballard, Gillian Yeo, Shayne Loft, Jeffrey B. Vancouver and Andrew Neal ( Journal of Applied Psychology , 2016[Sep], Vol 101[9], 1240-1265). Equation A3 contained an error. This correct equation is provided in the erratum. (The following abstract of the original article appeared in record 2016-28692-001.) We develop and test an integrative formal model of motivation and decision making. The model, referred to as the extended multiple-goal pursuit model (MGPM*), is an integration of the multiple-goal pursuit model (Vancouver, Weinhardt, & Schmidt, 2010) and decision field theory (Busemeyer & Townsend, 1993). Simulations of the model generated predictions regarding the effects of goal type (approach vs. avoidance), risk, and time sensitivity on prioritization. We tested these predictions in an experiment in which participants pursued different combinations of approach and avoidance goals under different levels of risk. The empirical results were consistent with the predictions of the MGPM*. Specifically, participants pursuing 1 approach and 1 avoidance goal shifted priority from the approach to the avoidance goal over time. Among participants pursuing 2 approach goals, those with low time sensitivity prioritized the goal with the larger discrepancy, whereas those with high time sensitivity prioritized the goal with the smaller discrepancy. Participants pursuing 2 avoidance goals generally prioritized the goal with the smaller discrepancy. Finally, all of these effects became weaker as the level of risk increased. We used quantitative model comparison to show that the MGPM* explained the data better than the original multiple-goal pursuit model, and that the major extensions from the original model were justified. The MGPM* represents a step forward in the development of a general theory of decision making during multiple-goal pursuit. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Predicting 30-Day Hospital Readmissions in Acute Myocardial Infarction: The AMI "READMITS" (Renal Function, Elevated Brain Natriuretic Peptide, Age, Diabetes Mellitus, Nonmale Sex, Intervention with Timely Percutaneous Coronary Intervention, and Low Systolic Blood Pressure) Score.

PubMed

Nguyen, Oanh Kieu; Makam, Anil N; Clark, Christopher; Zhang, Song; Das, Sandeep R; Halm, Ethan A

2018-04-17

Readmissions after hospitalization for acute myocardial infarction (AMI) are common. However, the few currently available AMI readmission risk prediction models have poor-to-modest predictive ability and are not readily actionable in real time. We sought to develop an actionable and accurate AMI readmission risk prediction model to identify high-risk patients as early as possible during hospitalization. We used electronic health record data from consecutive AMI hospitalizations from 6 hospitals in north Texas from 2009 to 2010 to derive and validate models predicting all-cause nonelective 30-day readmissions, using stepwise backward selection and 5-fold cross-validation. Of 826 patients hospitalized with AMI, 13% had a 30-day readmission. The first-day AMI model (the AMI "READMITS" score) included 7 predictors: renal function, elevated brain natriuretic peptide, age, diabetes mellitus, nonmale sex, intervention with timely percutaneous coronary intervention, and low systolic blood pressure, had an optimism-corrected C-statistic of 0.73 (95% confidence interval, 0.71-0.74) and was well calibrated. The full-stay AMI model, which included 3 additional predictors (use of intravenous diuretics, anemia on discharge, and discharge to postacute care), had an optimism-corrected C-statistic of 0.75 (95% confidence interval, 0.74-0.76) with minimally improved net reclassification and calibration. Both AMI models outperformed corresponding multicondition readmission models. The parsimonious AMI READMITS score enables early prospective identification of high-risk AMI patients for targeted readmissions reduction interventions within the first 24 hours of hospitalization. A full-stay AMI readmission model only modestly outperformed the AMI READMITS score in terms of discrimination, but surprisingly did not meaningfully improve reclassification. © 2018 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

A Meta-Analysis and Multisite Time-Series Analysis of the Differential Toxicity of Major Fine Particulate Matter Constituents

PubMed Central

Levy, Jonathan I.; Diez, David; Dou, Yiping; Barr, Christopher D.; Dominici, Francesca

2012-01-01

Health risk assessments of particulate matter less than 2.5 μm in diameter (PM2.5) often assume that all constituents of PM2.5 are equally toxic. While investigators in previous epidemiologic studies have evaluated health risks from various PM2.5 constituents, few have conducted the analyses needed to directly inform risk assessments. In this study, the authors performed a literature review and conducted a multisite time-series analysis of hospital admissions and exposure to PM2.5 constituents (elemental carbon, organic carbon matter, sulfate, and nitrate) in a population of 12 million US Medicare enrollees for the period 2000–2008. The literature review illustrated a general lack of multiconstituent models or insight about probabilities of differential impacts per unit of concentration change. Consistent with previous results, the multisite time-series analysis found statistically significant associations between short-term changes in elemental carbon and cardiovascular hospital admissions. Posterior probabilities from multiconstituent models provided evidence that some individual constituents were more toxic than others, and posterior parameter estimates coupled with correlations among these estimates provided necessary information for risk assessment. Ratios of constituent toxicities, commonly used in risk assessment to describe differential toxicity, were extremely uncertain for all comparisons. These analyses emphasize the subtlety of the statistical techniques and epidemiologic studies necessary to inform risk assessments of particle constituents. PMID:22510275

Bias Due to Correlation Between Times-at-Risk for Infection in Epidemiologic Studies Measuring Biological Interactions Between Sexually Transmitted Infections: A Case Study Using Human Papillomavirus Type Interactions

PubMed Central

Malagón, Talía; Lemieux-Mellouki, Philippe; Laprise, Jean-François; Brisson, Marc

2016-01-01

The clustering of human papillomavirus (HPV) infections in some individuals is often interpreted as the result of common risk factors rather than biological interactions between different types of HPV. The intraindividual correlation between times-at-risk for all HPV infections is not generally considered in the analysis of epidemiologic studies. We used a deterministic transmission model to simulate cross-sectional and prospective epidemiologic studies measuring associations between 2 HPV types. When we assumed no interactions, the model predicted that studies would estimate odds ratios and incidence rate ratios greater than 1 between HPV types even after complete adjustment for sexual behavior. We demonstrated that this residual association is due to correlation between the times-at-risk for different HPV types, where individuals become concurrently at risk for all of their partners’ HPV types when they enter a partnership and are not at risk when they are single. This correlation can be controlled in prospective studies by restricting analyses to susceptible individuals with an infected sexual partner. The bias in the measured associations was largest in low-sexual-activity populations, cross-sectional studies, and studies which evaluated infection with a first HPV type as the exposure. These results suggest that current epidemiologic evidence does not preclude the existence of competitive biological interactions between HPV types. PMID:27927619

Time series analysis of demographic and temporal trends of tuberculosis in Singapore.

PubMed

Wah, Win; Das, Sourav; Earnest, Arul; Lim, Leo Kang Yang; Chee, Cynthia Bin Eng; Cook, Alex Richard; Wang, Yee Tang; Win, Khin Mar Kyi; Ong, Marcus Eng Hock; Hsu, Li Yang

2014-10-31

Singapore is an intermediate tuberculosis (TB) incidence country, with a recent rise in TB incidence from 2008, after a fall in incidence since 1998. This study identified population characteristics that were associated with the recent increase in TB cases, and built a predictive model of TB risk in Singapore. Retrospective time series analysis was used to study TB notification data collected from 1995 to 2011 from the Singapore Tuberculosis Elimination Program (STEP) registry. A predictive model was developed based on the data collected from 1995 to 2010 and validated using the data collected in 2011. There was a significant difference in demographic characteristics between resident and non-resident TB cases. TB risk was higher in non-residents than in residents throughout the period. We found no significant association between demographic and macro-economic factors and annual incidence of TB with or without adjusting for the population-at-risk. Despite growing non-resident population, there was a significant decrease in the non-resident TB risk (p < 0.0001). However, there was no evidence of trend in the resident TB risk over this time period, though differences between different demographic groups were apparent with ethnic minorities experiencing higher incidence rates. The study found that despite an increasing size of non-resident population, TB risk among non-residents was decreasing at a rate of about 3% per year. There was an apparent seasonality in the TB reporting.

A comparative ecological risk assessment of Orimulsion and Fuel Oil No. 6 in the coastal marine environment

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

Harwell, M.; Ault, J.; Gentile, J.

1995-12-31

The conduct of comparative ecological risk assessments (CERA) resulting from the release of anthropogenic stressors into coastal marine environments requires theoretical and methodological innovations to integrate contaminant exposure with populations at risk over time and space scales. Consequently, predicted risks must be scaled to allow comparisons of relative ecological impacts in three physical dimensions plus time. This study was designed to compare the risks from hypothetical spills of Orimulsion and Fuel Oil No. 6 into the Tampa Bay ecosystem. The CERA framework used in this study integrates numerical hydrodynamic and transport-and-fate, toxicological, and biological models with extensive spatially explicit databasesmore » that describe the distributions of critical species and habitats. The presentation of the comparative ecological risks is facilitated by visualization and GIS techniques to allow realistic comparisons of toxicant exposures and their co-occurrence with key biological resources over time and across the seascape. A scaling methodology is presented that uses toxicological data as scalars for graphically representing the ecological effects associated with exposure levels for each scenario simulation. The CERA model serves as an interactive tool for assessing the relative ecological consequences of a range of potential exposure scenarios and for forecasting the longer-term productivity of critical biological resources and habitats that are key to ecosystem structure and function.« less

Comprehensive schoolteachers at risk of early exit from work.

PubMed

Mykletun, R J; Mykletun, A

1999-01-01

Risk of early exit from work for teachers was operationalized as high burnout scores, working part-time due to heavy burden and illness or working part-time while also receiving partial disability pension. Data were collected by mailed questionnaires in a cross-sectional study to a random sample of Norwegian comprehensive schoolteachers, response rate = 86% (N = 1860 valid cases). High age increased the risk of early exit from work, but for cynicism the age effect disappeared when sense of competence and stress were introduced in the regression model. Age had no effect for low professional efficacy. Sense of competence effected burnout, but actual competence level and the gap between actual competence and teaching obligations did not. Stress effected all measures of risk of early exit, especially exhaustion. Change as stress factor increased the exhaustion scores, and were also relevant to risk of having a part-time position, and/or partial disability pension.

Biological and statistical approaches to predicting human lung cancer risk from silica.

PubMed

Kuempel, E D; Tran, C L; Bailer, A J; Porter, D W; Hubbs, A F; Castranova, V

2001-01-01

Chronic inflammation is a key step in the pathogenesis of particle-elicited fibrosis and lung cancer in rats, and possibly in humans. In this study, we compute the excess risk estimates for lung cancer in humans with occupational exposure to crystalline silica, using both rat and human data, and using both a threshold approach and linear models. From a toxicokinetic/dynamic model fit to lung burden and pulmonary response data from a subchronic inhalation study in rats, we estimated the minimum critical quartz lung burden (Mcrit) associated with reduced pulmonary clearance and increased neutrophilic inflammation. A chronic study in rats was also used to predict the human excess risk of lung cancer at various quartz burdens, including mean Mcrit (0.39 mg/g lung). We used a human kinetic lung model to link the equivalent lung burdens to external exposures in humans. We then computed the excess risk of lung cancer at these external exposures, using data of workers exposed to respirable crystalline silica and using Poisson regression and lifetable analyses. Finally, we compared the lung cancer excess risks estimated from male rat and human data. We found that the rat-based linear model estimates were approximately three times higher than those based on human data (e.g., 2.8% in rats vs. 0.9-1% in humans, at mean Mcrit lung burden or associated mean working lifetime exposure of 0.036 mg/m3). Accounting for variability and uncertainty resulted in 100-1000 times lower estimates of human critical lung burden and airborne exposure. This study illustrates that assumptions about the relevant biological mechanism, animal model, and statistical approach can all influence the magnitude of lung cancer risk estimates in humans exposed to crystalline silica.

Surface and subsurface geologic risk factors to ground water affecting brownfield redevelopment potential.

PubMed

Kaufman, Martin M; Murray, Kent S; Rogers, Daniel T

2003-01-01

A model is created for assessing the redevelopment potential of brownfields. The model is derived from a space and time conceptual framework that identifies and measures the surface and subsurface risk factors present at brownfield sites. The model then combines these factors with a contamination extent multiplier at each site to create an index of redevelopment potential. Results from the application of the model within an urbanized watershed demonstrate clear differences between the redevelopment potential present within five different near-surface geologic units, with those units containing clay being less vulnerable to subsurface contamination. With and without the extent multiplier, the total risk present at the brownfield sites within all the geologic units is also strongly correlated to the actual costs of remediation. Thus, computing the total surface and subsurface risk within a watershed can help guide the remediation efforts at broad geographic scales, and prioritize the locations for redevelopment.

Software reliability through fault-avoidance and fault-tolerance

NASA Technical Reports Server (NTRS)

Vouk, Mladen A.; Mcallister, David F.

1993-01-01

Strategies and tools for the testing, risk assessment and risk control of dependable software-based systems were developed. Part of this project consists of studies to enable the transfer of technology to industry, for example the risk management techniques for safety-concious systems. Theoretical investigations of Boolean and Relational Operator (BRO) testing strategy were conducted for condition-based testing. The Basic Graph Generation and Analysis tool (BGG) was extended to fully incorporate several variants of the BRO metric. Single- and multi-phase risk, coverage and time-based models are being developed to provide additional theoretical and empirical basis for estimation of the reliability and availability of large, highly dependable software. A model for software process and risk management was developed. The use of cause-effect graphing for software specification and validation was investigated. Lastly, advanced software fault-tolerance models were studied to provide alternatives and improvements in situations where simple software fault-tolerance strategies break down.

Evaluating Predictive Models of Software Quality

NASA Astrophysics Data System (ADS)

Ciaschini, V.; Canaparo, M.; Ronchieri, E.; Salomoni, D.

2014-06-01

Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

Modeling the survival kinetics of Salmonella in tree nuts for use in risk assessment.

PubMed

Santillana Farakos, Sofia M; Pouillot, Régis; Anderson, Nathan; Johnson, Rhoma; Son, Insook; Van Doren, Jane

2016-06-16

Salmonella has been shown to survive in tree nuts over long periods of time. This survival capacity and its variability are key elements for risk assessment of Salmonella in tree nuts. The aim of this study was to develop a mathematical model to predict survival of Salmonella in tree nuts at ambient storage temperatures that considers variability and uncertainty separately and can easily be incorporated into a risk assessment model. Data on Salmonella survival on raw almonds, pecans, pistachios and walnuts were collected from the peer reviewed literature. The Weibull model was chosen as the baseline model and various fixed effect and mixed effect models were fit to the data. The best model identified through statistical analysis testing was then used to develop a hierarchical Bayesian model. Salmonella in tree nuts showed slow declines at temperatures ranging from 21°C to 24°C. A high degree of variability in survival was observed across tree nut studies reported in the literature. Statistical analysis results indicated that the best applicable model was a mixed effect model that included a fixed and random variation of δ per tree nut (which is the time it takes for the first log10 reduction) and a fixed variation of ρ per tree nut (parameter which defines the shape of the curve). Higher estimated survival rates (δ) were obtained for Salmonella on pistachios, followed in decreasing order by pecans, almonds and walnuts. The posterior distributions obtained from Bayesian inference were used to estimate the variability in the log10 decrease levels in survival for each tree nut, and the uncertainty of these estimates. These modeled uncertainty and variability distributions of the estimates can be used to obtain a complete exposure assessment of Salmonella in tree nuts when including time-temperature parameters for storage and consumption data. The statistical approach presented in this study may be applied to any studies that aim to develop predictive models to be implemented in a probabilistic exposure assessment or a quantitative microbial risk assessment. Published by Elsevier B.V.

Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models.

PubMed

Chen, Zewei; Zhang, Xin; Zhang, Zhuoyong

2016-12-01

Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring. Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD. For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models. Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.

Independent and joint effects of sedentary time and cardiorespiratory fitness on all-cause mortality: the Cooper Center Longitudinal Study

PubMed Central

Shuval, Kerem; Finley, Carrie E; Barlow, Carolyn E; Nguyen, Binh T; Njike, Valentine Y; Pettee Gabriel, Kelley

2015-01-01

Objectives To examine the independent and joint effects of sedentary time and cardiorespiratory fitness (fitness) on all-cause mortality. Design, setting, participants A prospective study of 3141 Cooper Center Longitudinal Study participants. Participants provided information on television (TV) viewing and car time in 1982 and completed a maximal exercise test during a 1-year time frame; they were then followed until mortality or through 2010. TV viewing, car time, total sedentary time and fitness were the primary exposures and all-cause mortality was the outcome. The relationship between the exposures and outcome was examined utilising Cox proportional hazard models. Results A total of 581 deaths occurred over a median follow-up period of 28.7 years (SD=4.4). At baseline, participants’ mean age was 45.0 years (SD=9.6), 86.5% were men and their mean body mass index was 24.6 (SD=3.0). Multivariable analyses revealed a significant linear relationship between increased fitness and lower mortality risk, even while adjusting for total sedentary time and covariates (p=0.02). The effects of total sedentary time on increased mortality risk did not quite reach statistical significance once fitness and covariates were adjusted for (p=0.05). When examining this relationship categorically, in comparison to the reference category (≤10 h/week), being sedentary for ≥23 h weekly increased mortality risk by 29% without controlling for fitness (HR=1.29, 95% CI 1.03 to 1.63); however, once fitness and covariates were taken into account this relationship did not reach statistical significance (HR=1.20, 95% CI 0.95 to 1.51). Moreover, spending >10 h in the car weekly significantly increased mortality risk by 27% in the fully adjusted model. The association between TV viewing and mortality was not significant. Conclusions The relationship between total sedentary time and higher mortality risk is less pronounced when fitness is taken into account. Increased car time, but not TV viewing, is significantly related to higher mortality risk, even when taking fitness into account, in this cohort. PMID:26525421

Integrating Environmental and Mosquito Data to Model Disease: Evaluating Alternative Modeling Approaches for Forecasting West Nile Virus in South Dakota, USA

NASA Astrophysics Data System (ADS)

Davis, J. K.; Vincent, G. P.; Hildreth, M.; Kightlinger, L.; Carlson, C.; Wimberly, M. C.

2017-12-01

South Dakota has the highest annual incidence of human cases of West Nile virus (WNV) in all US states, and human cases can vary wildly among years; predicting WNV risk in advance is a necessary exercise if public health officials are to respond efficiently and effectively to risk. Case counts are associated with environmental factors that affect mosquitoes, avian hosts, and the virus itself. They are also correlated with entomological risk indices obtained by trapping and testing mosquitoes. However, neither weather nor insect data alone provide a sufficient basis to make timely and accurate predictions, and combining them into models of human disease is not necessarily straightforward. Here we present lessons learned in three years of making real-time forecasts of this threat to public health. Various methods of integrating data from NASA's North American Land Data Assimilation System (NLDAS) with mosquito surveillance data were explored in a model comparison framework. We found that a model of human disease summarizing weather data (by polynomial distributed lags with seasonally-varying coefficients) and mosquito data (by a mixed-effects model that smooths out these sparse and highly-variable data) made accurate predictions of risk, and was generalizable enough to be recommended in similar applications. A model based on lagged effects of temperature and humidity provided the most accurate predictions. We also found that model accuracy was improved by allowing coefficients to vary smoothly throughout the season, giving different weights to different predictor variables during different parts of the season.

[Homicides involving firearms in Argentina between 1991 and 2006: a multilevel analysis].

PubMed

Zunino, Marina Gabriela; Diez Roux, Ana Victoria; de Souza, Edinilsa Ramos

2012-12-01

The influence of variables at different levels of organization and the effect of time on the occurrence of firearm-related homicides (FRH) in Argentina between 1991 and 2006 was analyzed using multilevel analysis. A three-level Poisson regression model was used. The first level corresponded to the distribution of the number of FRH by sex and age group for each administrative region and (four-year) period; the second corresponded to the variation over time in the interior of each administrative region; the third modeled the variation between administrative regions in accordance with the Level of Urbanization, Percentage of Homes with Unsatisfied Basic Needs and the Percentage of Working Adults. There were 15,067 FRH in persons aged 14 and over between 1991 and 2006 in the 493 administrative regions. The risk of death was higher in males and persons of 15 to 29 years of age; ages above that were associated with a lower risk. The influence of age was greater in central-urban zones and between 1999 and 2002 than during other periods. The level of urbanization was the socioeconomic variable most strongly associated with FRH risk. The risk of death from FRH was 1.6 times higher in central-urban zones compared with non-central zones. In both zones, the risk was highest between 1999 and 2002.

Modeling Fecal Indicator Bacteria Like Salt in Newport Bay

NASA Astrophysics Data System (ADS)

Ciglar, A. M.; Rippy, M.; Grant, S. B.

2015-12-01

Newport Bay is a harbor and estuary located in Orange County, CA that provides many water sports and recreational activities for millions of southern California residents and tourists. The aim of this study is to quickly assess exceedances of FIB in the Newport Bay which pose a health risk to recreational users. The ability to quickly assess water quality is made possible with an advection-diffusion mass transport model that uses easily measurable parameters such as volumetric flow rate from tributaries. Current FIB assessment methods for Newport Bay take a minimum of 24 hours to evaluate health risk by either culturing for FIB or running a more complex fluid dynamics model. By this time the FIB may have already reached the ocean outlet thus no longer posing a risk in the bay or recreationists may have already come in close contact with contaminated waters. The advection-diffusion model can process and disseminate health risk information within a few hours of flow rate measurements, minimizing time between an FIB exceedance and public awareness about the event. Data used to calibrate and validate the model was collected from January 2006 through February 2007. Salinity data was used for calibration and FIB data was used for validation. Both steady-state and transient conditions were assessed to determine if dry weather patterns can be simplified to the steady-state condition.

On a perturbed Sparre Andersen risk model with multi-layer dividend strategy

NASA Astrophysics Data System (ADS)

Yang, Hu; Zhang, Zhimin

2009-10-01

In this paper, we consider a perturbed Sparre Andersen risk model, in which the inter-claim times are generalized Erlang(n) distributed. Under the multi-layer dividend strategy, piece-wise integro-differential equations for the discounted penalty functions are derived, and a recursive approach is applied to express the solutions. A numerical example to calculate the ruin probabilities is given to illustrate the solution procedure.

Case-based Influence in Conflict Management

DTIC Science & Technology

2014-10-31

AFRL-OSR-VA-TR-2014-0337 CASE-BASED INFLUENCE IN CONFLICT MANAGEMENT Robert Axelrod ARTIS RESEARCH & RISK MODELING Final Report 10/31/2014...FA9550-10-1-0373 Dr. Robert Axelrod - PI Dr. Richard Davis- PD ARTIS Research & Risk Modeling ARTIS 5741 Canyon Ridge North Cave Creek, AZ 85331-9318...analysis of the timing of cyber conflict that quickly received attention from over 30 countries. 3 1 Axelrod , Final Report and Publications Final

Individualized pharmacokinetic risk assessment for development of diabetes in high risk population.

PubMed

Gupta, N; Al-Huniti, N H; Veng-Pedersen, P

2007-10-01

The objective of this study is to propose a non-parametric pharmacokinetic prediction model that addresses the individualized risk of developing type-2 diabetes in subjects with family history of type-2 diabetes. All selected 191 healthy subjects had both parents as type-2 diabetic. Glucose was administered intravenously (0.5 g/kg body weight) and 13 blood samples taken at specified times were analyzed for plasma insulin and glucose concentrations. All subjects were followed for an average of 13-14 years for diabetic or normal (non-diabetic) outcome. The new logistic regression model predicts the development of diabetes based on body mass index and only one blood sample at 90 min analyzed for insulin concentration. Our model correctly identified 4.5 times more subjects (54% versus 11.6%) predicted to develop diabetes and more than twice the subjects (99% versus 46.4%) predicted not to develop diabetes compared to current non-pharmacokinetic probability estimates for development of type-2 diabetes. Our model can be useful for individualized prediction of development of type-2 diabetes in subjects with family history of type-2 diabetes. This improved prediction may be an important mediating factor for better perception of risk and may result in an improved intervention.

Discrimination measures for survival outcomes: connection between the AUC and the predictiveness curve.

PubMed

Viallon, Vivian; Latouche, Aurélien

2011-03-01

Finding out biomarkers and building risk scores to predict the occurrence of survival outcomes is a major concern of clinical epidemiology, and so is the evaluation of prognostic models. In this paper, we are concerned with the estimation of the time-dependent AUC--area under the receiver-operating curve--which naturally extends standard AUC to the setting of survival outcomes and enables to evaluate the discriminative power of prognostic models. We establish a simple and useful relation between the predictiveness curve and the time-dependent AUC--AUC(t). This relation confirms that the predictiveness curve is the key concept for evaluating calibration and discrimination of prognostic models. It also highlights that accurate estimates of the conditional absolute risk function should yield accurate estimates for AUC(t). From this observation, we derive several estimators for AUC(t) relying on distinct estimators of the conditional absolute risk function. An empirical study was conducted to compare our estimators with the existing ones and assess the effect of model misspecification--when estimating the conditional absolute risk function--on the AUC(t) estimation. We further illustrate the methodology on the Mayo PBC and the VA lung cancer data sets. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

Ramuhalli, Pradeep; Hirt, Evelyn H.; Veeramany, Arun

This research report summaries the development and evaluation of a prototypic enhanced risk monitor (ERM) methodology (framework) that includes alternative risk metrics and uncertainty analysis. This updated ERM methodology accounts for uncertainty in the equipment condition assessment (ECA), the prognostic result, and the probabilistic risk assessment (PRA) model. It is anticipated that the ability to characterize uncertainty in the estimated risk and update the risk estimates in real time based on equipment condition assessment (ECA) will provide a mechanism for optimizing plant performance while staying within specified safety margins. These results (based on impacting active component O&M using real-time equipmentmore » condition information) are a step towards ERMs that, if integrated with AR supervisory plant control systems, can help control O&M costs and improve affordability of advanced reactors.« less

A conceptual framework for road safety and mobility applied to cycling safety.

PubMed

Schepers, Paul; Hagenzieker, Marjan; Methorst, Rob; van Wee, Bert; Wegman, Fred

2014-01-01

Scientific literature lacks a model which combines exposure to risk, risk, and the relationship between them. This paper presents a conceptual road safety framework comprising mutually interacting factors for exposure to risk resulting from travel behaviour (volumes, modal split, and distribution of traffic over time and space) and for risk (crash and injury risk). The framework's three determinants for travel behaviour are locations of activities; resistances (generalized transport costs); needs, opportunities, and abilities. Crash and injury risks are modelled by the three 'safety pillars': infrastructure, road users and the vehicles they use. Creating a link in the framework between risk and exposure is important because of the 'non-linear relationship' between them, i.e. risk tends to decrease as exposure increases. Furthermore, 'perceived' risk (a type of travel resistance) plays a role in mode choice, i.e. the perception that a certain type of vehicle is unsafe can be a deterrent to its use. This paper uses theories to explain how the elements in the model interact. Cycling is an area where governments typically have goals for both mobility and safety. To exemplify application of the model, the paper uses the framework to link research on cycling (safety) to land use and infrastructure. The model's value lies in its ability to identify potential consequences of measures and policies for both exposure and risk. This is important from a scientific perspective and for policy makers who often have objectives for both mobility and safety. Copyright © 2013 Elsevier Ltd. All rights reserved.

Web-Based Real Time Earthquake Forecasting and Personal Risk Management

NASA Astrophysics Data System (ADS)

Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Turcotte, D. L.; Donnellan, A.

2012-12-01

Earthquake forecasts have been computed by a variety of countries and economies world-wide for over two decades. For the most part, forecasts have been computed for insurance, reinsurance and underwriters of catastrophe bonds. One example is the Working Group on California Earthquake Probabilities that has been responsible for the official California earthquake forecast since 1988. However, in a time of increasingly severe global financial constraints, we are now moving inexorably towards personal risk management, wherein mitigating risk is becoming the responsibility of individual members of the public. Under these circumstances, open access to a variety of web-based tools, utilities and information is a necessity. Here we describe a web-based system that has been operational since 2009 at www.openhazards.com and www.quakesim.org. Models for earthquake physics and forecasting require input data, along with model parameters. The models we consider are the Natural Time Weibull (NTW) model for regional earthquake forecasting, together with models for activation and quiescence. These models use small earthquakes ('seismicity-based models") to forecast the occurrence of large earthquakes, either through varying rates of small earthquake activity, or via an accumulation of this activity over time. These approaches use data-mining algorithms combined with the ANSS earthquake catalog. The basic idea is to compute large earthquake probabilities using the number of small earthquakes that have occurred in a region since the last large earthquake. Each of these approaches has computational challenges associated with computing forecast information in real time. Using 25 years of data from the ANSS California-Nevada catalog of earthquakes, we show that real-time forecasting is possible at a grid scale of 0.1o. We have analyzed the performance of these models using Reliability/Attributes and standard Receiver Operating Characteristic (ROC) tests. We show how the Reliability and ROC tests allow us to judge data completeness and estimate error. It is clear from much of the analysis that data quality is a major limitation on the accurate computation of earthquake probabilities. We discuss the challenges and pitfalls in serving up these datasets over the web.

Effect of prison-based opioid substitution treatment and post-release retention in treatment on risk of re-incarceration.

PubMed

Larney, Sarah; Toson, Barbara; Burns, Lucy; Dolan, Kate

2012-02-01

People who use heroin are frequently incarcerated multiple times. Reducing re-incarceration of this group is important for reducing both health risks associated with incarceration and the costs of correctional administration. Opioid substitution treatment (OST) in prisons may help to reduce re-incarceration, but research findings on this topic have been mixed. In this study, we examined the effect of OST in prison and after release on re-incarceration. Longitudinal cohort study. SETTING, PARTICIPANTS AND MEASUREMENTS: Data on OST and incarceration were linked for a cohort of 375 male heroin users recruited originally in prisons in New South Wales, Australia. Data were linked for the period 1 June 1997-31 December 2006. Re-incarceration was examined using recurrent-event survival analysis models. Model 1 examined the effect of OST status at release from prison (i.e. in treatment versus out of treatment on the day of release) on re-incarceration. Model 2 considered the effect of remaining in OST after release on risk of re-incarceration. Ninety per cent of participants were re-incarcerated following their first observed release. Pre-incarceration cocaine use was associated with a 13% increase in the average risk of re-incarceration. There was no significant association between simply being in OST at the time of release and risk of re-incarceration; however, in the model taking into account post-release retention in treatment, the average risk of re-incarceration was reduced by 20% while participants were in treatment. In New South Wales, Australia, opioid substitution treatment after release from prison has reduced the average risk of re-incarceration by one-fifth. © 2011 The Authors, Addiction © 2011 Society for the Study of Addiction.

Randomized controlled trial to test the RHANI Wives HIV intervention for women in India at risk for HIV from husbands.

PubMed

Raj, Anita; Saggurti, Niranjan; Battala, Madhusudana; Nair, Saritha; Dasgupta, Anindita; Naik, D D; Abramovitz, Daniela; Silverman, Jay G; Balaiah, Donta

2013-11-01

This study involved evaluation of the short-term impact of the RHANI Wives HIV intervention among wives at risk for HIV from husbands in Mumbai, India. A two-armed cluster RCT was conducted with 220 women surveyed on marital sex at baseline and 4-5 month follow-up. RHANI Wives was a multisession intervention focused on safer sex, marital communication, gender inequities and violence; control participants received basic HIV prevention education. Generalized linear mixed models were conducted to assess program impact, with cluster as a random effect and with time, treatment group, and the time by treatment interaction as fixed effects. A significant time by treatment effect on proportion of unprotected sex with husband (p = 0.01) was observed, and the rate of unprotected sex for intervention participants was lower than that of control participants at follow-up (RR = 0.83, 95 % CI = 0.75, 0.93). RHANI Wives is a promising model for women at risk for HIV from husbands.

Development of hydrate risk quantification in oil and gas production

NASA Astrophysics Data System (ADS)

Chaudhari, Piyush N.

Subsea flowlines that transport hydrocarbons from wellhead to the processing facility face issues from solid deposits such as hydrates, waxes, asphaltenes, etc. The solid deposits not only affect the production but also pose a safety concern; thus, flow assurance is significantly important in designing and operating subsea oil and gas production. In most subsea oil and gas operations, gas hydrates form at high pressure and low temperature conditions, causing the risk of plugging flowlines, with a undesirable impact on production. Over the years, the oil and gas industry has shifted their perspective from hydrate avoidance to hydrate management given several parameters such as production facility, production chemistry, economic and environmental concerns. Thus, understanding the level of hydrate risk associated with subsea flowlines is an important in developing efficient hydrate management techniques. In the past, hydrate formation models were developed for various flow-systems (e.g., oil dominated, water dominated, and gas dominated) present in the oil and gas production. The objective of this research is to extend the application of the present hydrate prediction models for assessing the hydrate risk associated with subsea flowlines that are prone to hydrate formation. It involves a novel approach for developing quantitative hydrate risk models based on the conceptual models built from the qualitative knowledge obtained from experimental studies. A comprehensive hydrate risk model, that ranks the hydrate risk associated with the subsea production system as a function of time, hydrates, and several other parameters, which account for inertial, viscous, interfacial forces acting on the flow-system, is developed for oil dominated and condensate systems. The hydrate plugging risk for water dominated systems is successfully modeled using The Colorado School of Mines Hydrate Flow Assurance Tool (CSMHyFAST). It is found that CSMHyFAST can be used as a screening tool in order to reduce the parametric study that may require a long duration of time using The Colorado School of Mines Hydrate Kinetic Model (CSMHyK). The evolution of the hydrate plugging risk along flowline-riser systems is modeled for steady state and transient operations considering the effect of several critical parameters such as oil-hydrate slip, duration of shut-in, and water droplet size on a subsea tieback system. This research presents a novel platform for quantification of the hydrate plugging risk, which in-turn will play an important role in improving and optimizing current hydrate management strategies. The predictive strength of the hydrate risk quantification and hydrate prediction models will have a significant impact on flow assurance engineering and design with respect to building safe and efficient hydrate management techniques for future deep-water developments.

Stress and Sleep Reactivity: A Prospective Investigation of the Stress-Diathesis Model of Insomnia

PubMed Central

Drake, Christopher L.; Pillai, Vivek; Roth, Thomas

2014-01-01

Study Objectives: To prospectively assess sleep reactivity as a diathesis of insomnia, and to delineate the interaction between this diathesis and naturalistic stress in the development of insomnia among normal sleepers. Design: Longitudinal. Setting: Community-based. Participants: 2,316 adults from the Evolution of Pathways to Insomnia Cohort (EPIC) with no history of insomnia or depression (46.8 ± 13.2 y; 60% female). Interventions: None. Measurements and Results: Participants reported the number of stressful events they encountered at baseline (Time 1), as well as the level of cognitive intrusion they experienced in response to each stressor. Stressful events (OR = 1.13; P < 0.01) and stress-induced cognitive intrusion (OR = 1.61; P < 0.01) were significant predictors of risk for insomnia one year hence (Time 2). Intrusion mediated the effects of stressful events on risk for insomnia (P < 0.05). Trait sleep reactivity significantly increased risk for insomnia (OR = 1.78; P < 0.01). Further, sleep reactivity moderated the effects of stress-induced intrusion (P < 0.05), such that the risk for insomnia as a function of intrusion was significantly higher in individuals with high sleep reactivity. Trait sleep reactivity also constituted a significant risk for depression (OR = 1.67; P < 0.01) two years later (Time 3). Insomnia at Time 2 significantly mediated this effect (P < 0.05). Conclusions: This study suggests that premorbid sleep reactivity is a significant risk factor for incident insomnia, and that it triggers insomnia by exacerbating the effects of stress-induced intrusion. Sleep reactivity is also a precipitant of depression, as mediated by insomnia. These findings support the stress-diathesis model of insomnia, while highlighting sleep reactivity as an important diathesis. Citation: Drake CL, Pillai V, Roth T. Stress and sleep reactivity: a prospective investigation of the stress-diathesis model of insomnia. SLEEP 2014;37(8):1295-1304. PMID:25083009

Longitudinal pathways linking family factors and sibling relationship qualities to adolescent substance use and sexual risk behaviors.

PubMed

East, Patricia L; Khoo, Siek Toon

2005-12-01

This 3-wave, 5-year longitudinal study tested the contributions of family contextual factors and sibling relationship qualities to younger siblings' substance use, sexual risk behaviors, pregnancy, and sexually transmitted disease. More than 220 non-White families participated (67% Latino and 33% African American), all of which involved a younger sibling (133 girls and 89 boys; mean age = 13.6 years at Time 1) and an older sister (mean age = 17 years at Time 1). Results from structural equation latent growth curve modeling indicated that qualities of the sibling relationship (high older sister power, low warmth/closeness, and low conflict) mediated effects from several family risks (mothers' single parenting, older sisters' teen parenting, and family's receipt of aid) to younger sibling outcomes. Model results were generally stronger for sister-sister pairs than for sister-brother pairs. Findings add to theoretical models that emphasize the role of family and parenting processes in shaping sibling relationships, which, in turn, influence adolescent outcomes. Copyright 2006 APA, all rights reserved).

Future Bloom and Blossom Frost Risk for Malus domestica Considering Climate Model and Impact Model Uncertainties

PubMed Central

Hoffmann, Holger; Rath, Thomas

2013-01-01

The future bloom and risk of blossom frosts for Malus domestica were projected using regional climate realizations and phenological ( = impact) models. As climate impact projections are susceptible to uncertainties of climate and impact models and model concatenation, the significant horizon of the climate impact signal was analyzed by applying 7 impact models, including two new developments, on 13 climate realizations of the IPCC emission scenario A1B. Advancement of phenophases and a decrease in blossom frost risk for Lower Saxony (Germany) for early and late ripeners was determined by six out of seven phenological models. Single model/single grid point time series of bloom showed significant trends by 2021–2050 compared to 1971–2000, whereas the joint signal of all climate and impact models did not stabilize until 2043. Regarding blossom frost risk, joint projection variability exceeded the projected signal. Thus, blossom frost risk cannot be stated to be lower by the end of the 21st century despite a negative trend. As a consequence it is however unlikely to increase. Uncertainty of temperature, blooming date and blossom frost risk projection reached a minimum at 2078–2087. The projected phenophases advanced by 5.5 d K−1, showing partial compensation of delayed fulfillment of the winter chill requirement and faster completion of the following forcing phase in spring. Finally, phenological model performance was improved by considering the length of day. PMID:24116022

Future bloom and blossom frost risk for Malus domestica considering climate model and impact model uncertainties.

PubMed

Hoffmann, Holger; Rath, Thomas

2013-01-01

The future bloom and risk of blossom frosts for Malus domestica were projected using regional climate realizations and phenological ( = impact) models. As climate impact projections are susceptible to uncertainties of climate and impact models and model concatenation, the significant horizon of the climate impact signal was analyzed by applying 7 impact models, including two new developments, on 13 climate realizations of the IPCC emission scenario A1B. Advancement of phenophases and a decrease in blossom frost risk for Lower Saxony (Germany) for early and late ripeners was determined by six out of seven phenological models. Single model/single grid point time series of bloom showed significant trends by 2021-2050 compared to 1971-2000, whereas the joint signal of all climate and impact models did not stabilize until 2043. Regarding blossom frost risk, joint projection variability exceeded the projected signal. Thus, blossom frost risk cannot be stated to be lower by the end of the 21st century despite a negative trend. As a consequence it is however unlikely to increase. Uncertainty of temperature, blooming date and blossom frost risk projection reached a minimum at 2078-2087. The projected phenophases advanced by 5.5 d K(-1), showing partial compensation of delayed fulfillment of the winter chill requirement and faster completion of the following forcing phase in spring. Finally, phenological model performance was improved by considering the length of day.

Leisure-time physical activity and incident metabolic syndrome: a systematic review and dose-response meta-analysis of cohort studies.

PubMed

Zhang, Dongdong; Liu, Xuejiao; Liu, Yu; Sun, Xizhuo; Wang, Bingyuan; Ren, Yongcheng; Zhao, Yang; Zhou, Junmei; Han, Chengyi; Yin, Lei; Zhao, Jingzhi; Shi, Yuanyuan; Zhang, Ming; Hu, Dongsheng

2017-10-01

Leisure-time physical activity (LTPA) has been suggested to reduce risk of metabolic syndrome (MetS). However, a quantitative comprehensive assessment of the dose-response association between LTPA and incident MetS has not been reported. We performed a meta-analysis of studies assessing the risk of MetS with LTPA. MEDLINE via PubMed and EMBase databases were searched for relevant articles published up to March 13, 2017. Random-effects models were used to estimate the summary relative risk (RR) of MetS with LTPA. Restricted cubic splines were used to model the dose-response association. We identified 16 articles (18 studies including 76,699 participants and 13,871 cases of MetS). We found a negative linear association between LTPA and incident MetS, with a reduction of 8% in MetS risk per 10 metabolic equivalent of task (MET) h/week increment. According to the restricted cubic splines model, risk of MetS was reduced 10% with LTPA performed according to the basic guideline-recommended level of 150min of moderate PA (MPA) per week (10METh/week) versus inactivity (RR=0.90, 95% CI 0.86-0.94). It was reduced 20% and 53% with LTPA at twice (20METh/week) and seven times (70METh/week) the basic recommended level (RR=0.80, 95% CI 0.74-0.88 and 0.47, 95% CI 0.34-0.64, respectively). Our findings provide quantitative data suggesting that any amount of LTPA is better than none and that LTPA substantially exceeding the current LTPA guidelines is associated with an additional reduction in MetS risk. Copyright © 2017. Published by Elsevier Inc.

Some limitations of frequency as a component of risk: an expository note.

PubMed

Cox, Louis Anthony

2009-02-01

Students of risk analysis are often taught that "risk is frequency times consequence" or, more generally, that risk is determined by the frequency and severity of adverse consequences. But is it? This expository note reviews the concepts of frequency as average annual occurrence rate and as the reciprocal of mean time to failure (MTTF) or mean time between failures (MTBF) in a renewal process. It points out that if two risks (represented as two (frequency, severity) pairs for adverse consequences) have identical values for severity but different values of frequency, then it is not necessarily true that the one with the smaller value of frequency is preferable-and this is true no matter how frequency is defined. In general, there is not necessarily an increasing relation between the reciprocal of the mean time until an event occurs, its long-run average occurrences per year, and other criteria, such as the probability or expected number of times that it will happen over a specific interval of interest, such as the design life of a system. Risk depends on more than frequency and severity of consequences. It also depends on other information about the probability distribution for the time of a risk event that can become lost in simple measures of event "frequency." More flexible descriptions of risky processes, such as point process models can avoid these limitations.

Forecasting the value-at-risk of Chinese stock market using the HARQ model and extreme value theory

NASA Astrophysics Data System (ADS)

Liu, Guangqiang; Wei, Yu; Chen, Yongfei; Yu, Jiang; Hu, Yang

2018-06-01

Using intraday data of the CSI300 index, this paper discusses value-at-risk (VaR) forecasting of the Chinese stock market from the perspective of high-frequency volatility models. First, we measure the realized volatility (RV) with 5-minute high-frequency returns of the CSI300 index and then model it with the newly introduced heterogeneous autoregressive quarticity (HARQ) model, which can handle the time-varying coefficients of the HAR model. Second, we forecast the out-of-sample VaR of the CSI300 index by combining the HARQ model and extreme value theory (EVT). Finally, using several popular backtesting methods, we compare the VaR forecasting accuracy of HARQ model with other traditional HAR-type models, such as HAR, HAR-J, CHAR, and SHAR. The empirical results show that the novel HARQ model can beat other HAR-type models in forecasting the VaR of the Chinese stock market at various risk levels.

A method for determining weights for excess relative risk and excess absolute risk when applied in the calculation of lifetime risk of cancer from radiation exposure.

PubMed

Walsh, Linda; Schneider, Uwe

2013-03-01

Radiation-related risks of cancer can be transported from one population to another population at risk, for the purpose of calculating lifetime risks from radiation exposure. Transfer via excess relative risks (ERR) or excess absolute risks (EAR) or a mixture of both (i.e., from the life span study (LSS) of Japanese atomic bomb survivors) has been done in the past based on qualitative weighting. Consequently, the values of the weights applied and the method of application of the weights (i.e., as additive or geometric weighted means) have varied both between reports produced at different times by the same regulatory body and also between reports produced at similar times by different regulatory bodies. Since the gender and age patterns are often markedly different between EAR and ERR models, it is useful to have an evidence-based method for determining the relative goodness of fit of such models to the data. This paper identifies a method, using Akaike model weights, which could aid expert judgment and be applied to help to achieve consistency of approach and quantitative evidence-based results in future health risk assessments. The results of applying this method to recent LSS cancer incidence models are that the relative EAR weighting by cancer solid cancer site, on a scale of 0-1, is zero for breast and colon, 0.02 for all solid, 0.03 for lung, 0.08 for liver, 0.15 for thyroid, 0.18 for bladder and 0.93 for stomach. The EAR weighting for female breast cancer increases from 0 to 0.3, if a generally observed change in the trend between female age-specific breast cancer incidence rates and attained age, associated with menopause, is accounted for in the EAR model. Application of this method to preferred models from a study of multi-model inference from many models fitted to the LSS leukemia mortality data, results in an EAR weighting of 0. From these results it can be seen that lifetime risk transfer is most highly weighted by EAR only for stomach cancer. However, the generalization and interpretation of radiation effect estimates based on the LSS cancer data, when projected to other populations, are particularly uncertain if considerable differences exist between site-specific baseline rates in the LSS and the other populations of interest. Definitive conclusions, regarding the appropriate method for transporting cancer risks, are limited by a lack of knowledge in several areas including unknown factors and uncertainties in biological mechanisms and genetic and environmental risk factors for carcinogenesis; uncertainties in radiation dosimetry; and insufficient statistical power and/or incomplete follow-up in data from radio-epidemiological studies.

Impact of Relative Residence Times in Highly Distinct Environments on the Distribution of Heavy Drinkers

PubMed Central

Mubayi, Anuj; Greenwood, Priscilla E.; Castillo-Chávez, Carlos; Gruenewald, Paul; Gorman, Dennis M.

2009-01-01

Alcohol consumption is a function of social dynamics, environmental contexts, individuals’ preferences and family history. Empirical surveys have focused primarily on identification of risk factors for high-level drinking but have done little to clarify the underlying mechanisms at work. Also, there have been few attempts to apply nonlinear dynamics to the study of these mechanisms and processes at the population level. A simple framework where drinking is modeled as a socially contagious process in low- and high-risk connected environments is introduced. Individuals are classified as light, moderate (assumed mobile), and heavy drinkers. Moderate drinkers provide the link between both environments, that is, they are assumed to be the only individuals drinking in both settings. The focus here is on the effect of moderate drinkers, measured by the proportion of their time spent in “low-” versus “high-” risk drinking environments, on the distribution of drinkers. A simple model within our contact framework predicts that if the relative residence times of moderate drinkers is distributed randomly between low- and high-risk environments then the proportion of heavy drinkers is likely to be higher than expected. However, the full story even in a highly simplified setting is not so simple because “strong” local social mixing tends to increase high-risk drinking on its own. High levels of social interaction between light and moderate drinkers in low-risk environments can diminish the importance of the distribution of relative drinking times on the prevalence of heavy drinking. PMID:20161388

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.

Comparison of the risk factors effects between two populations: two alternative approaches illustrated by the analysis of first and second kidney transplant recipients

PubMed Central

2013-01-01

Background Whereas the prognosis of second kidney transplant recipients (STR) compared to the first ones has been frequently analyzed, no study has addressed the issue of comparing the risk factor effects on graft failure between both groups. Methods Here, we propose two alternative strategies to study the heterogeneity of risk factors between two groups of patients: (i) a multiplicative-regression model for relative survival (MRS) and (ii) a stratified Cox model (SCM) specifying the graft rank as strata and assuming subvectors of the explicatives variables. These developments were motivated by the analysis of factors associated with time to graft failure (return-to-dialysis or patient death) in second kidney transplant recipients (STR) compared to the first ones. Estimation of the parameters was based on partial likelihood maximization. Monte-Carlo simulations associated with bootstrap re-sampling was performed to calculate the standard deviations for the MRS. Results We demonstrate, for the first time in renal transplantation, that: (i) male donor gender is a specific risk factor for STR, (ii) the adverse effect of recipient age is enhanced for STR and (iii) the graft failure risk related to donor age is attenuated for STR. Conclusion While the traditional Cox model did not provide original results based on the renal transplantation literature, the proposed relative and stratified models revealed new findings that are useful for clinicians. These methodologies may be of interest in other medical fields when the principal objective is the comparison of risk factors between two populations. PMID:23915191

Potential of wind turbines to elicit seizures under various meteorological conditions.

PubMed

Smedley, Andrew R D; Webb, Ann R; Wilkins, Arnold J

2010-07-01

To determine the potential risk of epileptic seizures from wind turbine shadow flicker under various meteorologic conditions. We extend a previous model to include attenuation of sunlight by the atmosphere using the libradtran radiative transfer code. Under conditions in which observers look toward the horizon with their eyes open we find that there is risk when the observer is closer than 1.2 times the total turbine height when on land, and 2.8 times the total turbine height in marine environments, the risk limited by the size of the image of the sun's disc on the retina. When looking at the ground, where the shadow of the blade is cast, observers are at risk only when at a distance <36 times the blade width, the risk limited by image contrast. If the observer views the horizon and closes their eyes, however, the stimulus size and contrast ratio are epileptogenic for solar elevation angles down to approximately 5 degrees. Large turbines rotate at a rate below that at which the flicker is likely to present a risk, although there is a risk from smaller turbines that interrupt sunlight more than three times per second. For the scenarios considered, we find the risk is negligible at a distance more than about nine times the maximum height reached by the turbine blade, a distance similar to that in guidance from the United Kingdom planning authorities.

Predictive model of urinary tract infection after surgical treatment for women with endometrial cancer.

PubMed

Machida, Hiroko; Hom, Marianne S; Shabalova, Anastasiya; Grubbs, Brendan H; Matsuo, Koji

2017-08-01

The aim of the study was to identify risk factors associated with postoperative urinary tract infections (UTIs) following hysterectomy-based surgical staging in women with endometrial cancer. This is a retrospective study utilizing an institutional database (2008-2016) of stage I-IV endometrial cancer cases that underwent hysterectomy-based surgery. UTIs occurring within a 30-day time period after surgery were examined and correlated to patient clinico-pathological demographics. UTIs were observed in 44 (6.4%, 95% confidence interval 4.6-8.2) out of 687 cases subsequent to the diagnosis of endometrial cancer. UTI cases were significantly associated with obesity, advanced stage, prolonged operative time, hysterectomy type, pelvic lymphadenectomy, non-β-lactam antibiotics, and intraoperative urinary tract injury (all, p < 0.05). On multivariate analysis, three independent risk factors were identified for UTIs: prolonged operative time [odds ratio (OR) 3.36, 95% CI 1.65-6.87, p = 0.001], modified-radical/radical hysterectomy (OR 5.35, 95% CI 1.56-18.4, p = 0.008), and an absence of perioperative β-lactam antibiotics use (OR 3.50, 95% CI 1.46-8.38, p = 0.005). In a predictive model of UTI, the presence of multiple risk factors was associated with significantly increased risk of UTI: 4.1% for the group with no risk factors, 7.3-12.5% (OR 1.85-3.37) for single risk factor group, and 30.0-30.8% (OR 10.1-10.5) for two risk factor group. Urinary tract infections are common in women following surgical treatment for women with endometrial cancer with risk factors being a prolonged surgical time, radical hysterectomy, and non-guideline perioperative anti-microbial agent use. Consideration of prophylactic anti-microbial agent use in a high-risk group of postoperative urinary tract infection merits further investigation.

Development of models to predict early post-transplant recurrence of hepatocellular carcinoma that also integrate the quality and characteristics of the liver graft: A national registry study in China.

PubMed

Ling, Qi; Liu, Jimin; Zhuo, Jianyong; Zhuang, Runzhou; Huang, Haitao; He, Xiangxiang; Xu, Xiao; Zheng, Shusen

2018-04-27

Donor characteristics and graft quality were recently reported to play an important role in the recurrence of hepatocellular carcinoma after liver transplantation. Our aim was to establish a prognostic model by using both donor and recipient variables. Data of 1,010 adult patients (training/validation: 2/1) undergoing primary liver transplantation for hepatocellular carcinoma were extracted from the China Liver Transplant Registry database and analyzed retrospectively. A multivariate competing risk regression model was developed and used to generate a nomogram predicting the likelihood of post-transplant hepatocellular carcinoma recurrence. Of 673 patients in the training cohort, 70 (10.4%) had hepatocellular carcinoma recurrence with a median recurrence time of 6 months (interquartile range: 4-25 months). Cold ischemia time was the only independent donor prognostic factor for predicting hepatocellular carcinoma recurrence (hazard ratio = 2.234, P = .007). The optimal cutoff value was 12 hours when patients were grouped according to cold ischemia time at 2-hour intervals. Integrating cold ischemia time into the Milan criteria (liver transplantation candidate selection criteria) improved the accuracy for predicting hepatocellular carcinoma recurrence in both training and validation sets (P < .05). A nomogram composed of cold ischemia time, tumor burden, differentiation, and α-fetoprotein level proved to be accurate and reliable in predicting the likelihood of 1-year hepatocellular carcinoma recurrence after liver transplantation. Additionally, donor anti-hepatitis B core antibody positivity, prolonged cold ischemia time, and anhepatic time were linked to the intrahepatic recurrence, whereas older donor age, prolonged donor warm ischemia time, cold ischemia time, and ABO incompatibility were relevant to the extrahepatic recurrence. The graft quality integrated models exhibited considerable predictive accuracy in early hepatocellular carcinoma recurrence risk assessment. The identification of donor risks can further help understand the mechanism of different patterns of recurrence. Copyright © 2018 Elsevier Inc. All rights reserved.

Economic evaluation of using a genetic test to direct breast cancer chemoprevention in white women with a previous breast biopsy.

PubMed

Green, Linda E; Dinh, Tuan A; Hinds, David A; Walser, Bryan L; Allman, Richard

2014-04-01

Tamoxifen therapy reduces the risk of breast cancer but increases the risk of serious adverse events including endometrial cancer and thromboembolic events. The cost effectiveness of using a commercially available breast cancer risk assessment test (BREVAGen™) to inform the decision of which women should undergo chemoprevention by tamoxifen was modeled in a simulated population of women who had undergone biopsies but had no diagnosis of cancer. A continuous time, discrete event, mathematical model was used to simulate a population of white women aged 40-69 years, who were at elevated risk for breast cancer because of a history of benign breast biopsy. Women were assessed for clinical risk of breast cancer using the Gail model and for genetic risk using a panel of seven common single nucleotide polymorphisms. We evaluated the cost effectiveness of using genetic risk together with clinical risk, instead of clinical risk alone, to determine eligibility for 5 years of tamoxifen therapy. In addition to breast cancer, the simulation included health states of endometrial cancer, pulmonary embolism, deep-vein thrombosis, stroke, and cataract. Estimates of costs in 2012 US dollars were based on Medicare reimbursement rates reported in the literature and utilities for modeled health states were calculated as an average of utilities reported in the literature. A 50-year time horizon was used to observe lifetime effects including survival benefits. For those women at intermediate risk of developing breast cancer (1.2-1.66 % 5-year risk), the incremental cost-effectiveness ratio for the combined genetic and clinical risk assessment strategy over the clinical risk assessment-only strategy was US$47,000, US$44,000, and US$65,000 per quality-adjusted life-year gained, for women aged 40-49, 50-59, and 60-69 years, respectively (assuming a price of US$945 for genetic testing). Results were sensitive to assumptions about patient adherence, utility of life while taking tamoxifen, and cost of genetic testing. From the US payer's perspective, the combined genetic and clinical risk assessment strategy may be a moderately cost-effective alternative to using clinical risk alone to guide chemoprevention recommendations for women at intermediate risk of developing breast cancer.

Does the duration and time of sleep increase the risk of allergic rhinitis? Results of the 6-year nationwide Korea youth risk behavior web-based survey.

PubMed

Kwon, Jeoung A; Lee, Minjee; Yoo, Ki-Bong; Park, Eun-Cheol

2013-01-01

Allergic rhinitis (AR) is the most common chronic disorder in the pediatric population. Although several studies have investigated the correlation between AR and sleep-related issues, the association between the duration and time of sleep and AR has not been analyzed in long-term national data. This study investigated the relationship between sleep time and duration and AR risk in middle- and high-school students (adolescents aged 12-18). We analyzed national data from the Korea Youth Risk Behavior Web-based Survey by the Korea Centers for Disease Control and Prevention from 2007-2012. The sample size was 274,480, with an average response rate of 96.2%. Multivariate logistic regression analyses were conducted to determine the relationship between sleep and AR risk. Furthermore, to determine the best-fitted model among independent variables such as sleep duration, sleep time, and the combination of sleep duration and sleep time, we used Akaike Information Criteria (AIC) to compare models. A total of 43,337 boys and 41,665 girls reported a diagnosis of AR at baseline. The odds ratio increased with age and with higher education and economic status of the parents. Further, students in mid-sized and large cities had stronger relationships to AR than those in small cities. In both genders, AR was associated with depression and suicidal ideation. In the analysis of sleep duration and sleep time, the odds ratio increased in both genders when sleep duration was <7 hours, and when the time of sleep was later than 24:00 hours. Our results indicate an association between sleep time and duration and AR. This study is the first to focus on the relationship between sleep duration and time and AR in national survey data collected over 6 years.

A prospective study of sudden unexpected infant death after reported maltreatment.

PubMed

Putnam-Hornstein, Emily; Schneiderman, Janet U; Cleves, Mario A; Magruder, Joseph; Krous, Henry F

2014-01-01

To examine whether infants reported for maltreatment face a heightened risk of sudden infant death syndrome (SIDS) and other leading causes of sudden unexpected infant death (SUID). Linked birth and infant death records for all children born in California between 1999 and 2006 were matched to administrative child protection data. Infants were prospectively followed from birth through death or 1 year of age. A report of maltreatment was modeled as a time-varying covariate; risk factors at birth were included as baseline covariates. Multivariable competing risk survival models were used to estimate the adjusted relative hazard of postneonatal SIDS and other SUID. A previous maltreatment report emerged as a significant predictor of SIDS and other SUID. After adjusting for baseline risk factors, the rate of SIDS was more than 3 times as great among infants reported for possible maltreatment (hazard ratio: 3.22; 95% CI: 2.66, 3.89). Infants reported to child protective services have a heightened risk of SIDS and other SUID. Targeted services and improved communication between child protective services and the pediatric health care community may enhance infant well-being and reduce risk of death. Copyright © 2014 Mosby, Inc. All rights reserved.

MobRISK: a model for assessing the exposure of road users to flash flood events

NASA Astrophysics Data System (ADS)

Shabou, Saif; Ruin, Isabelle; Lutoff, Céline; Debionne, Samuel; Anquetin, Sandrine; Creutin, Jean-Dominique; Beaufils, Xavier

2017-09-01

Recent flash flood impact studies highlight that road networks are often disrupted due to adverse weather and flash flood events. Road users are thus particularly exposed to road flooding during their daily mobility. Previous exposure studies, however, do not take into consideration population mobility. Recent advances in transportation research provide an appropriate framework for simulating individual travel-activity patterns using an activity-based approach. These activity-based mobility models enable the prediction of the sequence of activities performed by individuals and locating them with a high spatial-temporal resolution. This paper describes the development of the MobRISK microsimulation system: a model for assessing the exposure of road users to extreme hydrometeorological events. MobRISK aims at providing an accurate spatiotemporal exposure assessment by integrating travel-activity behaviors and mobility adaptation with respect to weather disruptions. The model is applied in a flash-flood-prone area in southern France to assess motorists' exposure to the September 2002 flash flood event. The results show that risk of flooding mainly occurs in principal road links with considerable traffic load. However, a lag time between the timing of the road submersion and persons crossing these roads contributes to reducing the potential vehicle-related fatal accidents. It is also found that sociodemographic variables have a significant effect on individual exposure. Thus, the proposed model demonstrates the benefits of considering spatiotemporal dynamics of population exposure to flash floods and presents an important improvement in exposure assessment methods. Such improved characterization of road user exposures can present valuable information for flood risk management services.

Revised simulation model does not predict rebound in gonorrhoea prevalence where core groups are treated in the presence of antimicrobial resistance.

PubMed

Trecker, Molly A; Hogan, Daniel J; Waldner, Cheryl L; Dillon, Jo-Anne R; Osgood, Nathaniel D

2015-06-01

To determine the effects of using discrete versus continuous quantities of people in a compartmental model examining the contribution of antimicrobial resistance (AMR) to rebound in the prevalence of gonorrhoea. A previously published transmission model was reconfigured to represent the occurrence of gonorrhoea in discrete persons, rather than allowing fractions of infected individuals during simulations. In the revised model, prevalence only rebounded under scenarios reproduced from the original paper when AMR occurrence was increased by 10(5) times. In such situations, treatment of high-risk individuals yielded outcomes very similar to those resulting from treatment of low-risk and intermediate-risk individuals. Otherwise, in contrast with the original model, prevalence was the lowest when the high-risk group was treated, supporting the current policy of targeting treatment to high-risk groups. Simulation models can be highly sensitive to structural features. Small differences in structure and parameters can substantially influence predicted outcomes and policy prescriptions, and must be carefully considered. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people

NASA Astrophysics Data System (ADS)

Balbi, Stefano; Villa, Ferdinando; Mojtahed, Vahid; Hegetschweiler, Karin Tessa; Giupponi, Carlo

2016-06-01

This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; and produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of (1) likelihood of non-fatal physical injury, (2) likelihood of post-traumatic stress disorder and (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the effect of improving an existing early warning system, taking into account the reliability, lead time and scope (i.e., coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event.

Evaluating changes to reservoir rule curves using historical water-level data

USGS Publications Warehouse

Mower, Ethan; Miranda, Leandro E.

2013-01-01

Flood control reservoirs are typically managed through rule curves (i.e. target water levels) which control the storage and release timing of flood waters. Changes to rule curves are often contemplated and requested by various user groups and management agencies with no information available about the actual flood risk of such requests. Methods of estimating flood risk in reservoirs are not easily available to those unfamiliar with hydrological models that track water movement through a river basin. We developed a quantile regression model that uses readily available daily water-level data to estimate risk of spilling. Our model provided a relatively simple process for estimating the maximum applicable water level under a specific flood risk for any day of the year. This water level represents an upper-limit umbrella under which water levels can be operated in a variety of ways. Our model allows the visualization of water-level management under a user-specified flood risk and provides a framework for incorporating the effect of a changing environment on water-level management in reservoirs, but is not designed to replace existing hydrological models. The model can improve communication and collaboration among agencies responsible for managing natural resources dependent on reservoir water levels.

Using animal models to study post-partum psychiatric disorders

PubMed Central

Perani, C V; Slattery, D A

2014-01-01

The post-partum period represents a time during which all maternal organisms undergo substantial plasticity in a wide variety of systems in order to ensure the well-being of the offspring. Although this time is generally associated with increased calmness and decreased stress responses, for a substantial subset of mothers, this period represents a time of particular risk for the onset of psychiatric disorders. Thus, post-partum anxiety, depression and, to a lesser extent, psychosis may develop, and not only affect the well-being of the mother but also place at risk the long-term health of the infant. Although the risk factors for these disorders, as well as normal peripartum-associated adaptations, are well known, the underlying aetiology of post-partum psychiatric disorders remains poorly understood. However, there have been a number of attempts to model these disorders in basic research, which aim to reveal their underlying mechanisms. In the following review, we first discuss known peripartum adaptations and then describe post-partum mood and anxiety disorders, including their risk factors, prevalence and symptoms. Thereafter, we discuss the animal models that have been designed in order to study them and what they have revealed about their aetiology to date. Overall, these studies show that it is feasible to study such complex disorders in animal models, but that more needs to be done in order to increase our knowledge of these severe and debilitating mood and anxiety disorders. Linked Articles This article is part of a themed section on Animal Models in Psychiatry Research. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2014.171.issue-20 PMID:24527704

Health effects models for nuclear power plant accident consequence analysis: Low LET radiation: Part 2, Scientific bases for health effects models

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

Abrahamson, S.; Bender, M.; Book, S.

1989-05-01

This report provides dose-response models intended to be used in estimating the radiological health effects of nuclear power plant accidents. Models of early and continuing effects, cancers and thyroid nodules, and genetic effects are provided. Two-parameter Weibull hazard functions are recommended for estimating the risks of early and continuing health effects. Three potentially lethal early effects -- the hematopoietic, pulmonary and gastrointestinal syndromes -- are considered. Linear and linear-quadratic models are recommended for estimating cancer risks. Parameters are given for analyzing the risks of seven types of cancer in adults -- leukemia, bone, lung, breast, gastrointestinal, thyroid and ''other''. Themore » category, ''other'' cancers, is intended to reflect the combined risks of multiple myeloma, lymphoma, and cancers of the bladder, kidney, brain, ovary, uterus and cervix. Models of childhood cancers due to in utero exposure are also provided. For most cancers, both incidence and mortality are addressed. Linear and linear-quadratic models are also recommended for assessing genetic risks. Five classes of genetic disease -- dominant, x-linked, aneuploidy, unbalanced translocation and multifactorial diseases --are considered. In addition, the impact of radiation-induced genetic damage on the incidence of peri-implantation embryo losses is discussed. The uncertainty in modeling radiological health risks is addressed by providing central, upper, and lower estimates of all model parameters. Data are provided which should enable analysts to consider the timing and severity of each type of health risk. 22 refs., 14 figs., 51 tabs.« less

A model for hematopoietic death in man from irradiation of bone marrow during radioimmunotherapy.

PubMed

Scott, B R; Dillehay, L E

1990-11-01

There are numerous institutions worldwide performing clinical trials of radioimmunotherapy (RIT) for cancer. For RIT, an exponentially decaying radionuclide is attached by using a chelating agent to a specific monoclonal or polyclonal tumour antibody (e.g. antiferritin IgG). The major limitation to RIT is toxicity to normal tissue in organs other than the one containing the tumour (e.g. bone marrow). The focus of this manuscript is on modelling the risk (or probability) of hematopoietic death in man for exponentially decaying patterns of high-energy beta irradiation (e.g. 90Y) of bone marrow by radioimmunoglobulin injected into the blood. The analytical solutions presented are only applicable to protocols for which significant uptake of radioactivity by the bone marrow does not occur, and only for high energy beta emitters. However, the generic equation used to obtain the analytical solutions is applicable to any continuous pattern of high energy beta irradiation. A model called the "normalized dose model" was used to generate calculated values for the LD50 as a function of the effective half-time for the radioimmunoglobulin in the blood. A less complicated empirical model was used to describe the calculated values. This model is presumed to be valid for effective half-times in blood of up to about 20 days. For longer effective half-times, the LD50 can be estimated using the normalized-dose model presented. In this manuscript, we also provide a modified Weibull model that allows estimation of the risk of hematopoietic death for single or multiple injections (in one cycle) of radioimmunoglobulin, for patients with normal susceptibility to irradiation and for patients with heightened susceptibility. With the modified Weibull model, the risk of hematopoietic death depends on the level of medical treatment provided to mitigate radiation injuries.

Fifteen-minute Extravehicular Activity Prebreathe Protocol Using NASA's Exploration Atmosphere (8.2 psia/ 34% 02)

NASA Technical Reports Server (NTRS)

Abercromby, Andrew F. J.; Gernhardt, Michael L.; Conkin, Johnny

2013-01-01

A TBDM DCS probability model based on an existing biophysical model of inert gas bubble growth provides significant prediction and goodness-of-fit with 84 cases of DCS in 668 human altitude exposures. 2. Model predictions suggest that 15-minute O2 prebreathe protocols used in conjunction with suit ports and an 8.2 psi, 34% O2, 66% N2 atmosphere may enable rapid EVA capability for future exploration missions with the risk of DCS = 12%. ? EVA could begin immediately at 6.0 psi, with crewmembers decreasing suit pressure to 4.3 psi after completing the 15-minute in-suit prebreathe. 3. Model predictions suggest that intermittent recompression during exploration EVA may reduce decompression stress by 1.8% to 2.3% for 6 hours of total EVA time. Savings in gas consumables and crew time may be accumulated by abbreviating the EVA suit N2 purge to 2 minutes (20% N2) compared with 8 minutes (5% N2) at the expense of an increase in estimated decompression risk of up to 2.4% for an 8-hour EVA. ? Increased DCS risk could be offset by IR or by spending additional time at 6 psi at the beginning of the EVA. ? Savings of 0.48 lb of gas and 6 minutes per person per EVA corresponds to more than 31 hours of crew time and 1800 lb of gas and tankage under the Constellation lunar architecture. 6. Further research is needed to characterize and optimize breathing mixtures and intermittent recompression across the range of environments and operational conditions in which astronauts will live and work during future exploration missions. 7. Development of exploration prebreathe protocols will begin with definition of acceptable risk, followed by development of protocols based on models such as ours, and, ultimately, validation of protocols through ground trials before operational implementation.

Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait--a cohort study.

PubMed

Farran, Bassam; Channanath, Arshad Mohamed; Behbehani, Kazem; Thanaraj, Thangavel Alphonse

2013-05-14

We build classification models and risk assessment tools for diabetes, hypertension and comorbidity using machine-learning algorithms on data from Kuwait. We model the increased proneness in diabetic patients to develop hypertension and vice versa. We ascertain the importance of ethnicity (and natives vs expatriate migrants) and of using regional data in risk assessment. Retrospective cohort study. Four machine-learning techniques were used: logistic regression, k-nearest neighbours (k-NN), multifactor dimensionality reduction and support vector machines. The study uses fivefold cross validation to obtain generalisation accuracies and errors. Kuwait Health Network (KHN) that integrates data from primary health centres and hospitals in Kuwait. 270 172 hospital visitors (of which, 89 858 are diabetic, 58 745 hypertensive and 30 522 comorbid) comprising Kuwaiti natives, Asian and Arab expatriates. Incident type 2 diabetes, hypertension and comorbidity. Classification accuracies of >85% (for diabetes) and >90% (for hypertension) are achieved using only simple non-laboratory-based parameters. Risk assessment tools based on k-NN classification models are able to assign 'high' risk to 75% of diabetic patients and to 94% of hypertensive patients. Only 5% of diabetic patients are seen assigned 'low' risk. Asian-specific models and assessments perform even better. Pathological conditions of diabetes in the general population or in hypertensive population and those of hypertension are modelled. Two-stage aggregate classification models and risk assessment tools, built combining both the component models on diabetes (or on hypertension), perform better than individual models. Data on diabetes, hypertension and comorbidity from the cosmopolitan State of Kuwait are available for the first time. This enabled us to apply four different case-control models to assess risks. These tools aid in the preliminary non-intrusive assessment of the population. Ethnicity is seen significant to the predictive models. Risk assessments need to be developed using regional data as we demonstrate the applicability of the American Diabetes Association online calculator on data from Kuwait.

Accelerated failure time models for semi-competing risks data in the presence of complex censoring.

PubMed

Lee, Kyu Ha; Rondeau, Virginie; Haneuse, Sebastien

2017-12-01

Statistical analyses that investigate risk factors for Alzheimer's disease (AD) are often subject to a number of challenges. Some of these challenges arise due to practical considerations regarding data collection such that the observation of AD events is subject to complex censoring including left-truncation and either interval or right-censoring. Additional challenges arise due to the fact that study participants under investigation are often subject to competing forces, most notably death, that may not be independent of AD. Towards resolving the latter, researchers may choose to embed the study of AD within the "semi-competing risks" framework for which the recent statistical literature has seen a number of advances including for the so-called illness-death model. To the best of our knowledge, however, the semi-competing risks literature has not fully considered analyses in contexts with complex censoring, as in studies of AD. This is particularly the case when interest lies with the accelerated failure time (AFT) model, an alternative to the traditional multiplicative Cox model that places emphasis away from the hazard function. In this article, we outline a new Bayesian framework for estimation/inference of an AFT illness-death model for semi-competing risks data subject to complex censoring. An efficient computational algorithm that gives researchers the flexibility to adopt either a fully parametric or a semi-parametric model specification is developed and implemented. The proposed methods are motivated by and illustrated with an analysis of data from the Adult Changes in Thought study, an on-going community-based prospective study of incident AD in western Washington State. © 2017, The International Biometric Society.

Application of Ecosystem Models to Assess Environmental Drivers of Mosquito Abundance and Virus Transmission Risk and Associated Public Health Implications of Climate and Land Use Change

NASA Astrophysics Data System (ADS)

Melton, F.; Barker, C.; Park, B.; Reisen, W.; Michaelis, A.; Wang, W.; Hashimoto, H.; Milesi, C.; Hiatt, S.; Nemani, R.

2008-12-01

The NASA Terrestrial Observation and Prediction System (TOPS) is a modeling framework that integrates satellite observations, meteorological observations, and ancillary data to support monitoring and modeling of ecosystem and land surface conditions in near real-time. TOPS provides spatially continuous gridded estimates of a suite of measurements describing environmental conditions, and these data products are currently being applied to support the development of new models capable of forecasting estimated mosquito abundance and transmission risk for mosquito-borne diseases such as West Nile virus. We present results from the modeling analyses, describe their incorporation into the California Vectorborne Disease Surveillance System, and describe possible implications of projected climate and land use change for patterns in mosquito abundance and transmission risk for West Nile virus in California.

Perceived risks of HIV/AIDS and first sexual intercourse among youth in Cape Town, South Africa.

PubMed

Tenkorang, Eric Y; Rajulton, Fernando; Maticka-Tyndale, Eleanor

2009-04-01

The 'Health Belief Model' (HBM) identifies perception of HIV/AIDS risks, recognition of its seriousness, and knowledge about prevention as predictors of safer sexual activity. Using data from the Cape Area Panel Survey (CAPS) and hazard models, this study examines the impact of risk perception, considered the first step in HIV prevention, set within the context of the HBM and socio-economic, familial and school factors, on the timing of first sexual intercourse among youth aged 14-22 in Cape Town, South Africa. Of the HBM components, female youth who perceive their risk as 'very small' and males with higher knowledge, experience their sexual debut later than comparison groups, net of other influences. For both males and females socio-economic and familial factors also influence timing of sexual debut, confirming the need to consider the social embeddedness of this sexual behavior as well as the rational components of decision making when designing prevention programs.

A spatial assessment framework for evaluating flood risk under extreme climates.

PubMed

Chen, Yun; Liu, Rui; Barrett, Damian; Gao, Lei; Zhou, Mingwei; Renzullo, Luigi; Emelyanova, Irina

2015-12-15

Australian coal mines have been facing a major challenge of increasing risk of flooding caused by intensive rainfall events in recent years. In light of growing climate change concerns and the predicted escalation of flooding, estimating flood inundation risk becomes essential for understanding sustainable mine water management in the Australian mining sector. This research develops a spatial multi-criteria decision making prototype for the evaluation of flooding risk at a regional scale using the Bowen Basin and its surroundings in Queensland as a case study. Spatial gridded data, including climate, hydrology, topography, vegetation and soils, were collected and processed in ArcGIS. Several indices were derived based on time series of observations and spatial modeling taking account of extreme rainfall, evapotranspiration, stream flow, potential soil water retention, elevation and slope generated from a digital elevation model (DEM), as well as drainage density and proximity extracted from a river network. These spatial indices were weighted using the analytical hierarchy process (AHP) and integrated in an AHP-based suitability assessment (AHP-SA) model under the spatial risk evaluation framework. A regional flooding risk map was delineated to represent likely impacts of criterion indices at different risk levels, which was verified using the maximum inundation extent detectable by a time series of remote sensing imagery. The result provides baseline information to help Bowen Basin coal mines identify and assess flooding risk when making adaptation strategies and implementing mitigation measures in future. The framework and methodology developed in this research offers the Australian mining industry, and social and environmental studies around the world, an effective way to produce reliable assessment on flood risk for managing uncertainty in water availability under climate change. Copyright © 2015. Published by Elsevier B.V.

Sibling Influence on Mexican-Origin Adolescents’ Deviant and Sexual Risk Behaviors: The Role of Sibling Modeling

PubMed Central

Whiteman, Shawn D.; Zeiders, Katharine H.; Killoren, Sarah E.; Rodriguez, Sue Annie; Updegraff, Kimberly A.

2013-01-01

Purpose A growing body of research indicates that siblings uniquely influence each other’s health risk behaviors during adolescence and young adulthood. Mechanisms underlying these associations, however, are largely unknown because they are rarely tested directly. The present study addressed this gap by examining the role of sibling modeling in explaining changes in Mexican-origin youths’ deviant and sexual risk behaviors over time. Methods The sample included 380 Mexican-origin siblings (older sibling age: M = 21.18, SD = 1.59; younger sibling age: M = 18.19, SD = .46) from (N = 190) families. Participants provided self-reports of their sibling relationship qualities, including modeling, as well as their engagement in deviant and sexual risk taking behaviors in two home interviews across a two-year span. Results A series of residualized regression models revealed that younger siblings’ perceptions of modeling moderated the links between older siblings’ deviant and sexual risk behaviors and younger siblings’ subsequent behaviors in those same domains. Specifically, high levels of modeling predicted stronger associations between older siblings’ earlier and younger siblings’ later risk behaviors controlling for younger siblings’ earlier behaviors as well as variables that have been used as proxies for social learning in previous research. Conclusions Social learning mechanisms, especially modeling, are salient processes through which older siblings transmit norms and expectations regarding participation in health risk behaviors. Future research should continue to explore the ways in which siblings influence each other because such processes are emerging targets for intervention and prevention. PMID:24287013

Developing a Conceptually Equivalent Type 2 Diabetes Risk Score for Indian Gujaratis in the UK

PubMed Central

Patel, Naina; Stone, Margaret; Barber, Shaun; Gray, Laura; Davies, Melanie; Khunti, Kamlesh

2016-01-01

Aims. To apply and assess the suitability of a model consisting of commonly used cross-cultural translation methods to achieve a conceptually equivalent Gujarati language version of the Leicester self-assessment type 2 diabetes risk score. Methods. Implementation of the model involved multiple stages, including pretesting of the translated risk score by conducting semistructured interviews with a purposive sample of volunteers. Interviews were conducted on an iterative basis to enable findings to inform translation revisions and to elicit volunteers' ability to self-complete and understand the risk score. Results. The pretest stage was an essential component involving recruitment of a diverse sample of 18 Gujarati volunteers, many of whom gave detailed suggestions for improving the instructions for the calculation of the risk score and BMI table. Volunteers found the standard and level of Gujarati accessible and helpful in understanding the concept of risk, although many of the volunteers struggled to calculate their BMI. Conclusions. This is the first time that a multicomponent translation model has been applied to the translation of a type 2 diabetes risk score into another language. This project provides an invaluable opportunity to share learning about the transferability of this model for translation of self-completed risk scores in other health conditions. PMID:27703985

Markov Chain Model with Catastrophe to Determine Mean Time to Default of Credit Risky Assets

NASA Astrophysics Data System (ADS)

Dharmaraja, Selvamuthu; Pasricha, Puneet; Tardelli, Paola

2017-11-01

This article deals with the problem of probabilistic prediction of the time distance to default for a firm. To model the credit risk, the dynamics of an asset is described as a function of a homogeneous discrete time Markov chain subject to a catastrophe, the default. The behaviour of the Markov chain is investigated and the mean time to the default is expressed in a closed form. The methodology to estimate the parameters is given. Numerical results are provided to illustrate the applicability of the proposed model on real data and their analysis is discussed.

Explaining Spatial Variability in Wellbore Impairment Risk for Pennsylvania Oil and Gas Wells, 2000-2014

NASA Astrophysics Data System (ADS)

Santoro, R.; Ingraffea, A. R.

2015-12-01

Previous modeling (ingraffea et al. PNAS, 2014) indicated roughly two-times higher cumulative risk for wellbore impairment in unconventional wells, relative to conventional wells, and large spatial variation in risk for oil and gas wells drilled in the state of Pennsylvania. Impairment risk for wells in the northeast portion of the state were found to be 8.5-times greater than that of wells drilled in the rest of the state. Here, we set out to explain this apparent regional variability through Boosted Regression Tree (BRT) analysis of geographic, developmental, and general well attributes. We find that regional variability is largely driven by the nature of the development, i.e. whether conventional or unconventional development is dominant. Oil and natural gas market prices and total well depths present as major influences in wellbore impairment, with moderate influences from well densities and geologic factors. The figure depicts influence paths for predictors of impairments for the state (top left), SW region (top right), unconventional/NE region (bottom left) and conventional/NW region (bottom right) models. Influences are scaled to reflect percent contributions in explaining variability in the model.

Data-Driven Risk Assessment from Small Scale Epidemics: Estimation and Model Choice for Spatio-Temporal Data with Application to a Classical Swine Fever Outbreak

PubMed Central

Gamado, Kokouvi; Marion, Glenn; Porphyre, Thibaud

2017-01-01

Livestock epidemics have the potential to give rise to significant economic, welfare, and social costs. Incursions of emerging and re-emerging pathogens may lead to small and repeated outbreaks. Analysis of the resulting data is statistically challenging but can inform disease preparedness reducing potential future losses. We present a framework for spatial risk assessment of disease incursions based on data from small localized historic outbreaks. We focus on between-farm spread of livestock pathogens and illustrate our methods by application to data on the small outbreak of Classical Swine Fever (CSF) that occurred in 2000 in East Anglia, UK. We apply models based on continuous time semi-Markov processes, using data-augmentation Markov Chain Monte Carlo techniques within a Bayesian framework to infer disease dynamics and detection from incompletely observed outbreaks. The spatial transmission kernel describing pathogen spread between farms, and the distribution of times between infection and detection, is estimated alongside unobserved exposure times. Our results demonstrate inference is reliable even for relatively small outbreaks when the data-generating model is known. However, associated risk assessments depend strongly on the form of the fitted transmission kernel. Therefore, for real applications, methods are needed to select the most appropriate model in light of the data. We assess standard Deviance Information Criteria (DIC) model selection tools and recently introduced latent residual methods of model assessment, in selecting the functional form of the spatial transmission kernel. These methods are applied to the CSF data, and tested in simulated scenarios which represent field data, but assume the data generation mechanism is known. Analysis of simulated scenarios shows that latent residual methods enable reliable selection of the transmission kernel even for small outbreaks whereas the DIC is less reliable. Moreover, compared with DIC, model choice based on latent residual assessment correlated better with predicted risk. PMID:28293559

Habitual sleep as a contributor to racial differences in cardiometabolic risk.

PubMed

Curtis, David S; Fuller-Rowell, Thomas E; El-Sheikh, Mona; Carnethon, Mercedes R; Ryff, Carol D

2017-08-15

Insufficient and disrupted sleep is linked with cardiovascular and metabolic dysregulation and morbidity. The current study examines the degree to which differences in sleep between black/African American (AA) and white/European American (EA) adults explain racial differences in cardiometabolic (CMB) disease risk. Total sleep time and sleep efficiency (percent of time in bed asleep) were assessed via seven nights of wrist actigraphy among 426 participants in the Midlife in the United States Study (31% AA; 69% EA; 61% female; mean age = 56.8 y). CMB risk was indexed as a composite of seven biomarkers [blood pressure, waist circumference, hemoglobin A1c (HbA 1c ), insulin resistance, triglycerides, HDL cholesterol (HDL-C), and C-reactive protein]. Covariates included sociodemographic characteristics and relevant health behaviors. Results indicated that AAs relative to EAs obtained less sleep (341 vs. 381 min) and had lower sleep efficiency (72.3 vs. 82.2%) ( P values < 0.001). Further, 41% and 58% of the racial difference in CMB risk was explained by sleep time and sleep efficiency, respectively. In models stratified by sex, race was indirectly associated with CMB risk via sleep time and efficiency only among females (explaining 33% and 65% of the race difference, respectively). Indirect effects were robust to alternative model specifications that excluded participants with diabetes or heart disease. Consideration of sleep determinants and sleep health is therefore needed in efforts to reduce racial differences in CMB disease.

Habitual sleep as a contributor to racial differences in cardiometabolic risk

PubMed Central

Fuller-Rowell, Thomas E.; El-Sheikh, Mona; Carnethon, Mercedes R.; Ryff, Carol D.

2017-01-01

Insufficient and disrupted sleep is linked with cardiovascular and metabolic dysregulation and morbidity. The current study examines the degree to which differences in sleep between black/African American (AA) and white/European American (EA) adults explain racial differences in cardiometabolic (CMB) disease risk. Total sleep time and sleep efficiency (percent of time in bed asleep) were assessed via seven nights of wrist actigraphy among 426 participants in the Midlife in the United States Study (31% AA; 69% EA; 61% female; mean age = 56.8 y). CMB risk was indexed as a composite of seven biomarkers [blood pressure, waist circumference, hemoglobin A1c (HbA1c), insulin resistance, triglycerides, HDL cholesterol (HDL-C), and C-reactive protein]. Covariates included sociodemographic characteristics and relevant health behaviors. Results indicated that AAs relative to EAs obtained less sleep (341 vs. 381 min) and had lower sleep efficiency (72.3 vs. 82.2%) (P values < 0.001). Further, 41% and 58% of the racial difference in CMB risk was explained by sleep time and sleep efficiency, respectively. In models stratified by sex, race was indirectly associated with CMB risk via sleep time and efficiency only among females (explaining 33% and 65% of the race difference, respectively). Indirect effects were robust to alternative model specifications that excluded participants with diabetes or heart disease. Consideration of sleep determinants and sleep health is therefore needed in efforts to reduce racial differences in CMB disease. PMID:28760970

Frequency of Alcohol Use in Adolescence as a Marker for Subsequent Sexual Risk Behavior in Adulthood

PubMed Central

Muchimba, Maureen; Haberstick, Brett C.; Corley, Robin P.; McQueen, Matthew B.

2013-01-01

Purpose Although a number of studies have demonstrated an association between alcohol use frequency and sexual risk behavior, few have used longitudinal data. This study examined alohol use frequency in adolescence as a predictor of HIV sexual risk behavior in adulthood. Methods We collected data among 1368 participants in Colorado. During adolescence (Time 1), respondents were asked about the frequency of using alcohol during the previous 12 months. In adulthood (Time 2), the same respondents were asked about their sexual risk behavior during the previous 12 months. Sexual risk behavior items were used to construct an index, which was categorized to indicate low, medium and high risk study participants. The relationship between alcohol use patterns and risky sexual behavior was modeled using ordinal regression. Results Compared to individuals who drank no alcohol in the past 12 months at Time 1, the odds of being in a higher risk group of sexual behavior as opposed to a lower one at Time 2 were 1.56 (95% CI, 1.04-2.35) among those who drank 6-19 times. Similarly, the odds of being in a higher risk group relative to a lower one among those who drank ≥20 times or were 1.78 (95% CI, 1.05-3.02). Conclusion Alcohol use patterns in adolescence may be useful markers for programs that aim to prevent risky sexual behavior. Based on alcohol intake patterns, it may be possible to identify frequent alcohol users that need to be targeted with appropriate alcohol use and HIV risk reduction messages. PMID:23587785

A framework for quantifying net benefits of alternative prognostic models.

PubMed

Rapsomaniki, Eleni; White, Ian R; Wood, Angela M; Thompson, Simon G

2012-01-30

New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context. We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measures and can be used in full health economic evaluations of prognostic models used for screening and allocating risk reduction interventions. We extend previous work in this area by quantifying net benefits in life years, thus linking prognostic performance to health economic measures; by taking full account of the occurrence of events over time; and by considering estimation and cross-validation in a multiple-study setting. The method is illustrated in the context of cardiovascular disease risk prediction using an individual participant data meta-analysis. We estimate the number of cardiovascular-disease-free life years gained when statin treatment is allocated based on a risk prediction model with five established risk factors instead of a model with just age, gender and region. We explore methodological issues associated with the multistudy design and show that cost-effectiveness comparisons based on the proposed methodology are robust against a range of modelling assumptions, including adjusting for competing risks. Copyright © 2011 John Wiley & Sons, Ltd.

Patient-specific analysis of blood stasis in the left atrium

NASA Astrophysics Data System (ADS)

Flores, Oscar; Gonzalo, Alejandro; Garcia-Villalba, Manuel; Rossini, Lorenzo; Hsiao, Albert; McVeigh, Elliot; Kahn, Andrew M.; Del Alamo, Juan C.

2016-11-01

Atrial fibrillation (AF) is a common arrhythmia in which the left atrium (LA) beats rapidly and irregularly. Patients with AF are at increased risk of thromboembolic events (TE), particularly stroke. Anticoagulant therapy can reduce the risk of TE in AF, but it can also increase the risks of adverse events such as internal bleeding. The current lack of tools to predict each patient's risk of LA thrombogenesis makes it difficult to decide whether to anticoagulate patients with AF. The aim of this work is to evaluate blood stasis in patient-specific models of the LA, because stasis is a known thrombogenesis risk factor. To achieve our aim, we performed direct numerical simulations of left atrial flow using an immersed boundary solver developed at the UC3M, coupled to a 0D model for the pulmonary circulation. The LA geometry is obtained from time-resolved CT scans and the parameters of the 0D model are found by fitting pulmonary vein flow data obtained by 4D phase contrast MRI. Blood stasis is evaluated from the flow data by computing blood residence time together with other kinematic indices of the velocity field (e.g. strain and kinetic energy). We focus on the flow in the left atrial appendage, including a sensitivity analysis of the effect of the parameters of the 0D model. Funded by the Spanish MECD, the Clinical and Translational Research Institute at UCSD and the American Heart Association.

Clinical and physiological assessments for elucidating falls risk in Parkinson's disease.

PubMed

Latt, Mark D; Lord, Stephen R; Morris, John G L; Fung, Victor S C

2009-07-15

The study aims were to devise (1) a fall risk screen for people with PD using routine clinical measures and (2) an explanatory (physiological) fall risk assessment for guiding fall prevention interventions. One hundred thirteen people with PD (age 66 +/- 95% CI 1.6 years) underwent clinical assessments and quantitative tests of sway, gait, strength, reaction time, and lower limb sensation. Participants were then followed up for 12 months to determine fall incidence. In the follow-up year, 51 participants (45%) fell one or more times whereas 62 participants (55%) did not fall. Multivariate analyses of routine clinical measures revealed that a fall in the past year, abnormal axial posture, cognitive impairment, and freezing of gait were independent risk factors for falls and predicted 38/51 fallers (75%) and 45/62 non-fallers (73%). A multivariate model combining clinical and physiological measures that elucidate the pathophysiology of falls identified abnormal posture, freezing of gait, frontal impairment, poor leaning balance, and leg weakness as independent risk factors. This model correctly classified 39/51 fallers (77%) and 51/62 non-fallers (82%). Patients with PD at risk of falls can be identified accurately with routine clinical assessments and quantitative physiological tests. Many of the risk factors identified are amenable to targeted intervention. 2009 Movement Disorder Society.

A Multiphase Non-Linear Mixed Effects Model: An Application to Spirometry after Lung Transplantation

PubMed Central

Rajeswaran, Jeevanantham; Blackstone, Eugene H.

2014-01-01

In medical sciences, we often encounter longitudinal temporal relationships that are non-linear in nature. The influence of risk factors may also change across longitudinal follow-up. A system of multiphase non-linear mixed effects model is presented to model temporal patterns of longitudinal continuous measurements, with temporal decomposition to identify the phases and risk factors within each phase. Application of this model is illustrated using spirometry data after lung transplantation using readily available statistical software. This application illustrates the usefulness of our flexible model when dealing with complex non-linear patterns and time varying coefficients. PMID:24919830

Exploring heterogeneous market hypothesis using realized volatility

NASA Astrophysics Data System (ADS)

Chin, Wen Cheong; Isa, Zaidi; Mohd Nor, Abu Hassan Shaari

2013-04-01

This study investigates the heterogeneous market hypothesis using high frequency data. The cascaded heterogeneous trading activities with different time durations are modelled by the heterogeneous autoregressive framework. The empirical study indicated the presence of long memory behaviour and predictability elements in the financial time series which supported heterogeneous market hypothesis. Besides the common sum-of-square intraday realized volatility, we also advocated two power variation realized volatilities in forecast evaluation and risk measurement in order to overcome the possible abrupt jumps during the credit crisis. Finally, the empirical results are used in determining the market risk using the value-at-risk approach. The findings of this study have implications for informationally market efficiency analysis, portfolio strategies and risk managements.

Quantifying risk over the life course - latency, age-related susceptibility, and other time-varying exposure metrics.

PubMed

Wang, Molin; Liao, Xiaomei; Laden, Francine; Spiegelman, Donna

2016-06-15

Identification of the latency period and age-related susceptibility, if any, is an important aspect of assessing risks of environmental, nutritional, and occupational exposures. We consider estimation and inference for latency and age-related susceptibility in relative risk and excess risk models. We focus on likelihood-based methods for point and interval estimation of the latency period and age-related windows of susceptibility coupled with several commonly considered exposure metrics. The method is illustrated in a study of the timing of the effects of constituents of air pollution on mortality in the Nurses' Health Study. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Modeling commodity salam contract between two parties for discrete and continuous time series

NASA Astrophysics Data System (ADS)

Hisham, Azie Farhani Badrol; Jaffar, Maheran Mohd

2017-08-01

In order for Islamic finance to remain competitive as the conventional, there needs a new development of Islamic compliance product such as Islamic derivative that can be used to manage the risk. However, under syariah principles and regulations, all financial instruments must not be conflicting with five syariah elements which are riba (interest paid), rishwah (corruption), gharar (uncertainty or unnecessary risk), maysir (speculation or gambling) and jahl (taking advantage of the counterparty's ignorance). This study has proposed a traditional Islamic contract namely salam that can be built as an Islamic derivative product. Although a lot of studies has been done on discussing and proposing the implementation of salam contract as the Islamic product however they are more into qualitative and law issues. Since there is lack of quantitative study of salam contract being developed, this study introduces mathematical models that can value the appropriate salam price for a commodity salam contract between two parties. In modeling the commodity salam contract, this study has modified the existing conventional derivative model and come out with some adjustments to comply with syariah rules and regulations. The cost of carry model has been chosen as the foundation to develop the commodity salam model between two parties for discrete and continuous time series. However, the conventional time value of money results from the concept of interest that is prohibited in Islam. Therefore, this study has adopted the idea of Islamic time value of money which is known as the positive time preference, in modeling the commodity salam contract between two parties for discrete and continuous time series.

Parent and child psychopathology and suicide attempts among children of parents with alcohol use disorder.

PubMed

Conner, Kenneth R; Bossarte, Robert M; Lu, Naiji; Kaukeinen, Kimberly; Chan, Grace; Wyman, Peter; Tu, Xin M; Goldston, David B; Houston, Rebecca J; Bucholz, Kathleen K; Hesselbrock, Victor M

2014-01-01

Parents with psychopathology such as alcohol use disorder (AUD) that confers risk for suicide attempt (SA) may have children who are more likely to develop such psychopathology and to attempt suicide, suggesting that risk may be "transmitted" from parents to children. We examined this phenomenon during the transition from childhood to adolescence, when risk for SA increases dramatically. A cohort of 418 children were examined at average age 9.4 (range 7-14) years at enrollment (Time 1, childhood) and approximately 5 years later, prior to reaching age 18 (Time 2, adolescence). One or both biological parents, oversampled for AUD, were also interviewed. Structural equation models (SEM) examined father-child, mother-child, and either/both parent-child associations. The primary outcome was SA over follow-up among offspring, assessed at Time 2. As hypothesized, parental antisocial personality disorder predicted conduct disorder symptoms in offspring both during childhood and adolescence (parent-child model, father-child model) and maternal AUD predicted conduct disorder symptoms during childhood (mother-child model). However, we did not find evidence to support transmission of depression from parents to offspring either during childhood or adolescence, and parent psychopathology did not show statistically significant associations with SA during adolescence. In conclusion, we conducted a rare study of parent-to-child "transmission" of risk for SA that used a prospective research design, included diagnostic interviews with both parents and offspring, and examined the transition from childhood to adolescence, and the first such study in children of parents with AUD. Results provided mixed support for hypothesized parent-child associations.

Parent and Child Psychopathology and Suicide Attempts among Children of Parents with Alcohol Use Disorder

PubMed Central

Conner, Kenneth R.; Bossarte, Robert M.; Lu, Naiji; Kaukeinen, Kimberly; Chan, Grace; Wyman, Peter; Tu, Xin M.; Goldston, David B.; Houston, Rebecca J.; Bucholz, Kathleen K.; Hesselbrock, Victor M.

2014-01-01

Parents with psychopathology such as alcohol use disorder (AUD) that confers risk for suicide attempt (SA) may have children who are more likely to develop such psychopathology and to attempt suicide, suggesting that risk may be “transmitted” from parents to children. We examined this phenomenon during the transition from childhood to adolescence, when risk for SA increases dramatically. A cohort of 418 children were examined at average age 9.4 (range 7–14) years at enrollment (Time 1, childhood) and approximately five years later, prior to reaching age 18 (Time 2, adolescence). One or both biological parents, oversampled for AUD, were also interviewed. Structural equation models (SEM) examined father-child, mother-child, and either/both parent-child associations. The primary outcome was SA over follow-up among offspring, assessed at Time 2. As hypothesized, parental antisocial personality disorder predicted conduct disorder symptoms in offspring both during childhood and adolescence (parent-child model, father-child model) and maternal AUD predicted conduct disorder symptoms during childhood (mother-child model). However, we did not find evidence to support transmission of depression from parents to offspring either during childhood or adolescence, and parent psychopathology did not show statistically significant associations with SA during adolescence. In conclusion, we conducted a rare study of parent-to-child “transmission” of risk for SA that used a prospective research design, included diagnostic interviews with both parents and offspring, and examined the transition from childhood to adolescence, and the first such study in children of parents with AUD. Results provided mixed support for hypothesized parent-child associations. PMID:24716789

Risk for psychopathology in the children of depressed mothers: a developmental model for understanding mechanisms of transmission.

PubMed

Goodman, S H; Gotlib, I H

1999-07-01

A large body of literature documents the adverse effects of maternal depression on the functioning and development of offspring. Although investigators have identified factors associated with risk for abnormal development and psychopathology in the children, little attention has been paid to the mechanisms explaining the transmission of risk from the mothers to the children. Moreover, no existing model both guides understanding of the various processes' interrelatedness and considers the role of development in explicating the manifestation of risk in the children. This article proposes a developmentally sensitive, integrative model for understanding children's risk in relation to maternal depression. Four mechanisms through which risk might be transmitted are evaluated: (a) heritability of depression; (b) innate dysfunctional neuroregulatory mechanisms; (c) exposure to negative maternal cognitions, behaviors, and affect; and (d) the stressful context of the children's lives. Three factors that might moderate this risk are considered: (a) the father's health and involvement with the child, (b) the course and timing of the mother's depression, and (c) characteristics of the child. Relevant issues are discussed, and promising directions for future research are suggested.

Self-Reported Arrests Among Indigenous Adolescents: a Longitudinal Analysis of Community, Family, and Individual Risk Factors

PubMed Central

Gentzler, Kari C.

2018-01-01

Purpose North American indigenous (American Indian/Canadian First Nations) adolescents are overrepresented in the juvenile justice systems in the USA and Canada. One explanation advanced for disproportionate numbers of racial and ethnic minorities in the justice systems is the unequal distribution of risk factors across groups. The purpose of this study is to investigate the prevalence of and risk factors for first arrest within a population sample of indigenous adolescents. Methods The data come from an 8-year longitudinal panel study of indigenous youth (n = 641) from the northern Midwest and Canada, spanning ages 10 to 19 years. We used a discrete-time survival model to estimate the overall hazard of first arrest and change in the arrest hazard over time and included both time-invariant and time varying risk factors. Results The risk of arrest increased over time, although the largest increase occurred between waves 3 and 4, when the adolescents averaged 13.1 and 14.3 years, respectively. The youth had a 55 % probability of being arrested at least once by the end of the study. Of the time-invariant risk factors, exposure to violence, parent arrest, age, and income were associated with overall risk of first arrest. Three time-varying risk factors (alcohol use, marijuana use, and peer delinquency) were associated with changes in the risk of first arrest. Conclusions Being arrested carries significant repercussions for young people, including involvement in the juvenile justice system as well as consequences into adulthood. Communities must go beyond programs that target problem behaviors because community, family, and peer factors are also important. PMID:29503797

Modeling Individual Patient Preferences for Colorectal Cancer Screening Based on Their Tolerance for Complications Risk.

PubMed

Taksler, Glen B; Perzynski, Adam T; Kattan, Michael W

2017-04-01

Recommendations for colorectal cancer screening encourage patients to choose among various screening methods based on individual preferences for benefits, risks, screening frequency, and discomfort. We devised a model to illustrate how individuals with varying tolerance for screening complications risk might decide on their preferred screening strategy. We developed a discrete-time Markov mathematical model that allowed hypothetical individuals to maximize expected lifetime utility by selecting screening method, start age, stop age, and frequency. Individuals could choose from stool-based testing every 1 to 3 years, flexible sigmoidoscopy every 1 to 20 years with annual stool-based testing, colonoscopy every 1 to 20 years, or no screening. We compared the life expectancy gained from the chosen strategy with the life expectancy available from a benchmark strategy of decennial colonoscopy. For an individual at average risk of colorectal cancer who was risk neutral with respect to screening complications (and therefore was willing to undergo screening if it would actuarially increase life expectancy), the model predicted that he or she would choose colonoscopy every 10 years, from age 53 to 73 years, consistent with national guidelines. For a similar individual who was moderately averse to screening complications risk (and therefore required a greater increase in life expectancy to accept potential risks of colonoscopy), the model predicted that he or she would prefer flexible sigmoidoscopy every 12 years with annual stool-based testing, with 93% of the life expectancy benefit of decennial colonoscopy. For an individual with higher risk aversion, the model predicted that he or she would prefer 2 lifetime flexible sigmoidoscopies, 20 years apart, with 70% of the life expectancy benefit of decennial colonoscopy. Mathematical models may formalize how individuals with different risk attitudes choose between various guideline-recommended colorectal cancer screening strategies.

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.

Conscious worst case definition for risk assessment, part I: a knowledge mapping approach for defining most critical risk factors in integrative risk management of chemicals and nanomaterials.

PubMed

Sørensen, Peter B; Thomsen, Marianne; Assmuth, Timo; Grieger, Khara D; Baun, Anders

2010-08-15

This paper helps bridge the gap between scientists and other stakeholders in the areas of human and environmental risk management of chemicals and engineered nanomaterials. This connection is needed due to the evolution of stakeholder awareness and scientific progress related to human and environmental health which involves complex methodological demands on risk management. At the same time, the available scientific knowledge is also becoming more scattered across multiple scientific disciplines. Hence, the understanding of potentially risky situations is increasingly multifaceted, which again challenges risk assessors in terms of giving the 'right' relative priority to the multitude of contributing risk factors. A critical issue is therefore to develop procedures that can identify and evaluate worst case risk conditions which may be input to risk level predictions. Therefore, this paper suggests a conceptual modelling procedure that is able to define appropriate worst case conditions in complex risk management. The result of the analysis is an assembly of system models, denoted the Worst Case Definition (WCD) model, to set up and evaluate the conditions of multi-dimensional risk identification and risk quantification. The model can help optimize risk assessment planning by initial screening level analyses and guiding quantitative assessment in relation to knowledge needs for better decision support concerning environmental and human health protection or risk reduction. The WCD model facilitates the evaluation of fundamental uncertainty using knowledge mapping principles and techniques in a way that can improve a complete uncertainty analysis. Ultimately, the WCD is applicable for describing risk contributing factors in relation to many different types of risk management problems since it transparently and effectively handles assumptions and definitions and allows the integration of different forms of knowledge, thereby supporting the inclusion of multifaceted risk components in cumulative risk management. Copyright 2009 Elsevier B.V. All rights reserved.

"Near-term" Natural Catastrophe Risk Management and Risk Hedging in a Changing Climate

NASA Astrophysics Data System (ADS)

Michel, Gero; Tiampo, Kristy

2014-05-01

Competing with analytics - Can the insurance market take advantage of seasonal or "near-term" forecasting and temporal changes in risk? Natural perils (re)insurance has been based on models following climatology i.e. the long-term "historical" average. This is opposed to considering the "near-term" and forecasting hazard and risk for the seasons or years to come. Variability and short-term changes in risk are deemed abundant for almost all perils. In addition to hydrometeorological perils whose changes are vastly discussed, earthquake activity might also change over various time-scales affected by earlier local (or even global) events, regional changes in the distribution of stresses and strains and more. Only recently has insurance risk modeling of (stochastic) hurricane-years or extratropical-storm-years started considering our ability to forecast climate variability herewith taking advantage of apparent correlations between climate indicators and the activity of storm events. Once some of these "near-term measures" were in the market, rating agencies and regulators swiftly adopted these concepts demanding companies to deploy a selection of more conservative "time-dependent" models. This was despite the fact that the ultimate effect of some of these measures on insurance risk was not well understood. Apparent short-term success over the last years in near-term seasonal hurricane forecasting was brought to a halt in 2013 when these models failed to forecast the exceptional shortage of hurricanes herewith contradicting an active-year forecast. The focus of earthquake forecasting has in addition been mostly on high rather than low temporal and regional activity despite the fact that avoiding losses does not by itself create a product. This presentation sheds light on new risk management concepts for over-regional and global (re)insurance portfolios that take advantage of forecasting changes in risk. The presentation focuses on the "upside" and on new opportunities in risk-taking rather than the "downside" and the general notion that catastrophes will get worse. The focus will be on the industry's ability to hedge and optimize risk more efficiently in a changing environment.

Adipose-Derived Mesenchymal Stem Cell Administration Does Not Improve Corneal Graft Survival Outcome

PubMed Central

Fuentes-Julián, Sherezade; Arnalich-Montiel, Francisco; Jaumandreu, Laia; Leal, Marina; Casado, Alfonso; García-Tuñon, Ignacio; Hernández-Jiménez, Enrique; López-Collazo, Eduardo; De Miguel, Maria P.

2015-01-01

The effect of local and systemic injections of mesenchymal stem cells derived from adipose tissue (AD-MSC) into rabbit models of corneal allograft rejection with either normal-risk or high-risk vascularized corneal beds was investigated. The models we present in this study are more similar to human corneal transplants than previously reported murine models. Our aim was to prevent transplant rejection and increase the length of graft survival. In the normal-risk transplant model, in contrast to our expectations, the injection of AD-MSC into the graft junction during surgery resulted in the induction of increased signs of inflammation such as corneal edema with increased thickness, and a higher level of infiltration of leukocytes. This process led to a lower survival of the graft compared with the sham-treated corneal transplants. In the high-risk transplant model, in which immune ocular privilege was undermined by the induction of neovascularization prior to graft surgery, we found the use of systemic rabbit AD-MSCs prior to surgery, during surgery, and at various time points after surgery resulted in a shorter survival of the graft compared with the non-treated corneal grafts. Based on our results, local or systemic treatment with AD-MSCs to prevent corneal rejection in rabbit corneal models at normal or high risk of rejection does not increase survival but rather can increase inflammation and neovascularization and break the innate ocular immune privilege. This result can be partially explained by the immunomarkers, lack of immunosuppressive ability and immunophenotypical secretion molecules characterization of AD-MSC used in this study. Parameters including the risk of rejection, the inflammatory/vascularization environment, the cell source, the time of injection, the immunosuppression, the number of cells, and the mode of delivery must be established before translating the possible benefits of the use of MSCs in corneal transplants to clinical practice. PMID:25730319

Developmental timing of housing mobility: longitudinal effects on externalizing behaviors among at-risk youth.

PubMed

Fowler, Patrick J; Henry, David B; Schoeny, Michael; Taylor, Jeremy; Chavira, Dina

2014-02-01

This longitudinal study tested whether developmental timing of exposure to housing mobility exacerbates behavior problems in an at-risk sample of youth. Participants were 2,442 youth 4 to 16 years old at risk for child maltreatment followed at 3 time points over a 36-month follow-up. Caregivers reported on youth externalizing behaviors at each assessment. Latent growth models examined the effect of housing mobility on behavior problems after accounting for change in cognitive development, family instability, child gender, ethnicity, family income, and caregiver mental health at baseline. Findings suggested increased housing mobility predicted greater behavior problems when children were exposed at key developmental periods. Preschoolers exhibited significantly higher rates of behavior problems that remained stable across the 3-year follow-up. Likewise, adolescents exposed to more mobility became relatively more disruptive over time. No effects were found for school-age children. Children who moved frequently during infancy and more recently demonstrated significantly worse behavior over time. The developmental timing of housing mobility affects child behavioral outcomes. Youth in developmental transition at the time of mobility are at greatest risk for disturbances to residential contexts. Assessing housing history represents an important component of interventions with at-risk families. Copyright © 2014 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Spatial clustering of average risks and risk trends in Bayesian disease mapping.

PubMed

Anderson, Craig; Lee, Duncan; Dean, Nema

2017-01-01

Spatiotemporal disease mapping focuses on estimating the spatial pattern in disease risk across a set of nonoverlapping areal units over a fixed period of time. The key aim of such research is to identify areas that have a high average level of disease risk or where disease risk is increasing over time, thus allowing public health interventions to be focused on these areas. Such aims are well suited to the statistical approach of clustering, and while much research has been done in this area in a purely spatial setting, only a handful of approaches have focused on spatiotemporal clustering of disease risk. Therefore, this paper outlines a new modeling approach for clustering spatiotemporal disease risk data, by clustering areas based on both their mean risk levels and the behavior of their temporal trends. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Proactive assessment of accident risk to improve safety on a system of freeways.

DOT National Transportation Integrated Search

2012-05-01

This report describes the development and evaluation of real-time crash risk-assessment models for four freeway corridors: U.S. Route 101 NB (northbound) and SB (southbound) and Interstate 880 NB and SB. Crash data for these freeway segments for the ...

Challenges for modelling spatio-temporal variations of malaria risk in Malawi

NASA Astrophysics Data System (ADS)

Lowe, R.; Chirombo, J.; Tompkins, A. M.

2012-04-01

Malaria is the leading cause of morbidity and mortality in Malawi with more than 6 million episodes reported each year. Malaria poses a huge economic burden to Malawi in terms of the direct cost of treating malaria patients and also indirect costs resulting from workdays lost in agriculture and industry and absenteeism from school. Malawi implements malaria control activities within the Roll Back Malaria framework, with the objective to provide those most at risk (i.e. children under five years, pregnant woman and individuals with suppressed immune systems) access to personal and community protective measures. However, at present there is no mechanism by which to target the most 'at risk' populations ahead of an impending epidemic. Malaria transmission is influenced by variations in meteorological conditions, which impact the biology of the mosquito and the availability of breeding sites, but also socio-economic conditions such as levels of urbanisation, poverty and education, which influence human vulnerability and vector habitat. The many potential drivers of malaria, both extrinsic, such as climate, and intrinsic, such as population immunity are often difficult to disentangle. This presents a challenge for modelling of malaria risk in space and time. Using an age-stratified spatio-temporal dataset of malaria cases at the district level from July 2004 - June 2011, we use a spatio-temporal modelling framework to model variations in malaria risk in Malawi. Climatic and topographic variations are accounted for using an interpolation method to relate gridded products to administrative districts. District level data is tested in the model to account for confounding factors, including the proportion of the population living in urban areas; residing in traditional housing; with no toilet facilities; who do not attend school, etc, the number of health facilities per population and yearly estimates of insecticide-treated mosquito net distribution. In order to account for the unobserved confounding factors that influence malaria, which are not accounted for using measured covariates, a negative binomial generalised linear mixed model (GLMM) is adopted, which includes structured and unstructured spatial and temporal random effects. The parameters in this spatio-temporal Bayesian hierarchical model are estimated using Markov Chain Monte Carlo (MCMC). This allows posterior predictive distributions for disease risk to be derived for each spatial location and time period. A novel visualisation technique is then used to display seasonal probabilistic forecasts of malaria risk, derived from the developed model using pre-defined risk category thresholds, on a map. This technique allows decision makers to identify areas where the model predicts with certainty a particular malaria risk category (high, medium or low); in order to effectively target limited resources to those districts most at risk for a given season.

Advanced age, cardiovascular risk burden, and timed up and go test performance in Parkinson disease.

PubMed

Kotagal, Vikas; Albin, Roger L; Müller, Martijn L T M; Koeppe, Robert A; Studenski, Stephanie; Frey, Kirk A; Bohnen, Nicolaas I

2014-12-01

Cardiovascular comorbidities are a known risk factor for impaired mobility in elderly individuals. Motor impairments in Parkinson disease are conventionally ascribed to nigrostriatal dopaminergic denervation although progressive gait and balance impairments become more common with aging and often show limited response to dopaminergic replacement therapies. We explored the association between elevated cardiovascular risk factors and performance on the Timed Up and Go test in cross-sectional of Parkinson disease subjects (n = 83). Cardiovascular risk factor status was estimated using the Framingham General Cardiovascular Disease risk-scoring algorithm in order to dichotomize the cohort into those with and without elevated modifiable cardiovascular risk compared with normative scores for age and gender. All subjects underwent clinical and neuroimaging evaluations including a 3-m Timed Up and Go test, [(11)C]dihydrotetrabenazine positron emission tomography imaging to estimate nigrostriatal dopamine terminal loss, and an magnetic resonance imaging assessment of leukoaraiosis. A similar analysis was performed in 49 healthy controls. After adjusting for disease duration, leukoaraiosis, and nigrostriatal dopaminergic denervation, Parkinson disease subjects with elevated Framingham risk scores (n = 61) displayed slower Timed Up and Go test performance (β = 1.86, t = 2.41, p = .018) compared with subjects with normal range Framingham risk scores (n = 22). When age ≥65 was added to the model in a post hoc analysis, the strength of effect seen with older age (β = 1.51, t = 2.44, p = .017) was similar to that of elevated Framingham risk scoring (β = 1.87, t = 2.51, p = .014). In a multivariable regression model studying the healthy control population, advanced age (t = 2.15, p = .037) was a significant predictor of Timed Up and Go speed though striatal [(11)C]dihydrotetrabenazine (t = -1.30, p = .19) and elevated Framingham risk scores (t = 1.32, p = .19) were not. Modifiable cardiovascular risk factors and older age may independently exacerbate balance-related disability in Parkinson disease and may exert additive or synergistic pathological effects. The pathophysiology of these impairments cannot be explained completely by nigrostriatal dopaminergic denervation or leukoaraiosis burden and may relate to systemic factors seen with accelerated aging. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Adoption of Building Information Modelling in project planning risk management

NASA Astrophysics Data System (ADS)

Mering, M. M.; Aminudin, E.; Chai, C. S.; Zakaria, R.; Tan, C. S.; Lee, Y. Y.; Redzuan, A. A.

2017-11-01

An efficient and effective risk management required a systematic and proper methodology besides knowledge and experience. However, if the risk management is not discussed from the starting of the project, this duty is notably complicated and no longer efficient. This paper presents the adoption of Building Information Modelling (BIM) in project planning risk management. The objectives is to identify the traditional risk management practices and its function, besides, determine the best function of BIM in risk management and investigating the efficiency of adopting BIM-based risk management during the project planning phase. In order to obtain data, a quantitative approach is adopted in this research. Based on data analysis, the lack of compliance with project requirements and failure to recognise risk and develop responses to opportunity are the risks occurred when traditional risk management is implemented. When using BIM in project planning, it works as the tracking of cost control and cash flow give impact on the project cycle to be completed on time. 5D cost estimation or cash flow modeling benefit risk management in planning, controlling and managing budget and cost reasonably. There were two factors that mostly benefit a BIM-based technology which were formwork plan with integrated fall plan and design for safety model check. By adopting risk management, potential risks linked with a project and acknowledging to those risks can be identified to reduce them to an acceptable extent. This means recognizing potential risks and avoiding threat by reducing their negative effects. The BIM-based risk management can enhance the planning process of construction projects. It benefits the construction players in various aspects. It is important to know the application of BIM-based risk management as it can be a lesson learnt to others to implement BIM and increase the quality of the project.

An extended reinforcement learning model of basal ganglia to understand the contributions of serotonin and dopamine in risk-based decision making, reward prediction, and punishment learning

PubMed Central

Balasubramani, Pragathi P.; Chakravarthy, V. Srinivasa; Ravindran, Balaraman; Moustafa, Ahmed A.

2014-01-01

Although empirical and neural studies show that serotonin (5HT) plays many functional roles in the brain, prior computational models mostly focus on its role in behavioral inhibition. In this study, we present a model of risk based decision making in a modified Reinforcement Learning (RL)-framework. The model depicts the roles of dopamine (DA) and serotonin (5HT) in Basal Ganglia (BG). In this model, the DA signal is represented by the temporal difference error (δ), while the 5HT signal is represented by a parameter (α) that controls risk prediction error. This formulation that accommodates both 5HT and DA reconciles some of the diverse roles of 5HT particularly in connection with the BG system. We apply the model to different experimental paradigms used to study the role of 5HT: (1) Risk-sensitive decision making, where 5HT controls risk assessment, (2) Temporal reward prediction, where 5HT controls time-scale of reward prediction, and (3) Reward/Punishment sensitivity, in which the punishment prediction error depends on 5HT levels. Thus the proposed integrated RL model reconciles several existing theories of 5HT and DA in the BG. PMID:24795614

Simulating the Risk of Liver Fluke Infection using a Mechanistic Hydro-epidemiological Model

NASA Astrophysics Data System (ADS)

Beltrame, Ludovica; Dunne, Toby; Rose, Hannah; Walker, Josephine; Morgan, Eric; Vickerman, Peter; Wagener, Thorsten

2016-04-01

Liver Fluke (Fasciola hepatica) is a common parasite found in livestock and responsible for considerable economic losses throughout the world. Risk of infection is strongly influenced by climatic and hydrological conditions, which characterise the host environment for parasite development and transmission. Despite on-going control efforts, increases in fluke outbreaks have been reported in recent years in the UK, and have been often attributed to climate change. Currently used fluke risk models are based on empirical relationships derived between historical climate and incidence data. However, hydro-climate conditions are becoming increasingly non-stationary due to climate change and direct anthropogenic impacts such as land use change, making empirical models unsuitable for simulating future risk. In this study we introduce a mechanistic hydro-epidemiological model for Liver Fluke, which explicitly simulates habitat suitability for disease development in space and time, representing the parasite life cycle in connection with key environmental conditions. The model is used to assess patterns of Liver Fluke risk for two catchments in the UK under current and potential future climate conditions. Comparisons are made with a widely used empirical model employing different datasets, including data from regional veterinary laboratories. Results suggest that mechanistic models can achieve adequate predictive ability and support adaptive fluke control strategies under climate change scenarios.

Reduced order models for prediction of groundwater quality impacts from CO₂ and brine leakage

DOE PAGES

Zheng, Liange; Carroll, Susan; Bianchi, Marco; ...

2014-12-31

A careful assessment of the risk associated with geologic CO₂ storage is critical to the deployment of large-scale storage projects. A potential risk is the deterioration of groundwater quality caused by the leakage of CO₂ and brine leakage from deep subsurface reservoirs. In probabilistic risk assessment studies, numerical modeling is the primary tool employed to assess risk. However, the application of traditional numerical models to fully evaluate the impact of CO₂ leakage on groundwater can be computationally complex, demanding large processing times and resources, and involving large uncertainties. As an alternative, reduced order models (ROMs) can be used as highlymore » efficient surrogates for the complex process-based numerical models. In this study, we represent the complex hydrogeological and geochemical conditions in a heterogeneous aquifer and subsequent risk by developing and using two separate ROMs. The first ROM is derived from a model that accounts for the heterogeneous flow and transport conditions in the presence of complex leakage functions for CO₂ and brine. The second ROM is obtained from models that feature similar, but simplified flow and transport conditions, and allow for a more complex representation of all relevant geochemical reactions. To quantify possible impacts to groundwater aquifers, the basic risk metric is taken as the aquifer volume in which the water quality of the aquifer may be affected by an underlying CO₂ storage project. The integration of the two ROMs provides an estimate of the impacted aquifer volume taking into account uncertainties in flow, transport and chemical conditions. These two ROMs can be linked in a comprehensive system level model for quantitative risk assessment of the deep storage reservoir, wellbore leakage, and shallow aquifer impacts to assess the collective risk of CO₂ storage projects.« less

Maximizing Energy Savings Reliability in BC Hydro Industrial Demand-side Management Programs: An Assessment of Performance Incentive Models

NASA Astrophysics Data System (ADS)

Gosman, Nathaniel

For energy utilities faced with expanded jurisdictional energy efficiency requirements and pursuing demand-side management (DSM) incentive programs in the large industrial sector, performance incentive programs can be an effective means to maximize the reliability of planned energy savings. Performance incentive programs balance the objectives of high participation rates with persistent energy savings by: (1) providing financial incentives and resources to minimize constraints to investment in energy efficiency, and (2) requiring that incentive payments be dependent on measured energy savings over time. As BC Hydro increases its DSM initiatives to meet the Clean Energy Act objective to reduce at least 66 per cent of new electricity demand with DSM by 2020, the utility is faced with a higher level of DSM risk, or uncertainties that impact the costeffective acquisition of planned energy savings. For industrial DSM incentive programs, DSM risk can be broken down into project development and project performance risks. Development risk represents the project ramp-up phase and is the risk that planned energy savings do not materialize due to low customer response to program incentives. Performance risk represents the operational phase and is the risk that planned energy savings do not persist over the effective measure life. DSM project development and performance risks are, in turn, a result of industrial economic, technological and organizational conditions, or DSM risk factors. In the BC large industrial sector, and characteristic of large industrial sectors in general, these DSM risk factors include: (1) capital constraints to investment in energy efficiency, (2) commodity price volatility, (3) limited internal staffing resources to deploy towards energy efficiency, (4) variable load, process-based energy saving potential, and (5) a lack of organizational awareness of an operation's energy efficiency over time (energy performance). This research assessed the capacity of alternative performance incentive program models to manage DSM risk in BC. Three performance incentive program models were assessed and compared to BC Hydro's current large industrial DSM incentive program, Power Smart Partners -- Transmission Project Incentives, itself a performance incentive-based program. Together, the selected program models represent a continuum of program design and implementation in terms of the schedule and level of incentives provided, the duration and rigour of measurement and verification (M&V), energy efficiency measures targeted and involvement of the private sector. A multi criteria assessment framework was developed to rank the capacity of each program model to manage BC large industrial DSM risk factors. DSM risk management rankings were then compared to program costeffectiveness, targeted energy savings potential in BC and survey results from BC industrial firms on the program models. The findings indicate that the reliability of DSM energy savings in the BC large industrial sector can be maximized through performance incentive program models that: (1) offer incentives jointly for capital and low-cost operations and maintenance (O&M) measures, (2) allow flexible lead times for project development, (3) utilize rigorous M&V methods capable of measuring variable load, process-based energy savings, (4) use moderate contract lengths that align with effective measure life, and (5) integrate energy management software tools capable of providing energy performance feedback to customers to maximize the persistence of energy savings. While this study focuses exclusively on the BC large industrial sector, the findings of this research have applicability to all energy utilities serving large, energy intensive industrial sectors.