A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka

PubMed Central

2011-01-01

Background The deprived physical environments present in slums are well-known to have adverse health effects on their residents. However, little is known about the health effects of the social environments in slums. Moreover, neighbourhood quantitative spatial analyses of the mental health status of slum residents are still rare. The aim of this paper is to study self-rated mental health data in several slums of Dhaka, Bangladesh, by accounting for neighbourhood social and physical associations using spatial statistics. We hypothesised that mental health would show a significant spatial pattern in different population groups, and that the spatial patterns would relate to spatially-correlated health-determining factors (HDF). Methods We applied a spatial epidemiological approach, including non-spatial ANOVA/ANCOVA, as well as global and local univariate and bivariate Moran's I statistics. The WHO-5 Well-being Index was used as a measure of self-rated mental health. Results We found that poor mental health (WHO-5 scores < 13) among the adult population (age ≥15) was prevalent in all slum settlements. We detected spatially autocorrelated WHO-5 scores (i.e., spatial clusters of poor and good mental health among different population groups). Further, we detected spatial associations between mental health and housing quality, sanitation, income generation, environmental health knowledge, education, age, gender, flood non-affectedness, and selected properties of the natural environment. Conclusions Spatial patterns of mental health were detected and could be partly explained by spatially correlated HDF. We thereby showed that the socio-physical neighbourhood was significantly associated with health status, i.e., mental health at one location was spatially dependent on the mental health and HDF prevalent at neighbouring locations. Furthermore, the spatial patterns point to severe health disparities both within and between the slums. In addition to examining health outcomes, the methodology used here is also applicable to residuals of regression models, such as helping to avoid violating the assumption of data independence that underlies many statistical approaches. We assume that similar spatial structures can be found in other studies focussing on neighbourhood effects on health, and therefore argue for a more widespread incorporation of spatial statistics in epidemiological studies. PMID:21599932

Spatio-Temporal Epidemiology of Viral Hepatitis in China (2003-2015): Implications for Prevention and Control Policies.

PubMed

Zhu, Bin; Liu, Jinlin; Fu, Yang; Zhang, Bo; Mao, Ying

2018-04-02

Viral hepatitis, as one of the most serious notifiable infectious diseases in China, takes heavy tolls from the infected and causes a severe economic burden to society, yet few studies have systematically explored the spatio-temporal epidemiology of viral hepatitis in China. This study aims to explore, visualize and compare the epidemiologic trends and spatial changing patterns of different types of viral hepatitis (A, B, C, E and unspecified, based on the classification of CDC) at the provincial level in China. The growth rates of incidence are used and converted to box plots to visualize the epidemiologic trends, with the linear trend being tested by chi-square linear by linear association test. Two complementary spatial cluster methods are used to explore the overall agglomeration level and identify spatial clusters: spatial autocorrelation analysis (measured by global and local Moran's I) and space-time scan analysis. Based on the spatial autocorrelation analysis, the hotspots of hepatitis A remain relatively stable and gradually shrunk, with Yunnan and Sichuan successively moving out the high-high (HH) cluster area. The HH clustering feature of hepatitis B in China gradually disappeared with time. However, the HH cluster area of hepatitis C has gradually moved towards the west, while for hepatitis E, the provincial units around the Yangtze River Delta region have been revealing HH cluster features since 2005. The space-time scan analysis also indicates the distinct spatial changing patterns of different types of viral hepatitis in China. It is easy to conclude that there is no one-size-fits-all plan for the prevention and control of viral hepatitis in all the provincial units. An effective response requires a package of coordinated actions, which should vary across localities regarding the spatial-temporal epidemic dynamics of each type of virus and the specific conditions of each provincial unit.

Spatial analysis for the epidemiological study of cardiovascular diseases: A systematic literature search.

PubMed

Mena, Carlos; Sepúlveda, Cesar; Fuentes, Eduardo; Ormazábal, Yony; Palomo, Iván

2018-05-07

Cardiovascular diseases (CVDs) are the primary cause of death and disability in de world, and the detection of populations at risk as well as localization of vulnerable areas is essential for adequate epidemiological management. Techniques developed for spatial analysis, among them geographical information systems and spatial statistics, such as cluster detection and spatial correlation, are useful for the study of the distribution of the CVDs. These techniques, enabling recognition of events at different geographical levels of study (e.g., rural, deprived neighbourhoods, etc.), make it possible to relate CVDs to factors present in the immediate environment. The systemic literature presented here shows that this group of diseases is clustered with regard to incidence, mortality and hospitalization as well as obesity, smoking, increased glycated haemoglobin levels, hypertension physical activity and age. In addition, acquired variables such as income, residency (rural or urban) and education, contribute to CVD clustering. Both local cluster detection and spatial regression techniques give statistical weight to the findings providing valuable information that can influence response mechanisms in the health services by indicating locations in need of intervention and assignment of available resources.

Ecogeographic Genetic Epidemiology

PubMed Central

Sloan, Chantel D.; Duell, Eric J.; Shi, Xun; Irwin, Rebecca; Andrew, Angeline S.; Williams, Scott M.; Moore, Jason H.

2009-01-01

Complex diseases such as cancer and heart disease result from interactions between an individual's genetics and environment, i.e. their human ecology. Rates of complex diseases have consistently demonstrated geographic patterns of incidence, or spatial “clusters” of increased incidence relative to the general population. Likewise, genetic subpopulations and environmental influences are not evenly distributed across space. Merging appropriate methods from genetic epidemiology, ecology and geography will provide a more complete understanding of the spatial interactions between genetics and environment that result in spatial patterning of disease rates. Geographic Information Systems (GIS), which are tools designed specifically for dealing with geographic data and performing spatial analyses to determine their relationship, are key to this kind of data integration. Here the authors introduce a new interdisciplinary paradigm, ecogeographic genetic epidemiology, which uses GIS and spatial statistical analyses to layer genetic subpopulation and environmental data with disease rates and thereby discern the complex gene-environment interactions which result in spatial patterns of incidence. PMID:19025788

Linking Meteorology, Air Quality Models and Observations to ...

EPA Pesticide Factsheets

Epidemiologic studies are critical in establishing the association between exposure to air pollutants and adverse health effects. Results of epidemiologic studies are used by U.S. EPA in developing air quality standards to protect the public from the health effects of air pollutants. A major challenge in environmental epidemiology is adequate exposure characterization. Numerous health studies have used measurements from a few central-site ambient monitors to characterize air pollution exposures. Relying solely on central-site ambient monitors does not account for the spatial-heterogeneity of ambient air pollution patterns, the temporal variability in ambient concentrations, nor the influence of infiltration and indoor sources. Central-site monitoring becomes even more problematic for certain air pollutants that exhibit significant spatial heterogeneity. Statistical interpolation techniques and passive monitoring methods can provide additional spatial resolution in ambient concentration estimates. In addition, spatio-temporal models, which integrate GIS data and other factors, such as meteorology, have also been developed to produce more resolved estimates of ambient concentrations. Models, such as the Community Multi-Scale Air Quality (CMAQ) model, estimate ambient concentrations by combining information on meteorology, source emissions, and chemical-fate and transport. Hybrid modeling approaches, which integrate regional scale models with local scale dispersion

The impact of strain-specific immunity on Lyme disease incidence is spatially heterogeneous.

PubMed

Khatchikian, Camilo E; Nadelman, Robert B; Nowakowski, John; Schwartz, Ira; Wormser, Gary P; Brisson, Dustin

2017-12-01

Lyme disease, caused by the bacterium Borrelia burgdorferi, is the most common tick-borne infection in the US. Recent studies have demonstrated that the incidence of human Lyme disease would have been even greater were it not for the presence of strain-specific immunity, which protects previously infected patients against subsequent infections by the same B. burgdorferi strain. Here, spatial heterogeneity is incorporated into epidemiological models to accurately estimate the impact of strain-specific immunity on human Lyme disease incidence. The estimated reduction in the number of Lyme disease cases is greater in epidemiologic models that explicitly include the spatial distribution of Lyme disease cases reported at the county level than those that utilize nationwide data. strain-specific immunity has the greatest epidemiologic impact in geographic areas with the highest Lyme disease incidence due to the greater proportion of people that have been previously infected and have developed strain-specific immunity. Copyright © 2017 Elsevier Inc. All rights reserved.

Mind the Scales: Harnessing Spatial Big Data for Infectious Disease Surveillance and Inference

PubMed Central

Lee, Elizabeth C.; Asher, Jason M.; Goldlust, Sandra; Kraemer, John D.; Lawson, Andrew B.; Bansal, Shweta

2016-01-01

Spatial big data have the velocity, volume, and variety of big data sources and contain additional geographic information. Digital data sources, such as medical claims, mobile phone call data records, and geographically tagged tweets, have entered infectious diseases epidemiology as novel sources of data to complement traditional infectious disease surveillance. In this work, we provide examples of how spatial big data have been used thus far in epidemiological analyses and describe opportunities for these sources to improve disease-mitigation strategies and public health coordination. In addition, we consider the technical, practical, and ethical challenges with the use of spatial big data in infectious disease surveillance and inference. Finally, we discuss the implications of the rising use of spatial big data in epidemiology to health risk communication, and public health policy recommendations and coordination across scales. PMID:28830109

Landscape epidemiology of emerging infectious diseases in natural and human-altered ecosystems

Treesearch

Ross K. Meentemeyer; Sarah E. Haas; Tomas Vaclavik

2012-01-01

A central challenge to studying emerging infectious diseases (EIDs) is a landscape dilemma: Our best empirical understanding of disease dynamics occurs at local scales, whereas pathogen invasions and management occur over broad spatial extents. The burgeoning field of landscape epidemiology integrates concepts and approaches from disease ecology with the...

Epidemiologic evaluation of diarrhea in dogs in an animal shelter.

PubMed

Sokolow, Susanne H; Rand, Courtney; Marks, Stanley L; Drazenovich, Niki L; Kather, Elizabeth J; Foley, Janet E

2005-06-01

To determine associations among infectious pathogens and diarrheal disease in dogs in an animal shelter and demonstrate the use of geographic information systems (GISs) for tracking spatial distributions of diarrheal disease within shelters. Feces from 120 dogs. Fresh fecal specimens were screened for bacteria and bacterial toxins via bacteriologic culture and ELISA, parvovirus via ELISA, canine coronavirus via nested polymerase chain reaction assay, protozoal cysts and oocysts via a direct fluorescent antibody technique, and parasite ova and larvae via microscopic examination of direct wet mounts and zinc sulfate centrifugation flotation. Salmonella enterica and Brachyspira spp were not common, whereas other pathogens such as canine coronavirus and Helicobacter spp were common among the dogs that were surveyed. Only intestinal parasites and Campylobacterjejuni infection were significant risk factors for diarrhea by univariate odds ratio analysis. Giardia lamblia was significantly underestimated by fecal flotation, compared with a direct fluorescent antibody technique. Spatial analysis of case specimens by use of GIS indicated that diarrhea was widespread throughout the entire shelter, and spatial statistical analysis revealed no evidence of spatial clustering of case specimens. This study provided an epidemiologic overview of diarrhea and interacting diarrhea-associated pathogens in a densely housed, highly predisposed shelter population of dogs. Several of the approaches used in this study, such as use of a spatial representation of case specimens and considering multiple etiologies simultaneously, were novel and illustrate an integrated approach to epidemiologic investigations in shelter populations.

Mind the Scales: Harnessing Spatial Big Data for Infectious Disease Surveillance and Inference.

PubMed

Lee, Elizabeth C; Asher, Jason M; Goldlust, Sandra; Kraemer, John D; Lawson, Andrew B; Bansal, Shweta

2016-12-01

Spatial big data have the velocity, volume, and variety of big data sources and contain additional geographic information. Digital data sources, such as medical claims, mobile phone call data records, and geographically tagged tweets, have entered infectious diseases epidemiology as novel sources of data to complement traditional infectious disease surveillance. In this work, we provide examples of how spatial big data have been used thus far in epidemiological analyses and describe opportunities for these sources to improve disease-mitigation strategies and public health coordination. In addition, we consider the technical, practical, and ethical challenges with the use of spatial big data in infectious disease surveillance and inference. Finally, we discuss the implications of the rising use of spatial big data in epidemiology to health risk communication, and public health policy recommendations and coordination across scales. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America.

Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation

PubMed Central

2012-01-01

The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models. Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites. In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring, and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward. PMID:22591595

Landscape epidemiology of emerging infectious diseases in natural and human-altered ecosystems

Treesearch

Ross K. Meentemeyer; Sarah Haas; Tomáš Václavík

2013-01-01

A central challenge to studying emerging infectious diseases (EIDs) is a landscape dilemma: our best empirical understanding of disease dynamics occurs at local scales while pathogen invasions and management occur over broad spatial extents. The burgeoning field of landscape epidemiology integrates concepts and approaches from disease ecology with the macro-scale lens...

The spatial spread of schistosomiasis: A multidimensional network model applied to Saint-Louis region, Senegal

NASA Astrophysics Data System (ADS)

Ciddio, Manuela; Mari, Lorenzo; Sokolow, Susanne H.; De Leo, Giulio A.; Casagrandi, Renato; Gatto, Marino

2017-10-01

Schistosomiasis is a parasitic, water-related disease that is prevalent in tropical and subtropical areas of the world, causing severe and chronic consequences especially among children. Here we study the spatial spread of this disease within a network of connected villages in the endemic region of the Lower Basin of the Senegal River, in Senegal. The analysis is performed by means of a spatially explicit metapopulation model that couples local-scale eco-epidemiological dynamics with spatial mechanisms related to human mobility (estimated from anonymized mobile phone records), snail dispersal and hydrological transport of schistosome larvae along the main water bodies of the region. Results show that the model produces epidemiological patterns consistent with field observations, and point out the key role of spatial connectivity on the spread of the disease. These findings underline the importance of considering different transport pathways in order to elaborate disease control strategies that can be effective within a network of connected populations.

The Geography of Mental Health and General Wellness in Galveston Bay After Hurricane Ike: A Spatial Epidemiologic Study With Longitudinal Data.

PubMed

Gruebner, Oliver; Lowe, Sarah R; Tracy, Melissa; Cerdá, Magdalena; Joshi, Spruha; Norris, Fran H; Galea, Sandro

2016-04-01

To demonstrate a spatial epidemiologic approach that could be used in the aftermath of disasters to (1) detect spatial clusters and (2) explore geographic heterogeneity in predictors for mental health and general wellness. We used a cohort study of Hurricane Ike survivors (n=508) to assess the spatial distribution of postdisaster mental health wellness (most likely resilience trajectory for posttraumatic stress symptoms [PTSS] and depression) and general wellness (most likely resilience trajectory for PTSS, depression, functional impairment, and days of poor health) in Galveston, Texas. We applied the spatial scan statistic (SaTScan) and geographically weighted regression. We found spatial clusters of high likelihood wellness in areas north of Texas City and spatial concentrations of low likelihood wellness in Galveston Island. Geographic variation was found in predictors of wellness, showing increasing associations with both forms of wellness the closer respondents were located to Galveston City in Galveston Island. Predictors for postdisaster wellness may manifest differently across geographic space with concentrations of lower likelihood wellness and increased associations with predictors in areas of higher exposure. Our approach could be used to inform geographically targeted interventions to promote mental health and general wellness in disaster-affected communities.

Violent crime in San Antonio, Texas: an application of spatial epidemiological methods.

PubMed

Sparks, Corey S

2011-12-01

Violent crimes are rarely considered a public health problem or investigated using epidemiological methods. But patterns of violent crime and other health conditions are often affected by similar characteristics of the built environment. In this paper, methods and perspectives from spatial epidemiology are used in an analysis of violent crimes in San Antonio, TX. Bayesian statistical methods are used to examine the contextual influence of several aspects of the built environment. Additionally, spatial regression models using Bayesian model specifications are used to examine spatial patterns of violent crime risk. Results indicate that the determinants of violent crime depend on the model specification, but are primarily related to the built environment and neighborhood socioeconomic conditions. Results are discussed within the context of a rapidly growing urban area with a diverse population. Copyright © 2011 Elsevier Ltd. All rights reserved.

Epidemiology and Ecology of Tularemia in Sweden, 1984–2012

PubMed Central

Desvars, Amélie; Furberg, Maria; Hjertqvist, Marika; Vidman, Linda; Sjöstedt, Anders; Rydén, Patrik

2015-01-01

The zoonotic disease tularemia is endemic in large areas of the Northern Hemisphere, but research is lacking on patterns of spatial distribution and connections with ecologic factors. To describe the spatial epidemiology of and identify ecologic risk factors for tularemia incidence in Sweden, we analyzed surveillance data collected over 29 years (1984–2012). A total of 4,830 cases were notified, of which 3,524 met all study inclusion criteria. From the first to the second half of the study period, mean incidence increased 10-fold, from 0.26/100,000 persons during 1984–1998 to 2.47/100,000 persons during 1999–2012 (p<0.001). The incidence of tularemia was higher than expected in the boreal and alpine ecologic regions (p<0.001), and incidence was positively correlated with the presence of lakes and rivers (p<0.001). These results provide a comprehensive epidemiologic description of tularemia in Sweden and illustrate that incidence is higher in locations near lakes and rivers. PMID:25529978

Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: Insights into spatial variability using high-resolution satellite data

PubMed Central

Alexeeff, Stacey E.; Schwartz, Joel; Kloog, Itai; Chudnovsky, Alexandra; Koutrakis, Petros; Coull, Brent A.

2016-01-01

Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1km x 1km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R2 yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with greater than 0.9 out-of-sample R2 yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the standard errors. Land use regression models performed better in chronic effects simulations. These results can help researchers when interpreting health effect estimates in these types of studies. PMID:24896768

Spatial Risk Assessments Based on Vector-Borne Disease Epidemiologic Data: Importance of Scale for West Nile Virus Disease in Colorado

PubMed Central

Winters, Anna M.; Eisen, Rebecca J.; Delorey, Mark J.; Fischer, Marc; Nasci, Roger S.; Zielinski-Gutierrez, Emily; Moore, Chester G.; Pape, W. John; Eisen, Lars

2010-01-01

We used epidemiologic data for human West Nile virus (WNV) disease in Colorado from 2003 and 2007 to determine 1) the degree to which estimates of vector-borne disease occurrence is influenced by spatial scale of data aggregation (county versus census tract), and 2) the extent of concordance between spatial risk patterns based on case counts versus incidence. Statistical analyses showed that county, compared with census tract, accounted for approximately 50% of the overall variance in WNV disease incidence, and approximately 33% for the subset of cases classified as West Nile neuroinvasive disease. These findings indicate that sub-county scale presentation provides valuable risk information for stakeholders. There was high concordance between spatial patterns of WNV disease incidence and case counts for census tract (83%) but not for county (50%) or zip code (31%). We discuss how these findings impact on practices to develop spatial epidemiologic data for vector-borne diseases and present data to stakeholders. PMID:20439980

[John Snow, the cholera epidemic and the foundation of modern epidemiology].

PubMed

Cerda L, Jaime; Valdivia C, Gonzalo

2007-08-01

John Snow (1813-1858) was a brilliant British physician. Since young he stood out for his acute observation capacity, logical thinking and perseverance, first in anesthetics and later in epidemiology. The successive outbreaks of cholera that affected London, motivated him to study this disease from a populational point of view. He related the appearance of cases to the consumption of "morbid matter", responsible for the acute diarrhea with dehydration that characterizes this disease. Bravely, Snow opposed to certain theories present at his time, sacrificing his own prestige. He was a pioneer in the use of modern epidemiological investigation methodologies such as conducting surveys and spatial epidemiology. Fairly, he is considered nowadays as father of modern epidemiology by the scientific community.

Cellular automata and epidemiological models with spatial dependence

NASA Astrophysics Data System (ADS)

Fuentes, M. A.; Kuperman, M. N.

We present a cellular automata model developed to study the evolution of an infectivity nucleus in several conditions and for two kinds of epidemiologically different diseases. We analyse the role of the model parameters, concerning the epidemiological and demographic aspects of the problem, and of the evolution rules in relation to the spread of such infectious diseases, the arising of periodic temporal modulations related to the infectivity and recovery fronts, and the evolution of travelling waves. Among the obtained results we find analogies to endemic situations and pandemics.

Integrating the landscape epidemiology and genetics of RNA viruses: rabies in domestic dogs as a model.

PubMed

Brunker, K; Hampson, K; Horton, D L; Biek, R

2012-12-01

Landscape epidemiology and landscape genetics combine advances in molecular techniques, spatial analyses and epidemiological models to generate a more real-world understanding of infectious disease dynamics and provide powerful new tools for the study of RNA viruses. Using dog rabies as a model we have identified how key questions regarding viral spread and persistence can be addressed using a combination of these techniques. In contrast to wildlife rabies, investigations into the landscape epidemiology of domestic dog rabies requires more detailed assessment of the role of humans in disease spread, including the incorporation of anthropogenic landscape features, human movements and socio-cultural factors into spatial models. In particular, identifying and quantifying the influence of anthropogenic features on pathogen spread and measuring the permeability of dispersal barriers are important considerations for planning control strategies, and may differ according to cultural, social and geographical variation across countries or continents. Challenges for dog rabies research include the development of metapopulation models and transmission networks using genetic information to uncover potential source/sink dynamics and identify the main routes of viral dissemination. Information generated from a landscape genetics approach will facilitate spatially strategic control programmes that accommodate for heterogeneities in the landscape and therefore utilise resources in the most cost-effective way. This can include the efficient placement of vaccine barriers, surveillance points and adaptive management for large-scale control programmes.

Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data

PubMed Central

Vieira, Verónica; Webster, Thomas; Weinberg, Janice; Aschengrau, Ann; Ozonoff, David

2005-01-01

Background The availability of geographic information from cancer and birth defect registries has increased public demands for investigation of perceived disease clusters. Many neighborhood-level cluster investigations are methodologically problematic, while maps made from registry data often ignore latency and many known risk factors. Population-based case-control and cohort studies provide a stronger foundation for spatial epidemiology because potential confounders and disease latency can be addressed. Methods We investigated the association between residence and colorectal, lung, and breast cancer on upper Cape Cod, Massachusetts (USA) using extensive data on covariates and residential history from two case-control studies for 1983–1993. We generated maps using generalized additive models, smoothing on longitude and latitude while adjusting for covariates. The resulting continuous surface estimates disease rates relative to the whole study area. We used permutation tests to examine the overall importance of location in the model and identify areas of increased and decreased risk. Results Maps of colorectal cancer were relatively flat. Assuming 15 years of latency, lung cancer was significantly elevated just northeast of the Massachusetts Military Reservation, although the result did not hold when we restricted to residences of longest duration. Earlier non-spatial epidemiology had found a weak association between lung cancer and proximity to gun and mortar positions on the reservation. Breast cancer hot spots tended to increase in magnitude as we increased latency and adjusted for covariates, indicating that confounders were partly hiding these areas. Significant breast cancer hot spots were located near known groundwater plumes and the Massachusetts Military Reservation. Discussion Spatial epidemiology of population-based case-control studies addresses many methodological criticisms of cluster studies and generates new exposure hypotheses. Our results provide evidence for spatial clustering of breast cancer on upper Cape Cod. The analysis suggests further investigation of the potential association between breast cancer and pollution plumes based on detailed exposure modeling. PMID:15955253

Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: an application of generalized additive models to case-control data.

PubMed

Vieira, Verónica; Webster, Thomas; Weinberg, Janice; Aschengrau, Ann; Ozonoff, David

2005-06-14

The availability of geographic information from cancer and birth defect registries has increased public demands for investigation of perceived disease clusters. Many neighborhood-level cluster investigations are methodologically problematic, while maps made from registry data often ignore latency and many known risk factors. Population-based case-control and cohort studies provide a stronger foundation for spatial epidemiology because potential confounders and disease latency can be addressed. We investigated the association between residence and colorectal, lung, and breast cancer on upper Cape Cod, Massachusetts (USA) using extensive data on covariates and residential history from two case-control studies for 1983-1993. We generated maps using generalized additive models, smoothing on longitude and latitude while adjusting for covariates. The resulting continuous surface estimates disease rates relative to the whole study area. We used permutation tests to examine the overall importance of location in the model and identify areas of increased and decreased risk. Maps of colorectal cancer were relatively flat. Assuming 15 years of latency, lung cancer was significantly elevated just northeast of the Massachusetts Military Reservation, although the result did not hold when we restricted to residences of longest duration. Earlier non-spatial epidemiology had found a weak association between lung cancer and proximity to gun and mortar positions on the reservation. Breast cancer hot spots tended to increase in magnitude as we increased latency and adjusted for covariates, indicating that confounders were partly hiding these areas. Significant breast cancer hot spots were located near known groundwater plumes and the Massachusetts Military Reservation. Spatial epidemiology of population-based case-control studies addresses many methodological criticisms of cluster studies and generates new exposure hypotheses. Our results provide evidence for spatial clustering of breast cancer on upper Cape Cod. The analysis suggests further investigation of the potential association between breast cancer and pollution plumes based on detailed exposure modeling.

Virus evolution and transmission in an ever more connected world

PubMed Central

Pybus, Oliver G.; Tatem, Andrew J.; Lemey, Philippe

2015-01-01

The frequency and global impact of infectious disease outbreaks, particularly those caused by emerging viruses, demonstrate the need for a better understanding of how spatial ecology and pathogen evolution jointly shape epidemic dynamics. Advances in computational techniques and the increasing availability of genetic and geospatial data are helping to address this problem, particularly when both information sources are combined. Here, we review research at the intersection of evolutionary biology, human geography and epidemiology that is working towards an integrated view of spatial incidence, host mobility and viral genetic diversity. We first discuss how empirical studies have combined viral spatial and genetic data, focusing particularly on the contribution of evolutionary analyses to epidemiology and disease control. Second, we explore the interplay between virus evolution and global dispersal in more depth for two pathogens: human influenza A virus and chikungunya virus. We discuss the opportunities for future research arising from new analyses of human transportation and trade networks, as well as the associated challenges in accessing and sharing relevant spatial and genetic data. PMID:26702033

Analysis of Pollution Hazard Intensity: A Spatial Epidemiology Case Study of Soil Pb Contamination

PubMed Central

Ha, Hoehun; Rogerson, Peter A.; Olson, James R.; Han, Daikwon; Bian, Ling; Shao, Wanyun

2016-01-01

Heavy industrialization has resulted in the contamination of soil by metals from anthropogenic sources in Anniston, Alabama. This situation calls for increased public awareness of the soil contamination issue and better knowledge of the main factors contributing to the potential sources contaminating residential soil. The purpose of this spatial epidemiology research is to describe the effects of physical factors on the concentration of lead (Pb) in soil in Anniston AL, and to determine the socioeconomic and demographic characteristics of those residing in areas with higher soil contamination. Spatial regression models are used to account for spatial dependencies using these explanatory variables. After accounting for covariates and multicollinearity, results of the analysis indicate that lead concentration in soils varies markedly in the vicinity of a specific foundry (Foundry A), and that proximity to railroads explained a significant amount of spatial variation in soil lead concentration. Moreover, elevated soil lead levels were identified as a concern in industrial sites, neighborhoods with a high density of old housing, a high percentage of African American population, and a low percent of occupied housing units. The use of spatial modelling allows for better identification of significant factors that are correlated with soil lead concentrations. PMID:27649221

Analysis of Pollution Hazard Intensity: A Spatial Epidemiology Case Study of Soil Pb Contamination.

PubMed

Ha, Hoehun; Rogerson, Peter A; Olson, James R; Han, Daikwon; Bian, Ling; Shao, Wanyun

2016-09-14

Heavy industrialization has resulted in the contamination of soil by metals from anthropogenic sources in Anniston, Alabama. This situation calls for increased public awareness of the soil contamination issue and better knowledge of the main factors contributing to the potential sources contaminating residential soil. The purpose of this spatial epidemiology research is to describe the effects of physical factors on the concentration of lead (Pb) in soil in Anniston AL, and to determine the socioeconomic and demographic characteristics of those residing in areas with higher soil contamination. Spatial regression models are used to account for spatial dependencies using these explanatory variables. After accounting for covariates and multicollinearity, results of the analysis indicate that lead concentration in soils varies markedly in the vicinity of a specific foundry (Foundry A), and that proximity to railroads explained a significant amount of spatial variation in soil lead concentration. Moreover, elevated soil lead levels were identified as a concern in industrial sites, neighborhoods with a high density of old housing, a high percentage of African American population, and a low percent of occupied housing units. The use of spatial modelling allows for better identification of significant factors that are correlated with soil lead concentrations.

The Application of NASA Technology to Public Health

NASA Technical Reports Server (NTRS)

Rickman, Douglas L.; Watts, C.

2007-01-01

NASA scientists have a history of applying technologies created to handle satellite data to human health at various spatial scales. Scientists are now engaged in multiple public health application projects that integrate NASA satellite data with measures of public health. Such integration requires overcoming disparities between the environmental and the health data. Ground based sensors, satellite imagery, model outputs and other environmental sources have inconsistent spatial and temporal distributions. The MSFC team has recognized the approach used by environmental scientists to fill in the empty places can also be applied to outcomes, exposures and similar data. A revisit to the classic epidemiology study of 1854 using modern day surface modeling and GIS technology, demonstrates how spatial technology can enhance and change the future of environmental epidemiology. Thus, NASA brings to public health, not just a set of data, but an innovative way of thinking about the data.

[Poles of American tegumentary leishmaniasis production in northern Paraná State, Brazil].

PubMed

Monteiro, Wuelton Marcelo; Neitzke, Herintha Coeto; Silveira, Thaís Gomes Verzignassi; Lonardoni, Maria Valdrinez Campana; Teodoro, Ueslei; Ferreira, Maria Eugênia Moreira Costa

2009-05-01

American tegumentary leishmaniasis is endemic in the State of Paraná, with 99.3% of the cases reported in the South of Brazil. Spatial distribution of the disease in northern Paraná was verified, identifying the most relevant geographic areas in epidemiological terms. The study used data recorded on epidemiological forms from the Teaching and Research Clinical Test Laboratory of the State University in Maringá, from 1987 to 2004. The study only included individuals that were infected in the municipalities (counties) in northern Paraná. Identification of the epidemiological units (poles and circuits) was based on spatial density of cases, according to the model proposed by the National Health Foundation, considering the most likely infection sites. Considering 1,933 reported cases, 1,611 were infected in northern Paraná. American tegumentary leishmaniasis distribution in Paraná State suggests two circuits for production of the disease: Paraná-Paranapanema, highlighting the Cinzas-Laranjinha, Tibagi, Ivaí-Pirapó, Piquiri, and Baixo Iguaçu poles, and Ribeira, highlighting the Alto Ribeira pole.

Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission.

PubMed

Roche, Benjamin; Guégan, Jean-François; Bousquet, François

2008-10-15

Computational biology is often associated with genetic or genomic studies only. However, thanks to the increase of computational resources, computational models are appreciated as useful tools in many other scientific fields. Such modeling systems are particularly relevant for the study of complex systems, like the epidemiology of emerging infectious diseases. So far, mathematical models remain the main tool for the epidemiological and ecological analysis of infectious diseases, with SIR models could be seen as an implicit standard in epidemiology. Unfortunately, these models are based on differential equations and, therefore, can become very rapidly unmanageable due to the too many parameters which need to be taken into consideration. For instance, in the case of zoonotic and vector-borne diseases in wildlife many different potential host species could be involved in the life-cycle of disease transmission, and SIR models might not be the most suitable tool to truly capture the overall disease circulation within that environment. This limitation underlines the necessity to develop a standard spatial model that can cope with the transmission of disease in realistic ecosystems. Computational biology may prove to be flexible enough to take into account the natural complexity observed in both natural and man-made ecosystems. In this paper, we propose a new computational model to study the transmission of infectious diseases in a spatially explicit context. We developed a multi-agent system model for vector-borne disease transmission in a realistic spatial environment. Here we describe in detail the general behavior of this model that we hope will become a standard reference for the study of vector-borne disease transmission in wildlife. To conclude, we show how this simple model could be easily adapted and modified to be used as a common framework for further research developments in this field.

The Use of Satellite Remote Sensing in Epidemiological Studies

PubMed Central

Sorek-Hamer, Meytar; Just, Allan C.; Kloog, Itai

2016-01-01

Purpose of review Particulate matter (PM) air pollution is a ubiquitous exposure linked with multiple adverse health outcomes for children and across the life course. The recent development of satellite based remote sensing models for air pollution enables the quantification of these risks and addresses many limitations of previous air pollution research strategies. We review the recent literature on the applications of satellite remote sensing in air quality research, with a focus on their use in epidemiological studies. Recent findings Aerosol optical depth (AOD) is a focus of this review and a significant number of studies show that ground-level PM can be estimated from columnar AOD. Satellite measurements have been found to be an important source of data for PM model-based exposure estimates, and recently have been used in health studies to increase the spatial breadth and temporal resolution of these estimates. Summary It is suggested that satellite-based models improve our understanding of the spatial characteristics of air quality. Although the adoption of satellite-based measures of air quality in health studies is in its infancy, it is rapidly growing. Nevertheless, further investigation is still needed in order to have a better understanding of the AOD contribution to these prediction models in order to use them with higher accuracy in epidemiological studies. PMID:26859287

Satellite remote sensing in epidemiological studies.

PubMed

Sorek-Hamer, Meytar; Just, Allan C; Kloog, Itai

2016-04-01

Particulate matter air pollution is a ubiquitous exposure linked with multiple adverse health outcomes for children and across the life course. The recent development of satellite-based remote-sensing models for air pollution enables the quantification of these risks and addresses many limitations of previous air pollution research strategies. We review the recent literature on the applications of satellite remote sensing in air quality research, with a focus on their use in epidemiological studies. Aerosol optical depth (AOD) is a focus of this review and a significant number of studies show that ground-level particulate matter can be estimated from columnar AOD. Satellite measurements have been found to be an important source of data for particulate matter model-based exposure estimates, and recently have been used in health studies to increase the spatial breadth and temporal resolution of these estimates. It is suggested that satellite-based models improve our understanding of the spatial characteristics of air quality. Although the adoption of satellite-based measures of air quality in health studies is in its infancy, it is rapidly growing. Nevertheless, further investigation is still needed in order to have a better understanding of the AOD contribution to these prediction models in order to use them with higher accuracy in epidemiological studies.

Two new templates for epidemiology applications: linked micromap plots and conditioned choropleth maps.

PubMed

Carr, D B; Wallin, J F; Carr, D A

This paper describes two interactive templates for representing spatially indexed estimates. Both templates use a matrix layout of small panels. The first template, called linked micromap plots, can represent multivariate estimates associated with each spatially indexed study unit. The second template, called conditioned choropleth maps, shows the connection between a dependent variable, as represented in a classed choropleth map, and two explanatory variables. The paper describes the cognitive considerations that motivate the layouts and representation details. The discussion also addresses topics of data quality and access, hypothesis generation, and interactive features such as pan and zoom and dynamic conditioning via sliders. The examples show epidemiological (mortality rates) and environmental (toxic concentrations) applications. Copyright 2000 John Wiley & Sons, Ltd.

HIV Clustering in Mississippi: Spatial Epidemiological Study to Inform Implementation Science in the Deep South.

PubMed

Stopka, Thomas J; Brinkley-Rubinstein, Lauren; Johnson, Kendra; Chan, Philip A; Hutcheson, Marga; Crosby, Richard; Burke, Deirdre; Mena, Leandro; Nunn, Amy

2018-04-03

In recent years, more than half of new HIV infections in the United States occur among African Americans in the Southeastern United States. Spatial epidemiological analyses can inform public health responses in the Deep South by identifying HIV hotspots and community-level factors associated with clustering. The goal of this study was to identify and characterize HIV clusters in Mississippi through analysis of state-level HIV surveillance data. We used a combination of spatial epidemiology and statistical modeling to identify and characterize HIV hotspots in Mississippi census tracts (n=658) from 2008 to 2014. We conducted spatial analyses of all HIV infections, infections among men who have sex with men (MSM), and infections among African Americans. Multivariable logistic regression analyses identified community-level sociodemographic factors associated with HIV hotspots considering all cases. There were HIV hotspots for the entire population, MSM, and African American MSM identified in the Mississippi Delta region, Southern Mississippi, and in greater Jackson, including surrounding rural counties (P<.05). In multivariable models for all HIV cases, HIV hotspots were significantly more likely to include urban census tracts (adjusted odds ratio [AOR] 2.01, 95% CI 1.20-3.37) and census tracts that had a higher proportion of African Americans (AOR 3.85, 95% CI 2.23-6.65). The HIV hotspots were less likely to include census tracts with residents who had less than a high school education (AOR 0.95, 95% CI 0.92-0.98), census tracts with residents belonging to two or more racial/ethnic groups (AOR 0.46, 95% CI 0.30-0.70), and census tracts that had a higher percentage of the population living below the poverty level (AOR 0.51, 95% CI 0.28-0.92). We used spatial epidemiology and statistical modeling to identify and characterize HIV hotspots for the general population, MSM, and African Americans. HIV clusters concentrated in Jackson and the Mississippi Delta. African American race and urban location were positively associated with clusters, whereas having less than a high school education and having a higher percentage of the population living below the poverty level were negatively associated with clusters. Spatial epidemiological analyses can inform implementation science and public health response strategies, including improved HIV testing, targeted prevention and risk reduction education, and tailored preexposure prophylaxis to address HIV disparities in the South. ©Thomas J Stopka, Lauren Brinkley-Rubinstein, Kendra Johnson, Philip A Chan, Marga Hutcheson, Richard Crosby, Deirdre Burke, Leandro Mena, Amy Nunn. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 03.04.2018.

Balancing geo-privacy and spatial patterns in epidemiological studies.

PubMed

Chen, Chien-Chou; Chuang, Jen-Hsiang; Wang, Da-Wei; Wang, Chien-Min; Lin, Bo-Cheng; Chan, Ta-Chien

2017-11-08

To balance the protection of geo-privacy and the accuracy of spatial patterns, we developed a geo-spatial tool (GeoMasker) intended to mask the residential locations of patients or cases in a geographic information system (GIS). To elucidate the effects of geo-masking parameters, we applied 2010 dengue epidemic data from Taiwan testing the tool's performance in an empirical situation. The similarity of pre- and post-spatial patterns was measured by D statistics under a 95% confidence interval. In the empirical study, different magnitudes of anonymisation (estimated Kanonymity ≥10 and 100) were achieved and different degrees of agreement on the pre- and post-patterns were evaluated. The application is beneficial for public health workers and researchers when processing data with individuals' spatial information.

Space-temporal analysis of Chagas disease and its environmental and demographic risk factors in the municipality of Barcarena, Pará, Brazil.

PubMed

Sousa, Alcinês da Silva; Palácios, Vera Regina da Cunha Menezes; Miranda, Claúdia do Socorro; Costa, Rodrigo Junior Farias da; Catete, Clistenes Pamplona; Chagasteles, Eugenia Janis; Pereira, Alba Lucia Ribeiro Raithy; Gonçalves, Nelson Veiga

2017-01-01

Chagas disease is a parasitosis considered a serious problem of public health. In the municipality of Barcarena, Pará, from 2007 to 2014, occurred the highest prevalence of this disease in Brazil. To analyze the disease distribution related to epidemiological, environmental and demographic variables, in the area and period of the study. Epidemiological and demographic data of Barcarena Health Department and satellite images from the National Institute For Space Research (INPE) were used. The deforestation data were obtained through satellite image classification, using artificial neural network. The statistical significance was done with the χ2 test, and the spatial dependence tests among the variables were done using Kernel and Moran techniques. The epidemiological curve indicated a disease seasonal pattern. The major percentage of the cases were in male, brown skin color, adult, illiterate, urban areas and with probable oral contamination. It was confirmed the spatial dependence of the disease cases with the different types of deforestation identified in the municipality, as well as agglomerations of cases in urban and rural areas. Discussion: The disease distribution did not occur homogeneously, possibly due to the municipality demographic dynamics, with intense migratory flows that generates the deforestation. Different relationships among the variables studied and the occurrence of the disease in the municipality were observed. The technologies used were satisfactory to construct the disease epidemiological scenarios.

The Use of Spatial Analysis to Estimate the Prevalence of Canine Leishmaniasis in Greece and Cyprus to Predict Its Future Variation and Relate It to Human Disease

PubMed Central

Sifaki-Pistola, Dimitra; Ntais, Pantelis; Christodoulou, Vasiliki; Mazeris, Apostolos; Antoniou, Maria

2014-01-01

Climatic, environmental, and demographic changes favor the emergence of neglected vector-borne diseases like leishmaniasis, which is spreading through dogs, the principle host of the protozoan Leishmania infantum. Surveillance of the disease in dogs is important, because the number of infected animals in an area determines the local risk of human infection. However, dog epidemiological studies are costly. Our aim was to evaluate the Emerging Diseases in a Changing European Environment (EDEN) veterinary questionnaire as a cost-effective tool in providing reliable, spatially explicit indicators of canine leishmaniasis prevalence. For this purpose, the data from the questionnaire were compared with data from two epidemiological studies on leishmaniasis carried out in Greece and Cyprus at the same time using statistical methods and spatial statistics. Although the questionnaire data cannot provide a quantitative measure of leishmaniasis in an area, it indicates the dynamic of the disease; information is obtained in a short period of time at low cost. PMID:24957543

Geographic Variability in Geocoding Success for West Nile Virus Cases in South Dakota

PubMed Central

Wey, Christine L.; Griesse, Jennifer; Kightlinger, Lon; Wimberly, Michael C.

2009-01-01

Background Geocoding, the process of assigning each case a set of coordinates that closely approximates its true location, is an important component of spatial epidemiological studies. The failure to accurately geocode cases adversely affects the validity and strength of conclusions drawn from the analysis. We investigated whether there were differences among geographic locations and demographic classes in the ability to successfully geocode West Nile virus (WNV) cases in South Dakota. We successfully geocoded 1,354 cases (80.8%) to their street address locations and assigned all 1,676 cases to ZIP code tabulation areas (ZCTAs). Using spatial scan statistics, significant clusters of non-geocoded cases were identified in central and western South Dakota. Geocoding success rates were lower in areas of low population density and on Indian reservations than in other portions of the state. Geocoding success rates were lower for Native Americans than for other races. Spatial epidemiological studies should consider the potential biases that may result from excluding non-geocoded cases, particularly in rural portions of the Great Plains that contain large Native American populations. PMID:19577505

Application of spatial technology in malaria research & control: some new insights.

PubMed

Saxena, Rekha; Nagpal, B N; Srivastava, Aruna; Gupta, S K; Dash, A P

2009-08-01

Geographical information System (GIS) has emerged as the core of the spatial technology which integrates wide range of dataset available from different sources including Remote Sensing (RS) and Global Positioning System (GPS). Literature published during the decade (1998-2007) has been compiled and grouped into six categories according to the usage of the technology in malaria epidemiology. Different GIS modules like spatial data sources, mapping and geo-processing tools, distance calculation, digital elevation model (DEM), buffer zone and geo-statistical analysis have been investigated in detail, illustrated with examples as per the derived results. These GIS tools have contributed immensely in understanding the epidemiological processes of malaria and examples drawn have shown that GIS is now widely used for research and decision making in malaria control. Statistical data analysis currently is the most consistent and established set of tools to analyze spatial datasets. The desired future development of GIS is in line with the utilization of geo-statistical tools which combined with high quality data has capability to provide new insight into malaria epidemiology and the complexity of its transmission potential in endemic areas.

Spatial and temporal epidemiology of Mycobacterium leprae infection among leprosy patients and household contacts of an endemic region in Southeast Brazil.

PubMed

Nicchio, Mariana V C; Araujo, Sergio; Martins, Lorraine C; Pinheiro, Andressa V; Pereira, Daniela C; Borges, Angélica; Antunes, Douglas E; Barreto, Josafá G; Goulart, Isabela Maria B

2016-11-01

Leprosy is a chronic infectious disease that remains a public health problem in low- and middle-income countries. Household contacts of leprosy patients (HHCs) have increased risk of developing disease and are important links in the chain of transmission of Mycobacterium leprae. Based on epidemiological and operational factors, the global elimination strategy depends on the geographic stratification of endemic areas to intensify control activities. The purpose of the study was to integrate epidemiological indicators and serology into the spatial and temporal analysis of M. leprae infection, in order to understanding of the dynamics of transmission, essential information for the control of leprosy. Using location-based technologies and epidemiological data obtained from leprosy cases (N=371) and HHCs (N=53), during a 11year period (2004-2014), we explored the spatial and temporal distribution of diagnosed cases: stratified according their disease manifestation; and of subclinical infection among HHCs: determined by serology (anti-PGL-I ELISA and anti-NDO-LID rapid lateral-flow test); in order to assess the distribution pattern of the disease and the areas of greatest risk of illness, in a highly endemic municipality (Ituiutaba, MG) in the southeast region of Brazil. Seropositivity among HHCs was: 17% (9/53) for anti-PGL-I ELISA; and 42% for the NDO-LID rapid lateral-flow test. Forty-nine percent of the contacts were seropositive to at least one of the immunological tests. We observed substantial spatial heterogeneity of cases throughout the urban perimeter. Even so, four main clusters of patients and three main clusters of subclinical infection were identified. Spatio-temporal epidemiology associated to serological assessment can identify high-risk areas imbedded within the overall epidemic municipality, to prioritize active search of new cases as well support prevention strategies in these locations of greater disease burden and transmission. Such techniques should become increasingly useful and important in future action planning of health interventions, as decisions must be made to effectively allocate limited resources. Copyright © 2016 Elsevier B.V. All rights reserved.

Evolution of spatially structured host-parasite interactions.

PubMed

Lion, S; Gandon, S

2015-01-01

Spatial structure has dramatic effects on the demography and the evolution of species. A large variety of theoretical models have attempted to understand how local dispersal may shape the coevolution of interacting species such as host-parasite interactions. The lack of a unifying framework is a serious impediment for anyone willing to understand current theory. Here, we review previous theoretical studies in the light of a single epidemiological model that allows us to explore the effects of both host and parasite migration rates on the evolution and coevolution of various life-history traits. We discuss the impact of local dispersal on parasite virulence, various host defence strategies and local adaptation. Our analysis shows that evolutionary and coevolutionary outcomes crucially depend on the details of the host-parasite life cycle and on which life-history trait is involved in the interaction. We also discuss experimental studies that support the effects of spatial structure on the evolution of host-parasite interactions. This review highlights major similarities between some theoretical results, but it also reveals an important gap between evolutionary and coevolutionary models. We discuss possible ways to bridge this gap within a more unified framework that would reconcile spatial epidemiology, evolution and coevolution. © 2014 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Advances in satellite remote sensing of environmental variables for epidemiological applications.

PubMed

Goetz, S J; Prince, S D; Small, J

2000-01-01

Earth-observing satellites have provided an unprecedented view of the land surface but have been exploited relatively little for the measurement of environmental variables of particular relevance to epidemiology. Recent advances in techniques to recover continuous fields of air temperature, humidity, and vapour pressure deficit from remotely sensed observations have significant potential for disease vector monitoring and related epidemiological applications. We report on the development of techniques to map environmental variables with relevance to the prediction of the relative abundance of disease vectors and intermediate hosts. Improvements to current methods of obtaining information on vegetation properties, canopy and surface temperature and soil moisture over large areas are also discussed. Algorithms used to measure these variables incorporate visible, near-infrared and thermal infrared radiation observations derived from time series of satellite-based sensors, focused here primarily but not exclusively on the Advanced Very High Resolution Radiometer (AVHRR) instruments. The variables compare favourably with surface measurements over a broad array of conditions at several study sites, and maps of retrieved variables captured patterns of spatial variability comparable to, and locally more accurate than, spatially interpolated meteorological observations. Application of multi-temporal maps of these variables are discussed in relation to current epidemiological research on the distribution and abundance of some common disease vectors.

Need for improved methods to collect and present spatial epidemiologic data for vectorborne diseases.

PubMed

Eisen, Lars; Eisen, Rebecca J

2007-12-01

Improved methods for collection and presentation of spatial epidemiologic data are needed for vectorborne diseases in the United States. Lack of reliable data for probable pathogen exposure site has emerged as a major obstacle to the development of predictive spatial risk models. Although plague case investigations can serve as a model for how to ideally generate needed information, this comprehensive approach is cost-prohibitive for more common and less severe diseases. New methods are urgently needed to determine probable pathogen exposure sites that will yield reliable results while taking into account economic and time constraints of the public health system and attending physicians. Recent data demonstrate the need for a change from use of the county spatial unit for presentation of incidence of vectorborne diseases to more precise ZIP code or census tract scales. Such fine-scale spatial risk patterns can be communicated to the public and medical community through Web-mapping approaches.

Apropos: Plasmodium knowlesi malaria an emerging public health problem in Hulu Selangor, Selangor, Malaysia (2009-2013): epidemiologic and entomologic analysis.

PubMed

Waugh, Sheldon

2015-02-05

The use of detailed methodologies and legitimate settings justifications in spatial analysis is imperative to locating areas of significance. Studies missing this action may enact interventions in improper areas.

Epidemiological Characteristics and Space-Time Analysis of the 2015 Dengue Outbreak in the Metropolitan Region of Tainan City, Taiwan

PubMed Central

Ng, Ka-Chon; Nguyen, Thi Luong

2018-01-01

The metropolitan region of Tainan City in southern Taiwan experienced a dengue outbreak in 2015. This manuscript describes basic epidemiological features of this outbreak and uses spatial and temporal analysis tools to understand the spread of dengue during the outbreak. The analysis found that, independently of gender, dengue incidence rate increased with age, and proportionally affected more males below the age of 40 years but females above the age of 40 years. A spatial scan statistic was applied to detect clusters of disease transmission. The scan statistic found that dengue spread in a north-south diffusion direction, which is across the North, West-Central and South districts of Tainan City. Spatial regression models were used to quantify factors associated with transmission. This analysis indicated that neighborhoods with high proportions of residential area (or low wetland cover) were associated with dengue transmission. However, these association patterns were non-linear. The findings presented here can help Taiwanese public health agencies to understand the fundamental epidemiological characteristics and diffusion patterns of the 2015 dengue outbreak in Tainan City. This type of information is fundamental for policy making to prevent future uncontrolled dengue outbreaks, given that results from this study suggest that control interventions should be emphasized in the North and West-Central districts of Tainan city, in areas with a moderate percentage of residential land cover. PMID:29495351

Epidemiological Characteristics and Space-Time Analysis of the 2015 Dengue Outbreak in the Metropolitan Region of Tainan City, Taiwan.

PubMed

Chuang, Ting-Wu; Ng, Ka-Chon; Nguyen, Thi Luong; Chaves, Luis Fernando

2018-02-26

The metropolitan region of Tainan City in southern Taiwan experienced a dengue outbreak in 2015. This manuscript describes basic epidemiological features of this outbreak and uses spatial and temporal analysis tools to understand the spread of dengue during the outbreak. The analysis found that, independently of gender, dengue incidence rate increased with age, and proportionally affected more males below the age of 40 years but females above the age of 40 years. A spatial scan statistic was applied to detect clusters of disease transmission. The scan statistic found that dengue spread in a north-south diffusion direction, which is across the North, West-Central and South districts of Tainan City. Spatial regression models were used to quantify factors associated with transmission. This analysis indicated that neighborhoods with high proportions of residential area (or low wetland cover) were associated with dengue transmission. However, these association patterns were non-linear. The findings presented here can help Taiwanese public health agencies to understand the fundamental epidemiological characteristics and diffusion patterns of the 2015 dengue outbreak in Tainan City. This type of information is fundamental for policy making to prevent future uncontrolled dengue outbreaks, given that results from this study suggest that control interventions should be emphasized in the North and West-Central districts of Tainan city, in areas with a moderate percentage of residential land cover.

Epidemiological aspects of human and canine visceral leishmaniasis in Montes Claros, State of Minas Gerais, Brazil, between 2007 and 2009.

PubMed

Prado, Patrícia Fernandes do; Rocha, Marília Fonseca; Sousa, Joel Fontes de; Caldeira, Dênio Iuri; Paz, Gustavo Fontes; Dias, Edelberto Santos

2011-10-01

Visceral leishmaniasis (VL) is an expanding zoonosis in Brazil and is becoming urbanized in several Brazilian regions. This study aims to describe the epidemiological features of human and canine VL in the municipality of Montes Claros, State of Minas Gerais, by focusing on their spatial distribution. Data concerning human cases and reactive dogs for VL from 2007 to 2009 were obtained from the Information System for Disease Notification (SINAN) and from reports of the local Centro de Controle de Zoonoses (CCZ), respectively. The addresses of human and canine cases have been georeferenced and localized in thematic maps, allowing their spatial visualization as well as the identification of areas at risk of VL transmission. Ninety-five cases of human VL were reported in the period. The 0-9-year-old age group (48.4%) was the most affected, within which the majority consisted of male patients (64%). Of the samples collected for the canine serological survey, 2,919 (6.3%) were reactive to VL. The spatial localization of these cases shows that the disease was scattered in the urban area of the municipality. Areas showing a higher dissemination risk were concentrated in the central, northwestern, and southern regions of the city. Identifying the areas most at risk in urban Montes Claros may help guide actions toward local epidemiological vigilance and control.

Development of spatial density maps based on geoprocessing web services: application to tuberculosis incidence in Barcelona, Spain.

PubMed

Dominkovics, Pau; Granell, Carlos; Pérez-Navarro, Antoni; Casals, Martí; Orcau, Angels; Caylà, Joan A

2011-11-29

Health professionals and authorities strive to cope with heterogeneous data, services, and statistical models to support decision making on public health. Sophisticated analysis and distributed processing capabilities over geocoded epidemiological data are seen as driving factors to speed up control and decision making in these health risk situations. In this context, recent Web technologies and standards-based web services deployed on geospatial information infrastructures have rapidly become an efficient way to access, share, process, and visualize geocoded health-related information. Data used on this study is based on Tuberculosis (TB) cases registered in Barcelona city during 2009. Residential addresses are geocoded and loaded into a spatial database that acts as a backend database. The web-based application architecture and geoprocessing web services are designed according to the Representational State Transfer (REST) principles. These web processing services produce spatial density maps against the backend database. The results are focused on the use of the proposed web-based application to the analysis of TB cases in Barcelona. The application produces spatial density maps to ease the monitoring and decision making process by health professionals. We also include a discussion of how spatial density maps may be useful for health practitioners in such contexts. In this paper, we developed web-based client application and a set of geoprocessing web services to support specific health-spatial requirements. Spatial density maps of TB incidence were generated to help health professionals in analysis and decision-making tasks. The combined use of geographic information tools, map viewers, and geoprocessing services leads to interesting possibilities in handling health data in a spatial manner. In particular, the use of spatial density maps has been effective to identify the most affected areas and its spatial impact. This study is an attempt to demonstrate how web processing services together with web-based mapping capabilities suit the needs of health practitioners in epidemiological analysis scenarios.

Development of spatial density maps based on geoprocessing web services: application to tuberculosis incidence in Barcelona, Spain

PubMed Central

2011-01-01

Background Health professionals and authorities strive to cope with heterogeneous data, services, and statistical models to support decision making on public health. Sophisticated analysis and distributed processing capabilities over geocoded epidemiological data are seen as driving factors to speed up control and decision making in these health risk situations. In this context, recent Web technologies and standards-based web services deployed on geospatial information infrastructures have rapidly become an efficient way to access, share, process, and visualize geocoded health-related information. Methods Data used on this study is based on Tuberculosis (TB) cases registered in Barcelona city during 2009. Residential addresses are geocoded and loaded into a spatial database that acts as a backend database. The web-based application architecture and geoprocessing web services are designed according to the Representational State Transfer (REST) principles. These web processing services produce spatial density maps against the backend database. Results The results are focused on the use of the proposed web-based application to the analysis of TB cases in Barcelona. The application produces spatial density maps to ease the monitoring and decision making process by health professionals. We also include a discussion of how spatial density maps may be useful for health practitioners in such contexts. Conclusions In this paper, we developed web-based client application and a set of geoprocessing web services to support specific health-spatial requirements. Spatial density maps of TB incidence were generated to help health professionals in analysis and decision-making tasks. The combined use of geographic information tools, map viewers, and geoprocessing services leads to interesting possibilities in handling health data in a spatial manner. In particular, the use of spatial density maps has been effective to identify the most affected areas and its spatial impact. This study is an attempt to demonstrate how web processing services together with web-based mapping capabilities suit the needs of health practitioners in epidemiological analysis scenarios. PMID:22126392

Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand.

PubMed

Jeefoo, Phaisarn; Tripathi, Nitin Kumar; Souris, Marc

2011-01-01

In recent years, dengue has become a major international public health concern. In Thailand it is also an important concern as several dengue outbreaks were reported in last decade. This paper presents a GIS approach to analyze the spatial and temporal dynamics of dengue epidemics. The major objective of this study was to examine spatial diffusion patterns and hotspot identification for reported dengue cases. Geospatial diffusion pattern of the 2007 dengue outbreak was investigated. Map of daily cases was generated for the 153 days of the outbreak. Epidemiological data from Chachoengsao province, Thailand (reported dengue cases for the years 1999-2007) was used for this study. To analyze the dynamic space-time pattern of dengue outbreaks, all cases were positioned in space at a village level. After a general statistical analysis (by gender and age group), data was subsequently analyzed for temporal patterns and correlation with climatic data (especially rainfall), spatial patterns and cluster analysis, and spatio-temporal patterns of hotspots during epidemics. The results revealed spatial diffusion patterns during the years 1999-2007 representing spatially clustered patterns with significant differences by village. Villages on the urban fringe reported higher incidences. The space and time of the cases showed outbreak movement and spread patterns that could be related to entomologic and epidemiologic factors. The hotspots showed the spatial trend of dengue diffusion. This study presents useful information related to the dengue outbreak patterns in space and time and may help public health departments to plan strategies to control the spread of disease. The methodology is general for space-time analysis and can be applied for other infectious diseases as well.

SPATIAL ANALYSIS OF AIR POLLUTION AND DEVELOPMENT OF A LAND-USE REGRESSION ( LUR ) MODEL IN AN URBAN AIRSHED

EPA Science Inventory

The Detroit Children's Health Study is an epidemiologic study examining associations between chronic ambient environmental exposures to gaseous air pollutants and respiratory health outcomes among elementary school-age children in an urban airshed. The exposure component of this...

Modelling spatial connectivity in epidemiological systems, dengue fever in Thailand on networks from radiation models

NASA Astrophysics Data System (ADS)

Stollenwerk, Nico; Götz, Thomas; Mateus, Luis; Wijaya, Putra; Willems, David; Skwara, Urszula; Marguta, Ramona; Ghaffari, Peyman; Aguiar, Maíra

2016-06-01

We model the connectivity between Thai provinces in terms of human mobility via a radiation model in order to describe dengue fever spreading in Thailand, for which long term epidemiological data are available.

Landscape epidemiology and machine learning: A geospatial approach to modeling West Nile virus risk in the United States

NASA Astrophysics Data System (ADS)

Young, Sean Gregory

The complex interactions between human health and the physical landscape and environment have been recognized, if not fully understood, since the ancient Greeks. Landscape epidemiology, sometimes called spatial epidemiology, is a sub-discipline of medical geography that uses environmental conditions as explanatory variables in the study of disease or other health phenomena. This theory suggests that pathogenic organisms (whether germs or larger vector and host species) are subject to environmental conditions that can be observed on the landscape, and by identifying where such organisms are likely to exist, areas at greatest risk of the disease can be derived. Machine learning is a sub-discipline of artificial intelligence that can be used to create predictive models from large and complex datasets. West Nile virus (WNV) is a relatively new infectious disease in the United States, and has a fairly well-understood transmission cycle that is believed to be highly dependent on environmental conditions. This study takes a geospatial approach to the study of WNV risk, using both landscape epidemiology and machine learning techniques. A combination of remotely sensed and in situ variables are used to predict WNV incidence with a correlation coefficient as high as 0.86. A novel method of mitigating the small numbers problem is also tested and ultimately discarded. Finally a consistent spatial pattern of model errors is identified, indicating the chosen variables are capable of predicting WNV disease risk across most of the United States, but are inadequate in the northern Great Plains region of the US.

Evaluation of land use regression models in Detroit, Michigan

EPA Science Inventory

Introduction: Land use regression (LUR) models have emerged as a cost-effective tool for characterizing exposure in epidemiologic health studies. However, little critical attention has been focused on validation of these models as a step toward temporal and spatial extension of ...

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting.

PubMed

Young, Robin L; Weinberg, Janice; Vieira, Verónica; Ozonoff, Al; Webster, Thomas F

2010-07-19

A common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics. This research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases. The GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic.

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting

PubMed Central

2010-01-01

Background A common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics. Results This research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases. Conclusions The GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic. PMID:20642827

A scoping review of spatial cluster analysis techniques for point-event data.

PubMed

Fritz, Charles E; Schuurman, Nadine; Robertson, Colin; Lear, Scott

2013-05-01

Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

A METHOD OF ASSESSING AIR TOXICS CONCENTRATIONS IN URBAN AREAS USING MOBILE PLATFORM MEASUREMENTS

EPA Science Inventory

The objective of this paper is to demonstrate an approach to characterize the spatial variability in ambient air concentrations using mobile platform measurements. This approach may be useful for air toxic assessments in Environmental Justice applications, epidemiological studies...

Do factors related to combustion-based sources explain heterogeneity in PM-mortality associations across the United States?

EPA Science Inventory

Introduction: Spatial heterogeneity of effect estimates in associations between PM2.5 and total non-accidental mortality (TNA) in the United States (US), is an issue in epidemiology. This study uses rate ratios generated from the Multi-City/Multi-Pollutant study (1999-2005) for 3...

Need for Improved Methods to Collect and Present Spatial Epidemiologic Data for Vectorborne Diseases

PubMed Central

Eisen, Rebecca J.

2007-01-01

Improved methods for collection and presentation of spatial epidemiologic data are needed for vectorborne diseases in the United States. Lack of reliable data for probable pathogen exposure site has emerged as a major obstacle to the development of predictive spatial risk models. Although plague case investigations can serve as a model for how to ideally generate needed information, this comprehensive approach is cost-prohibitive for more common and less severe diseases. New methods are urgently needed to determine probable pathogen exposure sites that will yield reliable results while taking into account economic and time constraints of the public health system and attending physicians. Recent data demonstrate the need for a change from use of the county spatial unit for presentation of incidence of vectorborne diseases to more precise ZIP code or census tract scales. Such fine-scale spatial risk patterns can be communicated to the public and medical community through Web-mapping approaches. PMID:18258029

Parameter and uncertainty estimation for mechanistic, spatially explicit epidemiological models

NASA Astrophysics Data System (ADS)

Finger, Flavio; Schaefli, Bettina; Bertuzzo, Enrico; Mari, Lorenzo; Rinaldo, Andrea

2014-05-01

Epidemiological models can be a crucially important tool for decision-making during disease outbreaks. The range of possible applications spans from real-time forecasting and allocation of health-care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. Our spatially explicit, mechanistic models for cholera epidemics have been successfully applied to several epidemics including, the one that struck Haiti in late 2010 and is still ongoing. Calibration and parameter estimation of such models represents a major challenge because of properties unusual in traditional geoscientific domains such as hydrology. Firstly, the epidemiological data available might be subject to high uncertainties due to error-prone diagnosis as well as manual (and possibly incomplete) data collection. Secondly, long-term time-series of epidemiological data are often unavailable. Finally, the spatially explicit character of the models requires the comparison of several time-series of model outputs with their real-world counterparts, which calls for an appropriate weighting scheme. It follows that the usual assumption of a homoscedastic Gaussian error distribution, used in combination with classical calibration techniques based on Markov chain Monte Carlo algorithms, is likely to be violated, whereas the construction of an appropriate formal likelihood function seems close to impossible. Alternative calibration methods, which allow for accurate estimation of total model uncertainty, particularly regarding the envisaged use of the models for decision-making, are thus needed. Here we present the most recent developments regarding methods for parameter and uncertainty estimation to be used with our mechanistic, spatially explicit models for cholera epidemics, based on informal measures of goodness of fit.

SPATIAL ASSOCIATION BETWEEN SPECIATED FINE PARTICLES AND MORTALITY

EPA Science Inventory

Particulate matter (PM) has been linked to a range of serious cardiovascular and respiratory health problems. Some of the recent epidemiologic studies suggest that exposures to PM may result in tens of thousands of excess deaths per year and many more cases of illness among the ...

Epidemiological features and risk factors associated with the spatial and temporal distribution of human brucellosis in China

PubMed Central

2013-01-01

Background Human brucellosis incidence in China has been increasing dramatically since 1999. However, epidemiological features and potential factors underlying the re-emergence of the disease remain less understood. Methods Data on human and animal brucellosis cases at the county scale were collected for the year 2004 to 2010. Also collected were environmental and socioeconomic variables. Epidemiological features including spatial and temporal patterns of the disease were characterized, and the potential factors related to the spatial heterogeneity and the temporal trend of were analysed using Poisson regression analysis, Granger causality analysis, and autoregressive distributed lag (ADL) models, respectively. Results The epidemic showed a significantly higher spatial correlation with the number of sheep and goats than swine and cattle. The disease was most prevalent in grassland areas with elevation between 800–1,600 meters. The ADL models revealed that local epidemics were correlated with comparatively lower temperatures and less sunshine in winter and spring, with a 1–7 month lag before the epidemic peak in May. Conclusions Our findings indicate that human brucellosis tended to occur most commonly in grasslands at moderate elevation where sheep and goats were the predominant livestock, and in years with cooler winter and spring or less sunshine. PMID:24238301

Spatial-temporal pattern and risk factor analysis of bacillary dysentery in the Beijing-Tianjin-Tangshan urban region of China.

PubMed

Xiao, Gexin; Xu, Chengdong; Wang, Jinfeng; Yang, Dongyang; Wang, Li

2014-09-25

Bacillary dysentery remains a major public health concern in China. The Beijing-Tianjin-Tangshan urban region is one of the most heavily infected areas in the country. This study aimed to analyze epidemiological features of bacillary dysentery, detect spatial-temporal clusters of the disease, and analyze risk factors that may affect bacillary dysentery incidence in the region. Bacillary dysentery case data from January 2011 to December 2011 in Beijing-Tianjin-Tangshan were used in this study. The epidemiological features of cases were characterized, then scan statistics were performed to detect spatial temporal clusters of bacillary dysentery. A spatial panel model was used to identify potential risk factors. There were a total of 28,765 cases of bacillary dysentery in 2011. The results of the analysis indicated that compared with other age groups, the highest incidence (473.75/105) occurred in individuals <5 years of age. The incidence in males (530.57/105) was higher compared with females (409.06/105). On a temporal basis, incidence increased rapidly starting in April. Peak incidence occurred in August (571.10/105). Analysis of the spatial distribution model revealed that factors such as population density, temperature, precipitation, and sunshine hours were positively associated with incidence rate. Per capita gross domestic product was negatively associated with disease incidence. Meteorological and socio-economic factors have affected the transmission of bacillary dysentery in the urban Beijing-Tianjin-Tangshan region of China. The success of bacillary dysentery prevention and control department strategies would benefit from giving more consideration to climate variations and local socio-economic conditions.

Internal migration and transmission dynamics of tuberculosis in Shanghai, China: an epidemiological, spatial, genomic analysis.

PubMed

Yang, Chongguang; Lu, Liping; Warren, Joshua L; Wu, Jie; Jiang, Qi; Zuo, Tianyu; Gan, Mingyu; Liu, Mei; Liu, Qingyun; DeRiemer, Kathryn; Hong, Jianjun; Shen, Xin; Colijn, Caroline; Guo, Xiaoqin; Gao, Qian; Cohen, Ted

2018-04-19

Massive internal migration from rural to urban areas poses new challenges for tuberculosis control in China. We aimed to combine genomic, spatial, and epidemiological data to describe the dynamics of tuberculosis in an urban setting with large numbers of migrants. We did a population-based study of culture-positive Mycobacterium tuberculosis isolates in Songjiang, Shanghai. We used whole-genome sequencing to discriminate apparent genetic clusters of M tuberculosis sharing identical variable-number-tandem-repeat (VNTR) patterns, and analysed the relations between proximity of residence and the risk of genomically clustered M tuberculosis. Finally, we used genomic, spatial, and epidemiological data to estimate time of infection and transmission links among migrants and residents. Between Jan 1, 2009, and Dec 31, 2015, 1620 cases of culture-positive tuberculosis were recorded, 1211 (75%) of which occurred among internal migrants. 150 (69%) of 218 people sharing identical VNTR patterns had isolates within ten single-nucleotide polymorphisms (SNPs) of at least one other strain, consistent with recent transmission of M tuberculosis. Pairs of strains collected from individuals living in close proximity were more likely to be genetically similar than those from individuals who lived far away-for every additional km of distance between patients' homes, the odds that genotypically matched strains were within ten SNPs of each other decreased by about 10% (OR 0·89 [95% CI 0·87-0·91]; p<0·0001). We inferred that transmission from residents to migrants occurs as commonly as transmission from migrants to residents, and we estimated that more than two-thirds of migrants in genomic clusters were infected locally after migration. The primary mechanism driving local incidence of tuberculosis in urban centres is local transmission between both migrants and residents. Combined analysis of epidemiological, genomic, and spatial data contributes to a richer understanding of local transmission dynamics and should inform the design of more effective interventions. National Natural Science Foundation of China, National Science and Technology Major Project of China, and US National Institutes of Health. Copyright © 2018 Elsevier Ltd. All rights reserved.

SPATIAL VARIABILITY OF PM2.5 IN URBAN AREAS IN THE UNITED STATES

EPA Science Inventory

Epidemiologic time-series studies typically use either daily 24-hour PM concentrations averaged across several monitors in a city or data obtained at a ?central monitoring site' to relate to human health effects. If 24-hour average concentrations differ substantially across an ur...

Using the spatial filtering process to evaluate the nonbreeding range of Rusty Blackbird Euphagus carolinus

Treesearch

Paul Hamel; Esra Ozdenrol

2008-01-01

During the nonbreeding period, Rusty Blackbird (Euphagus carolinus) occurs predominantly in forested wetland habitats in the southeastern U.S. We used spatial filtering of Christmas Bird Count data to identify areas within the nonbreeding range where the species occurs at higher than expected probability. Spatial filtering is an epidemiological modeling process...

The moving-window Bayesian maximum entropy framework: estimation of PM(2.5) yearly average concentration across the contiguous United States.

PubMed

Akita, Yasuyuki; Chen, Jiu-Chiuan; Serre, Marc L

2012-09-01

Geostatistical methods are widely used in estimating long-term exposures for epidemiological studies on air pollution, despite their limited capabilities to handle spatial non-stationarity over large geographic domains and the uncertainty associated with missing monitoring data. We developed a moving-window (MW) Bayesian maximum entropy (BME) method and applied this framework to estimate fine particulate matter (PM(2.5)) yearly average concentrations over the contiguous US. The MW approach accounts for the spatial non-stationarity, while the BME method rigorously processes the uncertainty associated with data missingness in the air-monitoring system. In the cross-validation analyses conducted on a set of randomly selected complete PM(2.5) data in 2003 and on simulated data with different degrees of missing data, we demonstrate that the MW approach alone leads to at least 17.8% reduction in mean square error (MSE) in estimating the yearly PM(2.5). Moreover, the MWBME method further reduces the MSE by 8.4-43.7%, with the proportion of incomplete data increased from 18.3% to 82.0%. The MWBME approach leads to significant reductions in estimation error and thus is recommended for epidemiological studies investigating the effect of long-term exposure to PM(2.5) across large geographical domains with expected spatial non-stationarity.

Never mind the length, feel the quality: The Impact of Long-term Epidemiological Data Sets on Theory, Application and Policy

PubMed Central

Rohani, Pejman; King, Aaron A.

2010-01-01

Infectious diseases have been a prime testing ground for ecological theory. At the same time, the ecological perspective is increasingly recognized as essential in epidemiology. Long-term, spatially-resolved, reliable disease incidence data and the ability to confront them with mechanistic models have been critical in this cross-fertilization. Here, we review some of the key intellectual developments in epidemiology facilitated by long-term data. We proceed to identify research frontiers at the interface of ecology and epidemiology and their associated data needs. PMID:20800928

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.

Mapping child maltreatment risk: a 12-year spatio-temporal analysis of neighborhood influences.

PubMed

Gracia, Enrique; López-Quílez, Antonio; Marco, Miriam; Lila, Marisol

2017-10-18

'Place' matters in understanding prevalence variations and inequalities in child maltreatment risk. However, most studies examining ecological variations in child maltreatment risk fail to take into account the implications of the spatial and temporal dimensions of neighborhoods. In this study, we conduct a high-resolution small-area study to analyze the influence of neighborhood characteristics on the spatio-temporal epidemiology of child maltreatment risk. We conducted a 12-year (2004-2015) small-area Bayesian spatio-temporal epidemiological study with all families with child maltreatment protection measures in the city of Valencia, Spain. As neighborhood units, we used 552 census block groups. Cases were geocoded using the family address. Neighborhood-level characteristics analyzed included three indicators of neighborhood disadvantage-neighborhood economic status, neighborhood education level, and levels of policing activity-, immigrant concentration, and residential instability. Bayesian spatio-temporal modelling and disease mapping methods were used to provide area-specific risk estimations. Results from a spatio-temporal autoregressive model showed that neighborhoods with low levels of economic and educational status, with high levels of policing activity, and high immigrant concentration had higher levels of substantiated child maltreatment risk. Disease mapping methods were used to analyze areas of excess risk. Results showed chronic spatial patterns of high child maltreatment risk during the years analyzed, as well as stability over time in areas of low risk. Areas with increased or decreased child maltreatment risk over the years were also observed. A spatio-temporal epidemiological approach to study the geographical patterns, trends over time, and the contextual determinants of child maltreatment risk can provide a useful method to inform policy and action. This method can offer a more accurate description of the problem, and help to inform more localized prevention and intervention strategies. This new approach can also contribute to an improved epidemiological surveillance system to detect ecological variations in risk, and to assess the effectiveness of the initiatives to reduce this risk.

A spatial scan statistic for compound Poisson data.

PubMed

Rosychuk, Rhonda J; Chang, Hsing-Ming

2013-12-20

The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease-related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease-related visits. Copyright © 2013 John Wiley & Sons, Ltd.

[The occurrence of Echinococcus multilocularis in red foxes in lower Saxony: identification of a high risk area by spatial epidemiological cluster analysis].

PubMed

Berke, Olaf; von Keyserlingk, Michael; Broll, Susanne; Kreienbrock, Lothar

2002-01-01

There is considerable interest in the spatial distribution of Echinococcus multilocularis in red foxes (Vulpes vulpes L.), because this parasite causes the zoonoses of alveolar echinococcosis which is potentially of high fatality rate. High risk areas are known from France, Switzerland and the Swabian Alb in Germany for a long time. In this work, the spatial scan statistic is introduced as an instrument for identification and localisation of high risk areas, so called disease clusters in spatial epidemiology. The use of the spatial scan statistic along with data about the distribution of the parasite in 5365 red foxes in Lower Saxony, that were collected during 1991 to 1997, led to the identification of another high risk area. The relative risk for this disease cluster is approximated by RR = 5.03 (CI0.95(RR) = [4.27; 6.58]) for the period of 1991 to 1994 and by RR = 4.45 (CI0.95(RR) = [3.53; 5.59]) for the period of 1994 to 1997, respectively.

Inferring controls on the epidemiology of beech bark disease from spatial patterning of disease organisms

Treesearch

Jeffrey R. Garnas; David R. Houston; Mark J. Twery; Matthew P. Ayres; Celia Evans

2013-01-01

Spatial pattern in the distribution and abundance of organisms is an emergent property of collective rates of reproduction, survival and movement of individuals in a heterogeneous environment. The form, intensity and scale of spatial patterning can be used to test hypotheses regarding the relative importance of candidate processes to population dynamics. Using 84 plots...

Spatial autocorrelation among automated geocoding errors and its effects on testing for disease clustering

PubMed Central

Li, Jie; Fang, Xiangming

2010-01-01

Automated geocoding of patient addresses is an important data assimilation component of many spatial epidemiologic studies. Inevitably, the geocoding process results in positional errors. Positional errors incurred by automated geocoding tend to reduce the power of tests for disease clustering and otherwise affect spatial analytic methods. However, there are reasons to believe that the errors may often be positively spatially correlated and that this may mitigate their deleterious effects on spatial analyses. In this article, we demonstrate explicitly that the positional errors associated with automated geocoding of a dataset of more than 6000 addresses in Carroll County, Iowa are spatially autocorrelated. Furthermore, through two simulation studies of disease processes, including one in which the disease process is overlain upon the Carroll County addresses, we show that spatial autocorrelation among geocoding errors maintains the power of two tests for disease clustering at a level higher than that which would occur if the errors were independent. Implications of these results for cluster detection, privacy protection, and measurement-error modeling of geographic health data are discussed. PMID:20087879

Spatial analysis of MODIS aerosol optical depth, PM2.5, and chronic coronary heart disease.

PubMed

Hu, Zhiyong

2009-05-12

Numerous studies have found adverse health effects of acute and chronic exposure to fine particulate matter (PM2.5). Air pollution epidemiological studies relying on ground measurements provided by monitoring networks are often limited by sparse and unbalanced spatial distribution of the monitors. Studies have found correlations between satellite aerosol optical depth (AOD) and PM2.5 in some land regions. Satellite aerosol data may be used to extend the spatial coverage of PM2.5 exposure assessment. This study was to investigate correlation between PM2.5 and AOD in the conterminous USA, to derive a spatially complete PM2.5 surface by merging satellite AOD data and ground measurements based on the potential correlation, and to examine if there is an association of coronary heart disease with PM2.5. Years 2003 and 2004 daily MODIS (Moderate Resolution Imaging Spectrometer) Level 2 AOD images were collated with US EPA PM2.5 data covering the conterminous USA. Pearson's correlation analysis and geographically weighted regression (GWR) found that the relationship between PM2.5 and AOD is not spatially consistent across the conterminous states. The average correlation is 0.67 in the east and 0.22 in the west. GWR predicts well in the east and poorly in the west. The GWR model was used to derive a PM2.5 grid surface using the mean AOD raster calculated using the daily AOD data (RMSE = 1.67 microg/m3). Fitting of a Bayesian hierarchical model linking PM2.5 with age-race standardized mortality rates (SMRs) of chronic coronary heart disease found that areas with higher values of PM2.5 also show high rates of CCHD mortality: = 0.802, posterior 95% Bayesian credible interval (CI) = (0.386, 1.225). There is a spatial variation of the relationship between PM2.5 and AOD in the conterminous USA. In the eastern USA where AOD correlates well with PM2.5, AOD can be merged with ground PM2.5 data to derive a PM2.5 surface for epidemiological study. The study found that chronic coronary heart disease mortality rate increases with exposure to PM2.5.

An observational study of the temporal and spatial patterns of Marek's-disease-associated leukosis condemnation of young chickens in the United States of America

USDA-ARS?s Scientific Manuscript database

Marek's disease, a disease primarily affecting immature chickens, is a worldwide problem that has on at least three occasions threatened the poultry industry in the United States. A rich dataset to study the epidemiology of this disease is available because the United States Department of Agricultu...

Geo(spatial) Health Investigation of Rotavirus in an Endemic Region: Hydroclimatic Influences and Epidemiology of Rotavirus in Bangladesh

NASA Astrophysics Data System (ADS)

Hasan, M. A.; Akanda, A. S.; Jutla, A.; Colwell, R. R.

2016-12-01

Rotavirus is the leading cause of severe dehydrating diarrhea among children under 5. Over 80% of the approximate half a million child deaths every year occur in South Asia and sub-Saharan Africa alone. Although less explored than cholera as a climate driven and influenced global health problem, recent studies have showed that the disease shown strong seasonality and spatio-temporal variability depending on regional hydroclimatic and local environmental conditions. Understanding the epidemiology of this disease, especially the spatio-temporal incidence patterns with respect to environmental factors is vitally important to allow for identification of "hotspots", preventative preparations, and vaccination strategies to improve wellbeing of the vulnerable populations. With climate change, spatio-temporal signatures and footprints of the disease are changing along with increasing burden. However, a robust understanding of the relationships between rotavirus epidemiology and hydroclimatic drivers is yet to be developed. In this study, we evaluate the seasonality and epidemiologic characteristics of rotavirous infection and its spatio-temporal incidence patterns with respect to regional hydroclimatic variables and their extremes in an endemic region in South Asia. Hospital-based surveillance data from different geographic locations allowed us to explore the detailed spatial and temporal characteristics of rotavirus propagation under the influence of climate variables in both coastal and inland areas. The rotavirus transmission patterns show two peaks in a year in the capital city of Dhaka, where winter season (highest in January) shows a high peak and the July-August monsoon season shows a smaller peak. Correlation with climate variables revealed that minimum temperature has strong influence on the winter season outbreak, while rainfall extremes show a strong positive association with the secondary monsoon peak. Spatial analysis also revealed that humidity and soil wetness may influence the timing as drier areas experience earlier outbreaks than wetter areas. Accurate understanding of rotavirus propagation with respect to hydroclimatic and environmental variability can be utilized to establish global surveillance and forecast imminent risk of diarrheal outbreaks in vulnerable regions.

FREQUENCY DISTRIBUTIONS AND SPATIAL ANALYSIS OF FINE PARTICLE MEASUREMENTS IN ST. LOUIS DURING THE REGIONAL AIR POLLUTION STUDY/REGIONAL AIR MONITORING SYSTEM

EPA Science Inventory

Community, time-series epidemiology typically uses either 24-hour integrated particulate matter (PM) concentrations averaged across several monitors in a city or data obtained at a central monitoring site to relate PM concentrations to human health effects. If 24-hour integrated...

A simulation study to determine the attenuation and bias in health risk estimates due to exposure measurement error in bi-pollutant models

EPA Science Inventory

To understand the combined health effects of exposure to ambient air pollutant mixtures, it is becoming more common to include multiple pollutants in epidemiologic models. However, the complex spatial and temporal pattern of ambient pollutant concentrations and related exposures ...

A Context-sensitive Approach to Anonymizing Spatial Surveillance Data: Impact on Outbreak Detection

PubMed Central

Cassa, Christopher A.; Grannis, Shaun J.; Overhage, J. Marc; Mandl, Kenneth D.

2006-01-01

Objective: The use of spatially based methods and algorithms in epidemiology and surveillance presents privacy challenges for researchers and public health agencies. We describe a novel method for anonymizing individuals in public health data sets by transposing their spatial locations through a process informed by the underlying population density. Further, we measure the impact of the skew on detection of spatial clustering as measured by a spatial scanning statistic. Design: Cases were emergency department (ED) visits for respiratory illness. Baseline ED visit data were injected with artificially created clusters ranging in magnitude, shape, and location. The geocoded locations were then transformed using a de-identification algorithm that accounts for the local underlying population density. Measurements: A total of 12,600 separate weeks of case data with artificially created clusters were combined with control data and the impact on detection of spatial clustering identified by a spatial scan statistic was measured. Results: The anonymization algorithm produced an expected skew of cases that resulted in high values of data set k-anonymity. De-identification that moves points an average distance of 0.25 km lowers the spatial cluster detection sensitivity by less than 4% and lowers the detection specificity less than 1%. Conclusion: A population-density–based Gaussian spatial blurring markedly decreases the ability to identify individuals in a data set while only slightly decreasing the performance of a standardly used outbreak detection tool. These findings suggest new approaches to anonymizing data for spatial epidemiology and surveillance. PMID:16357353

Evaluation of the Gini Coefficient in Spatial Scan Statistics for Detecting Irregularly Shaped Clusters

PubMed Central

Kim, Jiyu; Jung, Inkyung

2017-01-01

Spatial scan statistics with circular or elliptic scanning windows are commonly used for cluster detection in various applications, such as the identification of geographical disease clusters from epidemiological data. It has been pointed out that the method may have difficulty in correctly identifying non-compact, arbitrarily shaped clusters. In this paper, we evaluated the Gini coefficient for detecting irregularly shaped clusters through a simulation study. The Gini coefficient, the use of which in spatial scan statistics was recently proposed, is a criterion measure for optimizing the maximum reported cluster size. Our simulation study results showed that using the Gini coefficient works better than the original spatial scan statistic for identifying irregularly shaped clusters, by reporting an optimized and refined collection of clusters rather than a single larger cluster. We have provided a real data example that seems to support the simulation results. We think that using the Gini coefficient in spatial scan statistics can be helpful for the detection of irregularly shaped clusters. PMID:28129368

Advances in diagnosis and spatial analysis of cysticercosis and taeniasis.

PubMed

Raoul, Francis; Li, Tiaoying; Sako, Yasuhito; Chen, Xingwang; Long, Changping; Yanagida, Tetsuya; Wu, Yunfei; Nakao, Minoru; Okamoto, Munehiro; Craig, Philip S; Giraudoux, Patrick; Ito, Akira

2013-11-01

Human cysticercosis, caused by accidental ingestion of eggs of Taenia solium, is one of the most pathogenic helminthiases and is listed among the 17 WHO Neglected Tropical Diseases. Controlling the life-cycle of T. solium between humans and pigs is essential for eradication of cysticercosis. One difficulty for the accurate detection and identification of T. solium species is the possible co-existence of two other human Taenia tapeworms (T. saginata and T. asiatica, which do not cause cysticercosis in humans). Several key issues for taeniasis/cysticercosis (T/C) evidence-based epidemiology and control are reviewed: (1) advances in immunological and molecular tools for screening of human and animals hosts and identification of Taenia species, with a focus on real-time detection of taeniasis carriers and infected animals in field community screenings, and (2) spatial ecological approaches that have been used to detect geospatial patterns of case distributions and to monitor pig activity and behaviour. Most recent eco-epidemiological studies undertaken in Sichuan province, China, are introduced and reviewed.

Thirty years of use and improvement of remote sensing, applied to epidemiology: from early promises to lasting frustration.

PubMed

Herbreteau, Vincent; Salem, Gérard; Souris, Marc; Hugot, Jean-Pierre; Gonzalez, Jean-Paul

2007-06-01

Remote sensing, referring to the remote study of objects, was originally developed for Earth observation, through the use of sensors on board planes or satellites. Improvements in the use and accessibility of multi-temporal satellite-derived environmental data have, for 30 years, contributed to a growing use in epidemiology. Despite the potential of remote-sensed images and processing techniques for a better knowledge of disease dynamics, an exhaustive analysis of the bibliography shows a generalized use of pre-processed spatial data and low-cost images, resulting in a limited adaptability when addressing biological questions.

The moving-window Bayesian Maximum Entropy framework: Estimation of PM2.5 yearly average concentration across the contiguous United States

PubMed Central

Akita, Yasuyuki; Chen, Jiu-Chiuan; Serre, Marc L.

2013-01-01

Geostatistical methods are widely used in estimating long-term exposures for air pollution epidemiological studies, despite their limited capabilities to handle spatial non-stationarity over large geographic domains and uncertainty associated with missing monitoring data. We developed a moving-window (MW) Bayesian Maximum Entropy (BME) method and applied this framework to estimate fine particulate matter (PM2.5) yearly average concentrations over the contiguous U.S. The MW approach accounts for the spatial non-stationarity, while the BME method rigorously processes the uncertainty associated with data missingnees in the air monitoring system. In the cross-validation analyses conducted on a set of randomly selected complete PM2.5 data in 2003 and on simulated data with different degrees of missing data, we demonstrate that the MW approach alone leads to at least 17.8% reduction in mean square error (MSE) in estimating the yearly PM2.5. Moreover, the MWBME method further reduces the MSE by 8.4% to 43.7% with the proportion of incomplete data increased from 18.3% to 82.0%. The MWBME approach leads to significant reductions in estimation error and thus is recommended for epidemiological studies investigating the effect of long-term exposure to PM2.5 across large geographical domains with expected spatial non-stationarity. PMID:22739679

Emerging Technologies for Assessing Physical Activity Behaviors in Space and Time

PubMed Central

Hurvitz, Philip M.; Moudon, Anne Vernez; Kang, Bumjoon; Saelens, Brian E.; Duncan, Glen E.

2014-01-01

Precise measurement of physical activity is important for health research, providing a better understanding of activity location, type, duration, and intensity. This article describes a novel suite of tools to measure and analyze physical activity behaviors in spatial epidemiology research. We use individual-level, high-resolution, objective data collected in a space-time framework to investigate built and social environment influences on activity. First, we collect data with accelerometers, global positioning system units, and smartphone-based digital travel and photo diaries to overcome many limitations inherent in self-reported data. Behaviors are measured continuously over the full spectrum of environmental exposures in daily life, instead of focusing exclusively on the home neighborhood. Second, data streams are integrated using common timestamps into a single data structure, the “LifeLog.” A graphic interface tool, “LifeLog View,” enables simultaneous visualization of all LifeLog data streams. Finally, we use geographic information system SmartMap rasters to measure spatially continuous environmental variables to capture exposures at the same spatial and temporal scale as in the LifeLog. These technologies enable precise measurement of behaviors in their spatial and temporal settings but also generate very large datasets; we discuss current limitations and promising methods for processing and analyzing such large datasets. Finally, we provide applications of these methods in spatially oriented research, including a natural experiment to evaluate the effects of new transportation infrastructure on activity levels, and a study of neighborhood environmental effects on activity using twins as quasi-causal controls to overcome self-selection and reverse causation problems. In summary, the integrative characteristics of large datasets contained in LifeLogs and SmartMaps hold great promise for advancing spatial epidemiologic research to promote healthy behaviors. PMID:24479113

Dynamics of aerial and terrestrial populations of Phytophthora ramorum in a California watershed under different climatic conditions

Treesearch

Catherine A. Eyre; Melina Kozanitas; Matteo Garbelotto

2013-01-01

We present a study of the epidemiology of sudden oak death (SOD) in California within a watershed based on temporally and spatially replicated surveys of symptoms, viability of the pathogen from symptomatic leaves, and genetic analyses using polymorphic SSR markers.Phytophthora ramorum is sensitive to climate; its...

Comparisons of Traffic-Related Ultrafine Particle Number Concentrations Measured in Two Urban Areas by Central, Residential, and Mobile Monitoring.

PubMed

Simon, Matthew C; Hudda, Neelakshi; Naumova, Elena N; Levy, Jonathan I; Brugge, Doug; Durant, John L

2017-11-01

Traffic-related ultrafine particles (UFP; <100 nanometers diameter) are ubiquitous in urban air. While studies have shown that UFP are toxic, epidemiological evidence of health effects, which is needed to inform risk assessment at the population scale, is limited due to challenges of accurately estimating UFP exposures. Epidemiologic studies often use empirical models to estimate UFP exposures; however, the monitoring strategies upon which the models are based have varied between studies. Our study compares particle number concentrations (PNC; a proxy for UFP) measured by three different monitoring approaches (central-site, short-term residential-site, and mobile on-road monitoring) in two study areas in metropolitan Boston (MA, USA). Our objectives were to quantify ambient PNC differences between the three monitoring platforms, compare the temporal patterns and the spatial heterogeneity of PNC between the monitoring platforms, and identify factors that affect correlations across the platforms. We collected >12,000 hours of measurements at the central sites, 1,000 hours of measurements at each of 20 residential sites in the two study areas, and >120 hours of mobile measurements over the course of ~1 year in each study area. Our results show differences between the monitoring strategies: mean one-minute PNC on-roads were higher (64,000 and 32,000 particles/cm 3 in Boston and Chelsea, respectively) compared to central-site measurements (23,000 and 19,000 particles/cm 3 ) and both were higher than at residences (14,000 and 15,000 particles/cm 3 ). Temporal correlations and spatial heterogeneity also differed between the platforms. Temporal correlations were generally highest between central and residential sites, and lowest between central-site and on-road measurements. We observed the greatest spatial heterogeneity across monitoring platforms during the morning rush hours (06:00-09:00) and the lowest during the overnight hours (18:00-06:00). Longer averaging times (days and hours vs. minutes) increased temporal correlations (Pearson correlations were 0.69 and 0.60 vs. 0.39 in Boston; 0.71 and 0.61 vs. 0.45 in Chelsea) and reduced spatial heterogeneity (coefficients of divergence were 0.24 and 0.29 vs. 0.33 in Boston; 0.20 and 0.27 vs. 0.31 in Chelsea). Our results suggest that combining stationary and mobile monitoring may lead to improved characterization of UFP in urban areas and thereby lead to improved exposure assignment for epidemiology studies.

Spatially explicit modelling of cholera epidemics

NASA Astrophysics Data System (ADS)

Finger, F.; Bertuzzo, E.; Mari, L.; Knox, A. C.; Gatto, M.; Rinaldo, A.

2013-12-01

Epidemiological models can provide crucial understanding about the dynamics of infectious diseases. Possible applications range from real-time forecasting and allocation of health care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. We apply a spatially explicit model to the cholera epidemic that struck Haiti in October 2010 and is still ongoing. The dynamics of susceptibles as well as symptomatic and asymptomatic infectives are modelled at the scale of local human communities. Dissemination of Vibrio cholerae through hydrological transport and human mobility along the road network is explicitly taken into account, as well as the effect of rainfall as a driver of increasing disease incidence. The model is calibrated using a dataset of reported cholera cases. We further model the long term impact of several types of interventions on the disease dynamics by varying parameters appropriately. Key epidemiological mechanisms and parameters which affect the efficiency of treatments such as antibiotics are identified. Our results lead to conclusions about the influence of different intervention strategies on the overall epidemiological dynamics.

Modelling spatial concordance between Rocky Mountain spotted fever disease incidence and habitat probability of its vector Dermacentor variabilis (American dog tick).

PubMed

Atkinson, Samuel F; Sarkar, Sahotra; Aviña, Aldo; Schuermann, Jim A; Williamson, Phillip

2012-11-01

The spatial distribution of Dermacentor variabilis, the most commonly identified vector of the bacterium Rickettsia rickettsii which causes Rocky Mountain spotted fever (RMSF) in humans, and the spatial distribution of RMSF, have not been previously studied in the south central United States of America, particularly in Texas. From an epidemiological perspective, one would tend to hypothesise that there would be a high degree of spatial concordance between the habitat suitability for the tick and the incidence of the disease. Both maximum-entropy modelling of the tick's habitat suitability and spatially adaptive filters modelling of the human incidence of RMSF disease provide reliable portrayals of the spatial distributions of these phenomenons. Even though rates of human cases of RMSF in Texas and rates of Dermacentor ticks infected with Rickettsia bacteria are both relatively low in Texas, the best data currently available allows a preliminary indication that the assumption of high levels of spatial concordance would not be correct in Texas (Kappa coefficient of agreement = 0.17). It will take substantially more data to provide conclusive findings, and to understand the results reported here, but this study provides an approach to begin understanding the discrepancy.

Epidemiological characteristics of cases of death from tuberculosis and vulnerable territories1

PubMed Central

Yamamura, Mellina; Santos-Neto, Marcelino; dos Santos, Rebeca Augusto Neman; Garcia, Maria Concebida da Cunha; Nogueira, Jordana de Almeida; Arcêncio, Ricardo Alexandre

2015-01-01

Objective: to characterize the differences in the clinical and epidemiological profile of cases of death that had tuberculosis as an immediate or associated cause, and to analyze the spatial distribution of the cases of death from tuberculosis within the territories of Ribeirão Preto, Brazil. Method: an ecological study, in which the population consisted of 114 cases of death from tuberculosis. Bivariate analysis was carried out, as well as point density analysis, defined with the Kernel estimate. Results: of the cases of death from tuberculosis, 50 were the immediate cause and 64 an associated cause. Age (p=.008) and sector responsible for the death certificate (p=.003) were the variables that presented statistically significant associations with the cause of death. The spatial distribution, in both events, did not occur randomly, forming clusters in areas of the municipality. Conclusion: the difference in the profiles of the cases of death from tuberculosis, as a basic cause and as an associated cause, was governed by the age and the sector responsible for the completion of the death certificate. The non-randomness of the spatial distribution of the cases suggests areas that are vulnerable to these events. Knowing these areas can contribute to the choice of disease control strategies. PMID:26487142

Epidemiological aspects and spatial distribution of human and canine visceral leishmaniasis in an endemic area in northeastern Brazil.

PubMed

Campos, Roseane; Santos, Márcio; Tunon, Gabriel; Cunha, Luana; Magalhães, Lucas; Moraes, Juliana; Ramalho, Danielle; Lima, Sanmy; Pacheco, José Antônio; Lipscomb, Michael; Ribeiro de Jesus, Amélia; Pacheco de Almeida, Roque

2017-05-11

Visceral leishmaniasis (VL) is a systemic disease endemic in tropical countries and transmitted through sand flies. In particular, Canis familiaris (or domesticated dogs) are believed to be a major urban reservoir for the parasite causing the disease Leishmania. The average number of human VL cases was 58 per year in the state of Sergipe. The city of Aracaju, capital of Sergipe in Northeastern Brazil, had 159 cases of VL in humans. Correlatively, the percentage of serologically positive dogs for leishmaniasis increased from 4.73% in 2008 to 12.69% in 2014. Thus, these studies aimed to delineate the spatial distribution and epidemiological aspects of human and canine VL as mutually supportive for increased incidence. The number of human cases of VL and the frequency of canine positive serology for VL both increased between 2008 and 2014. Spatial distribution analyses mapped areas of the city with the highest concentration of human and canine VL cases. The neighbourhoods that showed the highest disease frequency were located on the outskirts of the city and in urbanised areas or subjected to development. Exponential increase in VL-positive dogs further suggests that the disease is expanding in urban areas, where it can serve as a reservoir for transmission of dogs to humans via the sand fly vector.

Spatial data analysis and the use of maps in scientific health articles.

PubMed

Nucci, Luciana Bertoldi; Souccar, Patrick Theodore; Castilho, Silvia Diez

2016-07-01

Despite the growing number of studies with a characteristic element of spatial analysis, the application of the techniques is not always clear and its continuity in epidemiological studies requires careful evaluation. To verify the spread and use of those processes in national and international scientific papers. An assessment was made of periodicals according to the impact index. Among 8,281 journals surveyed, four national and four international were selected, of which 1,274 articles were analyzed regarding the presence or absence of spatial analysis techniques. Just over 10% of articles published in 2011 in high impact journals, both national and international, showed some element of geographical location. Although these percentages vary greatly from one journal to another, denoting different publication profiles, we consider this percentage as an indication that location variables have become an important factor in studies of health.

Approaches to understanding the impact of life-history features on plant-pathogen co-evolutionary dynamics

Treesearch

Jeremy J. Burdon; Peter H. Thrall; Adnane Nemri

2012-01-01

Natural plant-pathogen associations are complex interactions in which the interplay of environment, host, and pathogen factors results in spatially heterogeneous ecological and epidemiological dynamics. The evolutionary patterns that result from the interaction of these factors are still relatively poorly understood. Recently, integration of the appropriate spatial and...

Worldwide distribution and diversity of seabird ticks: implications for the ecology and epidemiology of tick-borne pathogens.

PubMed

Dietrich, Muriel; Gómez-Díaz, Elena; McCoy, Karen D

2011-05-01

The ubiquity of ticks and their importance in the transmission of pathogens involved in human and livestock diseases are reflected by the growing number of studies focusing on tick ecology and the epidemiology of tick-borne pathogens. Likewise, the involvement of wild birds in dispersing pathogens and their role as reservoir hosts are now well established. However, studies on tick-bird systems have mainly focused on land birds, and the role of seabirds in the ecology and epidemiology of tick-borne pathogens is rarely considered. Seabirds typically have large population sizes, wide geographic distributions, and high mobility, which make them significant potential players in the maintenance and dispersal of disease agents at large spatial scales. They are parasitized by at least 29 tick species found across all biogeographical regions of the world. We know that these seabird-tick systems can harbor a large diversity of pathogens, although detailed studies of this diversity remain scarce. In this article, we review current knowledge on the diversity and global distribution of ticks and tick-borne pathogens associated with seabirds. We discuss the relationship between seabirds, ticks, and their pathogens and examine the interesting characteristics of these relationships from ecological and epidemiological points of view. We also highlight some future research directions required to better understand the evolution of these systems and to assess the potential role of seabirds in the epidemiology of tick-borne pathogens.

The Molecular and Spatial Epidemiology of Typhoid Fever in Rural Cambodia.

PubMed

Pham Thanh, Duy; Thompson, Corinne N; Rabaa, Maia A; Sona, Soeng; Sopheary, Sun; Kumar, Varun; Moore, Catrin; Tran Vu Thieu, Nga; Wijedoru, Lalith; Holt, Kathryn E; Wong, Vanessa; Pickard, Derek; Thwaites, Guy E; Day, Nicholas; Dougan, Gordon; Turner, Paul; Parry, Christopher M; Baker, Stephen

2016-06-01

Typhoid fever, caused by the bacterium Salmonella Typhi, is an endemic cause of febrile disease in Cambodia. The aim of this study was to better understand the epidemiology of pediatric typhoid fever in Cambodia. We accessed routine blood culture data from Angkor Hospital for Children (AHC) in Siem Reap province between 2007 and 2014, and performed whole genome sequencing (WGS) on the isolated bacteria to characterize the S. Typhi population. The resulting phylogenetic information was combined with conventional epidemiological approaches to investigate the spatiotemporal distribution of S. Typhi and population-level risk factors for reported disease. During the study period, there were 262 cases of typhoid within a 100 km radius of AHC, with a median patient age of 8.2 years (IQR: 5.1-11.5 years). The majority of infections occurred during the rainy season, and commune incidences as high as 11.36/1,000 in children aged <15 years were observed over the study period. A population-based risk factor analysis found that access to water within households and increasing distance from Tonle Sap Lake were protective. Spatial mapping and WGS provided additional resolution for these findings, and confirmed that proximity to the lake was associated with discrete spatiotemporal disease clusters. We confirmed the dominance of MDR H58 S. Typhi in this population, and found substantial evidence of diversification (at least seven sublineages) within this single lineage. We conclude that there is a substantial burden of pediatric typhoid fever in rural communes in Cambodia. Our data provide a platform for additional population-based typhoid fever studies in this location, and suggest that this would be a suitable setting in which to introduce a school-based vaccination programme with Vi conjugate vaccines.

The Molecular and Spatial Epidemiology of Typhoid Fever in Rural Cambodia

PubMed Central

Rabaa, Maia A; Sona, Soeng; Sopheary, Sun; Kumar, Varun; Moore, Catrin; Tran Vu Thieu, Nga; Wijedoru, Lalith; Holt, Kathryn E.; Wong, Vanessa; Pickard, Derek; Thwaites, Guy E.; Day, Nicholas; Dougan, Gordon; Turner, Paul; Parry, Christopher M.; Baker, Stephen

2016-01-01

Typhoid fever, caused by the bacterium Salmonella Typhi, is an endemic cause of febrile disease in Cambodia. The aim of this study was to better understand the epidemiology of pediatric typhoid fever in Cambodia. We accessed routine blood culture data from Angkor Hospital for Children (AHC) in Siem Reap province between 2007 and 2014, and performed whole genome sequencing (WGS) on the isolated bacteria to characterize the S. Typhi population. The resulting phylogenetic information was combined with conventional epidemiological approaches to investigate the spatiotemporal distribution of S. Typhi and population-level risk factors for reported disease. During the study period, there were 262 cases of typhoid within a 100 km radius of AHC, with a median patient age of 8.2 years (IQR: 5.1–11.5 years). The majority of infections occurred during the rainy season, and commune incidences as high as 11.36/1,000 in children aged <15 years were observed over the study period. A population-based risk factor analysis found that access to water within households and increasing distance from Tonle Sap Lake were protective. Spatial mapping and WGS provided additional resolution for these findings, and confirmed that proximity to the lake was associated with discrete spatiotemporal disease clusters. We confirmed the dominance of MDR H58 S. Typhi in this population, and found substantial evidence of diversification (at least seven sublineages) within this single lineage. We conclude that there is a substantial burden of pediatric typhoid fever in rural communes in Cambodia. Our data provide a platform for additional population-based typhoid fever studies in this location, and suggest that this would be a suitable setting in which to introduce a school-based vaccination programme with Vi conjugate vaccines. PMID:27331909

[Worker's Health Surveillance

PubMed

Machado

1997-01-01

This paper is part of a broader discussion on the need for more in-depth study of workers' health surveillance practices, which are most often developed empirically, without well-defined theoretical or technical foundations. The paper presents a concept of surveillance in workers' health as a fulcrum for actions in the relationship between the work process and health. It emphasizes the exposure-based perspective involved in the epidemiological approach. Risk situations and effects are placed in spatial and technological context. The model provides an interdisciplinary approach with a technological, social, and epidemiological basis in a three-dimensional structure. A matrix for planning actions in workers' health surveillance is also presented, focusing on the connections between effects, risks, territory, and activities.

Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2.

PubMed

Lemey, Philippe; Rambaut, Andrew; Bedford, Trevor; Faria, Nuno; Bielejec, Filip; Baele, Guy; Russell, Colin A; Smith, Derek J; Pybus, Oliver G; Brockmann, Dirk; Suchard, Marc A

2014-02-01

Information on global human movement patterns is central to spatial epidemiological models used to predict the behavior of influenza and other infectious diseases. Yet it remains difficult to test which modes of dispersal drive pathogen spread at various geographic scales using standard epidemiological data alone. Evolutionary analyses of pathogen genome sequences increasingly provide insights into the spatial dynamics of influenza viruses, but to date they have largely neglected the wealth of information on human mobility, mainly because no statistical framework exists within which viral gene sequences and empirical data on host movement can be combined. Here, we address this problem by applying a phylogeographic approach to elucidate the global spread of human influenza subtype H3N2 and assess its ability to predict the spatial spread of human influenza A viruses worldwide. Using a framework that estimates the migration history of human influenza while simultaneously testing and quantifying a range of potential predictive variables of spatial spread, we show that the global dynamics of influenza H3N2 are driven by air passenger flows, whereas at more local scales spread is also determined by processes that correlate with geographic distance. Our analyses further confirm a central role for mainland China and Southeast Asia in maintaining a source population for global influenza diversity. By comparing model output with the known pandemic expansion of H1N1 during 2009, we demonstrate that predictions of influenza spatial spread are most accurate when data on human mobility and viral evolution are integrated. In conclusion, the global dynamics of influenza viruses are best explained by combining human mobility data with the spatial information inherent in sampled viral genomes. The integrated approach introduced here offers great potential for epidemiological surveillance through phylogeographic reconstructions and for improving predictive models of disease control.

Assessing the impact of fine particulate matter (PM2.5) on respiratory-cardiovascular chronic diseases in the New York City Metropolitan area using Hierarchical Bayesian Model estimates

EPA Science Inventory

An enhanced research paradigm is presented to address the spatial and temporal gaps in fine particulate matter (PM2.5) measurements and generate realistic and representative concentration fields for use in epidemiological studies of human exposure to ambient air particulate conce...

A three-dimensional point process model for the spatial distribution of disease occurrence in relation to an exposure source.

PubMed

Grell, Kathrine; Diggle, Peter J; Frederiksen, Kirsten; Schüz, Joachim; Cardis, Elisabeth; Andersen, Per K

2015-10-15

We study methods for how to include the spatial distribution of tumours when investigating the relation between brain tumours and the exposure from radio frequency electromagnetic fields caused by mobile phone use. Our suggested point process model is adapted from studies investigating spatial aggregation of a disease around a source of potential hazard in environmental epidemiology, where now the source is the preferred ear of each phone user. In this context, the spatial distribution is a distribution over a sample of patients rather than over multiple disease cases within one geographical area. We show how the distance relation between tumour and phone can be modelled nonparametrically and, with various parametric functions, how covariates can be included in the model and how to test for the effect of distance. To illustrate the models, we apply them to a subset of the data from the Interphone Study, a large multinational case-control study on the association between brain tumours and mobile phone use. Copyright © 2015 John Wiley & Sons, Ltd.

[Applying temporally-adjusted land use regression models to estimate ambient air pollution exposure during pregnancy].

PubMed

Zhang, Y J; Xue, F X; Bai, Z P

2017-03-06

The impact of maternal air pollution exposure on offspring health has received much attention. Precise and feasible exposure estimation is particularly important for clarifying exposure-response relationships and reducing heterogeneity among studies. Temporally-adjusted land use regression (LUR) models are exposure assessment methods developed in recent years that have the advantage of having high spatial-temporal resolution. Studies on the health effects of outdoor air pollution exposure during pregnancy have been increasingly carried out using this model. In China, research applying LUR models was done mostly at the model construction stage, and findings from related epidemiological studies were rarely reported. In this paper, the sources of heterogeneity and research progress of meta-analysis research on the associations between air pollution and adverse pregnancy outcomes were analyzed. The methods of the characteristics of temporally-adjusted LUR models were introduced. The current epidemiological studies on adverse pregnancy outcomes that applied this model were systematically summarized. Recommendations for the development and application of LUR models in China are presented. This will encourage the implementation of more valid exposure predictions during pregnancy in large-scale epidemiological studies on the health effects of air pollution in China.

Using a chemistry transport model to account for the spatial variability of exposure concentrations in epidemiologic air pollution studies.

PubMed

Valari, Myrto; Menut, Laurent; Chatignoux, Edouard

2011-02-01

Environmental epidemiology and more specifically time-series analysis have traditionally used area-averaged pollutant concentrations measured at central monitors as exposure surrogates to associate health outcomes with air pollution. However, spatial aggregation has been shown to contribute to the overall bias in the estimation of the exposure-response functions. This paper presents the benefit of adding features of the spatial variability of exposure by using concentration fields modeled with a chemistry transport model instead of monitor data and accounting for human activity patterns. On the basis of county-level census data for the city of Paris, France, and a Monte Carlo simulation, a simple activity model was developed accounting for the temporal variability between working and evening hours as well as during transit. By combining activity data with modeled concentrations, the downtown, suburban, and rural spatial patterns in exposure to nitrogen dioxide, ozone, and PM2.5 (particulate matter [PM] < or = 10 microm in aerodynamic diameter) were captured and parametrized. Exposures predicted with this model were used in a time-series study of the short-term effect of air pollution on total nonaccidental mortality for the 4-yr period from 2001 to 2004. It was shown that the time series of the exposure surrogates developed here are less correlated across co-pollutants than in the case of the area-averaged monitor data. This led to less biased exposure-response functions when all three co-pollutants were inserted simultaneously in the same regression model. This finding yields insight into pollutant-specific health effects that are otherwise masked by the high correlation among co-pollutants.

Monitoring Method of Cow Anthrax Based on Gis and Spatial Statistical Analysis

NASA Astrophysics Data System (ADS)

Li, Lin; Yang, Yong; Wang, Hongbin; Dong, Jing; Zhao, Yujun; He, Jianbin; Fan, Honggang

Geographic information system (GIS) is a computer application system, which possesses the ability of manipulating spatial information and has been used in many fields related with the spatial information management. Many methods and models have been established for analyzing animal diseases distribution models and temporal-spatial transmission models. Great benefits have been gained from the application of GIS in animal disease epidemiology. GIS is now a very important tool in animal disease epidemiological research. Spatial analysis function of GIS can be widened and strengthened by using spatial statistical analysis, allowing for the deeper exploration, analysis, manipulation and interpretation of spatial pattern and spatial correlation of the animal disease. In this paper, we analyzed the cow anthrax spatial distribution characteristics in the target district A (due to the secret of epidemic data we call it district A) based on the established GIS of the cow anthrax in this district in combination of spatial statistical analysis and GIS. The Cow anthrax is biogeochemical disease, and its geographical distribution is related closely to the environmental factors of habitats and has some spatial characteristics, and therefore the correct analysis of the spatial distribution of anthrax cow for monitoring and the prevention and control of anthrax has a very important role. However, the application of classic statistical methods in some areas is very difficult because of the pastoral nomadic context. The high mobility of livestock and the lack of enough suitable sampling for the some of the difficulties in monitoring currently make it nearly impossible to apply rigorous random sampling methods. It is thus necessary to develop an alternative sampling method, which could overcome the lack of sampling and meet the requirements for randomness. The GIS computer application software ArcGIS9.1 was used to overcome the lack of data of sampling sites.Using ArcGIS 9.1 and GEODA to analyze the cow anthrax spatial distribution of district A. we gained some conclusions about cow anthrax' density: (1) there is a spatial clustering model. (2) there is an intensely spatial autocorrelation. We established a prediction model to estimate the anthrax distribution based on the spatial characteristic of the density of cow anthrax. Comparing with the true distribution, the prediction model has a well coincidence and is feasible to the application. The method using a GIS tool facilitates can be implemented significantly in the cow anthrax monitoring and investigation, and the space statistics - related prediction model provides a fundamental use for other study on space-related animal diseases.

Phylogeography and epidemiological history of West Nile virus genotype 1a in Europe and the Mediterranean basin.

PubMed

Zehender, Gianguglielmo; Ebranati, Erika; Bernini, Flavia; Lo Presti, Alessandra; Rezza, Giovanni; Delogu, Mauro; Galli, Massimo; Ciccozzi, Massimo

2011-04-01

Aim of this study was to reconstruct the temporal and spatial phylodynamics of WNV-1a, the genotype to which the majority of European/Mediterranean viral strains belongs, by using sequences retrieved from public databases. WNV-1a isolates segregated into two major clades: the recent West Mediterranean sequences formed a single monophyletic group within clade A. Clade B included sequences from East Mediterranean and America. Phylogeographic analysis suggested that WNV-1a probably originated in sub-Saharan Africa in the early XXth century, and then spread northwards since the late 1970s, via two routes: one crossing Eastern Mediterranean and the other the Western Mediterranean countries. Our data suggest that the circulation of the virus in a given geographical area usually precedes the onset of the outbreak by one year or more, and underline the importance of the spatial-temporal phylodynamics reconstruction in clarifying the recent epidemiology and in setting up an efficient surveillance system for emerging/reemerging zoonosis. Copyright © 2011 Elsevier B.V. All rights reserved.

Spatial distribution of tuberculosis in Manaus and its social determinants, 2008-2013.

PubMed

de Castro, D B; Sadahiro, M; Pinto, R C; de Albuquerque, B C; Braga, J U

2018-02-01

Brazil ranks eighteenth worldwide in annual numbers of new tuberculosis (TB) cases. The municipality of Manaus, Amazonas State, has the highest incidence of TB in Brazil. To evaluate the quality of TB epidemiological surveillance, and to describe the spatial distribution pattern of TB incidence in Manaus and its social determinants. An ecological study was performed based on secondary data from TB epidemiological surveillance reports. An index was developed to classify neighborhoods in terms of the quality of surveillance and suspected underreporting. Based on data from neighborhoods with better surveillance performance, we observed that the average number of residents per room, the unemployment rate and the proportion of households connected to a sewage system were significant predictors of TB incidence. Seven neighborhoods in the south and west of the city had clusters of high TB transmission. Our results suggest that the association between TB and social vulnerability is obscured by the poor quality of TB surveillance data. We identified priority areas that require immediate TB control interventions and those where local surveillance efforts should be improved, and generated information useful for formulating more effective actions.

Descriptive epidemiology of typhoid fever during an epidemic in Harare, Zimbabwe, 2012.

PubMed

Polonsky, Jonathan A; Martínez-Pino, Isabel; Nackers, Fabienne; Chonzi, Prosper; Manangazira, Portia; Van Herp, Michel; Maes, Peter; Porten, Klaudia; Luquero, Francisco J

2014-01-01

Typhoid fever remains a significant public health problem in developing countries. In October 2011, a typhoid fever epidemic was declared in Harare, Zimbabwe - the fourth enteric infection epidemic since 2008. To orient control activities, we described the epidemiology and spatiotemporal clustering of the epidemic in Dzivaresekwa and Kuwadzana, the two most affected suburbs of Harare. A typhoid fever case-patient register was analysed to describe the epidemic. To explore clustering, we constructed a dataset comprising GPS coordinates of case-patient residences and randomly sampled residential locations (spatial controls). The scale and significance of clustering was explored with Ripley K functions. Cluster locations were determined by a random labelling technique and confirmed using Kulldorff's spatial scan statistic. We analysed data from 2570 confirmed and suspected case-patients, and found significant spatiotemporal clustering of typhoid fever in two non-overlapping areas, which appeared to be linked to environmental sources. Peak relative risk was more than six times greater than in areas lying outside the cluster ranges. Clusters were identified in similar geographical ranges by both random labelling and Kulldorff's spatial scan statistic. The spatial scale at which typhoid fever clustered was highly localised, with significant clustering at distances up to 4.5 km and peak levels at approximately 3.5 km. The epicentre of infection transmission shifted from one cluster to the other during the course of the epidemic. This study demonstrated highly localised clustering of typhoid fever during an epidemic in an urban African setting, and highlights the importance of spatiotemporal analysis for making timely decisions about targetting prevention and control activities and reinforcing treatment during epidemics. This approach should be integrated into existing surveillance systems to facilitate early detection of epidemics and identify their spatial range.

Descriptive Epidemiology of Typhoid Fever during an Epidemic in Harare, Zimbabwe, 2012

PubMed Central

Polonsky, Jonathan A.; Martínez-Pino, Isabel; Nackers, Fabienne; Chonzi, Prosper; Manangazira, Portia; Van Herp, Michel; Maes, Peter; Porten, Klaudia; Luquero, Francisco J.

2014-01-01

Background Typhoid fever remains a significant public health problem in developing countries. In October 2011, a typhoid fever epidemic was declared in Harare, Zimbabwe - the fourth enteric infection epidemic since 2008. To orient control activities, we described the epidemiology and spatiotemporal clustering of the epidemic in Dzivaresekwa and Kuwadzana, the two most affected suburbs of Harare. Methods A typhoid fever case-patient register was analysed to describe the epidemic. To explore clustering, we constructed a dataset comprising GPS coordinates of case-patient residences and randomly sampled residential locations (spatial controls). The scale and significance of clustering was explored with Ripley K functions. Cluster locations were determined by a random labelling technique and confirmed using Kulldorff's spatial scan statistic. Principal Findings We analysed data from 2570 confirmed and suspected case-patients, and found significant spatiotemporal clustering of typhoid fever in two non-overlapping areas, which appeared to be linked to environmental sources. Peak relative risk was more than six times greater than in areas lying outside the cluster ranges. Clusters were identified in similar geographical ranges by both random labelling and Kulldorff's spatial scan statistic. The spatial scale at which typhoid fever clustered was highly localised, with significant clustering at distances up to 4.5 km and peak levels at approximately 3.5 km. The epicentre of infection transmission shifted from one cluster to the other during the course of the epidemic. Conclusions This study demonstrated highly localised clustering of typhoid fever during an epidemic in an urban African setting, and highlights the importance of spatiotemporal analysis for making timely decisions about targetting prevention and control activities and reinforcing treatment during epidemics. This approach should be integrated into existing surveillance systems to facilitate early detection of epidemics and identify their spatial range. PMID:25486292

Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011

PubMed Central

Tahden, Maike; Manitz, Juliane; Baumgardt, Klaus; Fell, Gerhard; Kneib, Thomas; Hegasy, Guido

2016-01-01

In 2011, a large outbreak of entero-hemorrhagic E. coli (EHEC) and hemolytic uremic syndrome (HUS) occurred in Germany. The City of Hamburg was the first focus of the epidemic and had the highest incidences among all 16 Federal States of Germany. In this article, we present epidemiological characteristics of the Hamburg notification data. Evaluating the epicurves retrospectively, we found that the first epidemiological signal of the outbreak, which was in form of a HUS case cluster, was received by local health authorities when already 99 EHEC and 48 HUS patients had experienced their first symptoms. However, only two EHEC and seven HUS patients had been notified. Middle-aged women had the highest risk for contracting the infection in Hamburg. Furthermore, we studied timeliness of case notification in the course of the outbreak. To analyze the spatial distribution of EHEC/HUS incidences in 100 districts of Hamburg, we mapped cases' residential addresses using geographic information software. We then conducted an ecological study in order to find a statistical model identifying associations between local socio-economic factors and EHEC/HUS incidences in the epidemic. We employed a Bayesian Poisson model with covariates characterizing the Hamburg districts as well as incorporating structured and unstructured spatial effects. The Deviance Information Criterion was used for stepwise variable selection. We applied different modeling approaches by using primary data, transformed data, and preselected subsets of transformed data in order to identify socio-economic factors characterizing districts where EHEC/HUS outbreak cases had their residence. PMID:27723830

Epidemiology: Past, Present, and Future Impacts on Understanding Disease Dynamics and Improving Plant Disease Management-A Summary of Focus Issue Articles.

PubMed

Ojiambo, P S; Yuen, J; van den Bosch, F; Madden, L V

2017-10-01

Epidemiology has made significant contributions to plant pathology by elucidating the general principles underlying the development of disease epidemics. This has resulted in a greatly improved theoretical and empirical understanding of the dynamics of disease epidemics in time and space, predictions of disease outbreaks or the need for disease control in real-time basis, and tactical and strategic solutions to disease problems. Availability of high-resolution experimental data at multiple temporal and spatial scales has now provided a platform to test and validate theories on the spread of diseases at a wide range of spatial scales ranging from the local to the landscape level. Relatively new approaches in plant disease epidemiology, ranging from network to information theory, coupled with the availability of large-scale datasets and the rapid development of computer technology, are leading to revolutionary thinking about epidemics that can result in considerable improvement of strategic and tactical decision making in the control and management of plant diseases. Methods that were previously restricted to topics such as population biology or evolution are now being employed in epidemiology to enable a better understanding of the forces that drive the development of plant disease epidemics in space and time. This Focus Issue of Phytopathology features research articles that address broad themes in epidemiology including social and political consequences of disease epidemics, decision theory and support, pathogen dispersal and disease spread, disease assessment and pathogen biology and disease resistance. It is important to emphasize that these articles are just a sample of the types of research projects that are relevant to epidemiology. Below, we provide a succinct summary of the articles that are published in this Focus Issue .

Role of animal movement and indirect contact among farms in transmission of porcine epidemic diarrhea virus.

PubMed

VanderWaal, Kimberly; Perez, Andres; Torremorrell, Montse; Morrison, Robert M; Craft, Meggan

2018-04-12

Epidemiological models of the spread of pathogens in livestock populations primarily focus on direct contact between farms based on animal movement data, and in some cases, local spatial spread based on proximity between premises. The roles of other types of indirect contact among farms is rarely accounted for. In addition, data on animal movements is seldom available in the United States. However, the spread of porcine epidemic diarrhea virus (PEDv) in U.S. swine represents one of the best documented emergences of a highly infectious pathogen in the U.S. livestock industry, providing an opportunity to parameterize models of pathogen spread via direct and indirect transmission mechanisms in swine. Using observed data on pig movements during the initial phase of the PEDv epidemic, we developed a network-based and spatially explicit epidemiological model that simulates the spread of PEDv via both indirect and direct movement-related contact in order to answer unresolved questions concerning factors facilitating between-farm transmission. By modifying the likelihood of each transmission mechanism and fitting this model to observed epidemiological dynamics, our results suggest that between-farm transmission was primarily driven by direct mechanisms related to animal movement and indirect mechanisms related to local spatial spread based on geographic proximity. However, other forms of indirect transmission among farms, including contact via contaminated vehicles and feed, were responsible for high consequence transmission events resulting in the introduction of the virus into new geographic areas. This research is among the first reports of farm-level animal movements in the U.S. swine industry and, to our knowledge, represents the first epidemiological model of commercial U.S. swine using actual data on farm-level animal movement. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Pattern transitions in spatial epidemics: Mechanisms and emergent properties.

PubMed

Sun, Gui-Quan; Jusup, Marko; Jin, Zhen; Wang, Yi; Wang, Zhen

2016-12-01

Infectious diseases are a threat to human health and a hindrance to societal development. Consequently, the spread of diseases in both time and space has been widely studied, revealing the different types of spatial patterns. Transitions between patterns are an emergent property in spatial epidemics that can serve as a potential trend indicator of disease spread. Despite the usefulness of such an indicator, attempts to systematize the topic of pattern transitions have been few and far between. We present a mini-review on pattern transitions in spatial epidemics, describing the types of transitions and their underlying mechanisms. We show that pattern transitions relate to the complexity of spatial epidemics by, for example, being accompanied with phenomena such as coherence resonance and cyclic evolution. The results presented herein provide valuable insights into disease prevention and control, and may even be applicable outside epidemiology, including other branches of medical science, ecology, quantitative finance, and elsewhere. Copyright © 2016 Elsevier B.V. All rights reserved.

GIS-supported epidemiological analysis on canine Angiostrongylus vasorum and Crenosoma vulpis infections in Germany.

PubMed

Maksimov, Pavlo; Hermosilla, Carlos; Taubert, Anja; Staubach, Christoph; Sauter-Louis, Carola; Conraths, Franz J; Vrhovec, Majda Globokar; Pantchev, Nikola

2017-02-28

Angiostrongylus vasorum infections are the cause of severe cardiopulmonary diseases in dogs. In the past, canine angiostrongylosis has largely been neglected in Europe, although some recent studies indicated an expansion of historically known endemic areas, a phenomenon that might also apply to Crenosoma vulpis. The aim of the present study was to analyse temporal and spatial trends of canine A. vasorum and C. vulpis infections and to perform GIS-supported risk factor analysis to evaluate the role of landscape, age and seasonality in the life-cycle of these nematodes. A total of 12,682 faecal samples from German dogs (collected in 2003-2015) with clinical suspicion for lungworm infection were examined for the presence of A. vasorum and C. vulpis larvae by the Baermann funnel technique and respective epidemiological data (location and age of the sampled dogs, date of sampling) were subjected to GIS-supported risk factor analysis. Overall, A. vasorum and C. vulpis larvae were detected in 288 (2.3%) and 285 (2.2%) faecal samples, respectively. In general, both lungworm infections were found to be widely spread in Germany. GIS-supported analyses demonstrate spatial differences in the occurrence of canine A. vasorum and C. vulpis infections in Germany. also, risk factor analyses revealed an overlap but also diverging risk and protective factors for A. vasorum and C. vulpis infections. The current data also indicate a significant increase of A. vasorum and C. vulpis prevalences from 2003 to 2015 and from 2008 until 2015, respectively, and a potential spread of A. vasorum endemic areas to the northeastern part of Germany. The results of the present study show an insight into the epidemiological situation of lungworm infections (A. vasorum and C. vulpis) of the past 13 years in Germany. The data clearly demonstrate an increase of diagnosed A. vasorum prevalence in the tested dog population between 2003 and 2015 as well as spatial differences in the occurrence of diagnosed A. vasorum and C. vulpis infections of dogs in Germany. Risk factor analyses suggest possible differences in the biology of these parasites, presumably at the intermediate host level.

Controlling for unmeasured confounding and spatial misalignment in long-term air pollution and health studies.

PubMed

Lee, Duncan; Sarran, Christophe

2015-11-01

The health impact of long-term exposure to air pollution is now routinely estimated using spatial ecological studies, owing to the recent widespread availability of spatial referenced pollution and disease data. However, this areal unit study design presents a number of statistical challenges, which if ignored have the potential to bias the estimated pollution-health relationship. One such challenge is how to control for the spatial autocorrelation present in the data after accounting for the known covariates, which is caused by unmeasured confounding. A second challenge is how to adjust the functional form of the model to account for the spatial misalignment between the pollution and disease data, which causes within-area variation in the pollution data. These challenges have largely been ignored in existing long-term spatial air pollution and health studies, so here we propose a novel Bayesian hierarchical model that addresses both challenges and provide software to allow others to apply our model to their own data. The effectiveness of the proposed model is compared by simulation against a number of state-of-the-art alternatives proposed in the literature and is then used to estimate the impact of nitrogen dioxide and particulate matter concentrations on respiratory hospital admissions in a new epidemiological study in England in 2010 at the local authority level. © 2015 The Authors. Environmetrics published by John Wiley & Sons Ltd.

Generalized Accelerated Failure Time Spatial Frailty Model for Arbitrarily Censored Data

PubMed Central

Zhou, Haiming; Hanson, Timothy; Zhang, Jiajia

2017-01-01

Flexible incorporation of both geographical patterning and risk effects in cancer survival models is becoming increasingly important, due in part to the recent availability of large cancer registries. Most spatial survival models stochastically order survival curves from different subpopulations. However, it is common for survival curves from two subpopulations to cross in epidemiological cancer studies and thus interpretable standard survival models can not be used without some modification. Common fixes are the inclusion of time-varying regression effects in the proportional hazards model or fully non-parametric modeling, either of which destroys any easy interpretability from the fitted model. To address this issue, we develop a generalized accelerated failure time model which allows stratification on continuous or categorical covariates, as well as providing per-variable tests for whether stratification is necessary via novel approximate Bayes factors. The model is interpretable in terms of how median survival changes and is able to capture crossing survival curves in the presence of spatial correlation. A detailed Markov chain Monte Carlo algorithm is presented for posterior inference and a freely available function frailtyGAFT is provided to fit the model in the R package spBayesSurv. We apply our approach to a subset of the prostate cancer data gathered for Louisiana by the Surveillance, Epidemiology, and End Results program of the National Cancer Institute. PMID:26993982

Genomic Epidemiology of a Dengue Virus Epidemic in Urban Singapore▿ †

PubMed Central

Schreiber, Mark J.; Holmes, Edward C.; Ong, Swee Hoe; Soh, Harold S. H.; Liu, Wei; Tanner, Lukas; Aw, Pauline P. K.; Tan, Hwee Cheng; Ng, Lee Ching; Leo, Yee Sin; Low, Jenny G. H.; Ong, Adrian; Ooi, Eng Eong; Vasudevan, Subhash G.; Hibberd, Martin L.

2009-01-01

Dengue is one of the most important emerging diseases of humans, with no preventative vaccines or antiviral cures available at present. Although one-third of the world's population live at risk of infection, little is known about the pattern and dynamics of dengue virus (DENV) within outbreak situations. By exploiting genomic data from an intensively studied major outbreak, we are able to describe the molecular epidemiology of DENV at a uniquely fine-scaled temporal and spatial resolution. Two DENV serotypes (DENV-1 and DENV-3), and multiple component genotypes, spread concurrently and with similar epidemiological and evolutionary profiles during the initial outbreak phase of a major dengue epidemic that took place in Singapore during 2005. Although DENV-1 and DENV-3 differed in viremia and clinical outcome, there was no evidence for adaptive evolution before, during, or after the outbreak, indicating that ecological or immunological rather than virological factors were the key determinants of epidemic dynamics. PMID:19211734

Wave of chaos in a spatial eco-epidemiological system: Generating realistic patterns of patchiness in rabbit-lynx dynamics.

PubMed

Upadhyay, Ranjit Kumar; Roy, Parimita; Venkataraman, C; Madzvamuse, A

2016-11-01

In the present paper, we propose and analyze an eco-epidemiological model with diffusion to study the dynamics of rabbit populations which are consumed by lynx populations. Existence, boundedness, stability and bifurcation analyses of solutions for the proposed rabbit-lynx model are performed. Results show that in the presence of diffusion the model has the potential of exhibiting Turing instability. Numerical results (finite difference and finite element methods) reveal the existence of the wave of chaos and this appears to be a dominant mode of disease dispersal. We also show the mechanism of spatiotemporal pattern formation resulting from the Hopf bifurcation analysis, which can be a potential candidate for understanding the complex spatiotemporal dynamics of eco-epidemiological systems. Implications of the asymptotic transmission rate on disease eradication among rabbit population which in turn enhances the survival of Iberian lynx are discussed. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Social stressors and air pollution across New York City communities: a spatial approach for assessing correlations among multiple exposures.

PubMed

Shmool, Jessie L C; Kubzansky, Laura D; Newman, Ogonnaya Dotson; Spengler, John; Shepard, Peggy; Clougherty, Jane E

2014-11-06

Recent toxicological and epidemiological evidence suggests that chronic psychosocial stress may modify pollution effects on health. Thus, there is increasing interest in refined methods for assessing and incorporating non-chemical exposures, including social stressors, into environmental health research, towards identifying whether and how psychosocial stress interacts with chemical exposures to influence health and health disparities. We present a flexible, GIS-based approach for examining spatial patterns within and among a range of social stressors, and their spatial relationships with air pollution, across New York City, towards understanding their combined effects on health. We identified a wide suite of administrative indicators of community-level social stressors (2008-2010), and applied simultaneous autoregressive models and factor analysis to characterize spatial correlations among social stressors, and between social stressors and air pollutants, using New York City Community Air Survey (NYCCAS) data (2008-2009). Finally, we provide an exploratory ecologic analysis evaluating possible modification of the relationship between nitrogen dioxide (NO2) and childhood asthma Emergency Department (ED) visit rates by social stressors, to demonstrate how the methods used to assess stressor exposure (and/or consequent psychosocial stress) may alter model results. Administrative indicators of a range of social stressors (e.g., high crime rate, residential crowding rate) were not consistently correlated (rho = - 0.44 to 0.89), nor were they consistently correlated with indicators of socioeconomic position (rho = - 0.54 to 0.89). Factor analysis using 26 stressor indicators suggested geographically distinct patterns of social stressors, characterized by three factors: violent crime and physical disorder, crowding and poor access to resources, and noise disruption and property crimes. In an exploratory ecologic analysis, these factors were differentially associated with area-average NO2 and childhood asthma ED visits. For example, only the 'violent crime and disorder' factor was significantly associated with asthma ED visits, and only the 'crowding and resource access' factor modified the association between area-level NO2 and asthma ED visits. This spatial approach enabled quantification of complex spatial patterning and confounding between chemical and non-chemical exposures, and can inform study design for epidemiological studies of separate and combined effects of multiple urban exposures.

[Epidemiology's and epidemiologists' vices and virtues].

PubMed

Gennaro, Valerio; Ricci, Paolo; Levis, Angelo Gino; Crosignani, Paolo

2009-01-01

Epidemiology is a public health oriented discipline and is aimed to detect the spatial, temporal, social and causal distribution of diseases in the human population, in order to promote timely and effective preventive solutions. This paper highlights some gold standard of the epidemiological method and suggests some considerations for a critical comprehension of epidemiological studies, in particular those that deny or minimize the existence of public health risks. This paper will focus on some crucial elements such as definition, attribution, misclassification and underestimation of the harmful exposures caused by a multitude of factors, undervaluation of the possible interactions among harmful agents (even when law limits are respected), selection of exposed populations, insufficient number of studied diseases, importance of the right comparisons between similar groups when the reference population is used, disproportion between shortness of follow-up in respect with long latency period of the studied diseases, including cancer. This paper also observes that many epidemiologists carry out confirmative studies rather than exploratory ones, and they end to both underestimate and underevaluate the multiplicity of the causal associations in favour of a reductive and a critical approach to the statistics which, in final analysis, wants to replace epidemiology. It points out, moreover, that neglecting primary prevention as well as precautionary principles in the interpretation of public health studies, arises the suspicion of professional limitation or strong conflict of interests (business bias). Epidemiology is naturally oriented to both primary prevention and public health defense, and today, more than ever before, for many reasons, among which the overwhelming market power that is first and foremost the cause of the general increase of populations affected by avoidable pathologies, a systematic, correct and timely application of this precious discipline seems to be urgent to us. In conclusion, we would suggest that some technical-scientific and institutional initiatives should overcome the intrinsic limits of the current aetiological researches. The rigorous adhesion of epidemiology to its scientific method appears to be absolutely necessary in order to enforce the Italian constitutional principle which states that health defence is a fundamental right of each individual and a concern for the whole community.

Spatial variations in the consumption of illicit stimulant drugs across Australia: A nationwide application of wastewater-based epidemiology.

PubMed

Lai, Foon Yin; O'Brien, Jake; Bruno, Raimondo; Hall, Wayne; Prichard, Jeremy; Kirkbride, Paul; Gartner, Coral; Thai, Phong; Carter, Steve; Lloyd, Belinda; Burns, Lucy; Mueller, Jochen

2016-10-15

Obtaining representative information on illicit drug use and patterns across a country remains difficult using surveys because of low response rates and response biases. A range of studies have used wastewater-based epidemiology (WBE) as a complementary approach to monitor community-wide illicit drug use. In Australia, no large-scale WBE studies have been conducted to date to reveal illicit drug use profiles in a national context. In this study, we performed the first Australia-wide WBE monitoring to examine spatial patterns in the use of three illicit stimulants (cocaine, as its human metabolite benzoylecgonine; methamphetamine; and 3,4-methylendioxymethamphetamine (MDMA)). A total of 112 daily composite wastewater samples were collected from 14 wastewater treatment plants across four states and two territories. These covered approximately 40% of the Australian population. We identified and quantified illicit drug residues using liquid chromatography coupled with tandem mass spectrometry. There were distinctive spatial patterns of illicit stimulant use in Australia. Multivariate analyses showed that consumption of cocaine and MDMA was higher in the large cities than in rural areas. Also, cocaine consumption differed significantly between different jurisdictions. Methamphetamine consumption was more similar between urban and rural locations. Only a few cities had elevated levels of use. Extrapolation of the WBE estimates suggested that the annual consumption was 3tonnes for cocaine and 9tonnes combined for methamphetamine and MDMA, which outweighed the annual seizure amount by 25 times and 45 times, respectively. These ratios imply the difficulty of detecting the trafficking of these stimulants in Australia, possibly more so for methamphetamine than cocaine. The obtained spatial pattern of use was compared with that in the most recent national household survey. Together both WBE and survey methods provide a more comprehensive evaluation of drug use that can assist governments in developing policies to reduce drug use and harm in the communities. Copyright © 2016 Elsevier B.V. All rights reserved.

Prediction of daily fine particulate matter concentrations using aerosol optical depth retrievals from the Geostationary Operational Environmental Satellite (GOES).

PubMed

Chudnovsky, Alexandra A; Lee, Hyung Joo; Kostinski, Alex; Kotlov, Tanya; Koutrakis, Petros

2012-09-01

Although ground-level PM2.5 (particulate matter with aerodynamic diameter < 2.5 microm) monitoring sites provide accurate measurements, their spatial coverage within a given region is limited and thus often insufficient for exposure and epidemiological studies. Satellite data expand spatial coverage, enhancing our ability to estimate location- and/or subject-specific exposures to PM2.5. In this study, the authors apply a mixed-effects model approach to aerosol optical depth (AOD) retrievals from the Geostationary Operational Environmental Satellite (GOES) to predict PM2.5 concentrations within the New England area of the United States. With this approach, it is possible to control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles, and ground surface reflectance. The model-predicted PM2.5 mass concentration are highly correlated with the actual observations, R2 = 0.92. Therefore, adjustment for the daily variability in AOD-PM2.5 relationship allows obtaining spatially resolved PM2.5 concentration data that can be of great value to future exposure assessment and epidemiological studies. The authors demonstrated how AOD can be used reliably to predict daily PM2.5 mass concentrations, providing determination of their spatial and temporal variability. Promising results are found by adjusting for daily variability in the AOD-PM2.5 relationship, without the need to account for a wide variety of individual additional parameters. This approach is of a great potential to investigate the associations between subject-specific exposures to PM2.5 and their health effects. Higher 4 x 4-km resolution GOES AOD retrievals comparing with the conventional MODerate resolution Imaging Spectroradiometer (MODIS) 10-km product has the potential to capture PM2.5 variability within the urban domain.

Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006

PubMed Central

2009-01-01

Background Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to not only identify the location of such hotspots, but also their spatial patterns. Methods In this study, we utilize spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters, and areas in which these are situated, for the 20 leading causes of death in Taiwan. In addition, we use the fit to a logistic regression model to test the characteristics of similarity and dissimilarity by gender. Results Gender is compared in efforts to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis is utilized to identify spatial cluster patterns. There is naturally great interest in discovering the relationship between the leading causes of death and well-documented spatial risk factors. For example, in Taiwan, we found the geographical distribution of clusters where there is a prevalence of tuberculosis to closely correspond to the location of aboriginal townships. Conclusions Cluster mapping helps to clarify issues such as the spatial aspects of both internal and external correlations for leading health care events. This is of great aid in assessing spatial risk factors, which in turn facilitates the planning of the most advantageous types of health care policies and implementation of effective health care services. PMID:20003460

A New Hybrid Spatio-temporal Model for Estimating Daily Multi-year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

NASA Technical Reports Server (NTRS)

Kloog, Itai; Chudnovsky, Alexandra A.; Just, Allan C.; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A.; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

2014-01-01

The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter PM(sub 2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data.We developed and cross validated models to predict daily PM(sub 2.5) at a 1X 1 km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1 X 1 km grid predictions. We used mixed models regressing PM(sub 2.5) measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R(sup 2) = 0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R(sup 2) = 0.87, R(sup)2 = 0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.

A New Hybrid Spatio-Temporal Model For Estimating Daily Multi-Year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data.

PubMed

Kloog, Itai; Chudnovsky, Alexandra A; Just, Allan C; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

2014-10-01

The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM 2.5 ) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM 2.5 at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM 2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R 2 =0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R 2 =0.87, R 2 =0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.

A New Hybrid Spatio-Temporal Model For Estimating Daily Multi-Year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

PubMed Central

Kloog, Itai; Chudnovsky, Alexandra A.; Just, Allan C.; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A.; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

2017-01-01

Background The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. Methods We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM2.5 at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003–2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Results Our model performance was excellent (mean out-of-sample R2=0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R2=0.87, R2=0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Conclusion Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region. PMID:28966552

Epidemiological study of hazelnut bacterial blight in central Italy by using laboratory analysis and geostatistics.

PubMed

Lamichhane, Jay Ram; Fabi, Alfredo; Ridolfi, Roberto; Varvaro, Leonardo

2013-01-01

Incidence of Xanthomonas arboricola pv. corylina, the causal agent of hazelnut bacterial blight, was analyzed spatially in relation to the pedoclimatic factors. Hazelnut grown in twelve municipalities situated in the province of Viterbo, central Italy was studied. A consistent number of bacterial isolates were obtained from the infected tissues of hazelnut collected in three years (2010-2012). The isolates, characterized by phenotypic tests, did not show any difference among them. Spatial patterns of pedoclimatic data, analyzed by geostatistics showed a strong positive correlation of disease incidence with higher values of rainfall, thermal shock and soil nitrogen; a weak positive correlation with soil aluminium content and a strong negative correlation with the values of Mg/K ratio. No correlation of the disease incidence was found with soil pH. Disease incidence ranged from very low (<1%) to very high (almost 75%) across the orchards. Young plants (4-year old) were the most affected by the disease confirming a weak negative correlation of the disease incidence with plant age. Plant cultivars did not show any difference in susceptibility to the pathogen. Possible role of climate change on the epidemiology of the disease is discussed. Improved management practices are recommended for effective control of the disease.

Epidemiological Study of Hazelnut Bacterial Blight in Central Italy by Using Laboratory Analysis and Geostatistics

PubMed Central

Lamichhane, Jay Ram; Fabi, Alfredo; Ridolfi, Roberto; Varvaro, Leonardo

2013-01-01

Incidence of Xanthomonas arboricola pv. corylina, the causal agent of hazelnut bacterial blight, was analyzed spatially in relation to the pedoclimatic factors. Hazelnut grown in twelve municipalities situated in the province of Viterbo, central Italy was studied. A consistent number of bacterial isolates were obtained from the infected tissues of hazelnut collected in three years (2010–2012). The isolates, characterized by phenotypic tests, did not show any difference among them. Spatial patterns of pedoclimatic data, analyzed by geostatistics showed a strong positive correlation of disease incidence with higher values of rainfall, thermal shock and soil nitrogen; a weak positive correlation with soil aluminium content and a strong negative correlation with the values of Mg/K ratio. No correlation of the disease incidence was found with soil pH. Disease incidence ranged from very low (<1%) to very high (almost 75%) across the orchards. Young plants (4-year old) were the most affected by the disease confirming a weak negative correlation of the disease incidence with plant age. Plant cultivars did not show any difference in susceptibility to the pathogen. Possible role of climate change on the epidemiology of the disease is discussed. Improved management practices are recommended for effective control of the disease. PMID:23424654

Spatial and Temporal Distribution of Tuberculosis in the State of Mexico, Mexico

PubMed Central

Zaragoza Bastida, Adrian; Hernández Tellez, Marivel; Bustamante Montes, Lilia P.; Medina Torres, Imelda; Jaramillo Paniagua, Jaime Nicolás; Mendoza Martínez, Germán David; Ramírez Durán, Ninfa

2012-01-01

Tuberculosis (TB) is one of the oldest human diseases that still affects large population groups. According to the World Health Organization (WHO), there were approximately 9.4 million new cases worldwide in the year 2010. In Mexico, there were 18,848 new cases of TB of all clinical variants in 2010. The identification of clusters in space-time is of great interest in epidemiological studies. The objective of this research was to identify the spatial and temporal distribution of TB during the period 2006–2010 in the State of Mexico, using geographic information system (GIS) and SCAN statistics program. Nine significant clusters (P < 0.05) were identified using spatial and space-time analysis. The conclusion is that TB in the State of Mexico is not randomly distributed but is concentrated in areas close to Mexico City. PMID:22919337

Spatial patterns of natural hazards mortality in the United States

PubMed Central

Borden, Kevin A; Cutter, Susan L

2008-01-01

Background Studies on natural hazard mortality are most often hazard-specific (e.g. floods, earthquakes, heat), event specific (e.g. Hurricane Katrina), or lack adequate temporal or geographic coverage. This makes it difficult to assess mortality from natural hazards in any systematic way. This paper examines the spatial patterns of natural hazard mortality at the county-level for the U.S. from 1970–2004 using a combination of geographical and epidemiological methods. Results Chronic everyday hazards such as severe weather (summer and winter) and heat account for the majority of natural hazard fatalities. The regions most prone to deaths from natural hazards are the South and intermountain west, but sub-regional county-level mortality patterns show more variability. There is a distinct urban/rural component to the county patterns as well as a coastal trend. Significant clusters of high mortality are in the lower Mississippi Valley, upper Great Plains, and Mountain West, with additional areas in west Texas, and the panhandle of Florida, Significant clusters of low mortality are in the Midwest and urbanized Northeast. Conclusion There is no consistent source of hazard mortality data, yet improvements in existing databases can produce quality data that can be incorporated into spatial epidemiological studies as demonstrated in this paper. It is important to view natural hazard mortality through a geographic lens so as to better inform the public living in such hazard prone areas, but more importantly to inform local emergency practitioners who must plan for and respond to disasters in their community. PMID:19091058

A comparison of urban heat islands mapped using skin temperature, air temperature, and apparent temperature (Humidex), for the greater Vancouver area.

PubMed

Ho, Hung Chak; Knudby, Anders; Xu, Yongming; Hodul, Matus; Aminipouri, Mehdi

2016-02-15

Apparent temperature is more closely related to mortality during extreme heat events than other temperature variables, yet spatial epidemiology studies typically use skin temperature (also known as land surface temperature) to quantify heat exposure because it is relatively easy to map from satellite data. An empirical approach to map apparent temperature at the neighborhood scale, which relies on publicly available weather station observations and spatial data layers combined in a random forest regression model, was demonstrated for greater Vancouver, Canada. Model errors were acceptable (cross-validated RMSE=2.04 °C) and the resulting map of apparent temperature, calibrated for a typical hot summer day, corresponded well with past temperature research in the area. A comparison with field measurements as well as similar maps of skin temperature and air temperature revealed that skin temperature was poorly correlated with both air temperature (R(2)=0.38) and apparent temperature (R(2)=0.39). While the latter two were more similar (R(2)=0.87), apparent temperature was predicted to exceed air temperature by more than 5 °C in several urban areas as well as around the confluence of the Pitt and Fraser rivers. We conclude that skin temperature is not a suitable proxy for human heat exposure, and that spatial epidemiology studies could benefit from mapping apparent temperature, using an approach similar to the one reported here, to better quantify differences in heat exposure that exist across an urban landscape. Copyright © 2015 Elsevier B.V. All rights reserved.

Infection dynamics on spatial small-world network models

NASA Astrophysics Data System (ADS)

Iotti, Bryan; Antonioni, Alberto; Bullock, Seth; Darabos, Christian; Tomassini, Marco; Giacobini, Mario

2017-11-01

The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.

Syphilis in the economic center of South China: results from a real-time, web-based surveillance program.

PubMed

Zhang, Wangjian; Du, Zhicheng; Tang, Shaokai; Guo, Pi; Ye, Xingdong; Hao, Yuantao

2015-08-08

Guangzhou is the economic center of South China, which is currently suffering an insidious re-emergence of syphilis. Syphilis epidemic in this area is a matter of serious concern, because of the special economic position of Guangzhou and its large migrant population. Therefore, a comprehensive analysis of surveillance data is needed to provide further information for developing targeted control programs. Case-based surveillance data obtained from a real-time, web-based system were analyzed. A hierarchical clustering method was applied to classify the 12 districts of Guangzhou into several epidemiological regions. The district-level annual incidence and clustering results were displayed on the same map to show the spatial patterns of syphilis in Guangzhou. A total of 60,178 syphilis cases were reported during the period from 2005 to 2013, among which primary/secondary syphilis accounted for 15,864 cases (26.36 %), latent syphilis for 41,078 cases (68.26 %) and congenital syphilis for 2,090 cases (3.47 %). Moreover, primary/secondary syphilis burden slightly decreased from 17.5-18.0 cases per 100,000 people in the first years to 10.6 cases per 100,000 in 2013, with latent syphilis largely increasing from 18.5 cases per 100,000 to 43.4 cases per 100,000. Districts of Guangzhou could be classified into 3 epidemiological regions according to the syphilis burden over the last 3 years of the study period. The burden of primary/secondary syphilis appears to be decreasing in recent years, whereas that of latent syphilis is increasing. Given the epidemiological features and the annual changes found in this study, it is suggested that future control programs should be more population-specific and spatially targeted.

Spatial Analysis of HIV Positive Injection Drug Users in San Francisco, 1987 to 2005

PubMed Central

Martinez, Alexis N.; Mobley, Lee R.; Lorvick, Jennifer; Novak, Scott P.; Lopez, Andrea M.; Kral, Alex H.

2014-01-01

Spatial analyses of HIV/AIDS related outcomes are growing in popularity as a tool to understand geographic changes in the epidemic and inform the effectiveness of community-based prevention and treatment programs. The Urban Health Study was a serial, cross-sectional epidemiological study of injection drug users (IDUs) in San Francisco between 1987 and 2005 (N = 29,914). HIV testing was conducted for every participant. Participant residence was geocoded to the level of the United States Census tract for every observation in dataset. Local indicator of spatial autocorrelation (LISA) tests were used to identify univariate and bivariate Census tract clusters of HIV positive IDUs in two time periods. We further compared three tract level characteristics (% poverty, % African Americans, and % unemployment) across areas of clustered and non-clustered tracts. We identified significant spatial clustering of high numbers of HIV positive IDUs in the early period (1987–1995) and late period (1996–2005). We found significant bivariate clusters of Census tracts where HIV positive IDUs and tract level poverty were above average compared to the surrounding areas. Our data suggest that poverty, rather than race, was an important neighborhood characteristic associated with the spatial distribution of HIV in SF and its spatial diffusion over time. PMID:24722543

Intra-urban and street scale variability of BTEX, NO 2 and O 3 in Birmingham, UK: Implications for exposure assessment

NASA Astrophysics Data System (ADS)

Vardoulakis, Sotiris; Solazzo, Efisio; Lumbreras, Julio

2011-09-01

Automatic monitoring networks have the ability of capturing air pollution episodes, as well as short- and long-term air quality trends in urban areas that can be used in epidemiological studies. However, due to practical constraints (e.g. cost and bulk of equipment), the use of automatic analysers is restricted to a limited number of roadside and background locations within a city. As a result, certain localised air pollution hotspots may be overlooked or overemphasised, especially near heavily trafficked street canyons and intersections. This has implications for compliance with regulatory standards and may cause exposure misclassification in epidemiological studies. Apart from automatic analysers, low cost passive diffusion tubes can be used to characterise the spatial variability of air pollution in urban areas. In this study, BTEX, NO 2 and O 3 data from a one-year passive sampling survey were used to characterise the intra-urban and street scale spatial variability of traffic-related pollutants in Birmingham (UK). In addition, continuous monitoring of NO 2, NO x, O 3, CO, SO 2, PM 10 and PM 2.5 from three permanent monitoring sites was used to identify seasonal and annual pollution patterns. The passive sampling measurements allowed us to evaluate the representativeness of a permanent roadside monitoring site that has recorded some of the highest NO 2 and PM 10 concentrations in Birmingham in recent years. Dispersion modelling was also used to gain further insight into pollutant sources and dispersion characteristics at this location. The strong spatial concentration gradients observed in busy streets, as well as the differences between roadside and urban background levels highlight the importance of appropriate positioning of air quality monitoring equipment in cities.

Quantifying the impact of human mobility on malaria

PubMed Central

Wesolowski, Amy; Eagle, Nathan; Tatem, Andrew J.; Smith, David L.; Noor, Abdisalan M.; Snow, Robert W.; Buckee, Caroline O.

2013-01-01

Human movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal. Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation could greatly improve malaria control programs. Here we use spatially explicit mobile phone data and malaria prevalence information from Kenya to identify the dynamics of human carriers that drive parasite importation between regions. Our analysis identifies specific importation routes that contribute to malaria epidemiology on regional spatial scales. PMID:23066082

Epidemiological, evolutionary and co-evolutionary implications of context-dependent parasitism

PubMed Central

Vale, Pedro F.; Wilson, Alastair J.; Best, Alex; Boots, Mike; Little, Tom J.

2013-01-01

Victims of infection are expected to suffer increasingly as parasite population growth increases. Yet, under some conditions, faster growing parasites do not appear to cause more damage and infections can be quite tolerable. We studied these conditions by assessing how the relationship between parasite population growth and host health is sensitive to environmental variation. In experimental infections of the crustacean Daphnia magna and its bacterial parasite Pasteuria ramosa we show how easily an interaction can shift from a severe interaction, i.e. when host fitness declines substantially with each unit of parasite growth, to a tolerable relationship by changing only simple environmental variables: temperature and food availability. We explored the evolutionary and epidemiological implications of such a shift by modelling pathogen evolution and disease spread under different levels of infection severity, and find that environmental shifts that promote tolerance ultimately result in populations harbouring more parasitized individuals. We also find that the opportunity for selection, as indicated by the variance around traits, varied considerably with the environmental treatment. Thus our results suggest two mechanisms that could underlie co-evolutionary hot- and coldspots: spatial variation in tolerance and spatial variation in the opportunity for selection. PMID:21460572

Epidemiological, evolutionary, and coevolutionary implications of context-dependent parasitism.

PubMed

Vale, Pedro F; Wilson, Alastair J; Best, Alex; Boots, Mike; Little, Tom J

2011-04-01

Abstract Victims of infection are expected to suffer increasingly as parasite population growth increases. Yet, under some conditions, faster-growing parasites do not appear to cause more damage, and infections can be quite tolerable. We studied these conditions by assessing how the relationship between parasite population growth and host health is sensitive to environmental variation. In experimental infections of the crustacean Daphnia magna and its bacterial parasite Pasteuria ramosa, we show how easily an interaction can shift from a severe interaction, that is, when host fitness declines substantially with each unit of parasite growth, to a tolerable relationship by changing only simple environmental variables: temperature and food availability. We explored the evolutionary and epidemiological implications of such a shift by modeling pathogen evolution and disease spread under different levels of infection severity and found that environmental shifts that promote tolerance ultimately result in populations harboring more parasitized individuals. We also find that the opportunity for selection, as indicated by the variance around traits, varied considerably with the environmental treatment. Thus, our results suggest two mechanisms that could underlie coevolutionary hotspots and coldspots: spatial variation in tolerance and spatial variation in the opportunity for selection.

Impact of committed individuals on vaccination behavior

NASA Astrophysics Data System (ADS)

Liu, Xiao-Tao; Wu, Zhi-Xi; Zhang, Lianzhong

2012-11-01

We study how the presence of committed vaccinators, a small fraction of individuals who consistently hold the vaccinating strategy and are immune to influence, impact the vaccination dynamics in well-mixed and spatially structured populations. For this purpose, we develop an epidemiological game-theoretic model of a flu-like vaccination by integrating an epidemiological process into a simple agent-based model of adaptive learning, where individuals (except for those committed ones) use anecdotal evidence to estimate costs and benefits of vaccination. We show that the committed vaccinators, acting as “steadfast role models” in the populations, can efficiently avoid the clustering of susceptible individuals and stimulate other imitators to take vaccination, hence contributing to the promotion of vaccine uptake. We substantiate our findings by making comparative studies of our model on a full lattice and on a randomly diluted one. Our work is expected to provide valuable information for decision-making and design more effective disease-control strategy.

Using Social Network Measures in Wildlife Disease Ecology, Epidemiology, and Management

PubMed Central

Silk, Matthew J.; Croft, Darren P.; Delahay, Richard J.; Hodgson, David J.; Boots, Mike; Weber, Nicola; McDonald, Robbie A.

2017-01-01

Abstract Contact networks, behavioral interactions, and shared use of space can all have important implications for the spread of disease in animals. Social networks enable the quantification of complex patterns of interactions; therefore, network analysis is becoming increasingly widespread in the study of infectious disease in animals, including wildlife. We present an introductory guide to using social-network-analytical approaches in wildlife disease ecology, epidemiology, and management. We focus on providing detailed practical guidance for the use of basic descriptive network measures by suggesting the research questions to which each technique is best suited and detailing the software available for each. We also discuss how using network approaches can be used beyond the study of social contacts and across a range of spatial and temporal scales. Finally, we integrate these approaches to examine how network analysis can be used to inform the implementation and monitoring of effective disease management strategies. PMID:28596616

A flexible spatial scan statistic with a restricted likelihood ratio for detecting disease clusters.

PubMed

Tango, Toshiro; Takahashi, Kunihiko

2012-12-30

Spatial scan statistics are widely used tools for detection of disease clusters. Especially, the circular spatial scan statistic proposed by Kulldorff (1997) has been utilized in a wide variety of epidemiological studies and disease surveillance. However, as it cannot detect noncircular, irregularly shaped clusters, many authors have proposed different spatial scan statistics, including the elliptic version of Kulldorff's scan statistic. The flexible spatial scan statistic proposed by Tango and Takahashi (2005) has also been used for detecting irregularly shaped clusters. However, this method sets a feasible limitation of a maximum of 30 nearest neighbors for searching candidate clusters because of heavy computational load. In this paper, we show a flexible spatial scan statistic implemented with a restricted likelihood ratio proposed by Tango (2008) to (1) eliminate the limitation of 30 nearest neighbors and (2) to have surprisingly much less computational time than the original flexible spatial scan statistic. As a side effect, it is shown to be able to detect clusters with any shape reasonably well as the relative risk of the cluster becomes large via Monte Carlo simulation. We illustrate the proposed spatial scan statistic with data on mortality from cerebrovascular disease in the Tokyo Metropolitan area, Japan. Copyright © 2012 John Wiley & Sons, Ltd.

Spatial pattern and temporal trend of mortality due to tuberculosis 10

PubMed Central

de Queiroz, Ana Angélica Rêgo; Berra, Thaís Zamboni; Garcia, Maria Concebida da Cunha; Popolin, Marcela Paschoal; Belchior, Aylana de Souza; Yamamura, Mellina; dos Santos, Danielle Talita; Arroyo, Luiz Henrique; Arcêncio, Ricardo Alexandre

2018-01-01

ABSTRACT Objectives: To describe the epidemiological profile of mortality due to tuberculosis (TB), to analyze the spatial pattern of these deaths and to investigate the temporal trend in mortality due to tuberculosis in Northeast Brazil. Methods: An ecological study based on secondary mortality data. Deaths due to TB were included in the study. Descriptive statistics were calculated and gross mortality rates were estimated and smoothed by the Local Empirical Bayesian Method. Prais-Winsten’s regression was used to analyze the temporal trend in the TB mortality coefficients. The Kernel density technique was used to analyze the spatial distribution of TB mortality. Results: Tuberculosis was implicated in 236 deaths. The burden of tuberculosis deaths was higher amongst males, single people and people of mixed ethnicity, and the mean age at death was 51 years. TB deaths were clustered in the East, West and North health districts, and the tuberculosis mortality coefficient remained stable throughout the study period. Conclusions: Analyses of the spatial pattern and temporal trend in mortality revealed that certain areas have higher TB mortality rates, and should therefore be prioritized in public health interventions targeting the disease. PMID:29742272

Protecting the privacy of individual general practice patient electronic records for geospatial epidemiology research.

PubMed

Mazumdar, Soumya; Konings, Paul; Hewett, Michael; Bagheri, Nasser; McRae, Ian; Del Fante, Peter

2014-12-01

General practitioner (GP) practices in Australia are increasingly storing patient information in electronic databases. These practice databases can be accessed by clinical audit software to generate reports that inform clinical or population health decision making and public health surveillance. Many audit software applications also have the capacity to generate de-identified patient unit record data. However, the de-identified nature of the extracted data means that these records often lack geographic information. Without spatial references, it is impossible to build maps reflecting the spatial distribution of patients with particular conditions and needs. Links to socioeconomic, demographic, environmental or other geographically based information are also not possible. In some cases, relatively coarse geographies such as postcode are available, but these are of limited use and researchers cannot undertake precision spatial analyses such as calculating travel times. We describe a method that allows researchers to implement meaningful mapping and spatial epidemiological analyses of practice level patient data while preserving privacy. This solution has been piloted in a diabetes risk research project in the patient population of a practice in Adelaide. The method offers researchers a powerful means of analysing geographic clinic data in a privacy-protected manner. © 2014 Public Health Association of Australia.

Spatial epidemiology of dry eye disease: findings from South Korea.

PubMed

Um, Sun-Bi; Kim, Na Hyun; Lee, Hyung Keun; Song, Jong Suk; Kim, Hyeon Chang

2014-08-15

DED rate maps from diverse regions may allow us to understand world-wide spreading pattern of the disease. Only few studies compared the prevalence of DED between geographical regions in non-spatial context. Therefore, we examined the spatial epidemiological pattern of DED prevalence in South Korea using a nationally representative sample. We analyzed 16,431 Korean adults aged 30 years or older of the 5th Korea National Health and Nutrition Examination Survey. DED was defined as previously diagnosed by an ophthalmologist as well as symptoms experienced. Multiple logistic regression analysis was used to assess the spatial pattern in the prevalence of DED, and effects of environmental factors. Among seven metropolitan cities and nine provinces, three metropolitan cities located in the southeast of Korea revealed the highest prevalence of DED. After adjusting for sex, age and survey year, people living in urban areas had higher risk of having DED. Adjusted odds ratio for having previously diagnosed DED was 1.677 (95% CI 1.299-2.166) for metropolitan cities and 1.580 (95% CI 1.215-2.055) for other cities compared to rural areas. Corresponding odds ratio for presenting DED symptoms was 1.388 (95% CI 1.090-1.766) for metropolitan cities and 1.271 (95% CI 0.999-1.617) for other cities. Lower humidity and longer sunshine duration were significantly associated with DED. Among air pollutants, SO2 was associated with DED, while NO2, O3, CO, and PM10 were not. Our findings suggest that prevalence of DED can be affected by the degree of urbanization and environmental factors such as humidity and sunshine duration.

Variation in the Composition and In Vitro Proinflammatory Effect of Urban Particulate Matter from Different Sites

PubMed Central

Manzano-León, Natalia; Quintana, Raúl; Sánchez, Brisa; Serrano, Jesús; Vega, Elizabeth; Vázquez-López, Inés; Rojas-Bracho, Leonora; López-Villegas, Tania; O’Neill, Marie S.; Vadillo-Ortega, Felipe; De Vizcaya-Ruiz, Andrea; Rosas, Irma

2015-01-01

Spatial variation in particulate matter–related health and toxicological outcomes is partly due to its composition. We studied spatial variability in particle composition and induced cellular responses in Mexico City to complement an ongoing epidemiologic study. We measured elements, endotoxins, and polycyclic aromatic hydrocarbons in two particle size fractions collected in five sites. We compared the in vitro proinflammatory response of J774A.1 and THP-1 cells after exposure to particles, measuring subsequent TNFα and IL-6 secretion. Particle composition varied by site and size. Particle constituents were subjected to principal component analysis, identifying three components: C1 (Si, Sr, Mg, Ca, Al, Fe, Mn, endotoxin), C2 (polycyclic aromatic hydrocarbons), and C3 (Zn, S, Sb, Ni, Cu, Pb). Induced TNFα levels were higher and more heterogeneous than IL-6 levels. Cytokines produced by both cell lines only correlated with C1, suggesting that constituents associated with soil induced the inflammatory response and explain observed spatial differences. PMID:23335408

Spatial and seasonal heterogeneity of atmospheric particles induced reactive oxygen species in urban areas and the role of water-soluble metals.

PubMed

Gali, Nirmal Kumar; Yang, Fenhuan; Jiang, Sabrina Yanan; Chan, Ka Lok; Sun, Li; Ho, Kin-fai; Ning, Zhi

2015-03-01

Adverse health effects are associated with exposure to atmospheric particulate matter (PM), which carry various chemical constituents and induce both exogenous and endogenous oxidative stress. This study investigated the spatial and seasonal variability of PM-induced ROS at four sites with different characteristics in Hong Kong. Cytotoxicity, exogenous and endogenous ROS was determined on a dose and time dependent analysis. Large spatial variation of ROS was observed with fine PM at urban site showing highest ROS levels while coarse PM at traffic site ranks the top. No consistent seasonal difference was observed for ROS levels among all sites. The highly heterogeneous distribution of PM-induced ROS demonstrates the differential capability of PM to produce oxidative stress, and the need to use appropriate metrics as surrogates of exposure instead of PM mass in epidemiologic studies. Several transition metals were found associated with ROS by different degree illustrating the complexity of mechanisms involved. Copyright © 2015 Elsevier Ltd. All rights reserved.

Discussing State-of-the-Art Spatial Visualization Techniques Applicable for the Epidemiological Surveillance Data on the Example of Campylobacter spp. in Raw Chicken Meat.

PubMed

Plaza-Rodríguez, C; Appel, B; Kaesbohrer, A; Filter, M

2016-08-01

Within the European activities for the 'Monitoring and Collection of Information on Zoonoses', annually EFSA publishes a European report, including information related to the prevalence of Campylobacter spp. in Germany. Spatial epidemiology becomes here a fundamental tool for the generation of these reports, including the representation of prevalence as an essential element. Until now, choropleth maps are the default visualization technique applied in epidemiological monitoring and surveillance reports made by EFSA and German authorities. However, due to its limitations, it seems to be reasonable to explore alternative chart type. Four maps including choropleth, cartogram, graduated symbols and dot-density maps were created to visualize real-world sample data on the prevalence of Campylobacter spp. in raw chicken meat samples in Germany in 2011. In addition, adjacent and coincident maps were created to visualize also the associated uncertainty. As an outcome, we found that there is not a single data visualization technique that encompasses all the necessary features to visualize prevalence data alone or prevalence data together with their associated uncertainty. All the visualization techniques contemplated in this study demonstrated to have both advantages and disadvantages. To determine which visualization technique should be used for future reports, we recommend to create a dialogue between end-users and epidemiologists on the basis of sample data and charts. The final decision should also consider the knowledge and experience of end-users as well as the specific objective to be achieved with the charts. © 2015 The Authors. Zoonoses and Public Health Published by Blackwell Verlag GmbH.

Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures.

PubMed

Alexeeff, Stacey E; Carroll, Raymond J; Coull, Brent

2016-04-01

Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A spatial-temporal regression model to predict daily outdoor residential PAH concentrations in an epidemiologic study in Fresno, CA

NASA Astrophysics Data System (ADS)

Noth, Elizabeth M.; Hammond, S. Katharine; Biging, Gregory S.; Tager, Ira B.

2011-05-01

BackgroundPolycyclic aromatic hydrocarbons (PAHs) are generated as a byproduct of combustion, and are associated with respiratory symptoms and increased risk of asthma attacks. ObjectivesTo assign daily, outdoor exposures to participants in the Fresno Asthmatic Children's Environment Study (FACES) using land use regression models for the sum of 4-, 5- and 6-ring PAHs (PAH456). MethodsPAH data were collected daily at the EPA Supersite in Fresno, CA from 10/2000 through 2/2007. From 2/2002 to 2/2003, intensive air pollution sampling was conducted at 83 homes of participants in the FACES study. These measurement data were combined with meteorological data, source data, and other spatial variables to form a land use regression model to assign daily exposure at all FACES homes for all years of the study (2001-2008). ResultsThe model for daily, outdoor residential PAH456 concentrations accounted for 80% of the between-home variability and 18% of the within-home variability. Both temporal and spatial variables were significant in the model. Traffic characteristics and home heating fuel were the main spatial explanatory variables. ConclusionsBecause spatial and temporal distributions of PAHs vary on an intra-urban scale, the location of the child's home within the urban setting plays an important role in the level of exposure that each child has to PAHs.

A study on the use of Gumbel approximation with the Bernoulli spatial scan statistic.

PubMed

Read, S; Bath, P A; Willett, P; Maheswaran, R

2013-08-30

The Bernoulli version of the spatial scan statistic is a well established method of detecting localised spatial clusters in binary labelled point data, a typical application being the epidemiological case-control study. A recent study suggests the inferential accuracy of several versions of the spatial scan statistic (principally the Poisson version) can be improved, at little computational cost, by using the Gumbel distribution, a method now available in SaTScan(TM) (www.satscan.org). We study in detail the effect of this technique when applied to the Bernoulli version and demonstrate that it is highly effective, albeit with some increase in false alarm rates at certain significance thresholds. We explain how this increase is due to the discrete nature of the Bernoulli spatial scan statistic and demonstrate that it can affect even small p-values. Despite this, we argue that the Gumbel method is actually preferable for very small p-values. Furthermore, we extend previous research by running benchmark trials on 12 000 synthetic datasets, thus demonstrating that the overall detection capability of the Bernoulli version (i.e. ratio of power to false alarm rate) is not noticeably affected by the use of the Gumbel method. We also provide an example application of the Gumbel method using data on hospital admissions for chronic obstructive pulmonary disease. Copyright © 2013 John Wiley & Sons, Ltd.

Operational evaluation of the RLINE dispersion model for studies of traffic-related air pollutants

NASA Astrophysics Data System (ADS)

Milando, Chad W.; Batterman, Stuart A.

2018-06-01

Exposure to traffic-related air pollutants (TRAP) remains a key public health issue, and improved exposure measures are needed to support health impact and epidemiologic studies and inform regulatory responses. The recently developed Research LINE source model (RLINE), a Gaussian line source dispersion model, has been used in several epidemiologic studies of TRAP exposure, but evaluations of RLINE's performance in such applications have been limited. This study provides an operational evaluation of RLINE in which predictions of NOx, CO and PM2.5 are compared to observations at air quality monitoring stations located near high traffic roads in Detroit, MI. For CO and NOx, model performance was best at sites close to major roads, during downwind conditions, during weekdays, and during certain seasons. For PM2.5, the ability to discern local and particularly the traffic-related portion was limited, a result of high background levels, the sparseness of the monitoring network, and large uncertainties for certain processes (e.g., formation of secondary aerosols) and non-mobile sources (e.g., area, fugitive). Overall, RLINE's performance in near-road environments suggests its usefulness for estimating spatially- and temporally-resolved exposures. The study highlights considerations relevant to health impact and epidemiologic applications, including the importance of selecting appropriate pollutants, using appropriate monitoring approaches, considering prevailing wind directions during study design, and accounting for uncertainty.

Phylodynamics and Human-Mediated Dispersal of a Zoonotic Virus

PubMed Central

Talbi, Chiraz; Lemey, Philippe; Suchard, Marc A.; Abdelatif, Elbia; Elharrak, Mehdi; Jalal, Nourlil; Faouzi, Abdellah; Echevarría, Juan E.; Vazquez Morón, Sonia; Rambaut, Andrew; Campiz, Nicholas; Tatem, Andrew J.; Holmes, Edward C.; Bourhy, Hervé

2010-01-01

Understanding the role of humans in the dispersal of predominately animal pathogens is essential for their control. We used newly developed Bayesian phylogeographic methods to unravel the dynamics and determinants of the spread of dog rabies virus (RABV) in North Africa. Each of the countries studied exhibited largely disconnected spatial dynamics with major geo-political boundaries acting as barriers to gene flow. Road distances proved to be better predictors of the movement of dog RABV than accessibility or raw geographical distance, with occasional long distance and rapid spread within each of these countries. Using simulations that bridge phylodynamics and spatial epidemiology, we demonstrate that the contemporary viral distribution extends beyond that expected for RABV transmission in African dog populations. These results are strongly supportive of human-mediated dispersal, and demonstrate how an integrated phylogeographic approach will turn viral genetic data into a powerful asset for characterizing, predicting, and potentially controlling the spatial spread of pathogens. PMID:21060816

Comparison of exposure estimation methods for air pollutants: ambient monitoring data and regional air quality simulation.

PubMed

Bravo, Mercedes A; Fuentes, Montserrat; Zhang, Yang; Burr, Michael J; Bell, Michelle L

2012-07-01

Air quality modeling could potentially improve exposure estimates for use in epidemiological studies. We investigated this application of air quality modeling by estimating location-specific (point) and spatially-aggregated (county level) exposure concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM(2.5)) and ozone (O(3)) for the eastern U.S. in 2002 using the Community Multi-scale Air Quality (CMAQ) modeling system and a traditional approach using ambient monitors. The monitoring approach produced estimates for 370 and 454 counties for PM(2.5) and O(3), respectively. Modeled estimates included 1861 counties, covering 50% more population. The population uncovered by monitors differed from those near monitors (e.g., urbanicity, race, education, age, unemployment, income, modeled pollutant levels). CMAQ overestimated O(3) (annual normalized mean bias=4.30%), while modeled PM(2.5) had an annual normalized mean bias of -2.09%, although bias varied seasonally, from 32% in November to -27% in July. Epidemiology may benefit from air quality modeling, with improved spatial and temporal resolution and the ability to study populations far from monitors that may differ from those near monitors. However, model performance varied by measure of performance, season, and location. Thus, the appropriateness of using such modeled exposures in health studies depends on the pollutant and metric of concern, acceptable level of uncertainty, population of interest, study design, and other factors. Copyright © 2012 Elsevier Inc. All rights reserved.

Demographic characterization and spatial cluster analysis of human Salmonella 1,4,[5],12:i:- infections in Portugal: A 10year study.

PubMed

Seixas, R; Nunes, T; Machado, J; Tavares, L; Owen, S P; Bernardo, F; Oliveira, M

Salmonella 1,4,[5],12:i:- is presently considered one of the major serovars responsible for human salmonellosis worldwide. Due to its recent emergence, studies assessing the demographic characterization and spatial epidemiology of salmonellosis 1,4,[5],12:i:- at local- or country-level are lacking. In this study, a analysis was conducted over a 10year period, from 2000 to the first quarter of 2011 at the Portuguese National Laboratory in Portugal mainland, with a total of 215 Salmonella 1,4,[5],12:i:- serotyped isolates obtained from human infections by a passive surveillance system. Data regarding source, year and month of sampling, gender, age, district and municipality of the patients were registered. Descriptive statistical analysis and a spatial scan statistic combined with a geographic information system were employed to characterize the epidemiology and identify spatial clusters. Results showed that most districts have reports of Salmonella 1,4,[5],12:i:-, with a higher number of cases at the Portuguese coastland, including districts like Porto (n=60, 27.9%), Lisboa (n=29, 13.5%) and Aveiro (n=28, 13.0%). An increased incidence was observed in the period from 2004 to 2011 and most infections occurred during May and October. Spatial analysis revealed 4 clusters of higher than expected infection rates. Three were located in the north of Portugal, including two at the coastland (Cluster 1 [RR=3.58, p≤0.001] and 4 [RR=10.42 p≤0.230]), and one at the countryside (Cluster 3 [RR=17.76, p≤0.001]). A larger cluster was detected involving the center and south of Portugal (Cluster 2 [RR=4.85, p≤0.001]). The present study was elaborated with data provided by a passive surveillance system, which may originate an underestimation of disease burden. However, this is the first report describing the incidence and the distribution of areas with higher risk of infection in Portugal, revealing that Salmonella 1,4,[5],12:i:- displayed a significant geographic clustering and these areas should be further evaluated to identify risk factors in order to establish prevention programs. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Prediction and prevention of parasitic diseases using a landscape genomics framework

PubMed Central

Schwabl, Philipp; Llewellyn, Martin; Landguth, Erin L.; Andersson, Björn; Kitron, Uriel; Costales, Jaime A.; Ocaña, Sofía; Grijalva, Mario J.

2016-01-01

Summary Substantial heterogeneity exists in the dispersal, distribution and transmission of parasitic species. Understanding and predicting how such features are governed by the ecological variation of landscape they inhabit is the central goal of spatial epidemiology. Genetic data can further inform functional connectivity among parasite, host and vector populations in a landscape. Gene flow correlates with the spread of epidemiologically relevant phenotypes among parasite and vector populations (e.g., virulence, drug and pesticide resistance), as well as invasion and re-invasion risk where parasite transmission is absent due to current or past intervention measures. However, the formal integration of spatial and genetic data (‘landscape genetics’) is scarcely ever applied to parasites. Here, we discuss the specific challenges and practical prospects for the use of landscape genetics and genomics to understand the biology and control of parasitic disease and present a practical framework for doing so. PMID:27863902

Prediction and Prevention of Parasitic Diseases Using a Landscape Genomics Framework.

PubMed

Schwabl, Philipp; Llewellyn, Martin S; Landguth, Erin L; Andersson, Björn; Kitron, Uriel; Costales, Jaime A; Ocaña, Sofía; Grijalva, Mario J

2017-04-01

Substantial heterogeneity exists in the dispersal, distribution and transmission of parasitic species. Understanding and predicting how such features are governed by the ecological variation of landscape they inhabit is the central goal of spatial epidemiology. Genetic data can further inform functional connectivity among parasite, host and vector populations in a landscape. Gene flow correlates with the spread of epidemiologically relevant phenotypes among parasite and vector populations (e.g., virulence, drug and pesticide resistance), as well as invasion and re-invasion risk where parasite transmission is absent due to current or past intervention measures. However, the formal integration of spatial and genetic data ('landscape genetics') is scarcely ever applied to parasites. Here, we discuss the specific challenges and practical prospects for the use of landscape genetics and genomics to understand the biology and control of parasitic disease and present a practical framework for doing so. Copyright © 2016 Elsevier Ltd. All rights reserved.

Distance to health services affects local-level vaccine efficacy for pneumococcal conjugate vaccine (PCV) among rural Filipino children.

PubMed

Root, Elisabeth Dowling; Lucero, Marilla; Nohynek, Hanna; Anthamatten, Peter; Thomas, Deborah S K; Tallo, Veronica; Tanskanen, Antti; Quiambao, Beatriz P; Puumalainen, Taneli; Lupisan, Socorro P; Ruutu, Petri; Ladesma, Erma; Williams, Gail M; Riley, Ian; Simões, Eric A F

2014-03-04

Pneumococcal conjugate vaccines (PCVs) have demonstrated efficacy against childhood pneumococcal disease in several regions globally. We demonstrate how spatial epidemiological analysis of a PCV trial can assist in developing vaccination strategies that target specific geographic subpopulations at greater risk for pneumococcal pneumonia. We conducted a secondary analysis of a randomized, placebo-controlled, double-blind vaccine trial that examined the efficacy of an 11-valent PCV among children less than 2 y of age in Bohol, Philippines. Trial data were linked to the residential location of each participant using a geographic information system. We use spatial interpolation methods to create smoothed surface maps of vaccination rates and local-level vaccine efficacy across the study area. We then measure the relationship between distance to the main study hospital and local-level vaccine efficacy, controlling for ecological factors, using spatial autoregressive models with spatial autoregressive disturbances. We find a significant amount of spatial variation in vaccination rates across the study area. For the primary study endpoint vaccine efficacy increased with distance from the main study hospital from -14% for children living less than 1.5 km from Bohol Regional Hospital (BRH) to 55% for children living greater than 8.5 km from BRH. Spatial regression models indicated that after adjustment for ecological factors, distance to the main study hospital was positively related to vaccine efficacy, increasing at a rate of 4.5% per kilometer distance. Because areas with poor access to care have significantly higher VE, targeted vaccination of children in these areas might allow for a more effective implementation of global programs.

Spatio-temporal modeling of chronic PM 10 exposure for the Nurses' Health Study

NASA Astrophysics Data System (ADS)

Yanosky, Jeff D.; Paciorek, Christopher J.; Schwartz, Joel; Laden, Francine; Puett, Robin; Suh, Helen H.

2008-06-01

Chronic epidemiological studies of airborne particulate matter (PM) have typically characterized the chronic PM exposures of their study populations using city- or county-wide ambient concentrations, which limit the studies to areas where nearby monitoring data are available and which ignore within-city spatial gradients in ambient PM concentrations. To provide more spatially refined and precise chronic exposure measures, we used a Geographic Information System (GIS)-based spatial smoothing model to predict monthly outdoor PM10 concentrations in the northeastern and midwestern United States. This model included monthly smooth spatial terms and smooth regression terms of GIS-derived and meteorological predictors. Using cross-validation and other pre-specified selection criteria, terms for distance to road by road class, urban land use, block group and county population density, point- and area-source PM10 emissions, elevation, wind speed, and precipitation were found to be important determinants of PM10 concentrations and were included in the final model. Final model performance was strong (cross-validation R2=0.62), with little bias (-0.4 μg m-3) and high precision (6.4 μg m-3). The final model (with monthly spatial terms) performed better than a model with seasonal spatial terms (cross-validation R2=0.54). The addition of GIS-derived and meteorological predictors improved predictive performance over spatial smoothing (cross-validation R2=0.51) or inverse distance weighted interpolation (cross-validation R2=0.29) methods alone and increased the spatial resolution of predictions. The model performed well in both rural and urban areas, across seasons, and across the entire time period. The strong model performance demonstrates its suitability as a means to estimate individual-specific chronic PM10 exposures for large populations.

Satellite-based PM concentrations and their application to COPD in Cleveland, OH

PubMed Central

Kumar, Naresh; Liang, Dong; Comellas, Alejandro; Chu, Allen D.; Abrams, Thad

2014-01-01

A hybrid approach is proposed to estimate exposure to fine particulate matter (PM2.5) at a given location and time. This approach builds on satellite-based aerosol optical depth (AOD), air pollution data from sparsely distributed Environmental Protection Agency (EPA) sites and local time–space Kriging, an optimal interpolation technique. Given the daily global coverage of AOD data, we can develop daily estimate of air quality at any given location and time. This can assure unprecedented spatial coverage, needed for air quality surveillance and management and epidemiological studies. In this paper, we developed an empirical relationship between the 2 km AOD and PM2.5 data from EPA sites. Extrapolating this relationship to the study domain resulted in 2.3 million predictions of PM2.5 between 2000 and 2009 in Cleveland Metropolitan Statistical Area (MSA). We have developed local time–space Kriging to compute exposure at a given location and time using the predicted PM2.5. Daily estimates of PM2.5 were developed for Cleveland MSA between 2000 and 2009 at 2.5 km spatial resolution; 1.7 million (~79.8%) of 2.13 million predictions required for multiyear and geographic domain were robust. In the epidemiological application of the hybrid approach, admissions for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) was examined with respect to time–space lagged PM2.5 exposure. Our analysis suggests that the risk of AECOPD increases 2.3% with a unit increase in PM2.5 exposure within 9 days and 0.05° (~5 km) distance lags. In the aggregated analysis, the exposed groups (who experienced exposure to PM2.5 >15.4 μg/m3) were 54% more likely to be admitted for AECOPD than the reference group. The hybrid approach offers greater spatiotemporal coverage and reliable characterization of ambient concentration than conventional in situ monitoring-based approaches. Thus, this approach can potentially reduce exposure misclassification errors in the conventional air pollution epidemiology studies. PMID:24045428

Measuring spatial and temporal trends of nicotine and alcohol consumption in Australia using wastewater-based epidemiology.

PubMed

Lai, Foon Yin; Gartner, Coral; Hall, Wayne; Carter, Steve; O'Brien, Jake; Tscharke, Benjamin J; Been, Frederic; Gerber, Cobus; White, Jason; Thai, Phong; Bruno, Raimondo; Prichard, Jeremy; Kirkbride, K Paul; Mueller, Jochen F

2018-06-01

Tobacco and alcohol consumption remain priority public health issues world-wide. As participation in population-based surveys has fallen, it is increasingly challenging to estimate accurately the prevalence of alcohol and tobacco use. Wastewater-based epidemiology (WBE) is an alternative approach for estimating substance use at the population level that does not rely upon survey participation. This study examined spatio-temporal patterns in nicotine (a proxy for tobacco) and alcohol consumption in the Australian population via WBE. Daily wastewater samples (n = 164) were collected at 18 selected wastewater treatment plants across Australia, covering approximately 45% of the total population. Nicotine and alcohol metabolites in the samples were measured using liquid chromatography-tandem mass spectrometry. Daily consumption of nicotine and alcohol and its associated uncertainty were computed using Monte Carlo simulations. Nation-wide daily average and weekly consumption of these two substances were extrapolated using ordinary least squares and mixed-effect models. Nicotine and alcohol consumption was observed in all communities. Consumption of these substances in rural towns was three to four times higher than in urban communities. The spatial consumption pattern of these substances was consistent across the monitoring periods in 2014-15. Nicotine metabolites significantly reduced by 14-25% (P = 0.001-0.008) (2014-15) in some catchments. Alcohol consumption remained constant over the studied periods. Strong weekly consumption patterns were observed for alcohol but not nicotine. Nation-wide, the daily average consumption per person (aged 15-79 years) was estimated at approximately 2.5 cigarettes and 1.3-2.0 standard drinks (weekday-weekend) of alcohol. These estimates were close to the sale figure and apparent consumption, respectively. Wastewater-based epidemiology is a feasible method for objectively evaluating the geographic, temporal and weekly profiles of nicotine and alcohol consumption in different communities nationally. © 2018 Society for the Study of Addiction.

Does consideration of larger study areas yield more accurate estimates of air pollution health effects? An illustration of the bias-variance trade-off in air pollution epidemiology.

PubMed

Pedersen, Marie; Siroux, Valérie; Pin, Isabelle; Charles, Marie Aline; Forhan, Anne; Hulin, Agnés; Galineau, Julien; Lepeule, Johanna; Giorgis-Allemand, Lise; Sunyer, Jordi; Annesi-Maesano, Isabella; Slama, Rémy

2013-10-01

Spatially-resolved air pollution models can be developed in large areas. The resulting increased exposure contrasts and population size offer opportunities to better characterize the effect of atmospheric pollutants on respiratory health. However the heterogeneity of these areas may also enhance the potential for confounding. We aimed to discuss some analytical approaches to handle this trade-off. We modeled NO2 and PM10 concentrations at the home addresses of 1082 pregnant mothers from EDEN cohort living in and around urban areas, using ADMS dispersion model. Simulations were performed to identify the best strategy to limit confounding by unmeasured factors varying with area type. We examined the relation between modeled concentrations and respiratory health in infants using regression models with and without adjustment or interaction terms with area type. Simulations indicated that adjustment for area limited the bias due to unmeasured confounders varying with area at the costs of a slight decrease in statistical power. In our cohort, rural and urban areas differed for air pollution levels and for many factors associated with respiratory health and exposure. Area tended to modify effect measures of air pollution on respiratory health. Increasing the size of the study area also increases the potential for residual confounding. Our simulations suggest that adjusting for type of area is a good option to limit residual confounding due to area-associated factors without restricting the area size. Other statistical approaches developed in the field of spatial epidemiology are an alternative to control for poorly-measured spatially-varying confounders. © 2013 Elsevier Ltd. All rights reserved.

Cluster detection methods applied to the Upper Cape Cod cancer data.

PubMed

Ozonoff, Al; Webster, Thomas; Vieira, Veronica; Weinberg, Janice; Ozonoff, David; Aschengrau, Ann

2005-09-15

A variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets. We have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM) method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions. The three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering. The comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component.

Spatial Analysis of Rice Blast in China at Three Different Scales.

PubMed

Guo, Fangfang; Chen, Xinglong; Lu, Minghong; Yang, Li; Wang, Shi Wei; Wu, Bo Ming

2018-05-22

In this study, spatial analyses were conducted at three different scales to better understand the epidemiology of rice blast, a major rice disease caused by Magnaporthe oryzae. At regional scale, across the major rice production regions in China, rice blast incidence was monitored on 101 dates at 193 stations from June 10 th to Sep. 10 th during 2009-2014, and surveyed in 143 fields in September, 2016; at county scale, 3 surveys were done covering 1-5 counties in 2015-2016; and at field scale, blast was evaluated in 6 fields in 2015-2016. Spatial cluster and hot spot analyses were conducted in GIS on the geographical pattern of the disease at regional scale, and geostatistical analysis performed at all the three scales. Cluster and hot spot analyses revealed that high-disease areas were clustered in mountainous areas in China. Geostatistical analyses detected spatial dependence of blast incidence with influence ranges of 399 to 1080 km at regional scale, and 5 to 10 m at field scale, but not at county scale. The spatial patterns at different scales might be determined by inherent properties of rice blast and environmental driving forces, and findings from this study provide helpful information to sampling and management of rice blast.

Spatial analysis to identify hotspots of prevalence of schizophrenia.

PubMed

Moreno, Berta; García-Alonso, Carlos R; Negrín Hernández, Miguel A; Torres-González, Francisco; Salvador-Carulla, Luis

2008-10-01

The geographical distribution of mental health disorders is useful information for epidemiological research and health services planning. To determine the existence of geographical hotspots with a high prevalence of schizophrenia in a mental health area in Spain. The study included 774 patients with schizophrenia who were users of the community mental health care service in the area of South Granada. Spatial analysis (Kernel estimation) and Bayesian relative risks were used to locate potential hotspots. Availability and accessibility were both rated in each zone and spatial algebra was applied to identify hotspots in a particular zone. The age-corrected prevalence rate of schizophrenia was 2.86 per 1,000 population in the South Granada area. Bayesian analysis showed a relative risk varying from 0.43 to 2.33. The area analysed had a non-uniform spatial distribution of schizophrenia, with one main hotspot (zone S2). This zone had poor accessibility to and availability of mental health services. A municipality-based variation exists in the prevalence of schizophrenia and related disorders in the study area. Spatial analysis techniques are useful tools to analyse the heterogeneous distribution of a variable and to explain genetic/environmental factors in hotspots related with a lack of easy availability of and accessibility to adequate health care services.

Characterization of the epidemiology of bat-borne rabies in Chile between 2003 and 2013.

PubMed

Alegria-Moran, Raul; Miranda, Daniela; Barnard, Matt; Parra, Alonso; Lapierre, Lisette

2017-08-01

Rabies is a zoonotic disease of great impact to public health. According to the World Health Organization, the country of Chile is currently declared free from human rabies transmitted by dogs. An epidemiological characterization and description was conducted using rabies data from 2003 to 2013 held by the National Program for Prevention and Control of Rabies from the Ministry of Health, consisting of bats samples reported as suspect and samples taken by active surveillance (bats brain tissue). Spatial autocorrelation analysis was performed using Local Indicators of Spatial Association (LISA) statistics, particularly Moran's I index, for the detection of spatial clusters. Temporal descriptive analysis was also carried out. Nine hundred and twenty-seven positive cases were reported, presenting an average of 84 cases per year, mainly originated from passive surveillance (98.5%), whilst only 1.5% of cases were reported by active surveillance. Global positivity for the study period was 7.02% and 0.1% in passive and active surveillance respectively. Most of the cases were reported in the central zone of Chile (88.1%), followed by south zone (9.1%) and north zone (2.8%). At a regional level, Metropolitana (40.6%), Valparaíso (19.1%) and Maule (11.8%) regions reported the majority of the cases. Tadarida brasiliensis (92%) presented the majority of the cases reported, with viral variant 4 (82%) being most commonly diagnosed. Only two cases were detected in companion animals. The central zone presented a positive spatial autocorrelation (Moran's I index=0.1537, 95% CI=0.1141-0.1933; p-value=0.02); north and south zones returned non-significant results (Moran's I index=0.0517 and -0.0117, 95% CI=-0.0358-0.1392 and -0.0780-0.0546, and p-values=0.21 and 0.34 respectively). The number of rabies cases decreased between May and August (late fall and winter) and tended to increase during the hot season (December to March), confirmed with the evidence from Autocorrelation analysis and the Ljun-Box test (X 2 =234.85 and p-value<0.0001). Knowledge of animal rabies epidemiologic behaviour becomes relevant when designing prevention and control measures and surveillance programs. This is especially important considering the high impact to Public Health of this disease and that wildlife rabies in bats remains endemic in Chile. Copyright © 2017 Elsevier B.V. All rights reserved.

Geographic information systems: introduction.

PubMed

Calistri, Paolo; Conte, Annamaria; Freier, Jerome E; Ward, Michael P

2007-01-01

The recent exponential growth of the science and technology of geographic information systems (GIS) has made a tremendous contribution to epidemiological analysis and has led to the development of new powerful tools for the surveillance of animal diseases. GIS, spatial analysis and remote sensing provide valuable methods to collect and manage information for epidemiological surveys. Spatial patterns and trends of disease can be correlated with climatic and environmental information, thus contributing to a better understanding of the links between disease processes and explanatory spatial variables. Until recently, these tools were underexploited in the field of veterinary public health, due to the prohibitive cost of hardware and the complexity of GIS software that required a high level of expertise. The revolutionary developments in computer performance of the last decade have not only reduced the costs of equipment but have made available easy-to-use Web-based software which in turn have meant that GIS are more widely accessible by veterinary services at all levels. At the same time, the increased awareness of the possibilities offered by these tools has created new opportunities for decision-makers to enhance their planning, analysis and monitoring capabilities. These technologies offer a new way of sharing and accessing spatial and non-spatial data across groups and institutions. The series of papers included in this compilation aim to: - define the state of the art in the use of GIS in veterinary activities - identify priority needs in the development of new GIS tools at the international level for the surveillance of animal diseases and zoonoses - define practical proposals for their implementation. The topics addressed are presented in the following order in this book: - importance of GIS for the monitoring of animal diseases and zoonoses - GIS application in surveillance activities - spatial analysis in veterinary epidemiology - data collection and remote sensing applications - Web - GIS as a tool for data and knowledge sharing. All 43 manuscripts selected for this book have been peer-reviewed. These contributions were originally commissioned for the First international conference on the use of GIS in veterinary activities organised by the Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise 'G. Caporale', Teramo, Italy, and the World Organisation for Animal Health (OIE: Office International des Epizooties) that was held in Silvi Marina, Italy, from 8 to 11 October 2006. The editors would like to thank all authors for their valuable contributions.

Clinical, epidemiological, and spatial characteristics of Vibrio parahaemolyticus diarrhea and cholera in the urban slums of Kolkata, India

PubMed Central

2012-01-01

Background There is not much information on the differences in clinical, epidemiological and spatial characteristics of diarrhea due to V. cholerae and V. parahaemolyticus from non-coastal areas. We investigated the differences in clinical, epidemiological and spatial characteristics of the two Vibrio species in the urban slums of Kolkata, India. Methods The data of a cluster randomized cholera vaccine trial were used. We restricted the analysis to clusters assigned to placebo. Survival analysis of the time to the first episode was used to analyze risk factors for V. parahaemolyticus diarrhea or cholera. A spatial scan test was used to identify high risk areas for cholera and for V. parahaemolyticus diarrhea. Results In total, 54,519 people from the placebo clusters were assembled. The incidence of cholera (1.30/1000/year) was significantly higher than that of V. parahaemolyticus diarrhea (0.63/1000/year). Cholera incidence was inversely related to age, whereas the risk of V. parahaemolyticus diarrhea was age-independent. The seasonality of diarrhea due to the two Vibrio species was similar. Cholera was distinguished by a higher frequency of severe dehydration, and V. parahaemolyticus diarrhea was by abdominal pain. Hindus and those who live in household not using boiled or treated water were more likely to have V. parahaemolyticus diarrhea. Young age, low socioeconomic status, and living closer to a project healthcare facility were associated with an increased risk for cholera. The high risk area for cholera differed from the high risk area for V. parahaemolyticus diarrhea. Conclusion We report coexistence of the two vibrios in the slums of Kolkata. The two etiologies of diarrhea had a similar seasonality but had distinguishing clinical features. The risk factors and the high risk areas for the two diseases differ from one another suggesting different modes of transmission of these two pathogens. PMID:23020794

Relation of air pollution with epidemiology of respiratory diseases in isfahan, Iran from 2005 to 2009.

PubMed

Rashidi, Maasoumeh; Ramesht, Mohammad Hossein; Zohary, Moein; Poursafa, Parinaz; Kelishadi, Roya; Rashidi, Zeinab; Rouzbahani, Reza

2013-12-01

National Institute of Environmental Health Sciences (NIEHS) scientists shows that long-term exposure to air pollutants increases the risk of respiratory diseases such as allergies, asthma, chronic obstructive pulmonary disease, and lung cancer. Children and the elderly are particularly vulnerable to the health effects of ozone, fine particles, and other airborne toxicants. Air pollution factors are considered as one of the underlying causes of respiratory diseases. This study aimed to determine the association of respiratory diseases documented in medical records and air pollution (Map distribution) of accumulation in Isfahan province, Iran. By plotting the prevalence and spatial distribution maps, important differences from different points can be observed. The geographic information system (GIS), pollutant standards index (PSI) measurements, and remote Sensing (RS) technology were used after entering data in the mapping information table; spatial distribution was mapped and distribution of Geographical Epidemiology of Respiratory Diseases in Isfahan province (Iran) was determined in this case study from 2005 to 2009. Space with tracing the distribution of respiratory diseases was scattered based on the distribution of air pollution in the points is an important part of this type of diseases in Isfahan province where air pollution was more abundant. The findings of this study emphasis on the importance of preventing the exposure to air pollution, and to control air pollution product industries, to improve work environmental health, and to increase the health professionals and public knowledge in this regard.

Relation of air pollution with epidemiology of respiratory diseases in isfahan, Iran from 2005 to 2009

PubMed Central

Rashidi, Maasoumeh; Ramesht, Mohammad Hossein; Zohary, Moein; Poursafa, Parinaz; Kelishadi, Roya; Rashidi, Zeinab; Rouzbahani, Reza

2013-01-01

Background: National Institute of Environmental Health Sciences (NIEHS) scientists shows that long-term exposure to air pollutants increases the risk of respiratory diseases such as allergies, asthma, chronic obstructive pulmonary disease, and lung cancer. Children and the elderly are particularly vulnerable to the health effects of ozone, fine particles, and other airborne toxicants. Air pollution factors are considered as one of the underlying causes of respiratory diseases. This study aimed to determine the association of respiratory diseases documented in medical records and air pollution (Map distribution) of accumulation in Isfahan province, Iran. By plotting the prevalence and spatial distribution maps, important differences from different points can be observed. Materials and Methods: The geographic information system (GIS), pollutant standards index (PSI) measurements, and remote Sensing (RS) technology were used after entering data in the mapping information table; spatial distribution was mapped and distribution of Geographical Epidemiology of Respiratory Diseases in Isfahan province (Iran) was determined in this case study from 2005 to 2009. Results: Space with tracing the distribution of respiratory diseases was scattered based on the distribution of air pollution in the points is an important part of this type of diseases in Isfahan province where air pollution was more abundant. Conclusion: The findings of this study emphasis on the importance of preventing the exposure to air pollution, and to control air pollution product industries, to improve work environmental health, and to increase the health professionals and public knowledge in this regard. PMID:24523799

Optimising and communicating options for the control of invasive plant disease when there is epidemiological uncertainty.

PubMed

Cunniffe, Nik J; Stutt, Richard O J H; DeSimone, R Erik; Gottwald, Tim R; Gilligan, Christopher A

2015-04-01

Although local eradication is routinely attempted following introduction of disease into a new region, failure is commonplace. Epidemiological principles governing the design of successful control are not well-understood. We analyse factors underlying the effectiveness of reactive eradication of localised outbreaks of invading plant disease, using citrus canker in Florida as a case study, although our results are largely generic, and apply to other plant pathogens (as we show via our second case study, citrus greening). We demonstrate how to optimise control via removal of hosts surrounding detected infection (i.e. localised culling) using a spatially-explicit, stochastic epidemiological model. We show how to define optimal culling strategies that take account of stochasticity in disease spread, and how the effectiveness of disease control depends on epidemiological parameters determining pathogen infectivity, symptom emergence and spread, the initial level of infection, and the logistics and implementation of detection and control. We also consider how optimal culling strategies are conditioned on the levels of risk acceptance/aversion of decision makers, and show how to extend the analyses to account for potential larger-scale impacts of a small-scale outbreak. Control of local outbreaks by culling can be very effective, particularly when started quickly, but the optimum strategy and its performance are strongly dependent on epidemiological parameters (particularly those controlling dispersal and the extent of any cryptic infection, i.e. infectious hosts prior to symptoms), the logistics of detection and control, and the level of local and global risk that is deemed to be acceptable. A version of the model we developed to illustrate our methodology and results to an audience of stakeholders, including policy makers, regulators and growers, is available online as an interactive, user-friendly interface at http://www.webidemics.com/. This version of our model allows the complex epidemiological principles that underlie our results to be communicated to a non-specialist audience.

Optimising and Communicating Options for the Control of Invasive Plant Disease When There Is Epidemiological Uncertainty

PubMed Central

Cunniffe, Nik J.; Stutt, Richard O. J. H.; DeSimone, R. Erik; Gottwald, Tim R.; Gilligan, Christopher A.

2015-01-01

Although local eradication is routinely attempted following introduction of disease into a new region, failure is commonplace. Epidemiological principles governing the design of successful control are not well-understood. We analyse factors underlying the effectiveness of reactive eradication of localised outbreaks of invading plant disease, using citrus canker in Florida as a case study, although our results are largely generic, and apply to other plant pathogens (as we show via our second case study, citrus greening). We demonstrate how to optimise control via removal of hosts surrounding detected infection (i.e. localised culling) using a spatially-explicit, stochastic epidemiological model. We show how to define optimal culling strategies that take account of stochasticity in disease spread, and how the effectiveness of disease control depends on epidemiological parameters determining pathogen infectivity, symptom emergence and spread, the initial level of infection, and the logistics and implementation of detection and control. We also consider how optimal culling strategies are conditioned on the levels of risk acceptance/aversion of decision makers, and show how to extend the analyses to account for potential larger-scale impacts of a small-scale outbreak. Control of local outbreaks by culling can be very effective, particularly when started quickly, but the optimum strategy and its performance are strongly dependent on epidemiological parameters (particularly those controlling dispersal and the extent of any cryptic infection, i.e. infectious hosts prior to symptoms), the logistics of detection and control, and the level of local and global risk that is deemed to be acceptable. A version of the model we developed to illustrate our methodology and results to an audience of stakeholders, including policy makers, regulators and growers, is available online as an interactive, user-friendly interface at http://www.webidemics.com/. This version of our model allows the complex epidemiological principles that underlie our results to be communicated to a non-specialist audience. PMID:25874622

Pityriasis Rosea, Gianotti-Crosti Syndrome, Asymmetric Periflexural Exanthem, Papular-Purpuric Gloves and Socks Syndrome, Eruptive Pseudoangiomatosis, and Eruptive Hypomelanosis: Do Their Epidemiological Data Substantiate Infectious Etiologies?

PubMed Central

Zawar, Vijay; Sciallis, Gabriel F.; Kempf, Werner; Lee, Albert

2016-01-01

Many clinical and laboratory-based studies have been reported for skin rashes which may be due to viral infections, namely pityriasis rosea (PR), Gianotti-Crosti syndrome (GCS), asymmetric periflexural exanthem/unilateral laterothoracic exanthem (APE/ULE), papular-purpuric gloves and socks syndrome (PPGSS), and eruptive pseudo-angiomatosis (EP). Eruptive hypomelanosis (EH) is a newly discovered paraviral rash. Novel tools are now available to investigate the epidemiology of these rashes. To retrieve epidemiological data of these exanthema and analyze whether such substantiates or refutes infectious etiologies. We searched for articles published over the last 60 years and indexed by PubMed database. We then analyzed them for universality, demography, concurrent patients, temporal and spatial-temporal clustering, mini-epidemics, epidemics, and other clinical and geographical associations. Based on our criteria, we selected 55, 60, 29, 36, 20, and 4 articles for PR, GCS, APE/ULE, PPGSS, EP, and EH respectively. Universality or multiple-continental reports are found for all exanthema except EH. The ages of patients are compatible with infectious causes for PR, GCS, APE/ULE, and EH. Concurrent patients are reported for all. Significant patient clustering is demonstrated for PR and GCS. Mini-epidemics and epidemics have been reported for GCS, EP, and EH. The current epidemiological data supports, to a moderate extent, that PR, GCS, and APE could be caused by infectious agents. Support for PPGSS is marginal. Epidemiological evidences for infectious origins for EP and EH are inadequate. There might be growing epidemiological evidence to substantiate or to refute our findings in the future. PMID:27103975

Comparisons of traffic-related ultrafine particle number concentrations measured in two urban areas by central, residential, and mobile monitoring

NASA Astrophysics Data System (ADS)

Simon, Matthew C.; Hudda, Neelakshi; Naumova, Elena N.; Levy, Jonathan I.; Brugge, Doug; Durant, John L.

2017-11-01

Traffic-related ultrafine particles (UFP; <100 nm diameter) are ubiquitous in urban air. While studies have shown that UFP are toxic, epidemiological evidence of health effects, which is needed to inform risk assessment at the population scale, is limited due to challenges of accurately estimating UFP exposures. Epidemiologic studies often use empirical models to estimate UFP exposures; however, the monitoring strategies upon which the models are based have varied between studies. Our study compares particle number concentrations (PNC; a proxy for UFP) measured by three different monitoring approaches (central-site, short-term residential-site, and mobile on-road monitoring) in two study areas in metropolitan Boston (MA, USA). Our objectives were to quantify ambient PNC differences between the three monitoring platforms, compare the temporal patterns and the spatial heterogeneity of PNC between the monitoring platforms, and identify factors that affect correlations across the platforms. We collected >12,000 h of measurements at the central sites, 1000 h of measurements at each of 20 residential sites in the two study areas, and >120 h of mobile measurements over the course of ∼1 year in each study area. Our results show differences between the monitoring strategies: mean 1 min PNC on-roads were higher (64,000 and 32,000 particles/cm3 in Boston and Chelsea, respectively) compared to central-site measurements (23,000 and 19,000 particles/cm3) and both were higher than at residences (14,000 and 15,000 particles/cm3). Temporal correlations and spatial heterogeneity also differed between the platforms. Temporal correlations were generally highest between central and residential sites, and lowest between central-site and on-road measurements. We observed the greatest spatial heterogeneity across monitoring platforms during the morning rush hours (06:00-09:00) and the lowest during the overnight hours (18:00-06:00). Longer averaging times (days and hours vs. minutes) increased temporal correlations (Pearson correlations were 0.69 and 0.60 vs. 0.39 in Boston; 0.71 and 0.61 vs. 0.45 in Chelsea) and reduced spatial heterogeneity (coefficients of divergence were 0.24 and 0.29 vs. 0.33 in Boston; 0.20 and 0.27 vs. 0.31 in Chelsea). Our results suggest that combining stationary and mobile monitoring may lead to improved characterization of UFP in urban areas.

Early Detection for Dengue Using Local Indicator of Spatial Association (LISA) Analysis.

PubMed

Parra-Amaya, Mayra Elizabeth; Puerta-Yepes, María Eugenia; Lizarralde-Bejarano, Diana Paola; Arboleda-Sánchez, Sair

2016-03-29

Dengue is a viral disease caused by a flavivirus that is transmitted by mosquitoes of the genus Aedes . There is currently no specific treatment or commercial vaccine for its control and prevention; therefore, mosquito population control is the only alternative for preventing the occurrence of dengue. For this reason, entomological surveillance is recommended by World Health Organization (WHO) to measure dengue risk in endemic areas; however, several works have shown that the current methodology (aedic indices) is not sufficient for predicting dengue. In this work, we modified indices proposed for epidemic periods. The raw value of the epidemiological wave could be useful for detecting risk in epidemic periods; however, risk can only be detected if analyses incorporate the maximum epidemiological wave. Risk classification was performed according to Local Indicators of Spatial Association (LISA) methodology. The modified indices were analyzed using several hypothetical scenarios to evaluate their sensitivity. We found that modified indices could detect spatial and differential risks in epidemic and endemic years, which makes them a useful tool for the early detection of a dengue outbreak. In conclusion, the modified indices could predict risk at the spatio-temporal level in endemic years and could be incorporated in surveillance activities in endemic places.

HexSim: a modeling environment for ecology and conservation.

EPA Science Inventory

HexSim is a powerful and flexible new spatially-explicit, individual based modeling environment intended for use in ecology, conservation, genetics, epidemiology, toxicology, and other disciplines. We describe HexSim, illustrate past applications that contributed to our >10 year ...

Mortality and Case Fatality Due to Visceral Leishmaniasis in Brazil: A Nationwide Analysis of Epidemiology, Trends and Spatial Patterns

PubMed Central

Martins-Melo, Francisco Rogerlândio; Lima, Mauricélia da Silveira; Ramos, Alberto Novaes; Alencar, Carlos Henrique; Heukelbach, Jorg

2014-01-01

Background Visceral leishmaniasis (VL) is a significant public health problem in Brazil and several regions of the world. This study investigated the magnitude, temporal trends and spatial distribution of mortality related to VL in Brazil. Methods We performed a study based on secondary data obtained from the Brazilian Mortality Information System. We included all deaths in Brazil from 2000 to 2011, in which VL was recorded as cause of death. We present epidemiological characteristics, trend analysis of mortality and case fatality rates by joinpoint regression models, and spatial analysis using municipalities as geographical units of analysis. Results In the study period, 12,491,280 deaths were recorded in Brazil. VL was mentioned in 3,322 (0.03%) deaths. Average annual age-adjusted mortality rate was 0.15 deaths per 100,000 inhabitants and case fatality rate 8.1%. Highest mortality rates were observed in males (0.19 deaths/100,000 inhabitants), <1 year-olds (1.03 deaths/100,000 inhabitants) and residents in Northeast region (0.30 deaths/100,000 inhabitants). Highest case fatality rates were observed in males (8.8%), ≥70 year-olds (43.8%) and residents in South region (17.7%). Mortality and case fatality rates showed a significant increase in Brazil over the period, with different patterns between regions: increasing mortality rates in the North (Annual Percent Change – APC: 9.4%; 95% confidence interval – CI: 5.3 to 13.6), and Southeast (APC: 8.1%; 95% CI: 2.6 to 13.9); and increasing case fatality rates in the Northeast (APC: 4.0%; 95% CI: 0.8 to 7.4). Spatial analysis identified a major cluster of high mortality encompassing a wide geographic range in North and Northeast Brazil. Conclusions Despite ongoing control strategies, mortality related to VL in Brazil is increasing. Mortality and case fatality vary considerably between regions, and surveillance and control measures should be prioritized in high-risk clusters. Early diagnosis and treatment are fundamental strategies for reducing case fatality of VL in Brazil. PMID:24699517

Epidemic characteristics and spatio-temporal patterns of scrub typhus during 2006-2013 in Tai'an, Northern China.

PubMed

Zheng, L; Yang, H-L; Bi, Z-W; Kou, Z-Q; Zhang, L-Y; Zhang, A-H; Yang, L; Zhao, Z-T

2015-08-01

Tai'an, a famous cultural tourist district, is a new endemic foci of scrub typhus in northern China. Frequent reports of travel-acquired cases and absence of effective vaccine indicated a significant health problem of scrub typhus in Tai'an. Thus, descriptive epidemiological methods and spatial-temporal scan statistics were used to describe the epidemic characteristics and detect the significant clusters of the high incidence of scrub typhus at the town level in Tai'an. Results of descriptive epidemiological analysis showed a total of 490 cases were reported in Tai'an with the annual average incidence ranging from 0·48 to 2·27/100 000 during 2006-2013. Females, the elderly and farmers are the high-risk groups. Monthly changes of scrub typhus cases indicated an obvious epidemic period in autumn. Spatial-temporal distribution analysis, showed significant clusters of high incidence mainly located in eastern and northern Tai'an. Our study suggests that more effective, targeted measures for local residents should be implemented in the eastern and northern areas of Tai'an in autumn. Meanwhile, it may prove beneficial for health policy makers to advise travellers to take preventive measures in order to minimize the risk of infection of scrub typhus in Tai'an.

Spatiotemporal distribution of diurnal yellow fever vectors (Diptera: Culicidae) at two sylvan interfaces in Kenya, East Africa.

PubMed

Ellis, Brett Richard; Wesson, Dawn M; Sang, Rosemary C

2007-01-01

Yellow fever virus (YFV) remains a significant public health threat in sub-Saharan Africa in which 90% of the estimated 200,000 cases occur annually. In East Africa, human cases of YFV are characterized by unpredictable focal periodicity, lengthy inter-epidemic periods, and a precarious potential for large epidemics. YFV had remained undetected in this region for nearly 40 years until emerging in Kenya in 1992-93 and more recently in Sudan during 2003 and 2005. From an ecological perspective the emergence and epidemiological outcomes associated with YFV, and related vector-borne diseases, are critically dependent upon the underlying vector ecology at a local scale. The study here was aimed at defining the dynamics of important vector interactions at two important sites in Kenya with previous YFV or related arbovirus activity. The temporal abundance, spatial distribution, and human host seeking behavior of diurnal man-landing mosquito species along sylvan interfaces were investigated. A number of YFV vectors were identified including their abundances for the duration of the main rainy season. Spatially, results indicated that the greatest human-mosquito interactions occurred within the forest and decreased across more domesticated biotopes. A discussion of significant differences, ecological associations, and epidemiological implications is included.

Dynamically rich, yet parameter-sparse models for spatial epidemiology. Comment on "Coupled disease-behavior dynamics on complex networks: A review" by Z. Wang et al.

NASA Astrophysics Data System (ADS)

Jusup, Marko; Iwami, Shingo; Podobnik, Boris; Stanley, H. Eugene

2015-12-01

Since the very inception of mathematical modeling in epidemiology, scientists exploited the simplicity ingrained in the assumption of a well-mixed population. For example, perhaps the earliest susceptible-infectious-recovered (SIR) model developed by L. Reed and W.H. Frost in the 1920s [1], included the well-mixed assumption such that any two individuals in the population could meet each other. The problem was that, unlike many other simplifying assumptions used in epidemiological modeling whose validity holds in one situation or the other, well-mixed populations are almost non-existent in reality because the nature of human socio-economic interactions is, for the most part, highly heterogeneous (e.g. [2-6]).

Unveiling Spatial Epidemiology of HIV with Mobile Phone Data

NASA Astrophysics Data System (ADS)

Brdar, Sanja; Gavrić, Katarina; Ćulibrk, Dubravko; Crnojević, Vladimir

2016-01-01

An increasing amount of geo-referenced mobile phone data enables the identification of behavioral patterns, habits and movements of people. With this data, we can extract the knowledge potentially useful for many applications including the one tackled in this study - understanding spatial variation of epidemics. We explored the datasets collected by a cell phone service provider and linked them to spatial HIV prevalence rates estimated from publicly available surveys. For that purpose, 224 features were extracted from mobility and connectivity traces and related to the level of HIV epidemic in 50 Ivory Coast departments. By means of regression models, we evaluated predictive ability of extracted features. Several models predicted HIV prevalence that are highly correlated (>0.7) with actual values. Through contribution analysis we identified key elements that correlate with the rate of infections and could serve as a proxy for epidemic monitoring. Our findings indicate that night connectivity and activity, spatial area covered by users and overall migrations are strongly linked to HIV. By visualizing the communication and mobility flows, we strived to explain the spatial structure of epidemics. We discovered that strong ties and hubs in communication and mobility align with HIV hot spots.

Unveiling Spatial Epidemiology of HIV with Mobile Phone Data

PubMed Central

Brdar, Sanja; Gavrić, Katarina; Ćulibrk, Dubravko; Crnojević, Vladimir

2016-01-01

An increasing amount of geo-referenced mobile phone data enables the identification of behavioral patterns, habits and movements of people. With this data, we can extract the knowledge potentially useful for many applications including the one tackled in this study - understanding spatial variation of epidemics. We explored the datasets collected by a cell phone service provider and linked them to spatial HIV prevalence rates estimated from publicly available surveys. For that purpose, 224 features were extracted from mobility and connectivity traces and related to the level of HIV epidemic in 50 Ivory Coast departments. By means of regression models, we evaluated predictive ability of extracted features. Several models predicted HIV prevalence that are highly correlated (>0.7) with actual values. Through contribution analysis we identified key elements that correlate with the rate of infections and could serve as a proxy for epidemic monitoring. Our findings indicate that night connectivity and activity, spatial area covered by users and overall migrations are strongly linked to HIV. By visualizing the communication and mobility flows, we strived to explain the spatial structure of epidemics. We discovered that strong ties and hubs in communication and mobility align with HIV hot spots. PMID:26758042

Human Mobility Patterns and Cholera Epidemics: a Spatially Explicit Modeling Approach

NASA Astrophysics Data System (ADS)

Mari, L.; Bertuzzo, E.; Righetto, L.; Casagrandi, R.; Gatto, M.; Rodriguez-Iturbe, I.; Rinaldo, A.

2010-12-01

Cholera is an acute enteric disease caused by the ingestion of water or food contaminated by the bacterium Vibrio cholerae. Although most infected individuals do not develop severe symptoms, their stool may contain huge quantities of V.~cholerae cells. Therefore, while traveling or commuting, asymptomatic carriers can be responsible for the long-range dissemination of the disease. As a consequence, human mobility is an alternative and efficient driver for the spread of cholera, whose primary propagation pathway is hydrological transport through river networks. We present a multi-layer network model that accounts for the interplay between epidemiological dynamics, hydrological transport and long-distance dissemination of V.~cholerae due to human movement. In particular, building on top of state-of-the-art spatially explicit models for cholera spread through surface waters, we describe human movement and its effects on the propagation of the disease by means of a gravity-model approach borrowed from transportation theory. Gravity-like contact processes have been widely used in epidemiology, because they can satisfactorily depict human movement when data on actual mobility patterns are not available. We test our model against epidemiological data recorded during the cholera outbreak occurred in the KwaZulu-Natal province of South Africa during years 2000--2001. We show that human mobility does actually play an important role in the formation of the spatiotemporal patterns of cholera epidemics. In particular, long-range human movement may determine inter-catchment dissemination of V.~cholerae cells, thus in turn explaining the emergence of epidemic patterns that cannot be produced by hydrological transport alone. We also show that particular attention has to be devoted to study how heterogeneously distributed drinking water supplies and sanitation conditions may affect cholera transmission.

Routinely collected English birth data sets: comparisons and recommendations for reproductive epidemiology.

PubMed

Ghosh, Rebecca E; Ashworth, Danielle C; Hansell, Anna L; Garwood, Kevin; Elliott, Paul; Toledano, Mireille B

2016-09-01

In England there are four national routinely collected data sets on births: Office for National Statistics (ONS) births based on birth registrations; Hospital Episode Statistics (HES) deliveries (mothers' information); HES births (babies' information); and NHS Numbers for Babies (NN4B) based on ONS births plus gestational age and ethnicity information. This study describes and compares these data, with the aim of recommending the most appropriate data set(s) for use in epidemiological research and surveillance. We assessed the completeness and quality of the data sets in relation to use in epidemiological research and surveillance and produced detailed descriptive statistics on common reproductive outcomes for each data set including temporal and spatial trends. ONS births is a high quality complete data set but lacks interpretive and clinical information. HES deliveries showed good agreement with ONS births but HES births showed larger amounts of missing or unavailable data. Both HES data sets had improved quality from 2003 onwards, but showed some local spatial variability. NN4B showed excellent agreement with ONS and HES deliveries for the years available (2006-2010). Annual number of births increased by 17.6% comparing 2002 with 2010 (ONS births). Approximately 6% of births were of low birth weight (2.6% term low birth weight) and 0.5% were stillbirths. Routinely collected data on births provide a valuable resource for researchers. ONS and NN4B offer the most complete and accurate record of births. Where more detailed clinical information is required, HES deliveries offers a high quality data set that captures the majority of English births. 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/

Predominance and geo-mapping of avian influenza H5N1 in poultry sectors in Egypt.

PubMed

Arafa, Abdelsatar; El-Masry, Ihab; Khoulosy, Shereen; Hassan, Mohammed K; Soliman, Moussa; Fasanmi, Olubunmi G; Fasina, Folorunso O; Dauphin, Gwenaelle; Lubroth, Juan; Jobre, Yilma M

2016-11-28

Highly pathogenic avian influenza (HPAI) virus of the H5N1 subtype has been enzootic in the Egyptian poultry with significant human infections since 2008. This work evaluates the epidemiological and virological information from February 2006 to May 2015 in spatial and temporal terms. Only data with confirmed HPAI H5N1 sub-type were collected, and matched with the epidemiological data from various spatially and temporally-dispersed surveillances implemented between 2006 and 2015. Spatio-temporal analysis was conducted on a total of 3338 confirmed H5N1 HPAI poultry disease outbreaks and outputs described based on transmission patterns, poultry species, production types affected, trade, geographic and temporal distributions in Egypt. The H5N1 virus persists in the Egyptian poultry displaying a seasonal pattern with peak prevalence between January and March. There was no specific geographic pattern, but chickens and ducks were more affected. However, relatively higher disease incidences were recorded in the Nile Delta. Phylogenetic studies of the haemagglutinin gene sequences of H5N1 viruses indicated that multiple clusters circulated between 2006 and 2015, with significant deviations in circulation. Epidemiological dynamics of HPAI has changed with the origins of majority of outbreaks shifted to household poultry. The persistence of HPAI H5N1 in poultry with recurrent and sporadic infections in humans can influence virus evolution spatio-temporally. Household poultry plays significant roles in the H5N1 virus transmission to poultry and humans, but the role of commercial poultry needs further clarifications. While poultry trading supports the persistence and transmission of H5N1, the role of individual species may warrant further investigation. Surveillance activities, applying a multi-sectoral approach, are recommended.

A systematic review of Rift Valley Fever epidemiology 1931–2014

PubMed Central

Nanyingi, Mark O.; Munyua, Peninah; Kiama, Stephen G.; Muchemi, Gerald M.; Thumbi, Samuel M.; Bitek, Austine O.; Bett, Bernard; Muriithi, Reese M.; Njenga, M. Kariuki

2015-01-01

Background Rift Valley Fever (RVF) is a mosquito-borne viral zoonosis that was first isolated and characterized in 1931 in Kenya. RVF outbreaks have resulted in significant losses through human illness and deaths, high livestock abortions and deaths. This report provides an overview on epidemiology of RVF including ecology, molecular diversity spatiotemporal analysis, and predictive risk modeling. Methodology Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched for relevant RVF publications in repositories of the World Health Organization Library and Information Networks for Knowledge (WHOLIS), U.S Centers for Disease Control and Prevention (CDC), and Food and Agricultural Organization (FAO). Detailed searches were performed in Google Scholar, SpringerLink, and PubMed databases and included conference proceedings and books published from 1931 up to 31st January 2015. Results and discussion A total of 84 studies were included in this review; majority (50%) reported on common human and animal risk factors that included consumption of animal products, contact with infected animals and residing in low altitude areas associated with favorable climatic and ecological conditions for vector emergence. A total of 14 (16%) of the publications described RVF progressive spatial and temporal distribution and the use of risk modeling for timely prediction of imminent outbreaks. Using distribution maps, we illustrated the gradual spread and geographical extent of disease; we also estimated the disease burden using aggregate human mortalities and cumulative outbreak periods for endemic regions. Conclusion This review outlines common risk factors for RVF infections over wider geographical areas; it also emphasizes the role of spatial models in predicting RVF enzootics. It, therefore, explains RVF epidemiological status that may be used for design of targeted surveillance and control programs in endemic countries. PMID:26234531

A Comparison of Exposure Metrics for Traffic-Related Air Pollutants: Application to Epidemiology Studies in Detroit, Michigan

PubMed Central

Batterman, Stuart; Burke, Janet; Isakov, Vlad; Lewis, Toby; Mukherjee, Bhramar; Robins, Thomas

2014-01-01

Vehicles are major sources of air pollutant emissions, and individuals living near large roads endure high exposures and health risks associated with traffic-related air pollutants. Air pollution epidemiology, health risk, environmental justice, and transportation planning studies would all benefit from an improved understanding of the key information and metrics needed to assess exposures, as well as the strengths and limitations of alternate exposure metrics. This study develops and evaluates several metrics for characterizing exposure to traffic-related air pollutants for the 218 residential locations of participants in the NEXUS epidemiology study conducted in Detroit (MI, USA). Exposure metrics included proximity to major roads, traffic volume, vehicle mix, traffic density, vehicle exhaust emissions density, and pollutant concentrations predicted by dispersion models. Results presented for each metric include comparisons of exposure distributions, spatial variability, intraclass correlation, concordance and discordance rates, and overall strengths and limitations. While showing some agreement, the simple categorical and proximity classifications (e.g., high diesel/low diesel traffic roads and distance from major roads) do not reflect the range and overlap of exposures seen in the other metrics. Information provided by the traffic density metric, defined as the number of kilometers traveled (VKT) per day within a 300 m buffer around each home, was reasonably consistent with the more sophisticated metrics. Dispersion modeling provided spatially- and temporally-resolved concentrations, along with apportionments that separated concentrations due to traffic emissions and other sources. While several of the exposure metrics showed broad agreement, including traffic density, emissions density and modeled concentrations, these alternatives still produced exposure classifications that differed for a substantial fraction of study participants, e.g., from 20% to 50% of homes, depending on the metric, would be incorrectly classified into “low”, “medium” or “high” traffic exposure classes. These and other results suggest the potential for exposure misclassification and the need for refined and validated exposure metrics. While data and computational demands for dispersion modeling of traffic emissions are non-trivial concerns, once established, dispersion modeling systems can provide exposure information for both on- and near-road environments that would benefit future traffic-related assessments. PMID:25226412

Wildlife disease ecology from the individual to the population: Insights from a long-term study of a naturally infected European badger population.

PubMed

McDonald, Jenni L; Robertson, Andrew; Silk, Matthew J

2018-01-01

Long-term individual-based datasets on host-pathogen systems are a rare and valuable resource for understanding the infectious disease dynamics in wildlife. A study of European badgers (Meles meles) naturally infected with bovine tuberculosis (bTB) at Woodchester Park in Gloucestershire (UK) has produced a unique dataset, facilitating investigation of a diverse range of epidemiological and ecological questions with implications for disease management. Since the 1970s, this badger population has been monitored with a systematic mark-recapture regime yielding a dataset of >15,000 captures of >3,000 individuals, providing detailed individual life-history, morphometric, genetic, reproductive and disease data. The annual prevalence of bTB in the Woodchester Park badger population exhibits no straightforward relationship with population density, and both the incidence and prevalence of Mycobacterium bovis show marked variation in space. The study has revealed phenotypic traits that are critical for understanding the social structure of badger populations along with mechanisms vital for understanding disease spread at different spatial resolutions. Woodchester-based studies have provided key insights into how host ecology can influence infection at different spatial and temporal scales. Specifically, it has revealed heterogeneity in epidemiological parameters; intrinsic and extrinsic factors affecting population dynamics; provided insights into senescence and individual life histories; and revealed consistent individual variation in foraging patterns, refuge use and social interactions. An improved understanding of ecological and epidemiological processes is imperative for effective disease management. Woodchester Park research has provided information of direct relevance to bTB management, and a better appreciation of the role of individual heterogeneity in disease transmission can contribute further in this regard. The Woodchester Park study system now offers a rare opportunity to seek a dynamic understanding of how individual-, group- and population-level processes interact. The wealth of existing data makes it possible to take a more integrative approach to examining how the consequences of individual heterogeneity scale to determine population-level pathogen dynamics and help advance our understanding of the ecological drivers of host-pathogen systems. © 2017 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Spatial analysis of infection by the human immunodeficiency virus among pregnant women1

PubMed Central

de Holanda, Eliane Rolim; Galvão, Marli Teresinha Gimeniz; Pedrosa, Nathália Lima; Paiva, Simone de Sousa; de Almeida, Rosa Lívia Freitas

2015-01-01

OBJECTIVES: to analyze the spatial distribution of reported cases of pregnant women infected by the human immunodeficiency virus and to identify the urban areas with greater social vulnerability to the infection among pregnant women. METHOD: ecological study, developed by means of spatial analysis techniques of area data. Secondary data were used from the Brazilian National Disease Notification System for the city of Recife, Pernambuco. Birth data were obtained from the Brazilian Information System on Live Births and socioeconomic data from the 2010 Demographic Census. RESULTS: the presence of spatial self-correlation was verified. Moran's Index was significant for the distribution. Clusters were identified, considered as high-risk areas, located in grouped neighborhoods, with equally high infection rates among pregnant women. A neighborhood located in the Northwest of the city was distinguished, considered in an epidemiological transition phase. CONCLUSION: precarious living conditions, as evidenced by the indicators illiteracy, absence of prenatal care and poverty, were relevant for the risk of vertical HIV transmission, converging to the grouping of cases among disadvantaged regions. PMID:26155005

Distance to health services affects local-level vaccine efficacy for pneumococcal conjugate vaccine (PCV) among rural Filipino children

PubMed Central

Root, Elisabeth Dowling; Lucero, Marilla; Nohynek, Hanna; Anthamatten, Peter; Thomas, Deborah S. K.; Tallo, Veronica; Tanskanen, Antti; Quiambao, Beatriz P.; Puumalainen, Taneli; Lupisan, Socorro P.; Ruutu, Petri; Ladesma, Erma; Williams, Gail M.; Riley, Ian; Simões, Eric A. F.

2014-01-01

Pneumococcal conjugate vaccines (PCVs) have demonstrated efficacy against childhood pneumococcal disease in several regions globally. We demonstrate how spatial epidemiological analysis of a PCV trial can assist in developing vaccination strategies that target specific geographic subpopulations at greater risk for pneumococcal pneumonia. We conducted a secondary analysis of a randomized, placebo-controlled, double-blind vaccine trial that examined the efficacy of an 11-valent PCV among children less than 2 y of age in Bohol, Philippines. Trial data were linked to the residential location of each participant using a geographic information system. We use spatial interpolation methods to create smoothed surface maps of vaccination rates and local-level vaccine efficacy across the study area. We then measure the relationship between distance to the main study hospital and local-level vaccine efficacy, controlling for ecological factors, using spatial autoregressive models with spatial autoregressive disturbances. We find a significant amount of spatial variation in vaccination rates across the study area. For the primary study endpoint vaccine efficacy increased with distance from the main study hospital from −14% for children living less than 1.5 km from Bohol Regional Hospital (BRH) to 55% for children living greater than 8.5 km from BRH. Spatial regression models indicated that after adjustment for ecological factors, distance to the main study hospital was positively related to vaccine efficacy, increasing at a rate of 4.5% per kilometer distance. Because areas with poor access to care have significantly higher VE, targeted vaccination of children in these areas might allow for a more effective implementation of global programs. PMID:24550454

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.

Spatial Epidemiology of Plasmodium vivax, Afghanistan

PubMed Central

Leslie, Toby; Kolaczinski, Kate; Mohsen, Engineer; Mehboob, Najeebullah; Saleheen, Sarah; Khudonazarov, Juma; Freeman, Tim; Clements, Archie; Rowland, Mark; Kolaczinski, Jan

2006-01-01

Plasmodium vivax is endemic to many areas of Afghanistan. Geographic analysis helped highlight areas of malaria risk and clarified ecologic risk factors for transmission. Remote sensing enabled development of a risk map, thereby providing a valuable tool to help guide malaria control strategies. PMID:17176583

GIS-based environmental analysis of fox and canine lungworm distribution: an epidemiological study of Angiostrongylus vasorum and Crenosoma vulpis in red foxes from Slovakia.

PubMed

Čabanová, Viktória; Miterpáková, Martina; Druga, Michal; Hurníková, Zuzana; Valentová, Daniela

2018-02-01

Over a period of intervening years, the distribution of two canine cardiopulmonary metastrongylid nematodes, Angiostrongylus vasorum and Crenosoma vulpis, has been recognised in Central Europe. Here, we report the first epidemiological research conducted in red foxes from Slovakia and the potential influence of selected environmental variables on the parasites' occurrence, quantified by logistic regression. The environmental models revealed that distribution of C. vulpis is not significantly influenced by any environmental variables, and the parasite is present in the whole area under study. Models for A. vasorum revealed some weak influence of environmental variables, as it tends to occur in drier areas with lower proportion of forest. Moreover, A. vasorum shows a typical spatial clustering and occurs in endemic foci identified mainly in the eastern part of Slovakia. A cluster of A. vasorum infection foci was also found in the north-eastern region, where the average winter air temperature regularly falls below - 10 °C.

Radiofrequency exposure on fast patrol boats in the Royal Norwegian Navy--an approach to a dose assessment.

PubMed

Baste, Valborg; Mild, Kjell Hansson; Moen, Bente E

2010-07-01

Epidemiological studies related to radiofrequency (RF) electromagnetic fields (EMF) have mainly used crude proxies for exposure, such as job titles, distance to, or use of different equipment emitting RF EMF. The Royal Norwegian Navy (RNoN) has measured RF field emitted from high-frequency antennas and radars on several spots where the crew would most likely be located aboard fast patrol boats (FPB). These boats are small, with short distance between the crew and the equipment emitting RF field. We have described the measured RF exposure aboard FPB and suggested different methods for calculations of total exposure and annual dose. Linear and spatial average in addition to percentage of ICNIRP and squared deviation of ICNIRP has been used. The methods will form the basis of a job exposure matrix where relative differences in exposure between groups of crew members can be used in further epidemiological studies of reproductive health. 2010 Wiley-Liss, Inc.

The Sero-epidemiology of Coxiella burnetii in Humans and Cattle, Western Kenya: Evidence from a Cross-Sectional Study

PubMed Central

Thomas, Lian F.; Cook, Elizabeth A. J.; de Glanville, William A.; Atkinson, Peter M.; Wamae, Claire N.; Fèvre, Eric M.

2016-01-01

Evidence suggests that the intracellular bacterial pathogen Coxiella burnetii (which causes Q fever) is widespread, with a near global distribution. While there has been increasing attention to Q fever epidemiology in high-income settings, a recent systematic review highlighted significant gaps in our understanding of the prevalence, spatial distribution and risk factors for Q fever infection across Africa. This research aimed to provide a One Health assessment of Q fever epidemiology in parts of Western and Nyanza Provinces, Western Kenya, in cattle and humans. A cross-sectional survey was conducted: serum samples from 2049 humans and 955 cattle in 416 homesteads were analysed for C. burnetii antibodies. Questionnaires covering demographic, socio-economic and husbandry information were also administered. These data were linked to environmental datasets based on geographical locations (e.g., land cover). Correlation and spatial-cross correlation analyses were applied to assess the potential link between cattle and human seroprevalence. Multilevel regression analysis was used to assess the relationships between a range of socio-economic, demographic and environmental factors and sero-positivity in both humans and animals. The overall sero-prevalence of C. burnetii was 2.5% in humans and 10.5% in cattle, but we found no evidence of correlation between cattle and human seroprevalence either within households, or when incorporating spatial proximity to other households in the survey. Multilevel modelling indicated the importance of several factors for exposure to the organism. Cattle obtained from market (as opposed to those bred in their homestead) and those residing in areas with lower precipitation levels had the highest sero-prevalence. For humans, the youngest age group had the highest odds of seropositivity, variations were observed between ethnic groups, and frequent livestock contact (specifically grazing and dealing with abortion material) was also a risk factor. These results illustrate endemicity of C. burnetii in western Kenya, although prevalence is relatively low. The analysis indicates that while environmental factors may play a role in cattle exposure patterns, human exposure patterns are likely to be driven more strongly by livestock contacts. The implication of livestock markets in cattle exposure risks suggests these may be a suitable target for interventions. PMID:27716804

Spatial overlap links seemingly unconnected genotype-matched TB cases in rural Uganda

PubMed Central

Kato-Maeda, Midori; Emperador, Devy M.; Wandera, Bonnie; Mugagga, Olive; Crandall, John; Janes, Michael; Marquez, Carina; Kamya, Moses R.; Charlebois, Edwin D.; Havlir, Diane V.

2018-01-01

Introduction Incomplete understanding of TB transmission dynamics in high HIV prevalence settings remains an obstacle for prevention. Understanding where transmission occurs could provide a platform for case finding and interrupting transmission. Methods From 2012–2015, we sought to recruit all adults starting TB treatment in a Ugandan community. Participants underwent household (HH) contact investigation, and provided names of social contacts, sites of work, healthcare and socializing, and two sputum samples. Mycobacterium tuberculosis culture-positive specimens underwent 24-loci MIRU-VNTR and spoligotyping. We sought to identify epidemiologic links between genotype-matched cases by analyzing social networks and mapping locations where cases reported spending ≥12 hours over the one-month pre-treatment. Sites of spatial overlap (≤100m) between genotype-matched cases were considered potential transmission sites. We analyzed social networks stratified by genotype clustering status, with cases linked by shared locations, and compared network density by location type between clustered vs. non-clustered cases. Results Of 173 adults with TB, 131 (76%) were enrolled, 108 provided sputum, and 84/131 (78%) were MTB culture-positive: 52% (66/131) tested HIV-positive. Of 118 adult HH contacts, 105 (89%) were screened and 3 (2.5%) diagnosed with active TB. Overall, 33 TB cases (39%) belonged to 15 distinct MTB genotype-matched clusters. Within each cluster, no cases shared a HH or reported shared non-HH contacts. In 6/15 (40%) clusters, potential epidemiologic links were identified by spatial overlap at specific locations: 5/6 involved health care settings. Genotype-clustered TB social networks had significantly greater network density based on shared clinics (p<0.001) and decreased density based on shared marketplaces (p<0.001), compared to non-clustered networks. Conclusions In this molecular epidemiologic study, links between MTB genotype-matched cases were only identifiable via shared locations, healthcare locations in particular, rather than named contacts. This suggests most transmission is occurring between casual contacts, and emphasizes the need for improved infection control in healthcare settings in rural Africa. PMID:29438413

Design Issues in Small-Area Studies of Environment and Health

PubMed Central

Elliott, Paul; Savitz, David A.

2008-01-01

Background Small-area studies are part of the tradition of spatial epidemiology, which is concerned with the analysis of geographic patterns of disease with respect to environmental, demographic, socioeconomic, and other factors. We focus on etiologic research, where the aim is to make inferences about spatially varying environmental factors influencing the risk of disease. Methods and results We illustrate the approach through three exemplars: a) magnetic fields from overhead electric power lines and the occurrence of childhood leukemia, which illustrates the use of geographic information systems to focus on areas with high exposure prevalence; b) drinking-water disinfection by-products and reproductive outcomes, taking advantage of large between- to within-area variability in exposures from the water supply; and c) chronic exposure to air pollutants and cardiorespiratory health, where issues of socioeconomic confounding are particularly important. Discussion The small-area epidemiologic approach assigns exposure estimates to individuals based on location of residence or other geographic variables such as workplace or school. In this way, large populations can be studied, increasing the ability to investigate rare exposures or rare diseases. The approach is most effective when there is well-defined exposure variation across geographic units, limited within-area variation, and good control for potential confounding across areas. Conclusions In conjunction with traditional individual-based approaches, small-area studies offer a valuable addition to the armamentarium of the environmental epidemiologist. Modeling of exposure patterns coupled with collection of individual-level data on subsamples of the population should lead to improved risk estimates (i.e., less potential for bias) and help strengthen etiologic inference. PMID:18709174

Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

PubMed

Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

2015-04-01

Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for epidemiological surveillance.

Estimating ground-level PM(10) in a Chinese city by combining satellite data, meteorological information and a land use regression model.

PubMed

Meng, Xia; Fu, Qingyan; Ma, Zongwei; Chen, Li; Zou, Bin; Zhang, Yan; Xue, Wenbo; Wang, Jinnan; Wang, Dongfang; Kan, Haidong; Liu, Yang

2016-01-01

Development of exposure assessment model is the key component for epidemiological studies concerning air pollution, but the evidence from China is limited. Therefore, a linear mixed effects (LME) model was established in this study in a Chinese metropolis by incorporating aerosol optical depth (AOD), meteorological information and the land use regression (LUR) model to predict ground PM10 levels on high spatiotemporal resolution. The cross validation (CV) R(2) and the RMSE of the LME model were 0.87 and 19.2 μg/m(3), respectively. The relative prediction error (RPE) of daily and annual mean predicted PM10 concentrations were 19.1% and 7.5%, respectively. This study was the first attempt in China to estimate both short-term and long-term variation of PM10 levels with high spatial resolution in a Chinese metropolis with the LME model. The results suggested that the LME model could provide exposure assessment for short-term and long-term epidemiological studies in China. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spatial and Temporal Epidemiology of Nephropathia Epidemica Incidence and Hantavirus Seroprevalence in Rodent Hosts: Identification of the Main Environmental Factors in Europe.

PubMed

Monchatre-Leroy, E; Crespin, L; Boué, F; Marianneau, P; Calavas, D; Hénaux, V

2017-08-01

In Europe, the increasing number of nephropathia epidemica (NE) infections in humans, caused by Puumala virus carried by bank voles (Myodes glareolus), has triggered studies of environmental factors driving these infections. NE infections have been shown to occur in specific geographical areas characterized by environmental factors that influence the distribution and dynamics of host populations and virus persistence in the soil. Here, we review the influence of environmental conditions (including climate factors, food availability and habitat conditions) with respect to incidence in humans and seroprevalence in rodents, considering both direct and indirect transmission pathways. For each type of environmental factor, results and discrepancies between studies are presented and examined in the light of biological hypotheses. Overall, food availability and temperature appear to be the main drivers of host seroprevalence and NE incidence, but data quality and statistical approaches varied greatly among studies. We highlight the issues that now need to be addressed and suggest improvements for study design in regard to the current knowledge on hantavirus epidemiology. © 2016 Blackwell Verlag GmbH.

Geostatistics: a common link between medical geography, mathematical geology, and medical geology

PubMed Central

Goovaerts, P.

2015-01-01

Synopsis Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential ‘causes’ of disease, such as environmental exposure, diet and unhealthy behaviours, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentration across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level. PMID:25722963

Geostatistics: a common link between medical geography, mathematical geology, and medical geology.

PubMed

Goovaerts, P

2014-08-01

Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential 'causes' of disease, such as environmental exposure, diet and unhealthy behaviours, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentration across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level.

Air pollution exposure prediction approaches used in air pollution epidemiology studies.

PubMed

Özkaynak, Halûk; Baxter, Lisa K; Dionisio, Kathie L; Burke, Janet

2013-01-01

Epidemiological studies of the health effects of outdoor air pollution have traditionally relied upon surrogates of personal exposures, most commonly ambient concentration measurements from central-site monitors. However, this approach may introduce exposure prediction errors and misclassification of exposures for pollutants that are spatially heterogeneous, such as those associated with traffic emissions (e.g., carbon monoxide, elemental carbon, nitrogen oxides, and particulate matter). We review alternative air quality and human exposure metrics applied in recent air pollution health effect studies discussed during the International Society of Exposure Science 2011 conference in Baltimore, MD. Symposium presenters considered various alternative exposure metrics, including: central site or interpolated monitoring data, regional pollution levels predicted using the national scale Community Multiscale Air Quality model or from measurements combined with local-scale (AERMOD) air quality models, hybrid models that include satellite data, statistically blended modeling and measurement data, concentrations adjusted by home infiltration rates, and population-based human exposure model (Stochastic Human Exposure and Dose Simulation, and Air Pollutants Exposure models) predictions. These alternative exposure metrics were applied in epidemiological applications to health outcomes, including daily mortality and respiratory hospital admissions, daily hospital emergency department visits, daily myocardial infarctions, and daily adverse birth outcomes. This paper summarizes the research projects presented during the symposium, with full details of the work presented in individual papers in this journal issue.

Long-term particulate matter modeling for health effects studies in California - Part 1: Model performance on temporal and spatial variations

NASA Astrophysics Data System (ADS)

Hu, J.; Zhang, H.; Ying, Q.; Chen, S.-H.; Vandenberghe, F.; Kleeman, M. J.

2014-08-01

For the first time, a decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution and daily time resolution has been conducted in California to provide air quality data for health effects studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O3, PM2.5, EC, OC, nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM2.5, EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95%, 100%, 71%, 73%, and 92% of the simulated months, respectively. The base dataset provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated one day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to over-predict wind speed during stagnation events, leading to under-predictions of high PM concentrations, usually in winter months. The WRF model also generally under-predicts relative humidity, resulting in less particulate nitrate formation especially during winter months. These issues will be improved in future studies. All model results included in the current manuscript can be downloaded free of charge at http://faculty.engineering.ucdavis.edu/kleeman/.

Long-term particulate matter modeling for health effect studies in California - Part 1: Model performance on temporal and spatial variations

NASA Astrophysics Data System (ADS)

Hu, J.; Zhang, H.; Ying, Q.; Chen, S.-H.; Vandenberghe, F.; Kleeman, M. J.

2015-03-01

For the first time, a ~ decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution over populated regions and daily time resolution has been conducted for California to provide air quality data for health effect studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O3, PM2.5, elemental carbon (EC), organic carbon (OC), nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM2.5, EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95, 100, 71, 73, and 92% of the simulated months, respectively. The base data set provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated 1 day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to overpredict wind speed during stagnation events, leading to underpredictions of high PM concentrations, usually in winter months. The WRF model also generally underpredicts relative humidity, resulting in less particulate nitrate formation, especially during winter months. These limitations must be recognized when using data in health studies. All model results included in the current manuscript can be downloaded free of charge at http://faculty.engineering.ucdavis.edu/kleeman/ .

Spatio-temporal patterns of Campylobacter colonization in Danish broilers.

PubMed

Chowdhury, S; Themudo, G E; Sandberg, M; Ersbøll, A K

2013-05-01

Despite a number of risk-factor studies in different countries, the epidemiology of Campylobacter colonization in broilers, particularly spatial dependencies, is still not well understood. A series of analyses (visualization and exploratory) were therefore conducted in order to obtain a better understanding of the spatial and temporal distribution of Campylobacter in the Danish broiler population. In this study, we observed a non-random temporal occurrence of Campylobacter, with high prevalence during summer and low during winter. Significant spatio-temporal clusters were identified in the same areas in the summer months from 2007 to 2009. Range of influence between broiler farms were estimated at distances of 9.6 km and 13.5 km in different years. Identification of areas and time with greater risk indicates variable presence of risk factors with space and time. Implementation of safety measures on farms within high-risk clusters during summer could have an impact in reducing prevalence.

Exposure to atmospheric radon.

PubMed Central

Steck, D J; Field, R W; Lynch, C F

1999-01-01

We measured radon (222Rn) concentrations in Iowa and Minnesota and found that unusually high annual average radon concentrations occur outdoors in portions of central North America. In some areas, outdoor concentrations exceed the national average indoor radon concentration. The general spatial patterns of outdoor radon and indoor radon are similar to the spatial distribution of radon progeny in the soil. Outdoor radon exposure in this region can be a substantial fraction of an individual's total radon exposure and is highly variable across the population. Estimated lifetime effective dose equivalents for the women participants in a radon-related lung cancer study varied by a factor of two at the median dose, 8 mSv, and ranged up to 60 mSv (6 rem). Failure to include these doses can reduce the statistical power of epidemiologic studies that examine the lung cancer risk associated with residential radon exposure. Images Figure 1 Figure 2 Figure 3 Figure 4 PMID:9924007

[Important vector-borne infectious diseases among humans in Germany. Epidemiological aspects].

PubMed

Frank, C; Faber, M; Hellenbrand, W; Wilking, H; Stark, K

2014-05-01

Vector-borne infections pathogenic to humans play an important role in Germany. The relevant zoonotic pathogens are either endemic throughout Germany (e.g. Borrelia burgdorferi sensu latu) or only in specific regions, e.g. tick-borne encephalitis (TBE) virus and hantavirus. They cause a substantial burden of disease. Prevention and control largely rely on public advice and the application of personal protective measures (e.g. TBE virus vaccination and protection against vectors). High quality surveillance and targeted epidemiological studies are fundamental for the evaluation of temporal and spatial risks of infection and the effectiveness of preventive measures. Aside from endemic pathogens, vector-borne infections acquired abroad, mostly transmitted by mosquitoes, have to be systematically and intensively monitored as well, to assess the risk of infection for German residents traveling abroad and to adequately evaluate the risk of autochthonous transmission. Related issues, such as invasive species of mosquitoes in Germany and climate change, have to be taken into consideration. Such pathogens include West Nile, dengue and chikungunya viruses, as well as malaria parasites (Plasmodium species). The article presents an overview of the epidemiological situation of selected relevant vector-borne infections in Germany.

Networks in plant epidemiology: from genes to landscapes, countries, and continents.

PubMed

Moslonka-Lefebvre, Mathieu; Finley, Ann; Dorigatti, Ilaria; Dehnen-Schmutz, Katharina; Harwood, Tom; Jeger, Michael J; Xu, Xiangming; Holdenrieder, Ottmar; Pautasso, Marco

2011-04-01

There is increasing use of networks in ecology and epidemiology, but still relatively little application in phytopathology. Networks are sets of elements (nodes) connected in various ways by links (edges). Network analysis aims to understand system dynamics and outcomes in relation to network characteristics. Many existing natural, social, and technological networks have been shown to have small-world (local connectivity with short-cuts) and scale-free (presence of super-connected nodes) properties. In this review, we discuss how network concepts can be applied in plant pathology from the molecular to the landscape and global level. Wherever disease spread occurs not just because of passive/natural dispersion but also due to artificial movements, it makes sense to superimpose realistic models of the trade in plants on spatially explicit models of epidemic development. We provide an example of an emerging pathosystem (Phytophthora ramorum) where a theoretical network approach has proven particularly fruitful in analyzing the spread of disease in the UK plant trade. These studies can help in assessing the future threat posed by similar emerging pathogens. Networks have much potential in plant epidemiology and should become part of the standard curriculum.

A national assessment of the epidemiology of severe fever with thrombocytopenia syndrome, China.

PubMed

Liu, Kun; Zhou, Hang; Sun, Ruo-Xi; Yao, Hong-Wu; Li, Yu; Wang, Li-Ping; Mu, Di; Li, Xin-Lou; Yang, Yang; Gray, Gregory C; Cui, Ning; Yin, Wen-Wu; Fang, Li-Qun; Yu, Hong-Jie; Cao, Wu-Chun

2015-04-23

First discovered in rural areas of middle-eastern China in 2009, severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne zoonosis affecting hundreds of cases reported in China each year. Using the national surveillance data from 2010 to 2013, we conducted this retrospective epidemiological study and risk assessment of SFTS in China. We found that the incidence of SFTS and its epidemic areas are continuing to grow, but the case fatality rate (CFR) has steadily decreased. SFTS most commonly affected elderly farmers who acquired infection between May and July in middle-eastern China. However, other epidemiological characteristics such as incidence, sex ratio, CFR, and seasonality differ substantially across the affected provinces, which seem to be consistent with local agricultural activities and the seasonal abundance of ticks. Spatial scan statistics detected three hot spots of SFTS that accounted for 69.1% of SFTS cases in China. There was a strong association of SFTS incidence with temporal changes in the climate within the clusters. Multivariate modeling identified climate conditions, elevation, forest coverage, cattle density, and the presence of Haemaphysalis longicornis ticks as independent risk factors in the distribution of SFTS, based on which a predicted risk map of the disease was derived.

Quantifying Spatial Misclassification in Exposure to Noise Complaints Among Low-Income Housing Residents Across New York City Neighborhoods: A Global Positioning System (GPS) Study

PubMed Central

Duncan, Dustin T.; Tamura, Kosuke; Regan, Seann D.; Athens, Jessica; Elbel, Brian; Meline, Julie; Al-Ajlouni, Yazan A.; Chaix, Basile

2016-01-01

Purpose To examine if there was spatial misclassification in exposure to neighborhood noise complaints among a sample of low-income housing residents in New York City, comparing home-based spatial buffers and Global Positioning Systems (GPS) daily path buffers. Methods Data came from the community-based NYC Low-Income Housing, Neighborhoods and Health Study, where GPS tracking of the sample was conducted for a week (analytic n=102). We created a GPS daily path buffer (a buffering zone drawn around GPS tracks) of 200-meters and 400-meters. We also used home-based buffers of 200-meters and 400-meters. Using these “neighborhoods” (or exposure areas) we calculated neighborhood exposure to noisy events from 311 complaints data (analytic n=143,967). Friedman tests (to compare overall differences in neighborhood definitions) were applied. Results There were differences in neighborhood noise complaints according to the selected neighborhood definitions (p<0.05). For example, the mean neighborhood noise complaint count was 1196 per square kilometer for the 400-meter home-based and 812 per square kilometer for the 400-meter activity space buffer, illustrating how neighborhood definition influences the estimates of exposure to neighborhood noise complaints. Conclusions These analyses suggest that, whenever appropriate, GPS neighborhood definitions can be used in spatial epidemiology research in spatially mobile populations to understand people's lived experience. PMID:28063754

On the predictive ability of mechanistic models for the Haitian cholera epidemic.

PubMed

Mari, Lorenzo; Bertuzzo, Enrico; Finger, Flavio; Casagrandi, Renato; Gatto, Marino; Rinaldo, Andrea

2015-03-06

Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. We address the above issue in a formal model comparison framework and provide a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels and coupling mechanisms. Reference is made to records of the recent Haiti cholera epidemics. Our intensive computations and objective model comparisons show that spatially explicit models accounting for spatial connections have better explanatory power than spatially disconnected ones for short-to-intermediate calibration windows, while parsimonious, spatially disconnected models perform better with long training sets. On average, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Realist explanatory theory building method for social epidemiology: a protocol for a mixed method multilevel study of neighbourhood context and postnatal depression.

PubMed

Eastwood, John G; Jalaludin, Bin B; Kemp, Lynn A

2014-01-01

A recent criticism of social epidemiological studies, and multi-level studies in particular has been a paucity of theory. We will present here the protocol for a study that aims to build a theory of the social epidemiology of maternal depression. We use a critical realist approach which is trans-disciplinary, encompassing both quantitative and qualitative traditions, and that assumes both ontological and hierarchical stratification of reality. We describe a critical realist Explanatory Theory Building Method comprising of an: 1) emergent phase, 2) construction phase, and 3) confirmatory phase. A concurrent triangulated mixed method multilevel cross-sectional study design is described. The Emergent Phase uses: interviews, focus groups, exploratory data analysis, exploratory factor analysis, regression, and multilevel Bayesian spatial data analysis to detect and describe phenomena. Abductive and retroductive reasoning will be applied to: categorical principal component analysis, exploratory factor analysis, regression, coding of concepts and categories, constant comparative analysis, drawing of conceptual networks, and situational analysis to generate theoretical concepts. The Theory Construction Phase will include: 1) defining stratified levels; 2) analytic resolution; 3) abductive reasoning; 4) comparative analysis (triangulation); 5) retroduction; 6) postulate and proposition development; 7) comparison and assessment of theories; and 8) conceptual frameworks and model development. The strength of the critical realist methodology described is the extent to which this paradigm is able to support the epistemological, ontological, axiological, methodological and rhetorical positions of both quantitative and qualitative research in the field of social epidemiology. The extensive multilevel Bayesian studies, intensive qualitative studies, latent variable theory, abductive triangulation, and Inference to Best Explanation provide a strong foundation for Theory Construction. The study will contribute to defining the role that realism and mixed methods can play in explaining the social determinants and developmental origins of health and disease.

How much, how long, what, and where: air pollution exposure assessment for epidemiologic studies of respiratory disease.

PubMed

Brauer, Michael

2010-05-01

Epidemiology has played an important role in the understanding of air pollution as a risk factor for respiratory disease and in the evidence base for air quality standards. With the widespread availability of genetic information and increasingly sophisticated measurements of molecular markers of adverse effects, there is a need for more specific and precise assessment of exposure to maximize the potential information to be derived from epidemiologic studies. Here advances in air pollution exposure assessment and their applications to studies of respiratory disease are reviewed, with a focus on recent studies of traffic-related air pollution and asthma. Although continuous measurements of personal exposures for all study subjects for a complete study period might be considered the desired "gold standard" for exposure, this is rarely, if ever, achieved due to feasibility constraints. Given this, exposure is typically estimated using models. Recent applications of geospatial (e.g., land use regression) models to studies of respiratory disease have made possible new study designs focused on spatial variability in exposure within urban areas and have provided new insights into the potential role of traffic-related air pollution (TRAP) as a risk factor for the development of childhood asthma. Substantial uncertainty remains, however, regarding what agent(s) within TRAP might be responsible for the observed associations. Future research will require increasing the specificity of exposure assessment to identify the potential roles of individual air pollution components, to elucidate potential mechanisms, and to facilitate studies of mixtures and gene-air pollution interactions.

Analysis of the spatial and temporal distribution of malaria in an area of Northern Guatemala with seasonal malaria transmission.

PubMed

Malvisi, Lucio; Troisi, Catherine L; Selwyn, Beatrice J

2018-06-23

The risk of malaria infection displays spatial and temporal variability that is likely due to interaction between the physical environment and the human population. In this study, we performed a spatial analysis at three different time points, corresponding to three cross-sectional surveys conducted as part of an insecticide-treated bed nets efficacy study, to reveal patterns of malaria incidence distribution in an area of Northern Guatemala characterized by low malaria endemicity. A thorough understanding of the spatial and temporal patterns of malaria distribution is essential for targeted malaria control programs. Two methods, the local Moran's I and the Getis-Ord G * (d), were used for the analysis, providing two different statistical approaches and allowing for a comparison of results. A distance band of 3.5 km was considered to be the most appropriate distance for the analysis of data based on epidemiological and entomological factors. Incidence rates were higher at the first cross-sectional survey conducted prior to the intervention compared to the following two surveys. Clusters or hot spots of malaria incidence exhibited high spatial and temporal variations. Findings from the two statistics were similar, though the G * (d) detected cold spots using a higher distance band (5.5 km). The high spatial and temporal variability in the distribution of clusters of high malaria incidence seems to be consistent with an area of unstable malaria transmission. In such a context, a strong surveillance system and the use of spatial analysis may be crucial for targeted malaria control activities.

A cross-sectional epidemiological study of domestic animals related to human leptospirosis cases in Nicaragua.

PubMed

Flores, Byron J; Pérez-Sánchez, Tania; Fuertes, Héctor; Sheleby-Elías, Jessica; Múzquiz, José Luis; Jirón, William; Duttmann, Christianne; Halaihel, Nabil

2017-06-01

Leptospirosis is one of the most extended zoonosis worldwide and humans become infected most commonly through contact with the urine of carrier animals, either directly or via contaminated water or soil. The aim in this study was to analyse the epidemiological behaviour of Leptospira spp., from domestic animals around the sites of human leptospirosis cases in Nicaragua, from 2007 through 2013. We report the results of a cross-sectional epidemiological study with a non-probability sampling of blood (n=3050) and urine (n=299) from Domestic Animals (DA) around the sites of human leptospirosis cases in Nicaragua. We analysed data obtained through Microscopic Agglutination Test (MAT), in-vitro culture, real time PCR and sequencing of lfb1 locus. Frequencies of 30.31% (95% CI: 28.66-31.95) and 15.38% (95% CI: 11.12-19.64) were obtained from serological test and from in-vitro culture, respectively. Although similar frequencies from serology test (P≥0.05) were found in DA species, in-vitro culture frequencies were significantly higher from bovine, equine and sheep (P<0.05) in comparison with swine and canine species. Ten serogroups of pathogenic Leptospira spp. were encountered, with the highest presence of Icterohaemorrhagiae serogroup 34.65% (95% CI: 29.35-39.94). We identified 7 samples homologous to L. interrogans species Pyrogenes serovar and 3 samples as L. noguchii Louisiana or Panama serovars by analysis of lfb1 sequences. We were able to establish a temporal and spatial correlation from DA and cumulative incidence of human cases. Therefore an effective epidemiological surveillance should be implemented with a specific control program toward DA in order to reduce human leptospirosis incidence. Copyright © 2017 Elsevier B.V. All rights reserved.

RADSS: an integration of GIS, spatial statistics, and network service for regional data mining

NASA Astrophysics Data System (ADS)

Hu, Haitang; Bao, Shuming; Lin, Hui; Zhu, Qing

2005-10-01

Regional data mining, which aims at the discovery of knowledge about spatial patterns, clusters or association between regions, has widely applications nowadays in social science, such as sociology, economics, epidemiology, crime, and so on. Many applications in the regional or other social sciences are more concerned with the spatial relationship, rather than the precise geographical location. Based on the spatial continuity rule derived from Tobler's first law of geography: observations at two sites tend to be more similar to each other if the sites are close together than if far apart, spatial statistics, as an important means for spatial data mining, allow the users to extract the interesting and useful information like spatial pattern, spatial structure, spatial association, spatial outlier and spatial interaction, from the vast amount of spatial data or non-spatial data. Therefore, by integrating with the spatial statistical methods, the geographical information systems will become more powerful in gaining further insights into the nature of spatial structure of regional system, and help the researchers to be more careful when selecting appropriate models. However, the lack of such tools holds back the application of spatial data analysis techniques and development of new methods and models (e.g., spatio-temporal models). Herein, we make an attempt to develop such an integrated software and apply it into the complex system analysis for the Poyang Lake Basin. This paper presents a framework for integrating GIS, spatial statistics and network service in regional data mining, as well as their implementation. After discussing the spatial statistics methods involved in regional complex system analysis, we introduce RADSS (Regional Analysis and Decision Support System), our new regional data mining tool, by integrating GIS, spatial statistics and network service. RADSS includes the functions of spatial data visualization, exploratory spatial data analysis, and spatial statistics. The tool also includes some fundamental spatial and non-spatial database in regional population and environment, which can be updated by external database via CD or network. Utilizing this data mining and exploratory analytical tool, the users can easily and quickly analyse the huge mount of the interrelated regional data, and better understand the spatial patterns and trends of the regional development, so as to make a credible and scientific decision. Moreover, it can be used as an educational tool for spatial data analysis and environmental studies. In this paper, we also present a case study on Poyang Lake Basin as an application of the tool and spatial data mining in complex environmental studies. At last, several concluding remarks are discussed.

COVARIATE-ADAPTIVE CLUSTERING OF EXPOSURES FOR AIR POLLUTION EPIDEMIOLOGY COHORTS*

PubMed Central

Keller, Joshua P.; Drton, Mathias; Larson, Timothy; Kaufman, Joel D.; Sandler, Dale P.; Szpiro, Adam A.

2017-01-01

Cohort studies in air pollution epidemiology aim to establish associations between health outcomes and air pollution exposures. Statistical analysis of such associations is complicated by the multivariate nature of the pollutant exposure data as well as the spatial misalignment that arises from the fact that exposure data are collected at regulatory monitoring network locations distinct from cohort locations. We present a novel clustering approach for addressing this challenge. Specifically, we present a method that uses geographic covariate information to cluster multi-pollutant observations and predict cluster membership at cohort locations. Our predictive k-means procedure identifies centers using a mixture model and is followed by multi-class spatial prediction. In simulations, we demonstrate that predictive k-means can reduce misclassification error by over 50% compared to ordinary k-means, with minimal loss in cluster representativeness. The improved prediction accuracy results in large gains of 30% or more in power for detecting effect modification by cluster in a simulated health analysis. In an analysis of the NIEHS Sister Study cohort using predictive k-means, we find that the association between systolic blood pressure (SBP) and long-term fine particulate matter (PM2.5) exposure varies significantly between different clusters of PM2.5 component profiles. Our cluster-based analysis shows that for subjects assigned to a cluster located in the Midwestern U.S., a 10 μg/m3 difference in exposure is associated with 4.37 mmHg (95% CI, 2.38, 6.35) higher SBP. PMID:28572869

Changes in the epidemiology of visceral leishmaniasis in Brazil from 2001 to 2014.

PubMed

Reis, Lisiane Lappe Dos; Balieiro, Antônio Alcirley da Silva; Fonseca, Fernanda Rodrigues; Gonçalves, Maria Jacirema Ferreira

2017-01-01

Visceral leishmaniasis (VL) is a neglected disease, with territorial expansion and regional differences in Brazil that require explanation. This study aimed to describe changes in the epidemiology of VL in Brazil from 2001 to 2014. The incidence rates, sociodemographic and clinical data, and case evolution were subgrouped from 2001 to 2006 and from 2007 to 2014 and presented descriptively. Spatial distribution of disease incidence rates and changes in the spatial and temporal pattern were examined. In total, 47,859 VL cases were reported in Brazil between 2001 and 2014, with predominance in the Northeast macroregion (55%), though the incidence rate in this region declined between the two study periods. The State of Tocantins had the highest crude rate (26.2/100,000 inhabitants), which was responsible for VL increasing in the North macroregion. VL predominated in the urban zone (70%), in children under 4 years (34%); however, an increase in the incidence of VL in adults older than 40 years was identified, with 12.3% and 31% in the first and second period, respectively. The mapping of crude rates and autochthonous canine cases showed territorial expansion. The temporal distribution of VL was consistent in Brazil in general, with no pattern observed, but regional differences were found. The incidence of VL is increasing in Brazil. In addition to the State of Tocantins, which had the highest rate, new outbreaks of VL have occurred in the South macroregion of Brazil with small decreases identified in the incidence rate in the Northeast.

The role of live poultry movement and live bird market biosecurity in the epidemiology of influenza A (H7N9): A cross-sectional observational study in four eastern China provinces.

PubMed

Zhou, Xiaoyan; Li, Yin; Wang, Youming; Edwards, John; Guo, Fusheng; Clements, Archie C A; Huang, Baoxu; Magalhaes, Ricardo J Soares

2015-10-01

A new reassortant influenza A (H7N9) virus emerged early 2013 in eastern China. Exposure to H7N9 infected poultry at live bird markets (LBM) was implicated as the main risk factor for human infection. We aimed to identify the role of LBM biosecurity indicators and poultry movement in the affected areas. A cross-sectional survey was carried out in 24 LBMs at the beginning of H7N9 outbreak in all affected provinces. We used univariable analysis to identify the biosecurity factors associated with the H7N9 presence in LBMs and social network and spatial analysis to quantify the connectivity and geographic variation in the connectivity of poultry movements. Chickens were the predominant poultry species traded by affected LBMs. The presence of H7N9 in LBMs was significantly associated with the type of LBM and with LBMs that sold chicken to other markets. The chicken movements were significantly spatially clustered and was highest in counties from Jiangsu and Anhui provinces. LBM biosecurity and chicken movement played an important role in the emergence of H7N9. This study identified highly connected areas in eastern China which continue to report human infections highlighting candidate areas for more detailed epidemiological investigations. Copyright © 2015 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

Underestimating the effects of spatial heterogeneity due to individual movement and spatial scale: infectious disease as an example

USGS Publications Warehouse

Cross, Paul C.; Caillaud, Damien; Heisey, Dennis M.

2013-01-01

Many ecological and epidemiological studies occur in systems with mobile individuals and heterogeneous landscapes. Using a simulation model, we show that the accuracy of inferring an underlying biological process from observational data depends on movement and spatial scale of the analysis. As an example, we focused on estimating the relationship between host density and pathogen transmission. Observational data can result in highly biased inference about the underlying process when individuals move among sampling areas. Even without sampling error, the effect of host density on disease transmission is underestimated by approximately 50 % when one in ten hosts move among sampling areas per lifetime. Aggregating data across larger regions causes minimal bias when host movement is low, and results in less biased inference when movement rates are high. However, increasing data aggregation reduces the observed spatial variation, which would lead to the misperception that a spatially targeted control effort may not be very effective. In addition, averaging over the local heterogeneity will result in underestimating the importance of spatial covariates. Minimizing the bias due to movement is not just about choosing the best spatial scale for analysis, but also about reducing the error associated with using the sampling location as a proxy for an individual’s spatial history. This error associated with the exposure covariate can be reduced by choosing sampling regions with less movement, including longitudinal information of individuals’ movements, or reducing the window of exposure by using repeated sampling or younger individuals.

[Exposure to nanoparticle-rich diesel exhaust affects hippocampal functions in mice].

PubMed

Win-Shwe, Tin Tin; Fujitani, Yuji; Hirano, Seishiro; Fujimaki, Hidekazu

2011-09-01

Epidemiological studies have indicated associations between day-to-day particulate air pollution and increased risks of various adverse health outcomes. Although an association between exposure to diesel exhaust particles (DEPs) and the development of pulmonary inflammation has been reported, there are limited reports on the neurotoxic effects of DEPs, particularly those of nanoparticle-rich diesel exhaust (NRDE). In this minireview, we highlighted the effects of NRDE which was generated in the National Institute for Environmental Studies, on hippocampus-dependent spatial learning ability and the expression of memory-function-related genes, neurotrophins, and proinflammatory cytokines in a mouse model.

Perspectives on the role of mobility, behavior, and time scales in the spread of diseases.

PubMed

Castillo-Chavez, Carlos; Bichara, Derdei; Morin, Benjamin R

2016-12-20

The dynamics, control, and evolution of communicable and vector-borne diseases are intimately connected to the joint dynamics of epidemiological, behavioral, and mobility processes that operate across multiple spatial, temporal, and organizational scales. The identification of a theoretical explanatory framework that accounts for the pattern regularity exhibited by a large number of host-parasite systems, including those sustained by host-vector epidemiological dynamics, is but one of the challenges facing the coevolving fields of computational, evolutionary, and theoretical epidemiology. Host-parasite epidemiological patterns, including epidemic outbreaks and endemic recurrent dynamics, are characteristic to well-identified regions of the world; the result of processes and constraints such as strain competition, host and vector mobility, and population structure operating over multiple scales in response to recurrent disturbances (like El Niño) and climatological and environmental perturbations over thousands of years. It is therefore important to identify and quantify the processes responsible for observed epidemiological macroscopic patterns: the result of individual interactions in changing social and ecological landscapes. In this perspective, we touch on some of the issues calling for the identification of an encompassing theoretical explanatory framework by identifying some of the limitations of existing theory, in the context of particular epidemiological systems. Fostering the reenergizing of research that aims at disentangling the role of epidemiological and socioeconomic forces on disease dynamics, better understood as complex adaptive systems, is a key aim of this perspective.

Characterizing Air Pollution Exposure Misclassification Errors Using Detailed Cell Phone Location Data

NASA Astrophysics Data System (ADS)

Yu, H.; Russell, A. G.; Mulholland, J. A.

2017-12-01

In air pollution epidemiologic studies with spatially resolved air pollution data, exposures are often estimated using the home locations of individual subjects. Due primarily to lack of data or logistic difficulties, the spatiotemporal mobility of subjects are mostly neglected, which are expected to result in exposure misclassification errors. In this study, we applied detailed cell phone location data to characterize potential exposure misclassification errors associated with home-based exposure estimation of air pollution. The cell phone data sample consists of 9,886 unique simcard IDs collected on one mid-week day in October, 2013 from Shenzhen, China. The Community Multi-scale Air Quality model was used to simulate hourly ambient concentrations of six chosen pollutants at 3 km spatial resolution, which were then fused with observational data to correct for potential modeling biases and errors. Air pollution exposure for each simcard ID was estimated by matching hourly pollutant concentrations with detailed location data for corresponding IDs. Finally, the results were compared with exposure estimates obtained using the home location method to assess potential exposure misclassification errors. Our results show that the home-based method is likely to have substantial exposure misclassification errors, over-estimating exposures for subjects with higher exposure levels and under-estimating exposures for those with lower exposure levels. This has the potential to lead to a bias-to-the-null in the health effect estimates. Our findings suggest that the use of cell phone data has the potential for improving the characterization of exposure and exposure misclassification in air pollution epidemiology studies.

Identifying PM2.5 and PM0.1 sources for epidemiological studies in California.

PubMed

Hu, Jianlin; Zhang, Hongliang; Chen, Shuhua; Ying, Qi; Wiedinmyer, Christine; Vandenberghe, Francois; Kleeman, Michael J

2014-05-06

The University of California-Davis_Primary (UCD_P) model was applied to simultaneously track ∼ 900 source contributions to primary particulate matter (PM) in California for seven continuous years (January 1st, 2000 to December 31st, 2006). Predicted source contributions to primary PM2.5 mass, PM1.8 elemental carbon (EC), PM1.8 organic carbon (OC), PM0.1 EC, and PM0.1 OC were in general agreement with the results from previous source apportionment studies using receptor-based techniques. All sources were further subjected to a constraint check based on model performance for PM trace elemental composition. A total of 151 PM2.5 sources and 71 PM0.1 sources contained PM elements that were predicted at concentrations in general agreement with measured values at nearby monitoring sites. Significant spatial heterogeneity was predicted among the 151 PM2.5 and 71 PM0.1 source concentrations, and significantly different seasonal profiles were predicted for PM2.5 and PM0.1 in central California vs southern California. Population-weighted concentrations of PM emitted from various sources calculated using the UCD_P model spatial information differed from the central monitor estimates by up to 77% for primary PM2.5 mass and 148% for PM2.5 EC because the central monitor concentration is not representative of exposure for nearby population. The results from the UCD_P model provide enhanced source apportionment information for epidemiological studies to examine the relationship between health effects and concentrations of primary PM from individual sources.

Space-time epidemiology and effect of meteorological parameters on influenza-like illness in Phitsanulok, a northern province in Thailand.

PubMed

Nimbalkar, Prakash Madhav; Tripathi, Nitin Kumar

2016-11-21

Influenza-like illness (ILI) is an acute respiratory disease that remains a public health concern for its ability to circulate globally affecting any age group and gender causing serious illness with mortality risk. Comprehensive assessment of the spatio-temporal dynamics of ILI is a prerequisite for effective risk assessment and application of control measures. Though meteorological parameters, such as rainfall, average relative humidity and temperature, influence ILI and represent crucial information for control of this disease, the relation between the disease and these variables is not clearly understood in tropical climates. The aim of this study was to analyse the epidemiology of ILI cases using integrated methods (space-time analysis, spatial autocorrelation and other correlation statistics). After 2009s H1N1 influenza pandemic, Phitsanulok Province in northern Thailand was strongly affected by ILI for many years. This study is based on ILI cases in villages in this province from 2005 to 2012. We used highly precise weekly incidence records covering eight years, which allowed accurate estimation of the ILI outbreak. Comprehensive methodology was developed to analyse the global and local patterns of the spread of the disease. Significant space-time clusters were detected over the study region during eight different periods. ILI cases showed seasonal clustered patterns with a peak in 2010 (P>0.05-9.999 iterations). Local indicators of spatial association identified hotspots for each year. Statistically, the weather pattern showed a clear influence on ILI cases and it strongly correlated with humidity at a lag of 1 month, while temperature had a weaker correlation.

Spatial And Temporal Analysis Of Multiple Whitefly Transmitted Virus Infections In Watermelon

USDA-ARS?s Scientific Manuscript database

Squash vein yellowing virus (SqVYV), Cucurbit leaf crumple virus (CuLCrV), and Cucurbit yellow stunting disorder virus (CYSDV) are three whitefly-transmitted viruses recently introduced to Florida that induce visually distinguishable symptoms on watermelon. The epidemiology of these three viruses wa...

Source characterization and exposure modeling of gas-phase polycyclic aromatic hydrocarbon (PAH) concentrations in Southern California

NASA Astrophysics Data System (ADS)

Masri, Shahir; Li, Lianfa; Dang, Andy; Chung, Judith H.; Chen, Jiu-Chiuan; Fan, Zhi-Hua (Tina); Wu, Jun

2018-03-01

Airborne exposures to polycyclic aromatic hydrocarbons (PAHs) are associated with adverse health outcomes. Because personal air measurements of PAHs are labor intensive and costly, spatial PAH exposure models are useful for epidemiological studies. However, few studies provide adequate spatial coverage to reflect intra-urban variability of ambient PAHs. In this study, we collected 39-40 weekly gas-phase PAH samples in southern California twice in summer and twice in winter, 2009, in order to characterize PAH source contributions and develop spatial models that can estimate gas-phase PAH concentrations at a high resolution. A spatial mixed regression model was constructed, including such variables as roadway, traffic, land-use, vegetation index, commercial cooking facilities, meteorology, and population density. Cross validation of the model resulted in an R2 of 0.66 for summer and 0.77 for winter. Results showed higher total PAH concentrations in winter. Pyrogenic sources, such as fossil fuels and diesel exhaust, were the most dominant contributors to total PAHs. PAH sources varied by season, with a higher fossil fuel and wood burning contribution in winter. Spatial autocorrelation accounted for a substantial amount of the variance in total PAH concentrations for both winter (56%) and summer (19%). In summer, other key variables explaining the variance included meteorological factors (9%), population density (15%), and roadway length (21%). In winter, the variance was also explained by traffic density (16%). In this study, source characterization confirmed the dominance of traffic and other fossil fuel sources to total measured gas-phase PAH concentrations while a spatial exposure model identified key predictors of PAH concentrations. Gas-phase PAH source characterization and exposure estimation is of high utility to epidemiologist and policy makers interested in understanding the health impacts of gas-phase PAHs and strategies to reduce emissions.

Source Characterization and Exposure Modeling of Gas-Phase Polycyclic Aromatic Hydrocarbon (PAH) Concentrations in Southern California.

PubMed

Masri, Shahir; Li, Lianfa; Dang, Andy; Chung, Judith H; Chen, Jiu-Chiuan; Fan, Zhi-Hua Tina; Wu, Jun

2018-03-01

Airborne exposures to polycyclic aromatic hydrocarbons (PAHs) are associated with adverse health outcomes. Because personal air measurements of PAHs are labor intensive and costly, spatial PAH exposure models are useful for epidemiological studies. However, few studies provide adequate spatial coverage to reflect intra-urban variability of ambient PAHs. In this study, we collected 39-40 weekly gas-phase PAH samples in southern California twice in summer and twice in winter, 2009, in order to characterize PAH source contributions and develop spatial models that can estimate gas-phase PAH concentrations at a high resolution. A spatial mixed regression model was constructed, including such variables as roadway, traffic, land-use, vegetation index, commercial cooking facilities, meteorology, and population density. Cross validation of the model resulted in an R 2 of 0.66 for summer and 0.77 for winter. Results showed higher total PAH concentrations in winter. Pyrogenic sources, such as fossil fuels and diesel exhaust, were the most dominant contributors to total PAHs. PAH sources varied by season, with a higher fossil fuel and wood burning contribution in winter. Spatial autocorrelation accounted for a substantial amount of the variance in total PAH concentrations for both winter (56%) and summer (19%). In summer, other key variables explaining the variance included meteorological factors (9%), population density (15%), and roadway length (21%). In winter, the variance was also explained by traffic density (16%). In this study, source characterization confirmed the dominance of traffic and other fossil fuel sources to total measured gas-phase PAH concentrations while a spatial exposure model identified key predictors of PAH concentrations. Gas-phase PAH source characterization and exposure estimation is of high utility to epidemiologist and policy makers interested in understanding the health impacts of gas-phase PAHs and strategies to reduce emissions.

Early Detection for Dengue Using Local Indicator of Spatial Association (LISA) Analysis

PubMed Central

Parra-Amaya, Mayra Elizabeth; Puerta-Yepes, María Eugenia; Lizarralde-Bejarano, Diana Paola; Arboleda-Sánchez, Sair

2016-01-01

Dengue is a viral disease caused by a flavivirus that is transmitted by mosquitoes of the genus Aedes. There is currently no specific treatment or commercial vaccine for its control and prevention; therefore, mosquito population control is the only alternative for preventing the occurrence of dengue. For this reason, entomological surveillance is recommended by World Health Organization (WHO) to measure dengue risk in endemic areas; however, several works have shown that the current methodology (aedic indices) is not sufficient for predicting dengue. In this work, we modified indices proposed for epidemic periods. The raw value of the epidemiological wave could be useful for detecting risk in epidemic periods; however, risk can only be detected if analyses incorporate the maximum epidemiological wave. Risk classification was performed according to Local Indicators of Spatial Association (LISA) methodology. The modified indices were analyzed using several hypothetical scenarios to evaluate their sensitivity. We found that modified indices could detect spatial and differential risks in epidemic and endemic years, which makes them a useful tool for the early detection of a dengue outbreak. In conclusion, the modified indices could predict risk at the spatio-temporal level in endemic years and could be incorporated in surveillance activities in endemic places. PMID:28933396

Spatial Distribution and Trends of Waterborne Diseases in Tashkent Province

PubMed Central

Subramanian, Veluswami Saravanan; Cho, Min Jung; Tan, Siwei Zoe; Fayzieva, Dilorom; Sebaly, Christian

2017-01-01

Introduction: The cumulative effect of limited investment in public water systems, inadequate public health infrastructure, and gaps in infectious disease prevention increased the incidence of waterborne diseases in Uzbekistan. The objectives of this study were: (1) to spatially analyze the distribution of the diseases in Tashkent Province, (2) to identify the intensity of spatial trends in the province, (3) to identify urban-rural characteristics of the disease distribution, and (4) to identify the differences in disease incidence between pediatric and adult populations of the province. Methods: Data on four major waterborne diseases and socio-demographics factors were collected in Tashkent Province from 2011 to 2014. Descriptive epidemiological methods and spatial-temporal methods were used to investigate the distribution and trends, and to identify waterborne diseases hotspots and vulnerable population groups in the province. Results: Hepatitis A and enterobiasis had a high incidence in most of Tashkent Province, with higher incidences in the eastern and western districts. Residents of rural areas, including children, were found to be more vulnerable to the waterborne diseases compared to other populations living in the province. Conclusions: This pilot study calls for more scientific investigations of waterborne diseases and their effect on public health in the region, which could facilitate targeted public health interventions in vulnerable regions of Uzbekistan. PMID:29138738

Spatial Distribution and Trends of Waterborne Diseases in Tashkent Province.

PubMed

Subramanian, Veluswami Saravanan; Cho, Min Jung; Tan, Siwei Zoe; Fayzieva, Dilorom; Sebaly, Christian

2017-01-01

The cumulative effect of limited investment in public water systems, inadequate public health infrastructure, and gaps in infectious disease prevention increased the incidence of waterborne diseases in Uzbekistan. The objectives of this study were: (1) to spatially analyze the distribution of the diseases in Tashkent Province, (2) to identify the intensity of spatial trends in the province, (3) to identify urban-rural characteristics of the disease distribution, and (4) to identify the differences in disease incidence between pediatric and adult populations of the province. Data on four major waterborne diseases and socio-demographics factors were collected in Tashkent Province from 2011 to 2014. Descriptive epidemiological methods and spatial-temporal methods were used to investigate the distribution and trends, and to identify waterborne diseases hotspots and vulnerable population groups in the province. Hepatitis A and enterobiasis had a high incidence in most of Tashkent Province, with higher incidences in the eastern and western districts. Residents of rural areas, including children, were found to be more vulnerable to the waterborne diseases compared to other populations living in the province. This pilot study calls for more scientific investigations of waterborne diseases and their effect on public health in the region, which could facilitate targeted public health interventions in vulnerable regions of Uzbekistan.

Remote sensing of desert dust aerosols over the Sahel : potential use for health impact studies

NASA Astrophysics Data System (ADS)

Deroubaix, A. D.; Martiny, N. M.; Chiapello, I. C.; Marticorena, B. M.

2012-04-01

Since the end of the 70's, remote sensing monitors the desert dust aerosols due to their absorption and scattering properties and allows to make long time series which are necessary for air quality or health impact studies. In the Sahel, a huge health problem is the Meningitis Meningococcal (MM) epidemics that occur during the dry season : the dust has been suspected to be crucial to understand their onsets and dynamics. The Aerosol absorption Index (AI) is a semi-quantitative index derived from TOMS and OMI observations in the UV available at a spatial resolution of 1° (1979-2005) and 0.25° (2005-today) respectively. The comparison of the OMI-AI and AERONET Aerosol Optical thickness (AOT) shows a good agreement at a daily time-step (correlation ~0.7). The comparison of the OMI-AI with the Particle Matter (PM) measurement of the Sahelian Dust Transect is lower (~0.4) at a daily time-step but it increases at a weekly time-step (~0.6). The OMI-AI reproduces the dust seasonal cycle over the Sahel and we conclude that the OMI-AI product at a 0.25° spatial resolution is suitable for health impact studies, especially at a weekly epidemiological time-step. Despite the AI is sensitive to the aerosol altitude, it provides a daily spatial information on dust. A preliminary investigation analysis of the link between weekly OMI AI and weekly WHO epidemiological data sets is presented in Mali and Niger, showing a good agreement between the AI and the onset of the MM epidemics with a constant lag (between 1 and 2 week). The next of this study is to analyse a deeper AI time series constituted by TOMS and OMI data sets. Based on the weekly ratios PM/AI at 2 stations of the Sahelian Dust Transect, a spatialized proxy for PM from the AI has been developed. The AI as a proxy for PM and other climate variables such as Temperature (T°), Relative Humidity (RH%) and the wind (intensity and direction) could then be used to analyze the link between those variables and the MM epidemics in the most concerned countries in Western Africa, which would be an important step towards a forecasting tool for the epidemics risks in Western Africa.

Classification and prediction of river network ephemerality and its relevance for waterborne disease epidemiology

NASA Astrophysics Data System (ADS)

Perez-Saez, Javier; Mande, Theophile; Larsen, Joshua; Ceperley, Natalie; Rinaldo, Andrea

2017-12-01

The transmission of waterborne diseases hinges on the interactions between hydrology and ecology of hosts, vectors and parasites, with the long-term absence of water constituting a strict lower bound. However, the link between spatio-temporal patterns of hydrological ephemerality and waterborne disease transmission is poorly understood and difficult to account for. The use of limited biophysical and hydroclimate information from otherwise data scarce regions is therefore needed to characterize, classify, and predict river network ephemerality in a spatially explicit framework. Here, we develop a novel large-scale ephemerality classification and prediction methodology based on monthly discharge data, water and energy availability, and remote-sensing measures of vegetation, that is relevant to epidemiology, and maintains a mechanistic link to catchment hydrologic processes. Specifically, with reference to the context of Burkina Faso in sub-Saharan Africa, we extract a relevant set of catchment covariates that include the aridity index, annual runoff estimation using the Budyko framework, and hysteretical relations between precipitation and vegetation. Five ephemerality classes, from permanent to strongly ephemeral, are defined from the duration of 0-flow periods that also accounts for the sensitivity of river discharge to the long-lasting drought of the 70's-80's in West Africa. Using such classes, a gradient-boosted tree-based prediction yielded three distinct geographic regions of ephemerality. Importantly, we observe a strong epidemiological association between our predictions of hydrologic ephemerality and the known spatial patterns of schistosomiasis, an endemic parasitic waterborne disease in which infection occurs with human-water contact, and requires aquatic snails as an intermediate host. The general nature of our approach and its relevance for predicting the hydrologic controls on schistosomiasis occurrence provides a pathway for the explicit inclusion of hydrologic drivers within epidemiological models of waterborne disease transmission.

Measurement error in epidemiologic studies of air pollution based on land-use regression models.

PubMed

Basagaña, Xavier; Aguilera, Inmaculada; Rivera, Marcela; Agis, David; Foraster, Maria; Marrugat, Jaume; Elosua, Roberto; Künzli, Nino

2013-10-15

Land-use regression (LUR) models are increasingly used to estimate air pollution exposure in epidemiologic studies. These models use air pollution measurements taken at a small set of locations and modeling based on geographical covariates for which data are available at all study participant locations. The process of LUR model development commonly includes a variable selection procedure. When LUR model predictions are used as explanatory variables in a model for a health outcome, measurement error can lead to bias of the regression coefficients and to inflation of their variance. In previous studies dealing with spatial predictions of air pollution, bias was shown to be small while most of the effect of measurement error was on the variance. In this study, we show that in realistic cases where LUR models are applied to health data, bias in health-effect estimates can be substantial. This bias depends on the number of air pollution measurement sites, the number of available predictors for model selection, and the amount of explainable variability in the true exposure. These results should be taken into account when interpreting health effects from studies that used LUR models.

Assessing patterns of spatial behavior in health studies: their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study).

PubMed

Perchoux, Camille; Kestens, Yan; Thomas, Frédérique; Van Hulst, Andraea; Thierry, Benoit; Chaix, Basile

2014-10-01

Prior epidemiological studies have mainly focused on local residential neighborhoods to assess environmental exposures. However, individual spatial behavior may modify residential neighborhood influences, with weaker health effects expected for mobile populations. By examining individual patterns of daily mobility and associated socio-demographic profiles and transportation modes, this article seeks to develop innovative methods to account for daily mobility in health studies. We used data from the RECORD Cohort Study collected in 2011-2012 in the Paris metropolitan area, France. A sample of 2062 individuals was investigated. Participants' perceived residential neighborhood boundaries and regular activity locations were geocoded using the VERITAS application. Twenty-four indicators were created to qualify individual space-time patterns, using spatial analysis methods and a geographic information system. Three domains of indicators were considered: lifestyle indicators, indicators related to the geometry of the activity space, and indicators related to the importance of the residential neighborhood in the overall activity space. Principal component analysis was used to identify main dimensions of spatial behavior. Multilevel linear regression was used to determine which individual characteristics were associated with each spatial behavior dimension. The factor analysis generated five dimensions of spatial behavior: importance of the residential neighborhood in the activity space, volume of activities, and size, eccentricity, and specialization of the activity space. Age, socioeconomic status, and location of the household in the region were the main predictors of daily mobility patterns. Activity spaces of small sizes centered on the residential neighborhood and implying a large volume of activities were associated with walking and/or biking as a transportation mode. Examination of patterns of spatial behavior by individual socio-demographic characteristics and in relation to transportation modes is useful to identify populations with specific mobility/accessibility needs and has implications for investigating transportation-related physical activity and assessing environmental exposures and their effects on health. Copyright © 2014 Elsevier Ltd. All rights reserved.

Emerging vector-borne zoonoses: eco-epidemiology and public health implications in India.

PubMed

Dhiman, Ramesh C

2014-01-01

The diseases originating from animals or associated with man and animals are remerging and have resulted in considerable morbidity and mortality. The present review highlights the re-emergence of emerging mainly zoonotic diseases like chikungunya, scrub typhus, and extension of spatial distribution of cutaneous leishmaniasis from western Rajasthan to Himachal Pradesh, Kerala, and Haryana states; West Nile virus to Assam, and non-endemic areas of Japanese encephalitis (JE) like Maharashtra and JE to Delhi; Crimean-Congo hemorrhagic fever making inroads in Ahmedabad; and reporting fifth parasite of human malaria with possibility of zoonosis have been highlighted, which necessitates further studies for prevention and control. Emphasis has been given on understanding the ecology of reservoir hosts of pathogen, micro niche of vector species, climatic, socioeconomic risk factors, etc. Development of facilities for diagnosis of virus from insects, reservoirs, and human beings (like BSL4, which has been established in NIV, Pune), awareness about symptoms of new emerging viral and other zoonotic diseases, differential diagnosis, risk factors (climatic, ecological, and socioeconomic) and mapping of disease-specific vulnerable areas, and mathematical modeling for projecting epidemiological scenario is needed for preparedness of public health institutes. It is high time to understand the ecological link of zoonotic or anthroponotic diseases for updated risk maps and epidemiological knowledge for effective preventive and control measures. The public health stakeholders in India as well as in Southeast Asia should emphasize on understanding the eco-epidemiology of the discussed zoonotic diseases for taking preventive actions.

Linking remote sensing, land cover and disease.

PubMed

Curran, P J; Atkinson, P M; Foody, G M; Milton, E J

2000-01-01

Land cover is a critical variable in epidemiology and can be characterized remotely. A framework is used to describe both the links between land cover and radiation recorded in a remotely sensed image, and the links between land cover and the disease carried by vectors. The framework is then used to explore the issues involved when moving from remotely sensed imagery to land cover and then to vector density/disease risk. This exploration highlights the role of land cover; the need to develop a sound knowledge of each link in the predictive sequence; the problematic mismatch between the spatial units of the remotely sensed and epidemiological data and the challenges and opportunities posed by adding a temporal mismatch between the remotely sensed and epidemiological data. The paper concludes with a call for both greater understanding of the physical components of the proposed framework and the utilization of optimized statistical tools as prerequisites to progress in this field.

Land use regression models for the oxidative potential of fine particles (PM2.5) in five European areas.

PubMed

Gulliver, John; Morley, David; Dunster, Chrissi; McCrea, Adrienne; van Nunen, Erik; Tsai, Ming-Yi; Probst-Hensch, Nicoltae; Eeftens, Marloes; Imboden, Medea; Ducret-Stich, Regina; Naccarati, Alessio; Galassi, Claudia; Ranzi, Andrea; Nieuwenhuijsen, Mark; Curto, Ariadna; Donaire-Gonzalez, David; Cirach, Marta; Vermeulen, Roel; Vineis, Paolo; Hoek, Gerard; Kelly, Frank J

2018-01-01

Oxidative potential (OP) of particulate matter (PM) is proposed as a biologically-relevant exposure metric for studies of air pollution and health. We aimed to evaluate the spatial variability of the OP of measured PM 2.5 using ascorbate (AA) and (reduced) glutathione (GSH), and develop land use regression (LUR) models to explain this spatial variability. We estimated annual average values (m -3 ) of OP AA and OP GSH for five areas (Basel, CH; Catalonia, ES; London-Oxford, UK (no OP GSH ); the Netherlands; and Turin, IT) using PM 2.5 filters. OP AA and OP GSH LUR models were developed using all monitoring sites, separately for each area and combined-areas. The same variables were then used in repeated sub-sampling of monitoring sites to test sensitivity of variable selection; new variables were offered where variables were excluded (p > .1). On average, measurements of OP AA and OP GSH were moderately correlated (maximum Pearson's maximum Pearson's R = = .7) with PM 2.5 and other metrics (PM 2.5 absorbance, NO 2 , Cu, Fe). HOV (hold-out validation) R 2 for OP AA models was .21, .58, .45, .53, and .13 for Basel, Catalonia, London-Oxford, the Netherlands and Turin respectively. For OP GSH , the only model achieving at least moderate performance was for the Netherlands (R 2 = .31). Combined models for OP AA and OP GSH were largely explained by study area with weak local predictors of intra-area contrasts; we therefore do not endorse them for use in epidemiologic studies. Given the moderate correlation of OP AA with other pollutants, the three reasonably performing LUR models for OP AA could be used independently of other pollutant metrics in epidemiological studies. Copyright © 2017 Elsevier Inc. All rights reserved.

Reducing the Use of Pesticides with Site-Specific Application: The Chemical Control of Rhizoctonia solani as a Case of Study for the Management of Soil-Borne Diseases

PubMed Central

Le Cointe, Ronan; Simon, Thomas E.; Delarue, Patrick; Hervé, Maxime; Leclerc, Melen; Poggi, Sylvain

2016-01-01

Reducing our reliance on pesticides is an essential step towards the sustainability of agricultural production. One approach involves the rational use of pesticides combined with innovative crop management. Most control strategies currently focus on the temporal aspect of epidemics, e.g. determining the optimal date for spraying, regardless of the spatial mechanics and ecology of disease spread. Designing innovative pest management strategies incorporating the spatial aspect of epidemics involves thorough knowledge on how disease control affects the life-history traits of the pathogen. In this study, using Rhizoctonia solani/Raphanus sativus as an example of a soil-borne pathosystem, we investigated the effects of a chemical control currently used by growers, Monceren® L, on key epidemiological components (saprotrophic spread and infectivity). We tested the potential “shield effect” of Monceren® L on pathogenic spread in a site-specific application context, i.e. the efficiency of this chemical to contain the spread of the fungus from an infected host when application is spatially localized, in our case, a strip placed between the infected host and a recipient bait. Our results showed that Monceren® L mainly inhibits the saprotrophic spread of the fungus in soil and may prevent the fungus from reaching its host plant. However, perhaps surprisingly we did not detect any significant effect of the fungicide on the pathogen infectivity. Finally, highly localized application of the fungicide—a narrow strip of soil (12.5 mm wide) sprayed with Monceren® L—significantly decreased local transmission of the pathogen, suggesting lowered risk of occurrence of invasive epidemics. Our results highlight that detailed knowledge on epidemiological processes could contribute to the design of innovative management strategies based on precision agriculture tools to improve the efficacy of disease control and reduce pesticide use. PMID:27668731

Antidepressant sales and regional variations of suicide mortality in Germany.

PubMed

Blüml, Victor; Helbich, Marco; Mayr, Michael; Turnwald, Roland; Vyssoki, Benjamin; Lewitzka, Ute; Hartung, Sebastian; Plener, Paul L; Fegert, Jörg M; Kapusta, Nestor D

2017-04-01

Suicides account for over one million deaths per year worldwide with depression among the most important risk factors. Epidemiological research into the relationship between antidepressant utilization and suicide mortality has shown heterogeneous and contradictory results. Different methodological approaches and limitations could at least partially explain varying results. This is the first study assessing the association of suicide mortality and antidepressant sales across Germany using complex statistical approaches in order to control for possible confounding factors including spatial dependency of data. German suicide counts were analyzed on a district level (n = 402) utilizing ecological Poisson regressions within a hierarchical Bayesian framework. Due to significant spatial effects between adjacent districts spatial models were calculated in addition to a baseline non-spatial model. Models were adjusted for several confounders including socioeconomic variables, quality of psychosocial care, and depression prevalence. Separate analyses were performed for Eastern and Western Germany and for different classes of antidepressants (SSRIs and TCAs). Overall antidepressant sales were significantly negatively associated with suicide mortality in the non-spatial baseline model, while after adjusting for spatially structured and unstructured effects the association turned out to be insignificant. In sub-analyses, analogue results were found for SSRIs and TCAs separately. Suicide risk shows a distinct heterogeneous pattern with a pronounced relative risk in Southeast Germany. In conclusion, the results reflect the heterogeneous findings of previous studies on the association between suicide mortality and antidepressant sales and point to the complexity of this hypothesized link. Furthermore, the findings support tailored suicide preventive efforts within high risk areas. Copyright © 2016 Elsevier Ltd. All rights reserved.

Agent-based modeling of malaria vectors: the importance of spatial simulation.

PubMed

Bomblies, Arne

2014-07-03

The modeling of malaria vector mosquito populations yields great insight into drivers of malaria transmission at the village scale. Simulation of individual mosquitoes as "agents" in a distributed, dynamic model domain may be greatly beneficial for simulation of spatial relationships of vectors and hosts. In this study, an agent-based model is used to simulate the life cycle and movement of individual malaria vector mosquitoes in a Niger Sahel village, with individual simulated mosquitoes interacting with their physical environment as well as humans. Various processes that are known to be epidemiologically important, such as the dependence of parity on flight distance between developmental habitat and blood meal hosts and therefore spatial relationships of pools and houses, are readily simulated using this modeling paradigm. Impacts of perturbations can be evaluated on the basis of vectorial capacity, because the interactions between individuals that make up the population- scale metric vectorial capacity can be easily tracked for simulated mosquitoes and human blood meal hosts, without the need to estimate vectorial capacity parameters. As expected, model results show pronounced impacts of pool source reduction from larvicide application and draining, but with varying degrees of impact depending on the spatial relationship between pools and human habitation. Results highlight the importance of spatially-explicit simulation that can model individuals such as in an agent-based model. The impacts of perturbations on village scale malaria transmission depend on spatial locations of individual mosquitoes, as well as the tracking of relevant life cycle events and characteristics of individual mosquitoes. This study demonstrates advantages of using an agent-based approach for village-scale mosquito simulation to address questions in which spatial relationships are known to be important.

Quantifying spatial misclassification in exposure to noise complaints among low-income housing residents across New York City neighborhoods: a Global Positioning System (GPS) study.

PubMed

Duncan, Dustin T; Tamura, Kosuke; Regan, Seann D; Athens, Jessica; Elbel, Brian; Meline, Julie; Al-Ajlouni, Yazan A; Chaix, Basile

2017-01-01

To examine if there was spatial misclassification in exposure to neighborhood noise complaints among a sample of low-income housing residents in New York City, comparing home-based spatial buffers and Global Positioning System (GPS) daily path buffers. Data came from the community-based NYC Low-Income Housing, Neighborhoods and Health Study, where GPS tracking of the sample was conducted for a week (analytic n = 102). We created a GPS daily path buffer (a buffering zone drawn around GPS tracks) of 200 m and 400 m. We also used home-based buffers of 200 m and 400 m. Using these "neighborhoods" (or exposure areas), we calculated neighborhood exposure to noisy events from 311 complaints data (analytic n = 143,967). Friedman tests (to compare overall differences in neighborhood definitions) were applied. There were differences in neighborhood noise complaints according to the selected neighborhood definitions (P < .05). For example, the mean neighborhood noise complaint count was 1196 per square kilometer for the 400-m home-based and 812 per square kilometer for the 400-m activity space buffer, illustrating how neighborhood definition influences the estimates of exposure to neighborhood noise complaints. These analyses suggest that, whenever appropriate, GPS neighborhood definitions can be used in spatial epidemiology research in spatially mobile populations to understand people's lived experience. Copyright © 2016 Elsevier Inc. All rights reserved.

Spatial and Statistical Analysis of Leptospirosis in Guilan Province, Iran

NASA Astrophysics Data System (ADS)

Nia, A. Mohammadi; Alimohammadi, A.; Habibi, R.; Shirzadi, M. R.

2015-12-01

The most underdiagnosed water-borne bacterial zoonosis in the world is Leptospirosis which especially impacts tropical and humid regions. According to World Health Organization (WHO), the number of human cases is not known precisely. Available reports showed that worldwide incidences vary from 0.1-1 per 100 000 per year in temperate climates to 10-100 per 100 000 in the humid tropics. Pathogenic bacteria that is spread by the urines of rats is the main reason of water and soil infections. Rice field farmers who are in contact with infected water or soil, contain the most burden of leptospirosis prevalence. In recent years, this zoonotic disease have been occurred in north of Iran endemically. Guilan as the second rice production province (average=750 000 000 Kg, 40% of country production) after Mazandaran, has one of the most rural population (Male=487 679, Female=496 022) and rice workers (47 621 insured workers) among Iran provinces. The main objectives of this study were to analyse yearly spatial distribution and the possible spatial clusters of leptospirosis to better understand epidemiological aspects of them in the province. Survey was performed during the period of 2009-2013 at rural district level throughout the study area. Global clustering methods including the average nearest neighbour distance, Moran's I and General G indices were utilized to investigate the annual spatial distribution of diseases. At the end, significant spatial clusters have been detected with the objective of informing priority areas for public health planning and resource allocation.

Bayesian structured additive regression modeling of epidemic data: application to cholera

PubMed Central

2012-01-01

Background A significant interest in spatial epidemiology lies in identifying associated risk factors which enhances the risk of infection. Most studies, however, make no, or limited use of the spatial structure of the data, as well as possible nonlinear effects of the risk factors. Methods We develop a Bayesian Structured Additive Regression model for cholera epidemic data. Model estimation and inference is based on fully Bayesian approach via Markov Chain Monte Carlo (MCMC) simulations. The model is applied to cholera epidemic data in the Kumasi Metropolis, Ghana. Proximity to refuse dumps, density of refuse dumps, and proximity to potential cholera reservoirs were modeled as continuous functions; presence of slum settlers and population density were modeled as fixed effects, whereas spatial references to the communities were modeled as structured and unstructured spatial effects. Results We observe that the risk of cholera is associated with slum settlements and high population density. The risk of cholera is equal and lower for communities with fewer refuse dumps, but variable and higher for communities with more refuse dumps. The risk is also lower for communities distant from refuse dumps and potential cholera reservoirs. The results also indicate distinct spatial variation in the risk of cholera infection. Conclusion The study highlights the usefulness of Bayesian semi-parametric regression model analyzing public health data. These findings could serve as novel information to help health planners and policy makers in making effective decisions to control or prevent cholera epidemics. PMID:22866662

Spatial analysis of lymphatic filariasis distribution in the Nile Delta in relation to some environmental variables using geographic information system technology.

PubMed

Hassan, A N; Dister, S; Beck, L

1998-04-01

Geographic information system (GIS) was used to analyze the spatial distribution of filariasis in the Nile Delta. The study involved 201 villages belonging to Giza, Qalubiya, Monoufiya, Gharbiya, and Dakahliya governorates. Villages with similar microfilarial (mf) prevalence rates were observed to cluster within 1-2 km distance, then, clustering started to decrease significantly with distance up to 5 km (Pearson correlation coefficient = -0.98). the likelihood of negative and high prevalence villages being contiguous was very low (approximately 1.8%, n = 612 village-pairs) indicating homogeneity in disease processes within the defined spatial scales. Of the villages located within 2 km from the main Nile branches (n = 46), 95% exhibited low prevalence. In addition, the spatial pattern of mf prevalence was shown to be negatively associated with annual rainfall and relative humidity, while it was positively associated with annual daily temperature. Average mf prevalence in warmer, relatively drier areas receiving 25 mm of rain was significantly higher (3.9%) than that in less warmer but more humid areas receiving 50 mm of rain (1.6%) (P < 0.0001). Based on the results of the present study, GIS was used to generate a "filariasis risk map" that could be used by health authorities to efficiently direct surveillance and control efforts. This investigation identified some of the factors underlying filariasis spatial pattern, quantified clustering and demonstrated the potential of GIS application in vector-borne disease epidemiology.

Modeling spatial effects of PM{sub 2.5} on term low birth weight in Los Angeles County

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

Coker, Eric, E-mail: cokerer@onid.orst.edu; Ghosh, Jokay; Jerrett, Michael

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM{sub 2.5}) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure–response of PM{sub 2.5} on TLBW to be the same throughout a large geographical area. Health effects related to PM{sub 2.5} exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure–response relationship between individual-level exposure to PM{sub 2.5} and TLBW. Here, we examine the overall and spatially varying exposure–response relationship between PM{sub 2.5} and TLBW throughout urbanmore » Los Angeles (LA) County, California. We estimated PM{sub 2.5} from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM{sub 2.5} level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure–response for PM{sub 2.5} and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure–response estimates for PM{sub 2.5} on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective. - Highlights: • We model the spatial dependency of PM{sub 2.5} effects on term low birth weight (TLBW). • PM{sub 2.5} effects on TLBW are shown to vary spatially across urban LA County. • Modeling spatial dependency of PM{sub 2.5} health effects may identify effect 'hotspots'. • Birth outcomes studies should consider the spatial dependency of PM{sub 2.5} effects.« less

Repeated Δ9-Tetrahydrocannabinol Exposure in Adolescent Monkeys: Persistent Effects Selective for Spatial Working Memory

PubMed Central

Verrico, Christopher D.; Gu, Hong; Peterson, Melanie L.; Sampson, Allan R.; Lewis, David A.

2014-01-01

Objective Epidemiological findings suggest that, relative to adults, adolescents are more vulnerable to the adverse persistent effects of cannabis on working memory. However, the potential confounds inherent in human studies preclude direct determination of a cause-and-effect relationship between adolescent cannabis use and heightened susceptibility to persistent working memory impairments. Consequently, the authors examined the effects of repeated exposure to Δ9-tetrahydrocannabinol (THC) on performance of spatial and object working memory tasks in adolescent monkeys. Method Seven pairs of male adolescent rhesus monkeys, matched for baseline cognitive performance, received vehicle or THC intravenously 5 days/week for 6 months. Performance on spatial and object memory tasks was assessed 23 or 71 hours after drug administration throughout the study. In addition, acute effects on working memory were also assessed at the beginning and end of the 6-month period. Results Relative to the vehicle-exposed control animals, those with repeated THC exposure had a blunted trajectory of accuracy improvements on the spatial working memory task in a delay-dependent manner. Accuracy improvements on the object working memory task did not differ between groups. Relative to the acute effects of THC on working memory at the beginning of the study, neither sensitivity nor tolerance was evident after 6 months of THC exposure. Conclusions Because maturation of performance is later for spatial than for object working memory, these findings suggest that persistent effects of THC on cognitive abilities are more evident when exposure coincides with the developmental stage during which the underlying neural circuits are actively maturing. PMID:24577206

Repeated Δ9-tetrahydrocannabinol exposure in adolescent monkeys: persistent effects selective for spatial working memory.

PubMed

Verrico, Christopher D; Gu, Hong; Peterson, Melanie L; Sampson, Allan R; Lewis, David A

2014-04-01

Epidemiological findings suggest that, relative to adults, adolescents are more vulnerable to the adverse persistent effects of cannabis on working memory. However, the potential confounds inherent in human studies preclude direct determination of a cause-and-effect relationship between adolescent cannabis use and heightened susceptibility to persistent working memory impairments. Consequently, the authors examined the effects of repeated exposure to Δ9-tetrahydrocannabinol (THC) on performance of spatial and object working memory tasks in adolescent monkeys. Seven pairs of male adolescent rhesus monkeys, matched for baseline cognitive performance, received vehicle or THC intravenously 5 days/week for 6 months. Performance on spatial and object memory tasks was assessed 23 or 71 hours after drug administration throughout the study. In addition, acute effects on working memory were also assessed at the beginning and end of the 6-month period. Relative to the vehicle-exposed control animals, those with repeated THC exposure had a blunted trajectory of accuracy improvements on the spatial working memory task in a delay-dependent manner. Accuracy improvements on the object working memory task did not differ between groups. Relative to the acute effects of THC on working memory at the beginning of the study, neither sensitivity nor tolerance was evident after 6 months of THC exposure. Because maturation of performance is later for spatial than for object working memory, these findings suggest that persistent effects of THC on cognitive abilities are more evident when exposure coincides with the developmental stage during which the underlying neural circuits are actively maturing.

Ecology and diversity in upper respiratory tract microbial population structures from a cross-sectional community swabbing study.

PubMed

Coughtrie, Abigail L; Morris, Denise E; Anderson, Rebecca; Begum, Nelupha; Cleary, David W; Faust, Saul N; Jefferies, Johanna M; Kraaijeveld, Alex R; Moore, Michael V; Mullee, Mark A; Roderick, Paul J; Tuck, Andrew; Whittaker, Robert N; Yuen, Ho Ming; Doncaster, C Patrick; Clarke, Stuart C

2018-06-21

Respiratory tract infections (RTIs) are responsible for over 2.8 million deaths per year worldwide with pathobiont carriage a required precursor to infection. We sought to determine carriage epidemiology for both bacterial and viral respiratory pathogens as part of a large population-based cross-sectional carriage study. Nose self-swab samples were collected in two separate time-points, May to August 2012 (late spring/summer) and February to April 2013 (winter/early spring). The presence of six bacterial species: S. pneumoniae, H. influenzae, M. catarrhalis, S. aureus, P. aeruginosa and N. meningitidis in addition to respiratory syncytial virus, influenza viruses A and B, rhinovirus/enterovirus, coronavirus, parainfluenza viruses 1-3 and adenovirus was determined using culture and PCR methods.Results/Key findings. Carriage was shown to vary with age, recent RTI and the presence of other species. Spatial structures of microbial communities were more disordered in the 0-4 age group and those with recent RTI. Species frequency distributions were flatter than random expectation in young individuals (X 2 =20.42, P=0.002), indicating spatial clumping of species consistent with facilitative relationships. Deviations from a neutral model of ecological niches were observed in summer samples and from older individuals but not in the winter or younger individuals (0-4 years), suggesting the presence of seasonal and age-dependent niche processes in respiratory community assembly. The application of epidemiological methods and ecological theory to respiratory tract samples has yielded novel insights into the factors that drive microbial community composition.

The roosting spatial network of a bird-predator bat.

PubMed

Fortuna, Miguel A; Popa-Lisseanu, Ana G; Ibáñez, Carlos; Bascompte, Jordi

2009-04-01

The use of roosting sites by animal societies is important in conservation biology, animal behavior, and epidemiology. The giant noctule bat (Nyctalus lasiopterus) constitutes fission-fusion societies whose members spread every day in multiple trees for shelter. To assess how the pattern of roosting use determines the potential for information exchange or disease spreading, we applied the framework of complex networks. We found a social and spatial segregation of the population in well-defined modules or compartments, formed by groups of bats sharing the same trees. Inside each module, we revealed an asymmetric use of trees by bats representative of a nested pattern. By applying a simple epidemiological model, we show that there is a strong correlation between network structure and the rate and shape of infection dynamics. This modular structure slows down the spread of diseases and the exchange of information through the entire network. The implication for management is complex, affecting differently the cohesion inside and among colonies and the transmission of parasites and diseases. Network analysis can hence be applied to quantifying the conservation status of individual trees used by species depending on hollows for shelter.

Scale-dependent approaches to modeling spatial epidemiology of chronic wasting disease.

USGS Publications Warehouse

Conner, Mary M.; Gross, John E.; Cross, Paul C.; Ebinger, Michael R.; Gillies, Robert; Samuel, Michael D.; Miller, Michael W.

2007-01-01

For each scale, we presented a focal approach that would be useful for understanding the spatial pattern and epidemiology of CWD, as well as being a useful tool for CWD management. The focal approaches include risk analysis and micromaps for the regional scale, cluster analysis for the landscape scale, and individual based modeling for the fine scale of within population. For each of these methods, we used simulated data and walked through the method step by step to fully illustrate the “how to”, with specifics about what is input and output, as well as what questions the method addresses. We also provided a summary table to, at a glance, describe the scale, questions that can be addressed, and general data required for each method described in this e-book. We hope that this review will be helpful to biologists and managers by increasing the utility of their surveillance data, and ultimately be useful for increasing our understanding of CWD and allowing wildlife biologists and managers to move beyond retroactive fire-fighting to proactive preventative action.

Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis

PubMed Central

Zhou, Ying; Levy, Jonathan I

2007-01-01

Background There has been growing interest among exposure assessors, epidemiologists, and policymakers in the concept of "hot spots", or more broadly, the "spatial extent" of impacts from traffic-related air pollutants. This review attempts to quantitatively synthesize findings about the spatial extent under various circumstances. Methods We include both the peer-reviewed literature and government reports, and focus on four significant air pollutants: carbon monoxide, benzene, nitrogen oxides, and particulate matter (including both ultrafine particle counts and fine particle mass). From the identified studies, we extracted information about significant factors that would be hypothesized to influence the spatial extent within the study, such as the study type (e.g., monitoring, air dispersion modeling, GIS-based epidemiological studies), focus on concentrations or health risks, pollutant under study, background concentration, emission rate, and meteorological factors, as well as the study's implicit or explicit definition of spatial extent. We supplement this meta-analysis with results from some illustrative atmospheric dispersion modeling. Results We found that pollutant characteristics and background concentrations best explained variability in previously published spatial extent estimates, with a modifying influence of local meteorology, once some extreme values based on health risk estimates were removed from the analysis. As hypothesized, inert pollutants with high background concentrations had the largest spatial extent (often demonstrating no significant gradient), and pollutants formed in near-source chemical reactions (e.g., nitrogen dioxide) had a larger spatial extent than pollutants depleted in near-source chemical reactions or removed through coagulation processes (e.g., nitrogen oxide and ultrafine particles). Our illustrative dispersion model illustrated the complex interplay of spatial extent definitions, emission rates, background concentrations, and meteorological conditions on spatial extent estimates even for non-reactive pollutants. Our findings indicate that, provided that a health risk threshold is not imposed, the spatial extent of impact for mobile sources reviewed in this study is on the order of 100–400 m for elemental carbon or particulate matter mass concentration (excluding background concentration), 200–500 m for nitrogen dioxide and 100–300 m for ultrafine particle counts. Conclusion First, to allow for meaningful comparisons across studies, it is important to state the definition of spatial extent explicitly, including the comparison method, threshold values, and whether background concentration is included. Second, the observation that the spatial extent is generally within a few hundred meters for highway or city roads demonstrates the need for high resolution modeling near the source. Finally, our findings emphasize that policymakers should be able to develop reasonable estimates of the "zone of influence" of mobile sources, provided that they can clarify the pollutant of concern, the general site characteristics, and the underlying definition of spatial extent that they wish to utilize. PMID:17519039

An assessment of air pollution and its attributable mortality in Ulaanbaatar, Mongolia.

PubMed

Allen, Ryan W; Gombojav, Enkhjargal; Barkhasragchaa, Baldorj; Byambaa, Tsogtbaatar; Lkhasuren, Oyuntogos; Amram, Ofer; Takaro, Tim K; Janes, Craig R

2013-03-01

Epidemiologic studies have consistently reported associations between outdoor fine particulate matter (PM 2.5 ) air pollution and adverse health effects. Although Asia bears the majority of the public health burden from air pollution, few epidemiologic studies have been conducted outside of North America and Europe due in part to challenges in population exposure assessment. We assessed the feasibility of two current exposure assessment techniques, land use regression (LUR) modeling and mobile monitoring, and estimated the mortality attributable to air pollution in Ulaanbaatar, Mongolia. We developed LUR models for predicting wintertime spatial patterns of NO 2 and SO 2 based on 2-week passive Ogawa measurements at 37 locations and freely available geographic predictors. The models explained 74% and 78% of the variance in NO 2 and SO 2 , respectively. Land cover characteristics derived from satellite images were useful predictors of both pollutants. Mobile PM 2.5 monitoring with an integrating nephelometer also showed promise, capturing substantial spatial variation in PM 2.5 concentrations. The spatial patterns in SO 2 and PM, seasonal and diurnal patterns in PM 2.5 , and high wintertime PM 2.5 /PM 10 ratios were consistent with a major impact from coal and wood combustion in the city's low-income traditional housing (ger) areas. The annual average concentration of PM 2.5 measured at a centrally located government monitoring site was 75 μg/m 3 or more than seven times the World Health Organization's PM 2.5 air quality guideline, driven by a wintertime average concentration of 148 μg/m 3 . PM 2.5 concentrations measured in a traditional housing area were higher, with a wintertime mean PM 2.5 concentration of 250 μg/m 3 . We conservatively estimated that 29% (95% CI, 12-43%) of cardiopulmonary deaths and 40% (95% CI, 17-56%) of lung cancer deaths in the city are attributable to outdoor air pollution. These deaths correspond to nearly 10% of the city's total mortality, with estimates ranging to more than 13% of mortality under less conservative model assumptions. LUR models and mobile monitoring can be successfully implemented in developing country cities, thus cost-effectively improving exposure assessment for epidemiology and risk assessment. Air pollution represents a major threat to public health in Ulaanbaatar, Mongolia, and reducing home heating emissions in traditional housing areas should be the primary focus of air pollution control efforts.

Improving exposure assessment in environmental epidemiology: Application of spatio-temporal visualization tools

NASA Astrophysics Data System (ADS)

Meliker, Jaymie R.; Slotnick, Melissa J.; Avruskin, Gillian A.; Kaufmann, Andrew; Jacquez, Geoffrey M.; Nriagu, Jerome O.

2005-05-01

A thorough assessment of human exposure to environmental agents should incorporate mobility patterns and temporal changes in human behaviors and concentrations of contaminants; yet the temporal dimension is often under-emphasized in exposure assessment endeavors, due in part to insufficient tools for visualizing and examining temporal datasets. Spatio-temporal visualization tools are valuable for integrating a temporal component, thus allowing for examination of continuous exposure histories in environmental epidemiologic investigations. An application of these tools to a bladder cancer case-control study in Michigan illustrates continuous exposure life-lines and maps that display smooth, continuous changes over time. Preliminary results suggest increased risk of bladder cancer from combined exposure to arsenic in drinking water (>25 μg/day) and heavy smoking (>30 cigarettes/day) in the 1970s and 1980s, and a possible cancer cluster around automotive, paint, and organic chemical industries in the early 1970s. These tools have broad application for examining spatially- and temporally-specific relationships between exposures to environmental risk factors and disease.

Evolution and molecular epidemiology of classical swine fever virus during a multi-annual outbreak amongst European wild boar.

PubMed

Goller, Katja V; Gabriel, Claudia; Dimna, Mireille Le; Le Potier, Marie-Frédérique; Rossi, Sophie; Staubach, Christoph; Merboth, Matthias; Beer, Martin; Blome, Sandra

2016-03-01

Classical swine fever is a viral disease of pigs that carries tremendous socio-economic impact. In outbreak situations, genetic typing is carried out for the purpose of molecular epidemiology in both domestic pigs and wild boar. These analyses are usually based on harmonized partial sequences. However, for high-resolution analyses towards the understanding of genetic variability and virus evolution, full-genome sequences are more appropriate. In this study, a unique set of representative virus strains was investigated that was collected during an outbreak in French free-ranging wild boar in the Vosges-du-Nord mountains between 2003 and 2007. Comparative sequence and evolutionary analyses of the nearly full-length sequences showed only slow evolution of classical swine fever virus strains over the years and no impact of vaccination on mutation rates. However, substitution rates varied amongst protein genes; furthermore, a spatial and temporal pattern could be observed whereby two separate clusters were formed that coincided with physical barriers.

Management of invading pathogens should be informed by epidemiology rather than administrative boundaries.

PubMed

Thompson, Robin N; Cobb, Richard C; Gilligan, Christopher A; Cunniffe, Nik J

2016-03-24

Plant and animal disease outbreaks have significant ecological and economic impacts. The spatial extent of control is often informed solely by administrative geography - for example, quarantine of an entire county or state once an invading disease is detected - with little regard for pathogen epidemiology. We present a stochastic model for the spread of a plant pathogen that couples spread in the natural environment and transmission via the nursery trade, and use it to illustrate that control deployed according to administrative boundaries is almost always sub-optimal. We use sudden oak death (caused by Phytophthora ramorum ) in mixed forests in California as motivation for our study, since the decision as to whether or not to deploy plant trade quarantine is currently undertaken on a county-by-county basis for that system. However, our key conclusion is applicable more generally: basing management of any disease entirely upon administrative borders does not balance the cost of control with the possible economic and ecological costs of further spread in the optimal fashion.

Spatial and temporal patterning of bank vole demography and the epidemiology of the Puumala hantavirus in northeastern France

PubMed Central

AUGOT, D.; SAUVAGE, F.; BOUE, F.; BOULOY, M.; ARTOIS, M.; DEMERSON, J. M.; COMBES, B.; COUDRIER, D.; ZELLER, H.; CLIQUET, F.; PONTIER, D.

2008-01-01

SUMMARY Epidemiological data from bank voles, Myodes glareolus, naturally infected by the hantavirus Puumala (PUUV) were collected by a capture–mark–recapture protocol from 2000 to 2002 in the French department of Ardennes. Four monitored trapping sites were established in two forests located in two cantons (Flize and Monthermé). We captured 912 bank voles corresponding to 557 different individuals during 8820 trapping nights for an overall trapping success of 10·34%. The average PUUV seroprevalence was 22·4%. Characteristics of the system reported in North European countries are confirmed in France. PUUV seroprevalence and abundance of rodents appeared weakly linked. Adult voles were more frequently antibody-positive, but no difference between sexes was established. Anti-PUUV seropositive voles were captured and high seroprevalence was observed from both forests, without human infection reported in Flize canton during the study. One site among the four exhibited peculiar infection dynamics, where vole weight and infection risk were negatively correlated. PMID:18325126

Novel microbiological and spatial statistical methods to improve strength of epidemiological evidence in a community-wide waterborne outbreak.

PubMed

Jalava, Katri; Rintala, Hanna; Ollgren, Jukka; Maunula, Leena; Gomez-Alvarez, Vicente; Revez, Joana; Palander, Marja; Antikainen, Jenni; Kauppinen, Ari; Räsänen, Pia; Siponen, Sallamaari; Nyholm, Outi; Kyyhkynen, Aino; Hakkarainen, Sirpa; Merentie, Juhani; Pärnänen, Martti; Loginov, Raisa; Ryu, Hodon; Kuusi, Markku; Siitonen, Anja; Miettinen, Ilkka; Santo Domingo, Jorge W; Hänninen, Marja-Liisa; Pitkänen, Tarja

2014-01-01

Failures in the drinking water distribution system cause gastrointestinal outbreaks with multiple pathogens. A water distribution pipe breakage caused a community-wide waterborne outbreak in Vuorela, Finland, July 2012. We investigated this outbreak with advanced epidemiological and microbiological methods. A total of 473/2931 inhabitants (16%) responded to a web-based questionnaire. Water and patient samples were subjected to analysis of multiple microbial targets, molecular typing and microbial community analysis. Spatial analysis on the water distribution network was done and we applied a spatial logistic regression model. The course of the illness was mild. Drinking untreated tap water from the defined outbreak area was significantly associated with illness (RR 5.6, 95% CI 1.9-16.4) increasing in a dose response manner. The closer a person lived to the water distribution breakage point, the higher the risk of becoming ill. Sapovirus, enterovirus, single Campylobacter jejuni and EHEC O157:H7 findings as well as virulence genes for EPEC, EAEC and EHEC pathogroups were detected by molecular or culture methods from the faecal samples of the patients. EPEC, EAEC and EHEC virulence genes and faecal indicator bacteria were also detected in water samples. Microbial community sequencing of contaminated tap water revealed abundance of Arcobacter species. The polyphasic approach improved the understanding of the source of the infections, and aided to define the extent and magnitude of this outbreak.

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

PubMed

Dionisio, Kathie L; Chang, Howard H; Baxter, Lisa K

2016-11-25

Exposure 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 air pollution and health. ZIP-code level estimates of exposure for six pollutants (CO, NO x , EC, PM 2.5 , SO 4 , O 3 ) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error. Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs. Substantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3-85% for population error, and 31-85% for total error. When CO, NO x or EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copollutants based on the estimated type I error rate. The impact of exposure error must be considered when interpreting results of copollutant epidemiologic models, due to the possibility of attenuation of main pollutant RRs and the increased probability of false positives when measurement error is present.

Prenatal and postnatal polybrominated diphenyl ether exposure and visual spatial abilities in children

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

Vuong, Ann M.

Polybrominated diphenyl ethers (PBDEs) are associated with impaired visual spatial abilities in toxicological studies, but no epidemiologic study has investigated PBDEs and visual spatial abilities in children. The Health Outcomes and Measures of the Environment Study, a prospective birth cohort (2003–2006, Cincinnati, OH), was used to examine prenatal and childhood PBDEs and visual spatial abilities in 199 children. PBDEs were measured at 16±3 weeks gestation and at 1, 2, 3, 5, and 8 years using gas chromatography/isotope dilution high-resolution mass spectrometry. We used the Virtual Morris Water Maze to measure visual spatial abilities at 8 years. In covariate-adjusted models, 10-foldmore » increases in BDE-47, −99, and −100 at 5 years were associated with shorter completion times by 5.2 s (95% Confidence Interval [CI] −9.3, −1.1), 4.5 s (95% CI −8.1, −0.9), and 4.7 s (95% CI −9.0, −0.3), respectively. However, children with higher BDE-153 at 3 years had longer completion times (β=5.4 s, 95% CI −0.3, 11.1). Prenatal PBDEs were associated with improved visual spatial memory retention, with children spending a higher percentage of their search path in the correct quadrant. Child sex modified some associations between PBDEs and visual spatial learning. Longer path lengths were observed among males with increased BDE-47 at 2 and 3 years, while females had shorter paths. In conclusion, prenatal and postnatal BDE-28, −47, −99, and −100 at 5 and 8 years were associated with improved visual spatial abilities, whereas a pattern of impairments in visual spatial learning was noted with early childhood BDE-153 concentrations. - Highlights: • The VMWM test was used to assess visual spatial abilities in children at 8 years. • BDE-153 at 3 years was adversely associated with visual spatial learning. • BDE-47, −99, and −100 at 5 years was associated with better visual spatial learning. • Prenatal PBDEs were associated with improved visual spatial memory retention. • Male children were observed to perform more poorly on the VMWM than females.« less

Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens

PubMed Central

Rey, Jean-François; Barrett, Luke G.; Thrall, Peter H.

2018-01-01

Genetically-controlled plant resistance can reduce the damage caused by pathogens. However, pathogens have the ability to evolve and overcome such resistance. This often occurs quickly after resistance is deployed, resulting in significant crop losses and a continuing need to develop new resistant cultivars. To tackle this issue, several strategies have been proposed to constrain the evolution of pathogen populations and thus increase genetic resistance durability. These strategies mainly rely on varying different combinations of resistance sources across time (crop rotations) and space. The spatial scale of deployment can vary from multiple resistance sources occurring in a single cultivar (pyramiding), in different cultivars within the same field (cultivar mixtures) or in different fields (mosaics). However, experimental comparison of the efficiency (i.e. ability to reduce disease impact) and durability (i.e. ability to limit pathogen evolution and delay resistance breakdown) of landscape-scale deployment strategies presents major logistical challenges. Therefore, we developed a spatially explicit stochastic model able to assess the epidemiological and evolutionary outcomes of the four major deployment options described above, including both qualitative resistance (i.e. major genes) and quantitative resistance traits against several components of pathogen aggressiveness: infection rate, latent period duration, propagule production rate, and infectious period duration. This model, implemented in the R package landsepi, provides a new and useful tool to assess the performance of a wide range of deployment options, and helps investigate the effect of landscape, epidemiological and evolutionary parameters. This article describes the model and its parameterisation for rust diseases of cereal crops, caused by fungi of the genus Puccinia. To illustrate the model, we use it to assess the epidemiological and evolutionary potential of the combination of a major gene and different traits of quantitative resistance. The comparison of the four major deployment strategies described above will be the objective of future studies. PMID:29649208

Modeling Disease Vector Occurrence when Detection Is Imperfect: Infestation of Amazonian Palm Trees by Triatomine Bugs at Three Spatial Scales

PubMed Central

Abad-Franch, Fernando; Ferraz, Gonçalo; Campos, Ciro; Palomeque, Francisco S.; Grijalva, Mario J.; Aguilar, H. Marcelo; Miles, Michael A.

2010-01-01

Background Failure to detect a disease agent or vector where it actually occurs constitutes a serious drawback in epidemiology. In the pervasive situation where no sampling technique is perfect, the explicit analytical treatment of detection failure becomes a key step in the estimation of epidemiological parameters. We illustrate this approach with a study of Attalea palm tree infestation by Rhodnius spp. (Triatominae), the most important vectors of Chagas disease (CD) in northern South America. Methodology/Principal Findings The probability of detecting triatomines in infested palms is estimated by repeatedly sampling each palm. This knowledge is used to derive an unbiased estimate of the biologically relevant probability of palm infestation. We combine maximum-likelihood analysis and information-theoretic model selection to test the relationships between environmental covariates and infestation of 298 Amazonian palm trees over three spatial scales: region within Amazonia, landscape, and individual palm. Palm infestation estimates are high (40–60%) across regions, and well above the observed infestation rate (24%). Detection probability is higher (∼0.55 on average) in the richest-soil region than elsewhere (∼0.08). Infestation estimates are similar in forest and rural areas, but lower in urban landscapes. Finally, individual palm covariates (accumulated organic matter and stem height) explain most of infestation rate variation. Conclusions/Significance Individual palm attributes appear as key drivers of infestation, suggesting that CD surveillance must incorporate local-scale knowledge and that peridomestic palm tree management might help lower transmission risk. Vector populations are probably denser in rich-soil sub-regions, where CD prevalence tends to be higher; this suggests a target for research on broad-scale risk mapping. Landscape-scale effects indicate that palm triatomine populations can endure deforestation in rural areas, but become rarer in heavily disturbed urban settings. Our methodological approach has wide application in infectious disease research; by improving eco-epidemiological parameter estimation, it can also significantly strengthen vector surveillance-control strategies. PMID:20209149

Landscape epidemiology and control of pathogens with cryptic and long-distance dispersal: sudden oak death in northern Californian forests.

PubMed

Filipe, João A N; Cobb, Richard C; Meentemeyer, Ross K; Lee, Christopher A; Valachovic, Yana S; Cook, Alex R; Rizzo, David M; Gilligan, Christopher A

2012-01-01

Exotic pathogens and pests threaten ecosystem service, biodiversity, and crop security globally. If an invasive agent can disperse asymptomatically over long distances, multiple spatial and temporal scales interplay, making identification of effective strategies to regulate, monitor, and control disease extremely difficult. The management of outbreaks is also challenged by limited data on the actual area infested and the dynamics of spatial spread, due to financial, technological, or social constraints. We examine principles of landscape epidemiology important in designing policy to prevent or slow invasion by such organisms, and use Phytophthora ramorum, the cause of sudden oak death, to illustrate how shortfalls in their understanding can render management applications inappropriate. This pathogen has invaded forests in coastal California, USA, and an isolated but fast-growing epidemic focus in northern California (Humboldt County) has the potential for extensive spread. The risk of spread is enhanced by the pathogen's generalist nature and survival. Additionally, the extent of cryptic infection is unknown due to limited surveying resources and access to private land. Here, we use an epidemiological model for transmission in heterogeneous landscapes and Bayesian Markov-chain-Monte-Carlo inference to estimate dispersal and life-cycle parameters of P. ramorum and forecast the distribution of infection and speed of the epidemic front in Humboldt County. We assess the viability of management options for containing the pathogen's northern spread and local impacts. Implementing a stand-alone host-free "barrier" had limited efficacy due to long-distance dispersal, but combining curative with preventive treatments ahead of the front reduced local damage and contained spread. While the large size of this focus makes effective control expensive, early synchronous treatment in newly-identified disease foci should be more cost-effective. We show how the successful management of forest ecosystems depends on estimating the spatial scales of invasion and treatment of pathogens and pests with cryptic long-distance dispersal. © 2012 Filipe et al.

Iowa radon leukaemia study: a hierarchical population risk model for spatially correlated exposure measured with error.

PubMed

Smith, Brian J; Zhang, Lixun; Field, R William

2007-11-10

This paper presents a Bayesian model that allows for the joint prediction of county-average radon levels and estimation of the associated leukaemia risk. The methods are motivated by radon data from an epidemiologic study of residential radon in Iowa that include 2726 outdoor and indoor measurements. Prediction of county-average radon is based on a geostatistical model for the radon data which assumes an underlying continuous spatial process. In the radon model, we account for uncertainties due to incomplete spatial coverage, spatial variability, characteristic differences between homes, and detector measurement error. The predicted radon averages are, in turn, included as a covariate in Poisson models for incident cases of acute lymphocytic (ALL), acute myelogenous (AML), chronic lymphocytic (CLL), and chronic myelogenous (CML) leukaemias reported to the Iowa cancer registry from 1973 to 2002. Since radon and leukaemia risk are modelled simultaneously in our approach, the resulting risk estimates accurately reflect uncertainties in the predicted radon exposure covariate. Posterior mean (95 per cent Bayesian credible interval) estimates of the relative risk associated with a 1 pCi/L increase in radon for ALL, AML, CLL, and CML are 0.91 (0.78-1.03), 1.01 (0.92-1.12), 1.06 (0.96-1.16), and 1.12 (0.98-1.27), respectively. Copyright 2007 John Wiley & Sons, Ltd.

Using remote sensing and machine learning for the spatial modelling of a bluetongue virus vector

NASA Astrophysics Data System (ADS)

Van doninck, J.; Peters, J.; De Baets, B.; Ducheyne, E.; Verhoest, N. E. C.

2012-04-01

Bluetongue is a viral vector-borne disease transmitted between hosts, mostly cattle and small ruminants, by some species of Culicoides midges. Within the Mediterranean basin, C. imicola is the main vector of the bluetongue virus. The spatial distribution of this species is limited by a number of environmental factors, including temperature, soil properties and land cover. The identification of zones at risk of bluetongue outbreaks thus requires detailed information on these environmental factors, as well as appropriate epidemiological modelling techniques. We here give an overview of the environmental factors assumed to be constraining the spatial distribution of C. imicola, as identified in different studies. Subsequently, remote sensing products that can be used as proxies for these environmental constraints are presented. Remote sensing data are then used together with species occurrence data from the Spanish Bluetongue National Surveillance Programme to calibrate a supervised learning model, based on Random Forests, to model the probability of occurrence of the C. imicola midge. The model will then be applied for a pixel-based prediction over the Iberian peninsula using remote sensing products for habitat characterization.

A high-resolution genetic signature of demographic and spatial expansion in epizootic rabies virus

PubMed Central

Biek, Roman; Henderson, J. Caroline; Waller, Lance A.; Rupprecht, Charles E.; Real, Leslie A.

2007-01-01

Emerging pathogens potentially undergo rapid evolution while expanding in population size and geographic range during the course of invasion, yet it is generally difficult to demonstrate how these processes interact. Our analysis of a 30-yr data set covering a large-scale rabies virus outbreak among North American raccoons reveals the long lasting effect of the initial infection wave in determining how viral populations are genetically structured in space. We further find that coalescent-based estimates derived from the genetic data yielded an amazingly accurate reconstruction of the known spatial and demographic dynamics of the virus over time. Our study demonstrates the combined evolutionary and population dynamic processes characterizing the spread of pathogen after its introduction into a fully susceptible host population. Furthermore, the results provide important insights regarding the spatial scale of rabies persistence and validate the use of coalescent approaches for uncovering even relatively complex population histories. Such approaches will be of increasing relevance for understanding the epidemiology of emerging zoonotic diseases in a landscape context. PMID:17470818

Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment

PubMed Central

Guttmann, Aline; Li, Xinran; Feschet, Fabien; Gaudart, Jean; Demongeot, Jacques; Boire, Jean-Yves; Ouchchane, Lemlih

2015-01-01

In cluster detection of disease, the use of local cluster detection tests (CDTs) is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC) is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps. PMID:26086911

Multi-scale biomedical systems: measurement challenges

NASA Astrophysics Data System (ADS)

Summers, R.

2016-11-01

Multi-scale biomedical systems are those that represent interactions in materials, sensors, and systems from a holistic perspective. It is possible to view such multi-scale activity using measurement of spatial scale or time scale, though in this paper only the former is considered. The biomedical application paradigm comprises interactions that range from quantum biological phenomena at scales of 10-12 for one individual to epidemiological studies of disease spread in populations that in a pandemic lead to measurement at a scale of 10+7. It is clear that there are measurement challenges at either end of this spatial scale, but those challenges that relate to the use of new technologies that deal with big data and health service delivery at the point of care are also considered. The measurement challenges lead to the use, in many cases, of model-based measurement and the adoption of virtual engineering. It is these measurement challenges that will be uncovered in this paper.

Chaos and Forecasting - Proceedings of the Royal Society Discussion Meeting

NASA Astrophysics Data System (ADS)

Tong, Howell

1995-04-01

The Table of Contents for the full book PDF is as follows: * Preface * Orthogonal Projection, Embedding Dimension and Sample Size in Chaotic Time Series from a Statistical Perspective * A Theory of Correlation Dimension for Stationary Time Series * On Prediction and Chaos in Stochastic Systems * Locally Optimized Prediction of Nonlinear Systems: Stochastic and Deterministic * A Poisson Distribution for the BDS Test Statistic for Independence in a Time Series * Chaos and Nonlinear Forecastability in Economics and Finance * Paradigm Change in Prediction * Predicting Nonuniform Chaotic Attractors in an Enzyme Reaction * Chaos in Geophysical Fluids * Chaotic Modulation of the Solar Cycle * Fractal Nature in Earthquake Phenomena and its Simple Models * Singular Vectors and the Predictability of Weather and Climate * Prediction as a Criterion for Classifying Natural Time Series * Measuring and Characterising Spatial Patterns, Dynamics and Chaos in Spatially-Extended Dynamical Systems and Ecologies * Non-Linear Forecasting and Chaos in Ecology and Epidemiology: Measles as a Case Study

CRISPR-based herd immunity can limit phage epidemics in bacterial populations

PubMed Central

Geyrhofer, Lukas; Barton, Nicholas H

2018-01-01

Herd immunity, a process in which resistant individuals limit the spread of a pathogen among susceptible hosts has been extensively studied in eukaryotes. Even though bacteria have evolved multiple immune systems against their phage pathogens, herd immunity in bacteria remains unexplored. Here we experimentally demonstrate that herd immunity arises during phage epidemics in structured and unstructured Escherichia coli populations consisting of differing frequencies of susceptible and resistant cells harboring CRISPR immunity. In addition, we develop a mathematical model that quantifies how herd immunity is affected by spatial population structure, bacterial growth rate, and phage replication rate. Using our model we infer a general epidemiological rule describing the relative speed of an epidemic in partially resistant spatially structured populations. Our experimental and theoretical findings indicate that herd immunity may be important in bacterial communities, allowing for stable coexistence of bacteria and their phages and the maintenance of polymorphism in bacterial immunity. PMID:29521625

Spatial networks

NASA Astrophysics Data System (ADS)

Barthélemy, Marc

2011-02-01

Complex systems are very often organized under the form of networks where nodes and edges are embedded in space. Transportation and mobility networks, Internet, mobile phone networks, power grids, social and contact networks, and neural networks, are all examples where space is relevant and where topology alone does not contain all the information. Characterizing and understanding the structure and the evolution of spatial networks is thus crucial for many different fields, ranging from urbanism to epidemiology. An important consequence of space on networks is that there is a cost associated with the length of edges which in turn has dramatic effects on the topological structure of these networks. We will thoroughly explain the current state of our understanding of how the spatial constraints affect the structure and properties of these networks. We will review the most recent empirical observations and the most important models of spatial networks. We will also discuss various processes which take place on these spatial networks, such as phase transitions, random walks, synchronization, navigation, resilience, and disease spread.

Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza

NASA Astrophysics Data System (ADS)

Viboud, Cécile; Bjørnstad, Ottar N.; Smith, David L.; Simonsen, Lone; Miller, Mark A.; Grenfell, Bryan T.

2006-04-01

Quantifying long-range dissemination of infectious diseases is a key issue in their dynamics and control. Here, we use influenza-related mortality data to analyze the between-state progression of interpandemic influenza in the United States over the past 30 years. Outbreaks show hierarchical spatial spread evidenced by higher pairwise synchrony between more populous states. Seasons with higher influenza mortality are associated with higher disease transmission and more rapid spread than are mild ones. The regional spread of infection correlates more closely with rates of movement of people to and from their workplaces (workflows) than with geographical distance. Workflows are described in turn by a gravity model, with a rapid decay of commuting up to around 100 km and a long tail of rare longer range flow. A simple epidemiological model, based on the gravity formulation, captures the observed increase of influenza spatial synchrony with transmissibility; high transmission allows influenza to spread rapidly beyond local spatial constraints.

Prediction and analysis of near-road concentrations using a reduced-form emission/dispersion model

PubMed Central

2010-01-01

Background Near-road exposures of traffic-related air pollutants have been receiving increased attention due to evidence linking emissions from high-traffic roadways to adverse health outcomes. To date, most epidemiological and risk analyses have utilized simple but crude exposure indicators, most typically proximity measures, such as the distance between freeways and residences, to represent air quality impacts from traffic. This paper derives and analyzes a simplified microscale simulation model designed to predict short- (hourly) to long-term (annual average) pollutant concentrations near roads. Sensitivity analyses and case studies are used to highlight issues in predicting near-road exposures. Methods Process-based simulation models using a computationally efficient reduced-form response surface structure and a minimum number of inputs integrate the major determinants of air pollution exposures: traffic volume and vehicle emissions, meteorology, and receptor location. We identify the most influential variables and then derive a set of multiplicative submodels that match predictions from "parent" models MOBILE6.2 and CALINE4. The assembled model is applied to two case studies in the Detroit, Michigan area. The first predicts carbon monoxide (CO) concentrations at a monitoring site near a freeway. The second predicts CO and PM2.5 concentrations in a dense receptor grid over a 1 km2 area around the intersection of two major roads. We analyze the spatial and temporal patterns of pollutant concentration predictions. Results Predicted CO concentrations showed reasonable agreement with annual average and 24-hour measurements, e.g., 59% of the 24-hr predictions were within a factor of two of observations in the warmer months when CO emissions are more consistent. The highest concentrations of both CO and PM2.5 were predicted to occur near intersections and downwind of major roads during periods of unfavorable meteorology (e.g., low wind speeds) and high emissions (e.g., weekday rush hour). The spatial and temporal variation among predicted concentrations was significant, and resulted in unusual distributional and correlation characteristics, including strong negative correlation for receptors on opposite sides of a road and the highest short-term concentrations on the "upwind" side of the road. Conclusions The case study findings can likely be generalized to many other locations, and they have important implications for epidemiological and other studies. The reduced-form model is intended for exposure assessment, risk assessment, epidemiological, geographical information systems, and other applications. PMID:20579353

Spatiotemporal dynamics of the Southern California Asian citrus psyllid (Diaphorina citri) invasion.

PubMed

Bayles, Brett R; Thomas, Shyam M; Simmons, Gregory S; Grafton-Cardwell, Elizabeth E; Daugherty, Mathew P

2017-01-01

Biological invasions are governed by spatial processes that tend to be distributed in non-random ways across landscapes. Characterizing the spatial and temporal heterogeneities of the introduction, establishment, and spread of non-native insect species is a key aspect of effectively managing their geographic expansion. The Asian citrus psyllid (Diaphorina citri), a vector of the bacterium associated with huanglongbing (HLB), poses a serious threat to commercial and residential citrus trees. In 2008, D. citri first began expanding northward from Mexico into parts of Southern California. Using georeferenced D. citri occurrence data from 2008-2014, we sought to better understand the extent of the geographic expansion of this invasive vector species. Our objectives were to: 1) describe the spatial and temporal distribution of D. citri in Southern California, 2) identify the locations of statistically significant D. citri hotspots, and 3) quantify the dynamics of anisotropic spread. We found clear evidence that the spatial and temporal distribution of D. citri in Southern California is non-random. Further, we identified the existence of statistically significant hotspots of D. citri occurrence and described the anisotropic dispersion across the Southern California landscape. For example, the dominant hotspot surrounding Los Angeles showed rapid and strongly asymmetric spread to the south and east. Our study demonstrates the feasibility of quantitative invasive insect risk assessment with the application of a spatial epidemiology framework.

Spatiotemporal dynamics of the Southern California Asian citrus psyllid (Diaphorina citri) invasion

PubMed Central

Thomas, Shyam M.; Simmons, Gregory S.; Grafton-Cardwell, Elizabeth E.; Daugherty, Mathew P.

2017-01-01

Biological invasions are governed by spatial processes that tend to be distributed in non-random ways across landscapes. Characterizing the spatial and temporal heterogeneities of the introduction, establishment, and spread of non-native insect species is a key aspect of effectively managing their geographic expansion. The Asian citrus psyllid (Diaphorina citri), a vector of the bacterium associated with huanglongbing (HLB), poses a serious threat to commercial and residential citrus trees. In 2008, D. citri first began expanding northward from Mexico into parts of Southern California. Using georeferenced D. citri occurrence data from 2008–2014, we sought to better understand the extent of the geographic expansion of this invasive vector species. Our objectives were to: 1) describe the spatial and temporal distribution of D. citri in Southern California, 2) identify the locations of statistically significant D. citri hotspots, and 3) quantify the dynamics of anisotropic spread. We found clear evidence that the spatial and temporal distribution of D. citri in Southern California is non-random. Further, we identified the existence of statistically significant hotspots of D. citri occurrence and described the anisotropic dispersion across the Southern California landscape. For example, the dominant hotspot surrounding Los Angeles showed rapid and strongly asymmetric spread to the south and east. Our study demonstrates the feasibility of quantitative invasive insect risk assessment with the application of a spatial epidemiology framework. PMID:28278188

Spatial-temporal epidemiology of human Salmonella Enteritidis infections with major phage types (PTs 1, 4, 5b, 8, 13, and 13a) in Ontario, Canada, 2008-2009.

PubMed

Varga, Csaba; Pearl, David L; McEwen, Scott A; Sargeant, Jan M; Pollari, Frank; Guerin, Michele T

2015-12-17

In Ontario and Canada, the incidence of human Salmonella enterica serotype Enteritidis (S. Enteritidis) infections have increased steadily during the last decade. Our study evaluated the spatial and temporal epidemiology of the major phage types (PTs) of S. Enteritidis infections to aid public health practitioners design effective prevention and control programs. Data on S. Enteritidis infections between January 1, 2008 and December 31, 2009 were obtained from Ontario's disease surveillance system. Salmonella Enteritidis infections with major phage types were classified by their annual health region-level incidence rates (IRs), monthly IRs, clinical symptoms, and exposure settings. A scan statistic was employed to detect retrospective phage type-specific spatial, temporal, and space-time clusters of S. Enteritidis infections. Space-time cluster cases' exposure settings were evaluated to identify common exposures. 1,336 cases were available for analysis. The six most frequently reported S. Enteritidis PTs were 8 (n = 398), 13a (n = 218), 13 (n = 198), 1 (n = 132), 5b (n = 83), and 4 (n = 76). Reported rates of S. Enteritidis infections with major phage types varied by health region and month. International travel and unknown exposure settings were the most frequently reported settings for PT 5b, 4, and 1 cases, whereas unknown exposure setting, private home, food premise, and international travel were the most frequently reported settings for PT 8, 13, and 13a cases. Diarrhea, abdominal pain, and fever were the most commonly reported clinical symptoms. A number of phage type-specific spatial, temporal, and space-time clusters were identified. Space-time clusters of PTs 1, 4, and 5b occurred mainly during the winter and spring months in the North West, North East, Eastern, Central East, and Central West regions. Space-time clusters of PTs 13 and 13a occurred at different times of the year in the Toronto region. Space-time clusters of PT 8 occurred at different times of the year in the North West and South West regions. Phage type-specific differences in exposure settings, and spatial-temporal clustering of S. Enteritidis infections were demonstrated that might guide public health surveillance of disease outbreaks. Our study methodology could be applied to other foodborne disease surveillance data to detect retrospective high disease rate clusters, which could aid public health authorities in developing effective prevention and control programs.

Spatial analysis for the identification of risk areas for schistosomiasis mansoni in the State of Sergipe, Brazil, 2005-2014.

PubMed

Santos, Allan Dantas Dos; Lima, Ana Caroline Rodrigues; Santos, Márcio Bezerra; Alves, José Antônio Barreto; Góes, Marco Aurélio de Oliveira; Nunes, Marco Antônio Prado; Sá, Sidney Lourdes César Souza; Araújo, Karina Conceição Gomes Machado de

2016-01-01

Schistosomiasis is a parasitic infectious disease with a worldwide prevalence. The objective of this work is to identify risk areas for schistosomiasis mansoni transmission in the State of Sergipe, Brazil, during the period from 2005 to 2014. We conducted an epidemiological study with secondary data from the Information System Control Program of Schistosomiasis [Sistema de Informação do Programa de Controle da Esquistossomose (SISPCE)]. Temporal trends were analyzed to obtain the annual percentage change (APC) in the rates of annual prevalence. In addition to the description of general indicators of the disease, the spatial analysis was descriptive, by means of the estimator of intensity kernel, and showed spatial dependence by indicators of global Moran (I) and Local Index of Spatial Association (LISA). Thematic maps of spatial distribution were made, identifying priority intervention areas in need of healthcare. There were 78,663 cases of schistosomiasis, with an average of 8.7% positivity recorded; 79.8% of the cases were treated, and Sergipe showed a decreasing positive trend (APC: -2.78). There was the presence of spatial autocorrelation and a significant global Moran index (I = 0.19; p-value = 0.03). We identified clusters of high-risk areas, mainly located in the northeast and southcentral of the state, which each had equally high infection rates. There was a decreasing positive trend of schistosomiasis in Sergipe. Spatial analysis identified the geographic distribution of risk and allowed the definition of priority areas for the maintenance and intensification of control interventions.

Planning for smallpox outbreaks

NASA Astrophysics Data System (ADS)

Ferguson, Neil M.; Keeling, Matt J.; John Edmunds, W.; Gani, Raymond; Grenfell, Bryan T.; Anderson, Roy M.; Leach, Steve

2003-10-01

Mathematical models of viral transmission and control are important tools for assessing the threat posed by deliberate release of the smallpox virus and the best means of containing an outbreak. Models must balance biological realism against limitations of knowledge, and uncertainties need to be accurately communicated to policy-makers. Smallpox poses the particular challenge that key biological, social and spatial factors affecting disease spread in contemporary populations must be elucidated largely from historical studies undertaken before disease eradication in 1979. We review the use of models in smallpox planning within the broader epidemiological context set by recent outbreaks of both novel and re-emerging pathogens.

Epidemiology of soil-transmitted helminthiases-related mortality in Brazil.

PubMed

Martins-Melo, Francisco R; Ramos, Alberto N; Alencar, Carlos H; Lima, Mauricélia S; Heukelbach, Jorg

2017-04-01

Soil-transmitted helminth (STH) infections are widely distributed in tropical and subtropical areas, including Brazil. We performed a nationwide population-based study including all deaths in Brazil from 2000 to 2011, in which STHs (ascariasis, trichuriasis and/or hookworm infection) were mentioned on death certificates, either as underlying or as associated causes of death. Epidemiological characteristics, time trends and spatial analysis of STH-related mortality were analysed. STHs was identified on 853/12 491 280 death certificates: 827 (97·0%) deaths related to ascariasis, 25 (2·9%) to hookworm infections, and 1 (0·1%) to trichuriasis. The average annual age-adjusted mortality rate was 0·34/1 000 000 inhabitants (95% confidence interval: 0·27-0·44). Females, children <10 years of age, indigenous ethnic groups and residents in the Northeast region had highest STH-related mortality rates. Nationwide mortality decreased significantly over time (annual percent change: -5·7%; 95% CI: -6·9 to -4·4), with regional differences. We identified spatial high-risk clusters for STH-related mortality mainly in the North, Northeast and South regions. Diseases of the digestive system and infectious/parasitic diseases were the most commonly associated causes of death mentioned in the STH-related deaths. Despite decreasing mortality in Brazil, a considerable number of deaths is caused by STHs, with ascariasis responsible for the vast majority. There were marked regional differences, affecting mainly children and vulnerable populations.

Geospatial tools for the identification of a malaria corridor in Estado Sucre, a Venezuelan north-eastern state.

PubMed

Delgado-Petrocelli, Laura; Camardiel, Alberto; Aguilar, Víctor Hugo; Martinez, Néstor; Córdova, Karenia; Ramos, Santiago

2011-05-01

Landscape ecology research relies on frameworks based on geographical information systems (GIS), geostatistics and spatial-feature relationships. With regard to health, the approach consists of systems analysis using a set of powerful tools aimed at the reduction of community vulnerability through improved public policies. The north-oriental malaria focus, one of five such foci in Venezuela, situated in the north-eastern part of the Estado Sucre state, unites several social and environmental features and functions as an epidemiological corridor, i.e. an endemic zone characterised by permanent interaction between the mosquito vector and the human host allowing a continuous persistence of the malaria lifecycle. A GIS was developed based on official cartography with thematic overlays depicting malaria distribution, socio-economic conditions, basic environmental information and specific features associated with the natural wetlands present in the area. Generally, malaria foci are continuously active but when the malaria situation was modelled in the north-oriental focus, a differential, spatio-temporal distribution pattern situation was found, i.e. a situation oscillating between very active and dormant transmission. This pattern was displayed by spatial and statistical analysis based on the model generated in this study and the results were confirmed by municipal and county malaria records. Control of malaria, keeping the incidence at a permanently low level within the regional population, should be possible if these results are taken into account when designing and implementing epidemiological surveillance policies.

Effect of transmission intensity on hotspots and micro-epidemiology of malaria in sub-Saharan Africa.

PubMed

Mogeni, Polycarp; Omedo, Irene; Nyundo, Christopher; Kamau, Alice; Noor, Abdisalan; Bejon, Philip

2017-06-30

Malaria transmission intensity is heterogeneous, complicating the implementation of malaria control interventions. We provide a description of the spatial micro-epidemiology of symptomatic malaria and asymptomatic parasitaemia in multiple sites. We assembled data from 19 studies conducted between 1996 and 2015 in seven countries of sub-Saharan Africa with homestead-level geospatial data. Data from each site were used to quantify spatial autocorrelation and examine the temporal stability of hotspots. Parameters from these analyses were examined to identify trends over varying transmission intensity. Significant hotspots of malaria transmission were observed in most years and sites. The risk ratios of malaria within hotspots were highest at low malaria positive fractions (MPFs) and decreased with increasing MPF (p < 0.001). However, statistical significance of hotspots was lowest at extremely low and extremely high MPFs, with a peak in statistical significance at an MPF of ~0.3. In four sites with longitudinal data we noted temporal instability and variable negative correlations between MPF and average age of symptomatic malaria across all sites, suggesting varying degrees of temporal stability. We observed geographical micro-variation in malaria transmission at sites with a variety of transmission intensities across sub-Saharan Africa. Hotspots are marked at lower transmission intensity, but it becomes difficult to show statistical significance when cases are sparse at very low transmission intensity. Given the predictability with which hotspots occur as transmission intensity falls, malaria control programmes should have a low threshold for responding to apparent clustering of cases.

Modelling dwarf mistletoe at three scales: life history, ballistics and contagion

Treesearch

Donald C. E. Robinson; Brian W. Geils

2006-01-01

The epidemiology of dwarf mistletoe (Arceuthobium) is simulated for the reproduction, dispersal, and spatial patterns of these plant pathogens on conifer trees. A conceptual model for mistletoe spread and intensification is coded as sets of related subprograms that link to either of two individual-tree growth models (FVS and TASS) used by managers to develop...

Landscape epidemiology and control of pathogens with cryptic and long-distance dispersal: Sudden oak death in northern Californian forests

Treesearch

Joao A. N. Filipe; Richard C. Cobb; Ross K. Meentemeyer; Christopher A. Lee; Yana S. Valachovic; Alex R. Cook; David M. Rizzo; Christopher A. Gilligan

2012-01-01

Exotic pathogens and pests threaten ecosystem service, biodiversity, and crop security globally. If an invasive agent can disperse asymptomatically over long distances, multiple spatial and temporal scales interplay, making identification of effective strategies to regulate, monitor, and control disease extremely difficult. The management of outbreaks is also...

Disparities in the Geography of Mental Health: Implications for Social Work

ERIC Educational Resources Information Center

Hudson, Christopher G.

2012-01-01

This article reviews recent theory and research on geographic disparities in mental health and their implications for social work. It focuses on work emerging from the fields of mental health geography, psychiatric epidemiology, and social work, arguing that a wide range of spatial disparities in mental health are important to understand but that…

The role of geostatistics in medical geology

NASA Astrophysics Data System (ADS)

Goovaerts, Pierre

2014-05-01

Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences, to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential 'causes' of disease, such as environmental exposure, diet and unhealthy behaviors, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentrations across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level. Arsenic in drinking-water is a major problem and has received much attention because of the large human population exposed and the extremely high concentrations (e.g. 600 to 700 μg/L) recorded in many instances. Few studies have however assessed the risks associated with exposure to low levels of arsenic (say < 50 μg/L) most commonly found in drinking water in the United States. In the Michigan thumb region, arsenopyrite (up to 7% As by weight) has been identified in the bedrock of the Marshall Sandstone aquifer, one of the region's most productive aquifers. Epidemiologic studies have suggested a possible associationbetween exposure to inorganic arsenic and prostate cancer mortality, including a study of populations residing in Utah. The information available for the present ecological study (i.e. analysis of aggregated health outcomes) consist of: 1) 9,188 arsenic concentrations measured at 8,212 different private wells that were sampled between 1993 and 2002, 2) prostate cancer incidence recorded at the township level over the period 1985-2002, and 3) block-group population density that served as proxy for urbanization and use of regulated public water supply versus use of potentially contaminated private wells in rural areas.

Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect

NASA Astrophysics Data System (ADS)

Li, Lianfa; Wu, Anna H.; Cheng, Iona; Chen, Jiu-Chiuan; Wu, Jun

2017-10-01

Monitoring of fine particulate matter with diameter <2.5 μm (PM2.5) started from 1999 in the US and even later in many other countries. The lack of historical PM2.5 data limits epidemiological studies of long-term exposure of PM2.5 and health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter <10 μm (PM10) and meteorological variables. Monitoring data of PM10, PM2.5, and meteorological variables covering the entire state of California were obtained from 1999 through 2013. We developed a spatiotemporal model that quantified non-linear associations between PM2.5 concentrations and the following predictor variables: spatiotemporal factors (PM10 and meteorological variables), spatial factors (land-use patterns, traffic, elevation, distance to shorelines, and spatial autocorrelation), and season. Our model accounted for regional-(county) scale spatial autocorrelation, using spatial weight matrix, and local-scale spatiotemporal variability, using local covariates in additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R2 of 0.61 for the daily model when PM10 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available.

Ecological characterization and molecular differentiation of Culex pipiens complex taxa and Culex torrentium in eastern Austria.

PubMed

Zittra, Carina; Flechl, Eva; Kothmayer, Michael; Vitecek, Simon; Rossiter, Heidemarie; Zechmeister, Thomas; Fuehrer, Hans-Peter

2016-04-11

Culex pipiens complex taxa differ in behaviour, ecophysiology and epidemiologic importance. Despite their epidemiologic significance, information on genetic diversity, occurrence and seasonal and spatial distribution patterns of the Cx. pipiens complex is still insufficient. Assessment of seasonal and spatial distribution patterns of Culex pipiens forms and their congener Cx. torrentium is crucial for the understanding of their vector-pathogen dynamics. Female mosquitoes were trapped from April-October 2014 twice a month for a 24-h time period with BG-sentinel traps at 24 sampling sites in eastern Austria, using carbon dioxide as attractant. Ecological forms of Cx. pipiens s.l. and their hybrids were differentiated using the CQ11 locus, and Cx. pipiens forms and their congener Cx. torrentium using the ACE-2 gene. Differential exploitation of ecological niches by Cx. pipiens forms and Cx. torrentium was analysed using likelihood ratio tests. Possible effects of environmental parameters on these taxa were tested using PERMANOVA based on distance matrices and, if significant, were modelled in nMDS ordination space to estimate non-linear relationships. For this study, 1476 Culex spp. were sampled. Culex pipiens f. pipiens representing 87.33 % of the total catch was most abundant, followed by hybrids of both forms (5.62 %), Cx. torrentium (3.79 %) and Cx. pipiens f. molestus (3.25 %). Differences in proportional abundances were found between land cover classes. Ecological parameters affecting seasonal and spatial distribution of these taxa in eastern Austria are precipitation duration, air temperature, sunlight and the interaction term of precipitation amount and the Danube water level, which can be interpreted as a proxy for breeding habitat availability. The Cx. pipiens complex of eastern Austria comprises both ecologically different forms, the mainly ornithophilic form pipiens and the mainly mammalophilic and anthropophilic form molestus. Heterogeneous agricultural areas as areas of coexistence may serve as hybridization zones, resulting in potential bridge vectors between birds and humans. Occurrence, seasonal and spatial distribution patterns of the Cx. pipiens complex and Cx. torrentium and the presence of hybrids between both forms were quantified for the first time in Austria. These findings will improve the knowledge of their vector-pathogen dynamics in this country.

A study on spatial decision support systems for HIV/AIDS prevention based on COM GIS technology

NASA Astrophysics Data System (ADS)

Yang, Kun; Luo, Huasong; Peng, Shungyun; Xu, Quanli

2007-06-01

Based on the deeply analysis of the current status and the existing problems of GIS technology applications in Epidemiology, this paper has proposed the method and process for establishing the spatial decision support systems of AIDS epidemic prevention by integrating the COM GIS, Spatial Database, GPS, Remote Sensing, and Communication technologies, as well as ASP and ActiveX software development technologies. One of the most important issues for constructing the spatial decision support systems of AIDS epidemic prevention is how to integrate the AIDS spreading models with GIS. The capabilities of GIS applications in the AIDS epidemic prevention have been described here in this paper firstly. Then some mature epidemic spreading models have also been discussed for extracting the computation parameters. Furthermore, a technical schema has been proposed for integrating the AIDS spreading models with GIS and relevant geospatial technologies, in which the GIS and model running platforms share a common spatial database and the computing results can be spatially visualized on Desktop or Web GIS clients. Finally, a complete solution for establishing the decision support systems of AIDS epidemic prevention has been offered in this paper based on the model integrating methods and ESRI COM GIS software packages. The general decision support systems are composed of data acquisition sub-systems, network communication sub-systems, model integrating sub-systems, AIDS epidemic information spatial database sub-systems, AIDS epidemic information querying and statistical analysis sub-systems, AIDS epidemic dynamic surveillance sub-systems, AIDS epidemic information spatial analysis and decision support sub-systems, as well as AIDS epidemic information publishing sub-systems based on Web GIS.

On Spatially Explicit Models of Epidemic and Endemic Cholera: The Haiti and Lake Kivu Case Studies.

NASA Astrophysics Data System (ADS)

Rinaldo, A.; Bertuzzo, E.; Mari, L.; Finger, F.; Casagrandi, R.; Gatto, M.; Rodriguez-Iturbe, I.

2014-12-01

The first part of the Lecture deals with the predictive ability of mechanistic models for the Haitian cholera epidemic. Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. A formal model comparison framework provides a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels. Intensive computations and objective model comparisons show that parsimonious spatially explicit models accounting for spatial connections have superior explanatory power than spatially disconnected ones for short-to intermediate calibration windows. In general, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management. The second part deals with approaches suitable to describe patterns of endemic cholera. Cholera outbreaks have been reported in the Democratic Republic of the Congo since the 1970s. Here we employ a spatially explicit, inhomogeneous Markov chain model to describe cholera incidence in eight health zones on the shore of lake Kivu. Remotely sensed datasets of chlorophyll a concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers in addition to baseline seasonality. The effect of human mobility is also modelled mechanistically. We test several models on a multi-year dataset of reported cholera cases. Fourteen models, accounting for different environmental drivers, are selected in calibration. Among these, the one accounting for seasonality, El Nino Southern Oscillation, precipitation and human mobility outperforms the others in cross-validation.

Changing livestock vaccination policy alters the epidemiology of human anthrax, Georgia, 2000-2013.

PubMed

Kracalik, Ian; Malania, Lile; Broladze, Mariam; Navdarashvili, Archil; Imnadze, Paata; Ryan, Sadie J; Blackburn, Jason K

2017-11-01

Anthrax is a widely spread zoonotic disease found on nearly every continent. To control the disease in humans and animals, annual livestock vaccination is recommended. However, in 2007, the country of Georgia ended its policy of compulsory annual livestock anthrax vaccination. Our objective was to assess how the epidemiology of human anthrax has evolved from 2000-2013 in Georgia, in the wake of this cessation. We used passive surveillance data on epidemiological surveys of human anthrax case patients. Risk factors and rates of self-reported sources of infection were compared, before and after the change in livestock vaccination policy. We mapped ethnicity-adjusted incidence during the two periods and assessed changes in the spatial pattern of risk. The overall risk of human anthrax increased >5-fold, from 0.7 cases per 100,000 in 2000 to 3.7 cases per 100,000 by 2013. Ethnic disparities in risk became pronounced; from 2000 to 2013, incidence increased >60-fold in Azerbaijanis from 0.35 to 21.1 cases/100,000 Azerbaijanis compared to 0.61 to 1.9 cases/100,000 among ethnic Georgians. Food-borne exposures from purchasing meat increased from 11% in 2000-2006 to 21% in 2007-2013. Spatial analyses revealed a shift from a random pattern of reporting pre-policy change to clustering among district municipalities following the change in policy. Our findings indicate there were unintended human health consequences associated with changing livestock vaccination policy. Following a reduction in the immunizations administered, there was a major shift in the epidemiology of human anthrax in Georgia. Current infection risk is now highest among ethnic minorities. Increased reporting among individuals uncharacteristically at risk for anthrax from foodborne exposures suggests spillover from modes of agricultural production. Given the importance of human-livestock health linkages, careful evaluations of policy need to be undertaken before changes to animal vaccination are made. Copyright © 2017 Elsevier Ltd. All rights reserved.

Genomic Epidemiology Reconstructs the Introduction and Spread of Zika Virus in Central America and Mexico.

PubMed

Thézé, Julien; Li, Tony; du Plessis, Louis; Bouquet, Jerome; Kraemer, Moritz U G; Somasekar, Sneha; Yu, Guixia; de Cesare, Mariateresa; Balmaseda, Angel; Kuan, Guillermina; Harris, Eva; Wu, Chieh-Hsi; Ansari, M Azim; Bowden, Rory; Faria, Nuno R; Yagi, Shigeo; Messenger, Sharon; Brooks, Trevor; Stone, Mars; Bloch, Evan M; Busch, Michael; Muñoz-Medina, José E; González-Bonilla, Cesar R; Wolinsky, Steven; López, Susana; Arias, Carlos F; Bonsall, David; Chiu, Charles Y; Pybus, Oliver G

2018-06-13

The Zika virus (ZIKV) epidemic in the Americas established ZIKV as a major public health threat and uncovered its association with severe diseases, including microcephaly. However, genetic epidemiology in some at-risk regions, particularly Central America and Mexico, remains limited. We report 61 ZIKV genomes from this region, generated using metagenomic sequencing with ZIKV-specific enrichment, and combine phylogenetic, epidemiological, and environmental data to reconstruct ZIKV transmission. These analyses revealed multiple independent ZIKV introductions to Central America and Mexico. One introduction, likely from Brazil via Honduras, led to most infections and the undetected spread of ZIKV through the region from late 2014. Multiple lines of evidence indicate biannual peaks of ZIKV transmission in the region, likely driven by varying local environmental conditions for mosquito vectors and herd immunity. The spatial and temporal heterogeneity of ZIKV transmission in Central America and Mexico challenges arbovirus surveillance and disease control measures. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

A spatial epidemiological analysis of nontuberculous mycobacterial infections in Queensland, Australia.

PubMed

Chou, Michael P; Clements, Archie C A; Thomson, Rachel M

2014-05-21

The epidemiology of infections with nontuberculous mycobacteria (NTM) has been changing and the incidence has been increasing in some settings. The main route of transmission to humans is considered to be from the environment. We aimed to describe spatial clusters of cases of NTM infections and to identify associated climatic, environmental and socio-economic variables. NTM data were obtained from the Queensland Mycobacterial Reference Laboratory for the period 2001-2011. A Bayesian spatial conditional autoregressive model was constructed at the postcode level, with covariates including soil variables, maximum, mean and minimum rainfall and temperature, income (proportion of population earning < $32,000 and < $52,000) and land use category. Significant clusters of NTM infection were identified in the central Queensland region overlying the Surat sub-division of the Great Artesian Basin, as well as in the lower North Queensland Local Government Area known as the Whitsunday region. Our models estimated an expected increase of 21% per percentage increase of population earning < $52,000 (95% CI 9-34%) and an expected decrease of 13% for every metre increase of average topsoil depth for risk of Mycobacterium intracellulare infection (95% CI -3 - -22%). There was an estimated increase of 79% per mg/m3 increase of soil bulk density (95% CI 26-156%) and 19% decrease for every percentage increase in population earning < $32,000 for risk of M. kansasii infection (95% CI -3 - -49%). There were distinct spatial clusters of M. kansasii, M. intracellulare and M. abscessus infections in Queensland, and a number of socio-ecological, economic and environmental factors were found to be associated with NTM infection risk.

Identifying areas at risk of low birth weight using spatial epidemiology: A small area surveillance study.

PubMed

Insaf, Tabassum Z; Talbot, Thomas

2016-07-01

To assess the geographic distribution of Low Birth Weight (LBW) in New York State among singleton births using a spatial regression approach in order to identify priority areas for public health actions. LBW was defined as birth weight less than 2500g. Geocoded data from 562,586 birth certificates in New York State (years 2008-2012) were merged with 2010 census data at the tract level. To provide stable estimates and maintain confidentiality, data were aggregated to yield 1268 areas of analysis. LBW prevalence among singleton births was related with area-level behavioral, socioeconomic and demographic characteristics using a Poisson mixed effects spatial error regression model. Observed low birth weight showed statistically significant auto-correlation in our study area (Moran's I 0.16 p value 0.0005). After over-dispersion correction and accounting for fixed effects for selected social determinants, spatial autocorrelation was fully accounted for (Moran's I-0.007 p value 0.241). The proportion of LBW was higher in areas with larger Hispanic or Black populations and high smoking prevalence. Smoothed maps with predicted prevalence were developed to identify areas at high risk of LBW. Spatial patterns of residual variation were analyzed to identify unique risk factors. Neighborhood racial composition contributes to disparities in LBW prevalence beyond differences in behavioral and socioeconomic factors. Small-area analyses of LBW can identify areas for targeted interventions and display unique local patterns that should be accounted for in prevention strategies. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Comprehensive personal RF-EMF exposure map and its potential use in epidemiological studies.

PubMed

Gonzalez-Rubio, Jesus; Najera, Alberto; Arribas, Enrique

2016-08-01

In recent years, numerous epidemiological studies, which deal with the potential effects of mobile phone antennas on health, have almost exclusively focused on their distance to mobile phone base stations. Although it is known that this is not the best approach to the problem, this situation occurs due to the numerous difficulties when determining the personal exposure to the radiofrequency electromagnetic fields (RF-EMF). However, due to the rise of personal exposimeters, the evolution of spatial statistics, the development of geographical information systems and the use of powerful software, new alternatives are available to deal with these epidemiological studies and thus overcome the aforementioned difficulties. Using these tools, this paper presents a lattice map of personal RF-EMF exposure from exterior mobile phone base stations, covering the entire 110 administrative regions in the city of Albacete (Spain). For this purpose, we used a personal exposimeter, Satimo EME Spy 140 model, performing measurements every 4s The exposimeter was located inside the plastic basket of a bicycle, whose versatility permitted the access to all the zones of the city. Once the exposure map was prepared, its relation with the known antenna locations was studied. The 64 mobile telephone antennas of the city were also georeferenced; the randomness of both variables (exposure and antennas) were studied by means of the Moran's I test. Results showed that the distribution of the antennas follows a grouped pattern (p<0.001), while the distribution of the average exposure values have a random distribution (p=0.618). In addition, we showed two Spearman correlation studies: the first between the average exposure values and the number of mobile telephone antennas per administrative region, and the second, also considering the antennas of the neighbouring regions. No substantial correlation was detected in either of the two cases. This study also reveals the weaknesses of the epidemiological studies, which only take into account the distance to the antennas, which would provide a new approach to the problem. By precisely knowing the resident population of each administrative region of the city, this proves to be highly useful to rely on a prepared aggregate data map based on the mean exposure values to RF-EMF in these sections. The displayed map would permit the execution of more accurate epidemiological studies, since it would be possible to compare the exposure measurements with the incidence data of a disease. Copyright © 2016 Elsevier Inc. All rights reserved.

Multiplicity and molecular epidemiology of Plasmodium vivax and Plasmodium falciparum infections in East Africa.

PubMed

Zhong, Daibin; Lo, Eugenia; Wang, Xiaoming; Yewhalaw, Delenasaw; Zhou, Guofa; Atieli, Harrysone E; Githeko, Andrew; Hemming-Schroeder, Elizabeth; Lee, Ming-Chieh; Afrane, Yaw; Yan, Guiyun

2018-05-02

Parasite genetic diversity and multiplicity of infection (MOI) affect clinical outcomes, response to drug treatment and naturally-acquired or vaccine-induced immunity. Traditional methods often underestimate the frequency and diversity of multiclonal infections due to technical sensitivity and specificity. Next-generation sequencing techniques provide a novel opportunity to study complexity of parasite populations and molecular epidemiology. Symptomatic and asymptomatic Plasmodium vivax samples were collected from health centres/hospitals and schools, respectively, from 2011 to 2015 in Ethiopia. Similarly, both symptomatic and asymptomatic Plasmodium falciparum samples were collected, respectively, from hospitals and schools in 2005 and 2015 in Kenya. Finger-pricked blood samples were collected and dried on filter paper. Long amplicon (> 400 bp) deep sequencing of merozoite surface protein 1 (msp1) gene was conducted to determine multiplicity and molecular epidemiology of P. vivax and P. falciparum infections. The results were compared with those based on short amplicon (117 bp) deep sequencing. A total of 139 P. vivax and 222 P. falciparum samples were pyro-sequenced for pvmsp1 and pfmsp1, yielding a total of 21 P. vivax and 99 P. falciparum predominant haplotypes. The average MOI for P. vivax and P. falciparum were 2.16 and 2.68, respectively, which were significantly higher than that of microsatellite markers and short amplicon (117 bp) deep sequencing. Multiclonal infections were detected in 62.2% of the samples for P. vivax and 74.8% of the samples for P. falciparum. Four out of the five subjects with recurrent P. vivax malaria were found to be a relapse 44-65 days after clearance of parasites. No difference was observed in MOI among P. vivax patients of different symptoms, ages and genders. Similar patterns were also observed in P. falciparum except for one study site in Kenyan lowland areas with significantly higher MOI. The study used a novel method to evaluate Plasmodium MOI and molecular epidemiological patterns by long amplicon ultra-deep sequencing. The complexity of infections were similar among age groups, symptoms, genders, transmission settings (spatial heterogeneity), as well as over years (pre- vs. post-scale-up interventions). This study demonstrated that long amplicon deep sequencing is a useful tool to investigate multiplicity and molecular epidemiology of Plasmodium parasite infections.

Typhoid Fever and Its Association with Environmental Factors in the Dhaka Metropolitan Area of Bangladesh: A Spatial and Time-Series Approach

PubMed Central

Dewan, Ashraf M.; Corner, Robert; Hashizume, Masahiro; Ongee, Emmanuel T.

2013-01-01

Typhoid fever is a major cause of death worldwide with a major part of the disease burden in developing regions such as the Indian sub-continent. Bangladesh is part of this highly endemic region, yet little is known about the spatial and temporal distribution of the disease at a regional scale. This research used a Geographic Information System to explore, spatially and temporally, the prevalence of typhoid in Dhaka Metropolitan Area (DMA) of Bangladesh over the period 2005–9. This paper provides the first study of the spatio-temporal epidemiology of typhoid for this region. The aims of the study were: (i) to analyse the epidemiology of cases from 2005 to 2009; (ii) to identify spatial patterns of infection based on two spatial hypotheses; and (iii) to determine the hydro-climatological factors associated with typhoid prevalence. Case occurrences data were collected from 11 major hospitals in DMA, geocoded to census tract level, and used in a spatio-temporal analysis with a range of demographic, environmental and meteorological variables. Analyses revealed distinct seasonality as well as age and gender differences, with males and very young children being disproportionately infected. The male-female ratio of typhoid cases was found to be 1.36, and the median age of the cases was 14 years. Typhoid incidence was higher in male population than female (χ2 = 5.88, p<0.05). The age-specific incidence rate was highest for the 0–4 years age group (277 cases), followed by the 60+ years age group (51 cases), then there were 45 cases for 15–17 years, 37 cases for 18–34 years, 34 cases for 35–39 years and 11 cases for 10–14 years per 100,000 people. Monsoon months had the highest disease occurrences (44.62%) followed by the pre-monsoon (30.54%) and post-monsoon (24.85%) season. The Student's t test revealed that there is no significant difference on the occurrence of typhoid between urban and rural environments (p>0.05). A statistically significant inverse association was found between typhoid incidence and distance to major waterbodies. Spatial pattern analysis showed that there was a significant clustering of typhoid distribution in the study area. Moran's I was highest (0.879; p<0.01) in 2008 and lowest (0.075; p<0.05) in 2009. Incidence rates were found to form three large, multi-centred, spatial clusters with no significant difference between urban and rural rates. Temporally, typhoid incidence was seen to increase with temperature, rainfall and river level at time lags ranging from three to five weeks. For example, for a 0.1 metre rise in river levels, the number of typhoid cases increased by 4.6% (95% CI: 2.4–2.8) above the threshold of 4.0 metres (95% CI: 2.4–4.3). On the other hand, with a 1°C rise in temperature, the number of typhoid cases could increase by 14.2% (95% CI: 4.4–25.0). PMID:23359825

Genotyping and spatial analysis of pulmonary tuberculosis and diabetes cases in the state of Veracruz, Mexico.

PubMed

Blanco-Guillot, Francles; Castañeda-Cediel, M Lucía; Cruz-Hervert, Pablo; Ferreyra-Reyes, Leticia; Delgado-Sánchez, Guadalupe; Ferreira-Guerrero, Elizabeth; Montero-Campos, Rogelio; Bobadilla-Del-Valle, Miriam; Martínez-Gamboa, Rosa Areli; Torres-González, Pedro; Téllez-Vazquez, Norma; Canizales-Quintero, Sergio; Yanes-Lane, Mercedes; Mongua-Rodríguez, Norma; Ponce-de-León, Alfredo; Sifuentes-Osornio, José; García-García, Lourdes

2018-01-01

Genotyping and georeferencing in tuberculosis (TB) have been used to characterize the distribution of the disease and occurrence of transmission within specific groups and communities. The objective of this study was to test the hypothesis that diabetes mellitus (DM) and pulmonary TB may occur in spatial and molecular aggregations. Retrospective cohort study of patients with pulmonary TB. The study area included 12 municipalities in the Sanitary Jurisdiction of Orizaba, Veracruz, México. Patients with acid-fast bacilli in sputum smears and/or Mycobacterium tuberculosis in sputum cultures were recruited from 1995 to 2010. Clinical (standardized questionnaire, physical examination, chest X-ray, blood glucose test and HIV test), microbiological, epidemiological, and molecular evaluations were carried out. Patients were considered "genotype-clustered" if two or more isolates from different patients were identified within 12 months of each other and had six or more IS6110 bands in an identical pattern, or < 6 bands with identical IS6110 RFLP patterns and spoligotype with the same spacer oligonucleotides. Residential and health care centers addresses were georeferenced. We used a Jeep hand GPS. The coordinates were transferred from the GPS files to ArcGIS using ArcMap 9.3. We evaluated global spatial aggregation of patients in IS6110-RFLP/ spoligotype clusters using global Moran´s I. Since global distribution was not random, we evaluated "hotspots" using Getis-Ord Gi* statistic. Using bivariate and multivariate analysis we analyzed sociodemographic, behavioral, clinic and bacteriological conditions associated with "hotspots". We used STATA® v13.1 for all statistical analysis. From 1995 to 2010, 1,370 patients >20 years were diagnosed with pulmonary TB; 33% had DM. The proportion of isolates that were genotyped was 80.7% (n = 1105), of which 31% (n = 342) were grouped in 91 genotype clusters with 2 to 23 patients each; 65.9% of total clusters were small (2 members) involving 35.08% of patients. Twenty three (22.7) percent of cases were classified as recent transmission. Moran`s I indicated that distribution of patients in IS6110-RFLP/spoligotype clusters was not random (Moran`s I = 0.035468, Z value = 7.0, p = 0.00). Local spatial analysis showed statistically significant spatial aggregation of patients in IS6110-RFLP/spoligotype clusters identifying "hotspots" and "coldspots". GI* statistic showed that the hotspot for spatial clustering was located in Camerino Z. Mendoza municipality; 14.6% (50/342) of patients in genotype clusters were located in a hotspot; of these, 60% (30/50) lived with DM. Using logistic regression the statistically significant variables associated with hotspots were: DM [adjusted Odds Ratio (aOR) 7.04, 95% Confidence interval (CI) 3.03-16.38] and attending the health center in Camerino Z. Mendoza (aOR18.04, 95% CI 7.35-44.28). The combination of molecular and epidemiological information with geospatial data allowed us to identify the concurrence of molecular clustering and spatial aggregation of patients with DM and TB. This information may be highly useful for TB control programs.

Genotyping and spatial analysis of pulmonary tuberculosis and diabetes cases in the state of Veracruz, Mexico

PubMed Central

Blanco-Guillot, Francles; Ferreyra-Reyes, Leticia; Delgado-Sánchez, Guadalupe; Ferreira-Guerrero, Elizabeth; Montero-Campos, Rogelio; Bobadilla-del-Valle, Miriam; Martínez-Gamboa, Rosa Areli; Torres-González, Pedro; Téllez-Vazquez, Norma; Canizales-Quintero, Sergio; Yanes-Lane, Mercedes; Mongua-Rodríguez, Norma; Ponce-de-León, Alfredo; Sifuentes-Osornio, José

2018-01-01

Background Genotyping and georeferencing in tuberculosis (TB) have been used to characterize the distribution of the disease and occurrence of transmission within specific groups and communities. Objective The objective of this study was to test the hypothesis that diabetes mellitus (DM) and pulmonary TB may occur in spatial and molecular aggregations. Material and methods Retrospective cohort study of patients with pulmonary TB. The study area included 12 municipalities in the Sanitary Jurisdiction of Orizaba, Veracruz, México. Patients with acid-fast bacilli in sputum smears and/or Mycobacterium tuberculosis in sputum cultures were recruited from 1995 to 2010. Clinical (standardized questionnaire, physical examination, chest X-ray, blood glucose test and HIV test), microbiological, epidemiological, and molecular evaluations were carried out. Patients were considered “genotype-clustered” if two or more isolates from different patients were identified within 12 months of each other and had six or more IS6110 bands in an identical pattern, or < 6 bands with identical IS6110 RFLP patterns and spoligotype with the same spacer oligonucleotides. Residential and health care centers addresses were georeferenced. We used a Jeep hand GPS. The coordinates were transferred from the GPS files to ArcGIS using ArcMap 9.3. We evaluated global spatial aggregation of patients in IS6110-RFLP/ spoligotype clusters using global Moran´s I. Since global distribution was not random, we evaluated “hotspots” using Getis-Ord Gi* statistic. Using bivariate and multivariate analysis we analyzed sociodemographic, behavioral, clinic and bacteriological conditions associated with “hotspots”. We used STATA® v13.1 for all statistical analysis. Results From 1995 to 2010, 1,370 patients >20 years were diagnosed with pulmonary TB; 33% had DM. The proportion of isolates that were genotyped was 80.7% (n = 1105), of which 31% (n = 342) were grouped in 91 genotype clusters with 2 to 23 patients each; 65.9% of total clusters were small (2 members) involving 35.08% of patients. Twenty three (22.7) percent of cases were classified as recent transmission. Moran`s I indicated that distribution of patients in IS6110-RFLP/spoligotype clusters was not random (Moran`s I = 0.035468, Z value = 7.0, p = 0.00). Local spatial analysis showed statistically significant spatial aggregation of patients in IS6110-RFLP/spoligotype clusters identifying “hotspots” and “coldspots”. GI* statistic showed that the hotspot for spatial clustering was located in Camerino Z. Mendoza municipality; 14.6% (50/342) of patients in genotype clusters were located in a hotspot; of these, 60% (30/50) lived with DM. Using logistic regression the statistically significant variables associated with hotspots were: DM [adjusted Odds Ratio (aOR) 7.04, 95% Confidence interval (CI) 3.03–16.38] and attending the health center in Camerino Z. Mendoza (aOR18.04, 95% CI 7.35–44.28). Conclusions The combination of molecular and epidemiological information with geospatial data allowed us to identify the concurrence of molecular clustering and spatial aggregation of patients with DM and TB. This information may be highly useful for TB control programs. PMID:29534104

Using NASA Remotely Sensed Data to Help Characterize Environmental Risk Factors for National Public Health Applications

NASA Technical Reports Server (NTRS)

Al-Hamdan, Mohammad; Crosson, William; Economou, Sigrid; Estes,Maurice, Jr.; Estes, Sue; Hemmings, Sarah; Kent, Shia; Puckett, Mark; Quattrochi, Dale; Wade, Gina;

Using NASA Remotely Sensed Data to Help Characterize Environmental Risk Factors for National Public Health Applications

NASA Astrophysics Data System (ADS)

Al-Hamdan, M. Z.; Crosson, W. L.; Economou, S.; Estes, M., Jr.; Estes, S. M.; Hemmings, S. N.; Kent, S.; Loop, M.; Puckett, M.; Quattrochi, D. A.; Wade, G.; McClure, L.

2012-12-01

The overall goal of this study is to address issues of environmental health and enhance public health decision making by using NASA remotely sensed data and products. This study is a collaboration between NASA Marshall Space Flight Center, Universities Space Research Association (USRA), the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) Office of Surveillance, Epidemiology and Laboratory Services. The objectives of this study are to develop high-quality spatial data sets of environmental variables, link these with public health data from a national cohort study, and deliver the environmental data sets and associated public health analyses to local, state and federal end-user groups. Three daily environmental data sets were developed for the conterminous U.S. on different spatial resolutions for the period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) on a 10-km grid using US Environmental Protection Agency (EPA) ground observations and NASA's MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of MODIS Land Surface Temperature (LST); and (3) a 12-km grid of daily incoming solar radiation and maximum and minimum air temperature using the North American Land Data Assimilation System (NLDAS) data. These environmental datasets were linked with public health data from the UAB REasons for Geographic and Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline, stroke and other health outcomes. These environmental national datasets will also be made available to public health professionals, researchers and the general public via the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system, where they can be aggregated to the county-level, state-level, or regional-level as per users' need and downloaded in tabular, graphical, and map formats. This provides a significant addition to the CDC WONDER online system, allowing public health researchers and policy makers to better include environmental exposure data in the context of other health data available in CDC WONDER. It also substantially expands public access to NASA data, making their use by a wide range of decision-makers feasible.

Integrating Survey and Molecular Approaches to Better Understand Wildlife Disease Ecology

PubMed Central

Cowled, Brendan D.; Ward, Michael P.; Laffan, Shawn W.; Galea, Francesca; Garner, M. Graeme; MacDonald, Anna J.; Marsh, Ian; Muellner, Petra; Negus, Katherine; Quasim, Sumaiya; Woolnough, Andrew P.; Sarre, Stephen D.

2012-01-01

Infectious wildlife diseases have enormous global impacts, leading to human pandemics, global biodiversity declines and socio-economic hardship. Understanding how infection persists and is transmitted in wildlife is critical for managing diseases, but our understanding is limited. Our study aim was to better understand how infectious disease persists in wildlife populations by integrating genetics, ecology and epidemiology approaches. Specifically, we aimed to determine whether environmental or host factors were stronger drivers of Salmonella persistence or transmission within a remote and isolated wild pig (Sus scrofa) population. We determined the Salmonella infection status of wild pigs. Salmonella isolates were genotyped and a range of data was collected on putative risk factors for Salmonella transmission. We a priori identified several plausible biological hypotheses for Salmonella prevalence (cross sectional study design) versus transmission (molecular case series study design) and fit the data to these models. There were 543 wild pig Salmonella observations, sampled at 93 unique locations. Salmonella prevalence was 41% (95% confidence interval [CI]: 37–45%). The median Salmonella DICE coefficient (or Salmonella genetic similarity) was 52% (interquartile range [IQR]: 42–62%). Using the traditional cross sectional prevalence study design, the only supported model was based on the hypothesis that abundance of available ecological resources determines Salmonella prevalence in wild pigs. In the molecular study design, spatial proximity and herd membership as well as some individual risk factors (sex, condition score and relative density) determined transmission between pigs. Traditional cross sectional surveys and molecular epidemiological approaches are complementary and together can enhance understanding of disease ecology: abundance of ecological resources critical for wildlife influences Salmonella prevalence, whereas Salmonella transmission is driven by local spatial, social, density and individual factors, rather than resources. This enhanced understanding has implications for the control of diseases in wildlife populations. Attempts to manage wildlife disease using simplistic density approaches do not acknowledge the complexity of disease ecology. PMID:23071552

Zika virus infection and microcephaly: Evidence regarding geospatial associations.

PubMed

Vissoci, João Ricardo Nickenig; Rocha, Thiago Augusto Hernandes; Silva, Núbia Cristina da; de Sousa Queiroz, Rejane Christine; Thomaz, Erika Bárbara Abreu Fonseca; Amaral, Pedro Vasconcelos Maia; Lein, Adriana; Branco, Maria Dos Remédios Freitas Carvalho; Aquino, José; Rodrigues, Zulimar Márita Ribeiro; da Silva, Antônio Augusto Moura; Staton, Catherine

2018-04-01

Although the Zika virus (ZIKV) epidemic ceased to be a public health emergency by the end of 2016, studies to improve knowledge about this emerging disease are still needed, especially those investigating a causal relationship between ZIKV in pregnant women and microcephaly in neonates. However, there are still many challenges in describing the relationship between ZIKV and microcephaly. The few studies focusing on the epidemiological profile of ZIKV and its changes over time are largely limited to systematic reviews of case reports and dispersal mapping of ZIKV spread over time without quantitative methods to analyze patterns and their covariates. Since Brazil has been at the epicenter of the ZIKV epidemic, this study examines the geospatial association between ZIKV and microcephaly in Brazil. Our study is categorized as a retrospective, ecological study based on secondary databases. Data were obtained from January to December 2016, from the following data sources: Brazilian System for Epidemiological Surveillance, Disease Notification System, System for Specialized Management Support, and Brazilian Institute of Geography and Statistics. Data were aggregated by municipality. Incidence rates were estimated per 100,000 inhabitants. Analyses consisted of mapping the aggregated incidence rates of ZIKV and microcephaly, followed by a Getis-Ord-Gi spatial cluster analysis and a Bivariate Local Moran's I analysis. The incidence of ZIKV cases is changing the virus's spatial pattern, shifting from Brazil's Northeast region to the Midwest and North regions. The number of municipalities in clusters of microcephaly incidence is also shifting from the Northeast region to the Midwest and North, after a time lag is considered. Our findings suggest an increase in microcephaly incidence in the Midwest and North regions, associated with high levels of ZIKV infection months before. The greatest burden of microcephaly shifted from the Northeast to other Brazilian regions at the beginning of 2016. Brazil's Midwest region experienced an increase in microcephaly incidence associated with ZIKV incidence. This finding highlights an association between an increase in ZIKV infection with a rise in microcephaly cases after approximately three months.

Epidemiological, Serological, and Virological Features of Dengue in Nha Trang City, Vietnam

PubMed Central

Quyen, Duong Le; Thanh Le, Nguyen; Van Anh, Cao Thi; Nguyen, Nguyen Binh; Hoang, Dong Van; Montgomery, Jacqui L.; Kutcher, Simon C.; Hoang Le, Nguyen; Hien, Nguyen Tran; Hue Kien, Duong Thi; Rabaa, Maia; O’Neill, Scott L.; Simmons, Cameron P.; Anh, Dang Duc; Anders, Katherine L.

2018-01-01

Abstract. Vietnam is endemic for dengue. We conducted a series of retrospective and prospective studies to characterize the epidemiology of dengue and population mobility patterns in Nha Trang city, Vietnam, with a view to rational design of trials of community-level interventions. A 10-year time series of dengue case notifications showed pronounced interannual variability, as well as spatial heterogeneity in ward-level dengue incidence (median annual coefficient of variation k = 0.47). Of 451 children aged 1–10 years enrolled in a cross-sectional serosurvey, almost one-third had evidence of a past dengue virus (DENV) infection, with older children more likely to have a multitypic response indicative of past exposure to ≥ 1 serotype. All four DENV serotypes were detected in hospitalized patients during 8 months of sampling in 2015. Mobility data collected from 1,000 children and young adults via prospective travel diaries showed that, although all ages spent approximately half of their daytime hours (5:00 am–9:00 pm) at home, younger age groups (≤ 14 years) spent a significantly greater proportion of their time within 500 m of home than older respondents. Together these findings inform the rational design of future trials of dengue preventive interventions in this setting by identifying 1) children < 7 years as an optimal target group for a flavivirus-naive serological cohort, 2) children and young adults as the predominant patient population for a study with a clinical end point of symptomatic dengue, and 3) substantial spatial and temporal variations in DENV transmission, with a consequent requirement for a trial to be large enough and of long enough duration to overcome this heterogeneity. PMID:29313471

Validity of ambient levels of fine particles as surrogate for personal exposure to outdoor air pollution--results of the European EXPOLIS-EAS Study (Swiss Center Basel).

PubMed

Oglesby, L; Künzli, N; Röösli, M; Braun-Fahrländer, C; Mathys, P; Stern, W; Jantunen, M; Kousa, A

2000-07-01

To evaluate the validity of fixed-site fine particle levels as exposure surrogates in air pollution epidemiology, we considered four indicator groups: (1) PM2.5 total mass concentrations, (2) sulfur and potassium for regional air pollution, (3) lead and bromine for traffic-related particles, and (4) calcium for crustal particles. Using data from the European EXPOLIS (Air Pollution Exposure Distribution within Adult Urban Populations in Europe) study, we assessed the associations between 48-hr personal exposures and home outdoor levels of the indicators. Furthermore, within-city variability of fine particle levels was evaluated. Personal exposures to PM2.5 mass were not correlated to corresponding home outdoor levels (n = 44, rSpearman (Sp) = 0.07). In the group reporting neither relevant indoor sources nor relevant activities, personal exposures and home outdoor levels of sulfur were highly correlated (n = 40, rSp = 0.85). In contrast, the associations were weaker for traffic (Pb: n = 44, rSp = 0.53; Br: n = 44, rSp = 0.21) and crustal (Ca: n = 44, rSp = 0.12) indicators. This contrast is consistent with spatially homogeneous regional pollution and higher spatial variability of traffic and crustal indicators observed in Basel, Switzerland. We conclude that for regional air pollution, fixed-site fine particle levels are valid exposure surrogates. For source-specific exposures, however, fixed-site data are probably not the optimal measure. Still, in air pollution epidemiology, ambient PM2.5 levels may be more appropriate exposure estimates than total personal PM2.5 exposure, since the latter reflects a mixture of indoor and outdoor sources.

Validity of Ambient Levels of Fine Particles as Surrogate for Personal Exposure to Outdoor Air Pollution-Results of the European EXPOLIS-EAS Study (Swiss Center Basel).

PubMed

Oglesby, Lucy; Künzli, Nino; Röösli, Martin; Braun-Fahrländer, Charlotte; Mathys, Patrick; Stern, Willem; Jantunen, Matti; Kousa, Anu

2000-07-01

To evaluate the validity of fixed-site fine particle levels as exposure surrogates in air pollution epidemiology, we considered four indicator groups: (1) PM 25 total mass concentrations, (2) sulfur and potassium for regional air pollution, (3) lead and bromine for traffic-related particles, and (4) calcium for crustal particles. Using data from the European EXPOLIS (Air Pollution Exposure Distribution within Adult Urban Populations in Europe) study, we assessed the associations between 48-hr personal exposures and home outdoor levels of the indicators. Furthermore, within-city variability of fine particle levels was evaluated. Personal exposures to PM 2.5 mass were not correlated to corresponding home outdoor levels (n = 44, r S (S) =r o v ' Spearman (Sp) 0.07). In the group reporting neither relevant indoor sources nor relevant activities, personal exposures and home outdoor levels of sulfur were highly correlated (n = 40, r Sp = 0.85). In contrast, the associations were weaker for traffic (Pb: n = 44, r Sp = 0.53; Br: n = 44, r Sp = 0.21) and crustal (Ca: n = 44, r Sp = 0.12) indicators. This contrast is consistent with spatially homogeneous regional pollution and higher spatial variability of traffic and crustal indicators observed in Basel, Switzerland. We conclude that for regional air pollution, fixed-site fine particle levels are valid exposure surrogates. For source-specific exposures, however, fixed-site data are probably not the optimal measure. Still, in air pollution epidemiology, ambient PM 2.5 levels may be more appropriate exposure estimates than total personal PM 2.5 exposure, since the latter reflects a mixture of indoor and outdoor sources.

Epidemiological analysis of bovine ephemeral fever in 2012-2013 in the subtropical islands of Japan.

PubMed

Hayama, Yoko; Moriguchi, Sachiko; Yanase, Tohru; Suzuki, Moemi; Niwa, Tsuyoshi; Ikemiyagi, Kazufumi; Nitta, Yoshiki; Yamamoto, Takehisa; Kobayashi, Sota; Murai, Kiyokazu; Tsutsui, Toshiyuki

2016-03-09

Bovine ephemeral fever (BEF) is a febrile disease of cattle that is transmitted by arthropod vectors such as mosquitoes and Culicoides biting midges. An outbreak of BEF recently occurred in Ishigaki Island and surrounding islands that are located southwest of Japan. In this study, an epidemiological analysis was conducted to understand the temporal and spatial characteristics of the outbreak. Factors associated with the disease spread within Ishigaki Island were investigated by hierarchical Bayesian models. The possibility of between-island transmission by windborne vectors and transmission by long-distance migration of infected vectors were examined using atmospheric dispersion models. In September 2012, the first case of the disease was detected in the western part of Ishigaki Island. In 1 month, it had rapidly spread to the southern part of the island and to surrounding islands, and led to 225 suspected cases of BEF during the outbreak. The dispersion model demonstrated the high possibility of between-island transmission by wind. Spatial analysis showed that paddy fields, farmlands, and slope gradients had a significant impact on the 1-km cell-level incidence risk. These factors may have influenced the habitats and movements of the vectors with regard to the spread of BEF. A plausible incursion event of infected vectors from Southeast Asia to Ishigaki Island was estimated to have occurred at the end of August. This study revealed that the condition of a terrain and land use significantly influenced disease transmission. These factors are important in assessing favorable environments for related vectors. The results of the dispersion model indicated the likely transmission of the infected vectors by wind on the local scale and on the long-distance scale. These findings would be helpful for developing a surveillance program and developing preventive measures against BEF.

Geographical and epidemiological analysis of oncological incidence in paediatric and adolescent ages in a municipality of North-Western Italy: Vercelli, years 2002-2009.

PubMed

Salerno, C; Berchialla, P; Palin, L A; Barasolo, E; Fossale, P G; Marciani, P

2017-01-01

This study of the geographical incidence on the oncological mortality in young age (0-44 years) in the City of Vercelli, was aimed to address the concerns of the population and the request of municipal administrators. A detailed and sophisticated study for a City of medium-small size is due for the presence of various sources of pressure, such as a MSW incinerator just a few kilometres from the city and an intensive agricultural activity that characterizes the entire area. The study is based on the census analysis of the population, and of the hospital admission cards, and it considers epidemiological standardized estimators and spatial analysis through Bayesian models, as well. Both approaches highlight Major risks for the area south of the city for major tumours such as total cancer in women (SIR / SMR significant excess of about 50%), colorectal (mean increments SMR / SIR between 3 and 4 times), ovary (mean increments SMR / SIR between 3:04 and times), and nervous system (increases 3 times for both indicators). These results deserve further studies through inferential case-control and cohort analysis, given the marginal role of the possible occupational hazards in the aetiology of juvenile cancer disorders.

A Pilot Study of Microbial Contamination of Subtropical Recreational Waters

PubMed Central

Fleming, Lora E; Solo, Gabriele H.; Elmir, Samir; Shibata, Tomoyuki; Squicciarini, Dominick; Quirino, Wendy; Arguello, Margia; Van de Bogart, Gayl

2009-01-01

Microbial water quality indicators are used to determine whether a water body is safe for recreational purposes. There have been concerns raised about the appropriate use of microbial indicators to regulate recreational uses of water bodies, in particular those located in tropical and sub-tropical environments. This prospective cohort pilot study evaluated the relationship between microbial water quality indicators and public health within two public beaches without known sewage discharge, but with historically high microbial levels for one beach, in subtropical Miami-Dade County (Florida). Monitoring was conducted in three phases: daily water monitoring, beach sand sampling, and spatially intense water sampling. An epidemiological questionnaire from a Los Angeles recreational beach-goer study was used to assess the self-reported swimming-related symptoms and exposures. There was no significant association between the number nor the type of reported symptoms and the different sampling months or beach sites, although persons who returned repeatedly to the beach were more likely to report symptoms. The number of indicator organisms correlated negatively with the frequency of symptoms reported by recreational beach goers. Results of the daily monitoring indicated that different indicators provided conflicting results concerning beach water quality. Larger epidemiologic studies with individual exposure monitoring are recommended to further evaluate these potentially important associations in subtropical recreational waters. PMID:20151031

[Comparison of Google and Yahoo applications for geocoding of postal addresses in epidemiological studies].

PubMed

Quesada, Jose Antonio; Nolasco, Andreu; Moncho, Joaquín

2013-01-01

Geocoding is the assignment of geographic coordinates to spatial points, which often are postal addresses. The error made in applying this process can introduce bias in estimates of spatiotemporal models in epidemiological studies. No studies have been found to measure the error made in applying this process in Spanish cities. The objective is to evaluate the errors in magnitude and direction from two free sources (Google and Yahoo) with regard to a GPS in two Spanish cities. 30 addresses were geocoded with those two sources and the GPS in Santa Pola (Alicante) and Alicante city. The distances were calculated in metres (median, CI95%) between the sources and the GPS, globally and according to the status reported by each source. The directionality of the error was evaluated by calculating the location quadrant and applying a Chi-Square test. The GPS error was evaluated by geocoding 11 addresses twice at 4 days interval. The overall median in Google-GPS was 23,2 metres (16,0-32,1) for Santa Pola, and 21,4 meters (14,9-31,1) for Alicante. The overall median in Yahoo was 136,0 meters (19,2-318,5) for Santa Pola, and 23,8 meters (13,6- 29,2) for Alicante. Between the 73% and 90% were geocoded by status as "exact or interpolated" (minor error), where Goggle and Yahoo had a median error between 19 and 23 metres in the two cities. The GPS had a median error of 13.8 meters (6,7-17,8). No error directionality was detected. Google error is acceptable and stable in the two cities, so that it is a reliable source for Para medir elgeocoding addresses in Spain in epidemiological studies.

Spatial and temporal variability of fine particle composition and source types in five cities of Connecticut and Massachusetts

PubMed Central

Lee, Hyung Joo; Gent, Janneane F.; Leaderer, Brian P.; Koutrakis, Petros

2011-01-01

To protect public health from PM2.5 air pollution, it is critical to identify the source types of PM2.5 mass and chemical components associated with higher risks of adverse health outcomes. Source apportionment modeling using Positive Matrix Factorization (PMF), was used to identify PM2.5 source types and quantify the source contributions to PM2.5 in five cities of Connecticut and Massachusetts. Spatial and temporal variability of PM2.5 mass, components and source contributions were investigated. PMF analysis identified five source types: regional pollution as traced by sulfur, motor vehicle, road dust, oil combustion and sea salt. The sulfur-related regional pollution and traffic source type were major contributors to PM2.5. Due to sparse ground-level PM2.5 monitoring sites, current epidemiological studies are susceptible to exposure measurement errors. The higher correlations in concentrations and source contributions between different locations suggest less spatial variability, resulting in less exposure measurement errors. When concentrations and/or contributions were compared to regional averages, correlations were generally higher than between-site correlations. This suggests that for assigning exposures for health effects studies, using regional average concentrations or contributions from several PM2.5 monitors is more reliable than using data from the nearest central monitor. PMID:21429560

Simulation of population-based commuter exposure to NO₂ using different air pollution models.

PubMed

Ragettli, Martina S; Tsai, Ming-Yi; Braun-Fahrländer, Charlotte; de Nazelle, Audrey; Schindler, Christian; Ineichen, Alex; Ducret-Stich, Regina E; Perez, Laura; Probst-Hensch, Nicole; Künzli, Nino; Phuleria, Harish C

2014-05-12

We simulated commuter routes and long-term exposure to traffic-related air pollution during commute in a representative population sample in Basel (Switzerland), and evaluated three air pollution models with different spatial resolution for estimating commute exposures to nitrogen dioxide (NO2) as a marker of long-term exposure to traffic-related air pollution. Our approach includes spatially and temporally resolved data on actual commuter routes, travel modes and three air pollution models. Annual mean NO2 commuter exposures were similar between models. However, we found more within-city and within-subject variability in annual mean (±SD) NO2 commuter exposure with a high resolution dispersion model (40 ± 7 µg m(-3), range: 21-61) than with a dispersion model with a lower resolution (39 ± 5 µg m(-3); range: 24-51), and a land use regression model (41 ± 5 µg m(-3); range: 24-54). Highest median cumulative exposures were calculated along motorized transport and bicycle routes, and the lowest for walking. For estimating commuter exposure within a city and being interested also in small-scale variability between roads, a model with a high resolution is recommended. For larger scale epidemiological health assessment studies, models with a coarser spatial resolution are likely sufficient, especially when study areas include suburban and rural areas.

Novel Microbiological and Spatial Statistical Methods to Improve Strength of Epidemiological Evidence in a Community-Wide Waterborne Outbreak

PubMed Central

Jalava, Katri; Rintala, Hanna; Ollgren, Jukka; Maunula, Leena; Gomez-Alvarez, Vicente; Revez, Joana; Palander, Marja; Antikainen, Jenni; Kauppinen, Ari; Räsänen, Pia; Siponen, Sallamaari; Nyholm, Outi; Kyyhkynen, Aino; Hakkarainen, Sirpa; Merentie, Juhani; Pärnänen, Martti; Loginov, Raisa; Ryu, Hodon; Kuusi, Markku; Siitonen, Anja; Miettinen, Ilkka; Santo Domingo, Jorge W.; Hänninen, Marja-Liisa; Pitkänen, Tarja

2014-01-01

Failures in the drinking water distribution system cause gastrointestinal outbreaks with multiple pathogens. A water distribution pipe breakage caused a community-wide waterborne outbreak in Vuorela, Finland, July 2012. We investigated this outbreak with advanced epidemiological and microbiological methods. A total of 473/2931 inhabitants (16%) responded to a web-based questionnaire. Water and patient samples were subjected to analysis of multiple microbial targets, molecular typing and microbial community analysis. Spatial analysis on the water distribution network was done and we applied a spatial logistic regression model. The course of the illness was mild. Drinking untreated tap water from the defined outbreak area was significantly associated with illness (RR 5.6, 95% CI 1.9–16.4) increasing in a dose response manner. The closer a person lived to the water distribution breakage point, the higher the risk of becoming ill. Sapovirus, enterovirus, single Campylobacter jejuni and EHEC O157:H7 findings as well as virulence genes for EPEC, EAEC and EHEC pathogroups were detected by molecular or culture methods from the faecal samples of the patients. EPEC, EAEC and EHEC virulence genes and faecal indicator bacteria were also detected in water samples. Microbial community sequencing of contaminated tap water revealed abundance of Arcobacter species. The polyphasic approach improved the understanding of the source of the infections, and aided to define the extent and magnitude of this outbreak. PMID:25147923

Global biogeography of human infectious diseases.

PubMed

Murray, Kris A; Preston, Nicholas; Allen, Toph; Zambrana-Torrelio, Carlos; Hosseini, Parviez R; Daszak, Peter

2015-10-13

The distributions of most infectious agents causing disease in humans are poorly resolved or unknown. However, poorly known and unknown agents contribute to the global burden of disease and will underlie many future disease risks. Existing patterns of infectious disease co-occurrence could thus play a critical role in resolving or anticipating current and future disease threats. We analyzed the global occurrence patterns of 187 human infectious diseases across 225 countries and seven epidemiological classes (human-specific, zoonotic, vector-borne, non-vector-borne, bacterial, viral, and parasitic) to show that human infectious diseases exhibit distinct spatial grouping patterns at a global scale. We demonstrate, using outbreaks of Ebola virus as a test case, that this spatial structuring provides an untapped source of prior information that could be used to tighten the focus of a range of health-related research and management activities at early stages or in data-poor settings, including disease surveillance, outbreak responses, or optimizing pathogen discovery. In examining the correlates of these spatial patterns, among a range of geographic, epidemiological, environmental, and social factors, mammalian biodiversity was the strongest predictor of infectious disease co-occurrence overall and for six of the seven disease classes examined, giving rise to a striking congruence between global pathogeographic and "Wallacean" zoogeographic patterns. This clear biogeographic signal suggests that infectious disease assemblages remain fundamentally constrained in their distributions by ecological barriers to dispersal or establishment, despite the homogenizing forces of globalization. Pathogeography thus provides an overarching context in which other factors promoting infectious disease emergence and spread are set.

Nutritional status of children under 5 years of age in Brazil: evidence of nutritional epidemiological polarisation.

PubMed

Pereira, Ingrid Freitas da Silva; Andrade, Lára de Melo Barbosa; Spyrides, Maria Helena Constatino; Lyra, Clélia de Oliveira

2017-10-01

The objective of this study was to evaluate the nutritional status of children under 5 years of age in Brazil in 2009 and its association with social and demographic factors. Data from the Household Budget Survey (Pesquisa de Orçamento Familiar - POF 2008-2009) were used, in which the nutritional profile was evaluated according to the weight-for-age (W/A), height-for-age (H/A) and weight-for-height (W/H) indices (n = 14,569). The association was estimated by applying the Pearson association test, a logistic regression and a correspondence analysis. The correspondence analysis showed a higher association of thinness with children in the North and Northeast regions, in families with lower levels of income and in those of black colour/race. Overweight and obesity had a stronger relationship with children living in the South, Southeast and Central-West, in males, in those from urban areas, in those of Caucasian colour/race, in those aged 3 years and in those from families with intermediate income ranges. Overweight and obesity showed a heterogeneous spatial distribution amongst Brazilian states. A nutritional epidemiological polarisation that presents a major challenge for public health is indicated: we must reduce nutritional deficiencies and promote healthy eating habits from childhood to improve the nutritional and epidemiological profiles and mortality of the population.

Application of geo-spatial technology in schistosomiasis modelling in Africa: a review.

PubMed

Manyangadze, Tawanda; Chimbari, Moses John; Gebreslasie, Michael; Mukaratirwa, Samson

2015-11-04

Schistosomiasis continues to impact socio-economic development negatively in sub-Saharan Africa. The advent of spatial technologies, including geographic information systems (GIS), Earth observation (EO) and global positioning systems (GPS) assist modelling efforts. However, there is increasing concern regarding the accuracy and precision of the current spatial models. This paper reviews the literature regarding the progress and challenges in the development and utilization of spatial technology with special reference to predictive models for schistosomiasis in Africa. Peer-reviewed papers identified through a PubMed search using the following keywords: geo-spatial analysis OR remote sensing OR modelling OR earth observation OR geographic information systems OR prediction OR mapping AND schistosomiasis AND Africa were used. Statistical uncertainty, low spatial and temporal resolution satellite data and poor validation were identified as some of the factors that compromise the precision and accuracy of the existing predictive models. The need for high spatial resolution of remote sensing data in conjunction with ancillary data viz. ground-measured climatic and environmental information, local presence/absence intermediate host snail surveys as well as prevalence and intensity of human infection for model calibration and validation are discussed. The importance of a multidisciplinary approach in developing robust, spatial data capturing, modelling techniques and products applicable in epidemiology is highlighted.

Medical Mapping: The Revolution in Teaching--and Using--Maps for the Analysis of Medical Issues

ERIC Educational Resources Information Center

Koch, Tom; Denike, Ken

2004-01-01

As a result of advances in GIS, students from a range of disciplines (epidemiology, public health, medical sociology, and anthropology) are seeking to learn how to apply a mapped, spatial perspective to issues of disease and health. This paper describes a pilot program whose intent has been to develop an interdisciplinary, skill-based course in…

Mapping Exposure to Multi-Pollutants Using Environmental Biomonitors-A Multi-Exposure Index.

PubMed

Serrano, Helena C; Köbel, Melanie; Palma-Oliveira, José; Pinho, Pedro; Branquinho, Cristina

2017-01-01

Atmosphere is a major pathway for transport and deposition of pollutants in the environment. In industrial areas, organic compounds are released or formed as by-products, such as polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F's). Inorganic chemical elements, including lead and arsenic, are also part of the pollutants mixture, and even in low concentrations may potentially be toxic and carcinogenic. However, assessing the spatial pattern of their deposition is difficult due to high spatial and temporal heterogeneity. Lichens have been used as biomonitors of atmospheric deposition, because these organisms encompass greater spatial detail than air monitoring stations and provide an integration of overall pollution. Based upon the ability of lichens to concentrate pollutants such as PCDD/F and chemical elements, the main objectives of this study were to develop a new semi-quantitative multi-pollutant toxicity exposure index (TEQ-like), derived from risk estimates, in an attempt to correlate several atmospheric pollutants to human exposure levels. The actual pollutant concentrations were measured in the environment, from biomonitors (organisms that integrate multi-pollutants), enabling interpolation and mapping of contaminant deposition within the region. Thus, the TEQ-like index provides a spatial representation not from absolute accumulation of the different pollutants, but from the accumulation weighted by their relative risk. The assessment of environmental human exposure to multi-pollutants through atmospheric deposition may be applied to industries to improve mitigation processes or to health stakeholders to target populations for a comprehensive risk assessment, epidemiological studies, and health recommendations.

Aerobic Fitness is Associated With Hippocampal Volume in Elderly Humans

PubMed Central

Erickson, Kirk I.; Prakash, Ruchika S.; Voss, Michelle W.; Chaddock, Laura; Hu, Liang; Morris, Katherine S.; White, Siobhan M.; Wójcicki, Thomas R.; McAuley, Edward; Kramer, Arthur F.

2010-01-01

Deterioration of the hippocampus occurs in elderly individuals with and without dementia, yet individual variation exists in the degree and rate of hippocampal decay. Determining the factors that influence individual variation in the magnitude and rate of hippocampal decay may help promote lifestyle changes that prevent such deterioration from taking place. Aerobic fitness and exercise are effective at preventing cortical decay and cognitive impairment in older adults and epidemiological studies suggest that physical activity can reduce the risk for developing dementia. However, the relationship between aerobic fitness and hippocampal volume in elderly humans is unknown. In this study, we investigated whether individuals with higher levels of aerobic fitness displayed greater volume of the hippocampus and better spatial memory performance than individuals with lower fitness levels. Furthermore, in exploratory analyses, we assessed whether hippocampal volume mediated the relationship between fitness and spatial memory. Using a region-of-interest analysis on magnetic resonance images in 165 nondemented older adults, we found a triple association such that higher fitness levels were associated with larger left and right hippocampi after controlling for age, sex, and years of education, and larger hippocampi and higher fitness levels were correlated with better spatial memory performance. Furthermore, we demonstrated that hippocampal volume partially mediated the relationship between higher fitness levels and enhanced spatial memory. Our results clearly indicate that higher levels of aerobic fitness are associated with increased hippocampal volume in older humans, which translates to better memory function. PMID:19123237

Ambient ultrafine particle levels at residential and reference sites in urban and rural Switzerland.

PubMed

Meier, Reto; Eeftens, Marloes; Aguilera, Inmaculada; Phuleria, Harish C; Ineichen, Alex; Davey, Mark; Ragettli, Martina S; Fierz, Martin; Schindler, Christian; Probst-Hensch, Nicole; Tsai, Ming-Yi; Künzli, Nino

2015-03-03

Although there is evidence that ultrafine particles (UFP) do affect human health there are currently no legal ambient standards. The main reasons are the absence of spatially resolved exposure data to investigate long-term health effects and the challenge of defining representative reference sites for monitoring given the high dependence of UFP on proximity to sources. The objectives of this study were to evaluate the spatial distribution of UFP in four areas of the Swiss Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) and to investigate the representativeness of routine air monitoring stations for residential sites in these areas. Repeated UFP measurements during three seasons have been conducted at a total of 80 residential sites and four area specific reference sites over a median duration of 7 days. Arithmetic mean residential PNC scattered around the median of 10,800 particles/cm(3) (interquartile range [IQR] = 7800 particles/cm(3)). Spatial within area contrasts (90th/10th percentile ratios) were around two; increased contrasts were observed during weekday rush-hours. Temporal UFP patterns were comparable at reference and residential sites in all areas. Our data show that central monitoring sites can represent residential conditions when locations are well chosen with respect to the local sources--namely traffic. For epidemiological research, locally resolved spatial models are needed to estimate individuals' long-term exposures to UFP of outdoor origin at home, during commute and at work.

Geospatial patterns in influenza vaccination: evidence from uninsured and publicly insured children in North Carolina.

PubMed

Trogdon, Justin G; Ahn, Thomas

2015-03-01

The purpose of this study was to explore geospatial patterns in influenza vaccination. We conducted an ecological analysis of publicly funded influenza vaccinations at the ZIP code tabulation area (ZCTA) level using secondary data for publicly funded influenza vaccinations among eligible school-aged children (age range, 5-17 years) for the 2010-2011 and 2011-2012 influenza seasons from the North Carolina Immunization Registry (NCIR). NCIR data were merged by ZCTA with other publicly available data. We tested for spatial autocorrelation in unadjusted influenza vaccination rates using choropleth maps and Moran's I. We estimated nonspatial and spatial negative binomial models with spatially correlated random effects adjusted for demographic, economic, and health care variables. The study was conducted at the University of North Carolina at Chapel Hill in the spring of 2014. The NCIR demonstrated spatial autocorrelation in publicly funded influenza vaccinations among uninsured and means-tested, publicly insured school-aged children; ZCTAs tended to have influenza vaccination rates that were similar to their neighbors. This result was partially explained by included ZCTA characteristics, but not wholly. To the extent that the geospatial clustering of vaccination rates is the result of social influences, targeting interventions to increase influenza vaccination among school-aged children in one area could also lead to increases in neighboring areas. Copyright © 2015 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Spatial dynamics of bovine tuberculosis in the Autonomous Community of Madrid, Spain (2010-2012).

PubMed

de la Cruz, Maria Luisa; Perez, Andres; Bezos, Javier; Pages, Enrique; Casal, Carmen; Carpintero, Jesus; Romero, Beatriz; Dominguez, Lucas; Barker, Christopher M; Diaz, Rosa; Alvarez, Julio

2014-01-01

Progress in control of bovine tuberculosis (bTB) is often not uniform, usually due to the effect of one or more sometimes unknown epidemiological factors impairing the success of eradication programs. Use of spatial analysis can help to identify clusters of persistence of disease, leading to the identification of these factors thus allowing the implementation of targeted control measures, and may provide some insights of disease transmission, particularly when combined with molecular typing techniques. Here, the spatial dynamics of bTB in a high prevalence region of Spain were assessed during a three year period (2010-2012) using data from the eradication campaigns to detect clusters of positive bTB herds and of those infected with certain Mycobacterium bovis strains (characterized using spoligotyping and VNTR typing). In addition, the within-herd transmission coefficient (β) was estimated in infected herds and its spatial distribution and association with other potential outbreak and herd variables was evaluated. Significant clustering of positive herds was identified in the three years of the study in the same location ("high risk area"). Three spoligotypes (SB0339, SB0121 and SB1142) accounted for >70% of the outbreaks detected in the three years. VNTR subtyping revealed the presence of few but highly prevalent strains within the high risk area, suggesting maintained transmission in the area. The spatial autocorrelation found in the distribution of the estimated within-herd transmission coefficients in herds located within distances <14 km and the results of the spatial regression analysis, support the hypothesis of shared local factors affecting disease transmission in farms located at a close proximity.

Spatial vulnerability of fine particulate matter relative to the prevalence of diabetes in the United States.

PubMed

Chien, Lung-Chang; Alamgir, Hasanat; Yu, Hwa-Lung

2015-03-01

Recent research supports a link between diabetes and fine particulate matter (≤ 2.5μg in diameter; PM2.5) in both laboratory and epidemiology studies. However, research investigating the potential relationship of the spatial vulnerability of diabetes to concomitant PM2.5 levels is still sparse, and the level of diabetes geographic disparities attributed to PM2.5 levels has yet to be evaluated. We conducted a Bayesian structured additive regression modeling approach to determine whether long-term exposure to PM2.5 is spatially associated with diabetes prevalence after adjusting for the socioeconomic status of county residents. This study utilizes the following data sources from 2004 to 2010: the Behavioral Risk Factor Surveillance System, the American Community Survey, and the Environmental Protection Agency. We also conducted spatial comparisons with low, median-low, median-high, and high levels of PM2.5 concentrations. When PM2.5 concentrations increased 1 μg/m(3), the increase in the relative risk percentage for diabetes ranged from -5.47% (95% credible interval = -6.14, -4.77) to 2.34% (95% CI = 2.01, 2.70), where 1323 of 3109 counties (42.55%) displayed diabetes vulnerability with significantly positive relative risk percentages. These vulnerable counties are more likely located in the Southeast, Central, and South Regions of the U.S. A similar spatial vulnerability pattern for concentrations of low PM2.5 levels was also present in these same three regions. A clear cluster of vulnerable counties at median-high PM2.5 level was found in Michigan. This study identifies the spatial vulnerability of diabetes prevalence associated with PM2.5, and thereby provides the evidence needed to prompt and establish enhanced surveillance that can monitor diabetes vulnerability in areas with low PM2.5 pollution. Copyright © 2014 Elsevier B.V. All rights reserved.

Advanced analysis of forest fire clustering

NASA Astrophysics Data System (ADS)

Kanevski, Mikhail; Pereira, Mario; Golay, Jean

2017-04-01

Analysis of point pattern clustering is an important topic in spatial statistics and for many applications: biodiversity, epidemiology, natural hazards, geomarketing, etc. There are several fundamental approaches used to quantify spatial data clustering using topological, statistical and fractal measures. In the present research, the recently introduced multi-point Morisita index (mMI) is applied to study the spatial clustering of forest fires in Portugal. The data set consists of more than 30000 fire events covering the time period from 1975 to 2013. The distribution of forest fires is very complex and highly variable in space. mMI is a multi-point extension of the classical two-point Morisita index. In essence, mMI is estimated by covering the region under study by a grid and by computing how many times more likely it is that m points selected at random will be from the same grid cell than it would be in the case of a complete random Poisson process. By changing the number of grid cells (size of the grid cells), mMI characterizes the scaling properties of spatial clustering. From mMI, the data intrinsic dimension (fractal dimension) of the point distribution can be estimated as well. In this study, the mMI of forest fires is compared with the mMI of random patterns (RPs) generated within the validity domain defined as the forest area of Portugal. It turns out that the forest fires are highly clustered inside the validity domain in comparison with the RPs. Moreover, they demonstrate different scaling properties at different spatial scales. The results obtained from the mMI analysis are also compared with those of fractal measures of clustering - box counting and sand box counting approaches. REFERENCES Golay J., Kanevski M., Vega Orozco C., Leuenberger M., 2014: The multipoint Morisita index for the analysis of spatial patterns. Physica A, 406, 191-202. Golay J., Kanevski M. 2015: A new estimator of intrinsic dimension based on the multipoint Morisita index. Pattern Recognition, 48, 4070-4081.

Spatial vulnerability of fine particulate matter relative to the geographic disparities of adult's diabetes prevalence in the United States

NASA Astrophysics Data System (ADS)

Chien, Lung-Chang; Alamgir, Hassanat; Yu, Hwa-Lung

2015-04-01

Potentially larger regional effects of climate change have been revealed on the elevation of fine particulate matter (≤ 2.5 µg in diameter; PM2.5) in the U.S. In addition, recent research supports a link between diabetes and PM2.5 in both laboratory and epidemiology studies. However, research investigating the potential relationship of the spatial vulnerability of diabetes to concomitant PM2.5 levels is still sparse, and the level of diabetes geographic disparities attributed to PM2.5 levels has yet to be evaluated. We conducted a Bayesian structured additive regression modeling approach to determine whether long-term exposure to PM2.5 is spatially associated with diabetes prevalence after adjusting for the socioeconomic status of county residents. This study utilizes the following data sources from 2004-2010: the Behavioral Risk Factor Surveillance System, the American Community Survey, and the Environmental Protection Agency. We also conducted spatial comparisons with low, median-low, median-high, and high levels of PM2.5 concentrations. When PM2.5 concentrations increased 1 µg/m3, the increase in the relative risk percentage for diabetes ranged from -5.47% (95% credible interval = -6.14, -4.77) to 2.34% (95% CI = 2.01, 2.70), where 1,323 of 3,109 counties (42.55%) displayed diabetes vulnerability with significantly positive relative risk percentages. These vulnerable counties are more likely located in the Southeast, Central, and South Regions of the U.S. A similar spatial vulnerability pattern for concentrations of low PM2.5 levels was also present in these same three regions. A clear cluster of vulnerable counties at median-high PM2.5 level was found in Michigan. This study identifies the spatial vulnerability of diabetes prevalence associated with PM2.5, and thereby provides the evidence needed to prompt and establish enhanced surveillance that can monitor diabetes vulnerability in areas with low PM2.5 pollution.

Pathogenic landscapes: Interactions between land, people, disease vectors, and their animal hosts

PubMed Central

2010-01-01

Background Landscape attributes influence spatial variations in disease risk or incidence. We present a review of the key findings from eight case studies that we conducted in Europe and West Africa on the impact of land changes on emerging or re-emerging vector-borne diseases and/or zoonoses. The case studies concern West Nile virus transmission in Senegal, tick-borne encephalitis incidence in Latvia, sandfly abundance in the French Pyrenees, Rift Valley Fever in the Ferlo (Senegal), West Nile Fever and the risk of malaria re-emergence in the Camargue, and rodent-borne Puumala hantavirus and Lyme borreliosis in Belgium. Results We identified general principles governing landscape epidemiology in these diverse disease systems and geographic regions. We formulated ten propositions that are related to landscape attributes, spatial patterns and habitat connectivity, pathways of pathogen transmission between vectors and hosts, scale issues, land use and ownership, and human behaviour associated with transmission cycles. Conclusions A static view of the "pathogenecity" of landscapes overlays maps of the spatial distribution of vectors and their habitats, animal hosts carrying specific pathogens and their habitat, and susceptible human hosts and their land use. A more dynamic view emphasizing the spatial and temporal interactions between these agents at multiple scales is more appropriate. We also highlight the complementarity of the modelling approaches used in our case studies. Integrated analyses at the landscape scale allows a better understanding of interactions between changes in ecosystems and climate, land use and human behaviour, and the ecology of vectors and animal hosts of infectious agents. PMID:20979609

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

PubMed Central

2014-01-01

Background Manson’s schistosomiasis continues to be a severe public health problem in Brazil, where thousands of people live under the risk of contracting this parasitosis. In the Northeast of Brazil, schistosomiasis has expanded from rural areas to the coast of Pernambuco State, where the intermediate host is Biomphalaria glabrata snails. This study aims at presenting situational analyses on schistosomiasis at the coastal locality of Porto de Galinhas, Pernambuco, Brazil, by determining the risk factors relating to its occurrence from the epidemiological and spatial perspectives. Methods In order to gather prevalence data, a parasitological census surveys were conducted in 2010 in the light of the Kato-Katz technique. Furthermore, malacological surveys were also conducted in the same years so as to define the density and infection rates of the intermediate host. Lastly, socioeconomic-behavioral survey was also conducted to determine the odds ratio for infection by Schistosoma mansoni. Based on these data, spatial analyses were done, resulting in maps of the risk of disease transmission. To predict the risk of schistosomiasis occurrence, a multivariate logistic regression was performed using R 2.13 software. Results Based on prevalence, malacological and socioeconomic-behavioural surveys, it was identified a prevalence of 15.7% in the investigated population (2,757 individuals). Due to the malacological survey, 36 breeding sites were identified, of which 11 were classified as foci of schistosomiasis transmission since they pointed out snails which were infected by Schistosoma mansoni. Overall, 11,012 snails (Biomphalaria glabrata) were collected. The multivariate regression model identified six explanatory variables of environmental, socioeconomic and demographic nature. Spatial sweep analysis by means of the Bernoulli method identified one statistically significant cluster in Salinas (RR = 2.2; p-value < 0.000), the district with the highest occurrence of cases. Conclusions Based on the resulting information from this study, the epidemiological dimensions of this disease are significant and severe, within the scenario of schistosomiasis in Pernambuco state. The risk factors which were identified in the predictive model made it clear that the environmental and social conditions influence on the schistosomiasis occurrences. PMID:24559264

Spatial epidemiology in zoonotic parasitic diseases: insights gained at the 1st International Symposium on Geospatial Health in Lijiang, China, 2007

PubMed Central

Zhou, Xiao-Nong; Lv, Shan; Yang, Guo-Jing; Kristensen, Thomas K; Bergquist, N Robert; Utzinger, Jürg; Malone, John B

2009-01-01

The 1st International Symposium on Geospatial Health was convened in Lijiang, Yunnan province, People's Republic of China from 8 to 9 September, 2007. The objective was to review progress made with the application of spatial techniques on zoonotic parasitic diseases, particularly in Southeast Asia. The symposium featured 71 presentations covering soil-transmitted and water-borne helminth infections, as well as arthropod-borne diseases such as leishmaniasis, malaria and lymphatic filariasis. The work made public at this occasion is briefly summarized here to highlight the advances made and to put forth research priorities in this area. Approaches such as geographical information systems (GIS), global positioning systems (GPS) and remote sensing (RS), including spatial statistics, web-based GIS and map visualization of field investigations, figured prominently in the presentation. PMID:19193214

Descriptive analysis and spatial epidemiology of porcine reproductive and respiratory syndrome (PRRS) for swine sites participating in area regional control and elimination programs from 3 regions of Ontario

PubMed Central

Arruda, Andreia G.; Poljak, Zvonimir; Friendship, Robert; Carpenter, Jane; Hand, Karen

2015-01-01

The objectives of this study were to describe demographics, basic biosecurity practices, ownership structure, and prevalence of porcine reproductive and respiratory syndrome (PRRS) in swine sites located in 3 regions in Ontario, and investigate the presence of spatial clustering and clusters of PRRS positive sites in the 3 regions. A total of 370 swine sites were enrolled in Area Regional Control and Elimination projects in Niagara, Watford, and Perth from 2010 to 2013. Demographics, biosecurity, and site ownership data were collected using a standardized questionnaire and site locations were obtained from an industry organization. Status was assigned on the basis of available diagnostic tests and/or assessment by site veterinarians. Spatial dependence was investigated using the D-function, the spatial scan statistic test and the spatial relative risk method. Results showed that the use of strict all-in all-out (AIAO) pig flow and shower before entry are uncommon biosecurity practices in swine sites, but a larger proportion of sites reported having a Danish entry. The prevalence of PRRS in the 3 regions ranged from 17% to 48% and localized high and low risk clusters were detected. Sites enrolled in the PRRS control projects were characterized by membership in multiple and overlapping ownership structures and networks, which complicates the way the results of monitoring and disease management measures are communicated to the target population. PMID:26424906

Measuring Spatial Dependence for Infectious Disease Epidemiology

PubMed Central

Grabowski, M. Kate; Cummings, Derek A. T.

2016-01-01

Global spatial clustering is the tendency of points, here cases of infectious disease, to occur closer together than expected by chance. The extent of global clustering can provide a window into the spatial scale of disease transmission, thereby providing insights into the mechanism of spread, and informing optimal surveillance and control. Here the authors present an interpretable measure of spatial clustering, τ, which can be understood as a measure of relative risk. When biological or temporal information can be used to identify sets of potentially linked and likely unlinked cases, this measure can be estimated without knowledge of the underlying population distribution. The greater our ability to distinguish closely related (i.e., separated by few generations of transmission) from more distantly related cases, the more closely τ will track the true scale of transmission. The authors illustrate this approach using examples from the analyses of HIV, dengue and measles, and provide an R package implementing the methods described. The statistic presented, and measures of global clustering in general, can be powerful tools for analysis of spatially resolved data on infectious diseases. PMID:27196422

Performance-related increases in hippocampal N-acetylaspartate (NAA) induced by spatial navigation training are restricted to BDNF Val homozygotes.

PubMed

Lövdén, Martin; Schaefer, Sabine; Noack, Hannes; Kanowski, Martin; Kaufmann, Jörn; Tempelmann, Claus; Bodammer, Nils Christian; Kühn, Simone; Heinze, Hans-Jochen; Lindenberger, Ulman; Düzel, Emrah; Bäckman, Lars

2011-06-01

Recent evidence indicates experience-dependent brain volume changes in humans, but the functional and histological nature of such changes is unknown. Here, we report that adult men performing a cognitively demanding spatial navigation task every other day over 4 months display increases in hippocampal N-acetylaspartate (NAA) as measured with magnetic resonance spectroscopy. Unlike measures of brain volume, changes in NAA are sensitive to metabolic and functional aspects of neural and glia tissue and unlikely to reflect changes in microvasculature. Training-induced changes in NAA were, however, absent in carriers of the Met substitution in the brain-derived neurotrophic factor (BDNF) gene, which is known to reduce activity-dependent secretion of BDNF. Among BDNF Val homozygotes, increases in NAA were strongly related to the degree of practice-related improvement in navigation performance and normalized to pretraining levels 4 months after the last training session. We conclude that changes in demands on spatial navigation can alter hippocampal NAA concentrations, confirming epidemiological studies suggesting that mental experience may have direct effects on neural integrity and cognitive performance. BDNF genotype moderates these plastic changes, in line with the contention that gene-context interactions shape the ontogeny of complex phenotypes.

Identifying geographic hot spots of reassortment in a multipartite plant virus

PubMed Central

Savory, Fiona R; Varma, Varun; Ramakrishnan, Uma

2014-01-01

Reassortment between different species or strains plays a key role in the evolution of multipartite plant viruses and can have important epidemiological implications. Identifying geographic locations where reassortant lineages are most likely to emerge could be a valuable strategy for informing disease management and surveillance efforts. We developed a predictive framework to identify potential geographic hot spots of reassortment based upon spatially explicit analyses of genome constellation diversity. To demonstrate the utility of this approach, we examined spatial variation in the potential for reassortment among Cardamom bushy dwarf virus (CBDV; Nanoviridae, Babuvirus) isolates in Northeast India. Using sequence data corresponding to six discrete genome components for 163 CBDV isolates, a quantitative measure of genome constellation diversity was obtained for locations across the sampling region. Two key areas were identified where viruses with highly distinct genome constellations cocirculate, and these locations were designated as possible geographic hot spots of reassortment, where novel reassortant lineages could emerge. Our study demonstrates that the potential for reassortment can be spatially dependent in multipartite plant viruses and highlights the use of evolutionary analyses to identify locations which could be actively managed to facilitate the prevention of outbreaks involving novel reassortant strains. PMID:24944570

Temporal and spatial scaling of the genetic structure of a vector-borne plant pathogen.

PubMed

Coletta-Filho, Helvécio D; Francisco, Carolina S; Almeida, Rodrigo P P

2014-02-01

The ecology of plant pathogens of perennial crops is affected by the long-lived nature of their immobile hosts. In addition, changes to the genetic structure of pathogen populations may affect disease epidemiology and management practices; examples include local adaptation of more fit genotypes or introduction of novel genotypes from geographically distant areas via human movement of infected plant material or insect vectors. We studied the genetic structure of Xylella fastidiosa populations causing disease in sweet orange plants in Brazil at multiple scales using fast-evolving molecular markers (simple-sequence DNA repeats). Results show that populations of X. fastidiosa were regionally isolated, and that isolation was maintained for populations analyzed a decade apart from each other. However, despite such geographic isolation, local populations present in year 2000 were largely replaced by novel genotypes in 2009 but not as a result of migration. At a smaller spatial scale (individual trees), results suggest that isolates within plants originated from a shared common ancestor. In summary, new insights on the ecology of this economically important plant pathogen were obtained by sampling populations at different spatial scales and two different time points.

Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

PubMed

Aguero-Valverde, Jonathan

2013-10-01

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes. Copyright © 2013 Elsevier Ltd. All rights reserved.

The role of influenza in the epidemiology of pneumonia

PubMed Central

Shrestha, Sourya; Foxman, Betsy; Berus, Joshua; van Panhuis, Willem G.; Steiner, Claudia; Viboud, Cécile; Rohani, Pejman

2015-01-01

Interactions arising from sequential viral and bacterial infections play important roles in the epidemiological outcome of many respiratory pathogens. Influenza virus has been implicated in the pathogenesis of several respiratory bacterial pathogens commonly associated with pneumonia. Though clinical evidence supporting this interaction is unambiguous, its population-level effects—magnitude, epidemiological impact and variation during pandemic and seasonal outbreaks—remain unclear. To address these unknowns, we used longitudinal influenza and pneumonia incidence data, at different spatial resolutions and across different epidemiological periods, to infer the nature, timing and the intensity of influenza-pneumonia interaction. We used a mechanistic transmission model within a likelihood-based inference framework to carry out formal hypothesis testing. Irrespective of the source of data examined, we found that influenza infection increases the risk of pneumonia by ~100-fold. We found no support for enhanced transmission or severity impact of the interaction. For model-validation, we challenged our fitted model to make out-of-sample pneumonia predictions during pandemic and non-pandemic periods. The consistency in our inference tests carried out on several distinct datasets, and the predictive skill of our model increase confidence in our overall conclusion that influenza infection substantially enhances the risk of pneumonia, though only for a short period. PMID:26486591

Magnetic field measurements near stand-alone transformer stations.

PubMed

Kandel, Shaiela; Hareuveny, Ronen; Yitzhak, Nir-Mordechay; Ruppin, Raphael

2013-12-01

Extremely low-frequency (ELF) magnetic field (MF) measurements around and above three stand-alone 22/0.4-kV transformer stations have been performed. The low-voltage (LV) cables between the transformer and the LV switchgear were found to be the major source of strong ELF MFs of limited spatial extent. The strong fields measured above the transformer stations support the assessment method, to be used in future epidemiological studies, of classifying apartments located right above the transformer stations as highly exposed to MFs. The results of the MF measurements above the ground around the transformer stations provide a basis for the assessment of the option of implementing precautionary procedures.

Identifying irregularly shaped crime hot-spots using a multiobjective evolutionary algorithm

NASA Astrophysics Data System (ADS)

Wu, Xiaolan; Grubesic, Tony H.

2010-12-01

Spatial cluster detection techniques are widely used in criminology, geography, epidemiology, and other fields. In particular, spatial scan statistics are popular and efficient techniques for detecting areas of elevated crime or disease events. The majority of spatial scan approaches attempt to delineate geographic zones by evaluating the significance of clusters using likelihood ratio statistics tested with the Poisson distribution. While this can be effective, many scan statistics give preference to circular clusters, diminishing their ability to identify elongated and/or irregular shaped clusters. Although adjusting the shape of the scan window can mitigate some of these problems, both the significance of irregular clusters and their spatial structure must be accounted for in a meaningful way. This paper utilizes a multiobjective evolutionary algorithm to find clusters with maximum significance while quantitatively tracking their geographic structure. Crime data for the city of Cincinnati are utilized to demonstrate the advantages of the new approach and highlight its benefits versus more traditional scan statistics.

Epidemiological risk factors for adult dengue in Singapore: an 8-year nested test negative case control study.

PubMed

Yung, Chee Fu; Chan, Siew Pang; Thein, Tun Linn; Chai, Siaw Ching; Leo, Yee Sin

2016-07-08

Understanding changes in the ecology and epidemiology of dengue is important to ensure resource intensive control programmes are targeted effectively as well as to inform future dengue vaccination strategies. We analyzed data from a multicentre longitudinal prospective study of fever in adults using a nested test negative case control approach to identify epidemiological risk factors for dengue disease in Singapore. From April 2005 to February 2013, adult patients presenting with fever within 72 h at selected public primary healthcare clinics and a tertiary hospital in Singapore were recruited. Acute and convalescent blood samples were collected and used to diagnose dengue using both PCR and serology methods. A dengue case was defined as having a positive RT-PCR result for DENV OR evidence of serological conversion between acute and convalescent blood samples. Similarly, controls were chosen from patients in the cohort who tested negative for dengue using the same laboratory methods. The host epidemiological factors which increased the likelihood of dengue disease amongst adults in Singapore were those aged between 21 and 40 years old (2 fold increase) while in contrast, Malay ethnicity was protective (OR 0.57, 95%CI 0.35 to 0.91) against dengue disease. Spatial factors which increased the odds of acquiring dengue was residing at a foreign workers dormitory or hostel (OR 3.25, 95 % CI 1.84 to 5.73) while individuals living in the North-West region of the country were less likely to get dengue (OR 0.50, 95%CI 0.29 to 0.86). Other factors such as gender, whether one primarily works indoors or outdoors, general dwelling type or floor, the type of transportation one uses to work, travel history, as well as self-reported history of mosquito bite or household dengue/fever were not useful in helping to inform a diagnosis of dengue. We have demonstrated a test negative study design to better understand the epidemiological risk factors of adult dengue over multiple seasons. We were able to discount other previously speculated factors such as gender, whether one primarily works indoors or outdoors, dwelling floor in a building and the use of public transportation as having no effect on one's risk of getting dengue.

Circular epidemiology.

PubMed

Kuller, L H

1999-11-01

Circular epidemiology can be defined as the continuation of specific types of epidemiologic studies beyond the point of reasonable doubt of the true existence of an important association or the absence of such an association. Circular epidemiology is an extreme example of studies of the consistency of associations. A basic problem for epidemiology is the lack of a systematic approach to acquiring new knowledge to reach a goal of improving public health and preventive medicine. For epidemiologists, research support unfortunately is biased toward the continued study of already proven hypotheses. Circular epidemiology, however, freezes at one point in the evolution of epidemiologic studies, failing to move from descriptive to analytical case-control and longitudinal studies, for example, to experimental, clinical trials. Good epidemiology journals are filled with very well-conducted epidemiologic studies that primarily repeat the obvious or are variations on the theme.

A population-based spatio-temporal analysis of Clostridium difficile infection in Queensland, Australia over a 10-year period.

PubMed

Furuya-Kanamori, Luis; Robson, Jenny; Soares Magalhães, Ricardo J; Yakob, Laith; McKenzie, Samantha J; Paterson, David L; Riley, Thomas V; Clements, Archie C A

2014-11-01

To identify the spatio-temporal patterns and environmental factors associated with Clostridium difficile infection (CDI) in Queensland, Australia. Data from patients tested for CDI were collected from 392 postcodes across Queensland between May 2003 and December 2012. A binomial logistic regression model, with CDI status as the outcome, was built in a Bayesian framework, incorporating fixed effects for sex, age, source of the sample (healthcare facility or community), elevation, rainfall, land surface temperature, seasons of the year, time in months and spatially unstructured random effects at the postcode level. C. difficile was identified in 13.1% of the samples, the proportion significantly increased over the study period from 5.9% in 2003 to 18.8% in 2012. CDI peaked in summer (14.6%) and was at its lowest in autumn (10.1%). Other factors significantly associated with CDI included female sex (OR: 1.08; 95%CI: 1.01-1.14), community source samples (OR: 1.12; 95%CI: 1.05-1.20), and higher rainfall (OR: 1.09; 95%CI: 1.02-1.17). There was no significant spatial variation in CDI after accounting for the fixed effects in the model. There was an increasing annual trend in CDI in Queensland from 2003 to 2012. Peaks of CDI were found in summer (December-February), which is at odds with the current epidemiological pattern described for northern hemisphere countries. Epidemiologically plausible explanations for this disparity require further investigation. Copyright © 2014 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

Epidemiology of acute promyelocytic leukemia.

PubMed

Mele, A; Stazi, M A; Pulsoni, A; Visani, G; Monarca, B; Castelli, G; Rocchi, L; Avvisati, G; Mandelli, F

1995-01-01

The estimated incidence of acute promyelocytic leukemia (APL) is approximately 6 cases per 10 million people per year with no apparent differences between sexes. The age of APL cases is younger than that of other acute myeloid leukemias (AML). Spatial and temporal clusters of APL have been reported. These observations suggest a possible selective role for environmental and/or occupational factors in APL development. A multicenter case-control study was carried out on risk factors for acute leukemias and preleukemias. In this report data related to APL are selectively analyzed from the larger study to identify specific risk factors. The case-control study on 38 cases of APL showed a strong association with shoemaking (odds ration 6.3, 95% confidence interval 1.3-31.1). A moderate leukemogenic effect from living in houses built with tuff, a polous building material containing gamma-emitting radionuclides and having a high radon concentration, and from using hair dyes was also suggested. These data, together with the reported spatial and temporal clustering of APL, support the hypothesis of specific environmental and/or occupational risk factors for APL among other AML subtypes and indicate the need for additional ad hoc multicenter studies.

Beneficial effects of dietary EGCG and voluntary exercise on behavior in an Alzheimer's disease mouse model.

PubMed

Walker, Jennifer M; Klakotskaia, Diana; Ajit, Deepa; Weisman, Gary A; Wood, W Gibson; Sun, Grace Y; Serfozo, Peter; Simonyi, Agnes; Schachtman, Todd R

2015-01-01

Alzheimer's disease (AD) is a progressive, age-dependent neurodegenerative disorder affecting specific brain regions that control memory and cognitive functions. Epidemiological studies suggest that exercise and dietary antioxidants are beneficial in reducing AD risk. To date, botanical flavonoids are consistently associated with the prevention of age-related diseases. The present study investigated the effects of 4 months of wheel-running exercise, initiated at 2-months of age, in conjunction with the effects of the green tea catechin (-)-epigallocatechin-3-gallate (EGCG) administered orally in the drinking water (50 mg/kg daily) on: (1) behavioral measures: learning and memory performance in the Barnes maze, nest building, open-field, anxiety in the light-dark box; and (2) soluble amyloid-β (Aβ) levels in the cortex and hippocampus in TgCRND8 (Tg) mice. Untreated Tg mice showed hyperactivity, relatively poor nest building behaviors, and deficits in spatial learning in the Barnes maze. Both EGCG and voluntary exercise, separately and in combination, were able to attenuate nest building and Barnes maze performance deficits. Additionally, these interventions lowered soluble Aβ1-42 levels in the cortex and hippocampus. These results, together with epidemiological and clinical studies in humans, suggest that dietary polyphenols and exercise may have beneficial effects on brain health and slow the progression of AD.

Reassembling epidemiology: mapping, monitoring and making-up people in the context of HIV prevention in India.

PubMed

Lorway, Robert; Khan, Shamshad

2014-07-01

This paper explores how the Gates-funded HIV Initiative in India, known as Avahan, produces sociality. Drawing upon ethnographic research conducted between 2006 and 2012, we illustrate how epidemiological surveillance procedures, undergirded by contemporary managerial and entrepreneurial logics, entwine with and become transformed by the everyday practices of men who have sex with men (many of whom sell sex). The coevolution of epidemiology and sociality, with respect to these communities, is explored in relation to: 1) how individual identities are reproduced in association with standardized units of space and time; 2) how knowledge of mapping and enumeration data is employed in the making up of group membership boundaries, revealing how collective interests come to cohere around the project of epidemic prevention; and 3) how knowledge of epidemiological surveillance and procedures provides a basis on which groups collectively realize and execute local security strategies. While monitoring and evaluation (M&E) specialists continually track and standardize the identities, behaviours and social spaces of local populations (through various mapping, typologization and random sampling procedures, which treat space and time as predictable variables), community members simultaneously retranslate and reroute these standardizing processes into "the local" through everyday spatial management practices for health protection. These grounded epidemiologies, we argue, point to vital sites in the co-creation of scientific knowledge-where the quotidian practices of sex workers reassemble epidemiology, continually altering the very objects that surveillance experts are tracking. We further argue that attention to these re-workings can help us unravel the tremendous successes that have been claimed under Avahan in terms of HIV infections averted. Copyright © 2014 Elsevier Ltd. All rights reserved.

Bat trait, genetic and pathogen data from large-scale investigations of African fruit bats, Eidolon helvum.

PubMed

Peel, Alison J; Baker, Kate S; Hayman, David T S; Suu-Ire, Richard; Breed, Andrew C; Gembu, Guy-Crispin; Lembo, Tiziana; Fernández-Loras, Andrés; Sargan, David R; Fooks, Anthony R; Cunningham, Andrew A; Wood, James L N

2016-08-01

Bats, including African straw-coloured fruit bats (Eidolon helvum), have been highlighted as reservoirs of many recently emerged zoonotic viruses. This common, widespread and ecologically important species was the focus of longitudinal and continent-wide studies of the epidemiological and ecology of Lagos bat virus, henipaviruses and Achimota viruses. Here we present a spatial, morphological, demographic, genetic and serological dataset encompassing 2827 bats from nine countries over an 8-year period. Genetic data comprises cytochrome b mitochondrial sequences (n=608) and microsatellite genotypes from 18 loci (n=544). Tooth-cementum analyses (n=316) allowed derivation of rare age-specific serologic data for a lyssavirus, a henipavirus and two rubulaviruses. This dataset contributes a substantial volume of data on the ecology of E. helvum and its viruses and will be valuable for a wide range of studies, including viral transmission dynamic modelling in age-structured populations, investigation of seasonal reproductive asynchrony in wide-ranging species, ecological niche modelling, inference of island colonisation history, exploration of relationships between island and body size, and various spatial analyses of demographic, morphometric or serological data.

Epidemiological patterns of tick-borne encephalitis in Lithuania and clinical features in adults in the light of the high incidence in recent years: a retrospective study.

PubMed

Radzišauskienė, D; Žagminas, K; Ašoklienė, L; Jasionis, A; Mameniškienė, R; Ambrozaitis, A; Jančorienė, L; Jatužis, D; Petraitytė, I; Mockienė, E

2018-02-01

Lithuania is one of the countries with the highest incidence of tick-borne encephalitis (TBE) in Europe. The aim of this study was to describe the epidemiological patterns of TBE in Lithuania, and characterize clinical features in adults in the light of the high incidence in recent years. Surveillance data available on the website of the Centre for Communicable Diseases and AIDS of Lithuania were used to describe the epidemiological patterns of TBE. The retrospective study included 712 patients hospitalized in the Centre for Infectious Diseases and the Centre for Neurology of Vilnius University in the years 2005-2014. Tick-borne encephalitis incidence rates have been increasing by 8.5% per year for the 45-year period from 1970 to 2014. The joinpoint model finds two joinpoints at 1991 and 1994, with a significant decrease of 8.4% per year (P < 0.05) prior to the joinpoint at 1991, and a rise of 195.2% afterwards. TBE presented with meningoencephalitis in 556 cases (81.3%). A total of 129 patients (18%) had a severe case of the disease. The most common neurological signs were ataxia (579, 81.3%), meningeal signs (474, 66.5%) and tremor (338, 47.5%). Limb paresis was observed in 6.3% of patients. Five patients (0.7%) died, and 544 patients (76.7%) were discharged with sequelae. Intensified efforts in promoting TBE vaccination will be needed in the light of the high incidence and expanded spatial distribution. Significant prognostic factors for severe cases of the disease were age above 61 and delayed immune response of specific immunoglobulin G. © 2017 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.

Performance map of a cluster detection test using extended power

PubMed Central

2013-01-01

Background Conventional power studies possess limited ability to assess the performance of cluster detection tests. In particular, they cannot evaluate the accuracy of the cluster location, which is essential in such assessments. Furthermore, they usually estimate power for one or a few particular alternative hypotheses and thus cannot assess performance over an entire region. Takahashi and Tango developed the concept of extended power that indicates both the rate of null hypothesis rejection and the accuracy of the cluster location. We propose a systematic assessment method, using here extended power, to produce a map showing the performance of cluster detection tests over an entire region. Methods To explore the behavior of a cluster detection test on identical cluster types at any possible location, we successively applied four different spatial and epidemiological parameters. These parameters determined four cluster collections, each covering the entire study region. We simulated 1,000 datasets for each cluster and analyzed them with Kulldorff’s spatial scan statistic. From the area under the extended power curve, we constructed a map for each parameter set showing the performance of the test across the entire region. Results Consistent with previous studies, the performance of the spatial scan statistic increased with the baseline incidence of disease, the size of the at-risk population and the strength of the cluster (i.e., the relative risk). Performance was heterogeneous, however, even for very similar clusters (i.e., similar with respect to the aforementioned factors), suggesting the influence of other factors. Conclusions The area under the extended power curve is a single measure of performance and, although needing further exploration, it is suitable to conduct a systematic spatial evaluation of performance. The performance map we propose enables epidemiologists to assess cluster detection tests across an entire study region. PMID:24156765

Saturation sampling for spatial variation in multiple air pollutants across an inversion-prone metropolitan area of complex terrain

PubMed Central

2014-01-01

Background Characterizing intra-urban variation in air quality is important for epidemiological investigation of health outcomes and disparities. To date, however, few studies have been designed to capture spatial variation during select hours of the day, or to examine the roles of meteorology and complex terrain in shaping intra-urban exposure gradients. Methods We designed a spatial saturation monitoring study to target local air pollution sources, and to understand the role of topography and temperature inversions on fine-scale pollution variation by systematically allocating sampling locations across gradients in key local emissions sources (vehicle traffic, industrial facilities) and topography (elevation) in the Pittsburgh area. Street-level integrated samples of fine particulate matter (PM2.5), black carbon (BC), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) were collected during morning rush and probable inversion hours (6-11 AM), during summer and winter. We hypothesized that pollution concentrations would be: 1) higher under inversion conditions, 2) exacerbated in lower-elevation areas, and 3) vary by season. Results During July - August 2011 and January - March 2012, we observed wide spatial and seasonal variability in pollution concentrations, exceeding the range measured at regulatory monitors. We identified elevated concentrations of multiple pollutants at lower-elevation sites, and a positive association between inversion frequency and NO2 concentration. We examined temporal adjustment methods for deriving seasonal concentration estimates, and found that the appropriate reference temporal trend differs between pollutants. Conclusions Our time-stratified spatial saturation approach found some evidence for modification of inversion-concentration relationships by topography, and provided useful insights for refining and interpreting GIS-based pollution source indicators for Land Use Regression modeling. PMID:24735818

Saturation sampling for spatial variation in multiple air pollutants across an inversion-prone metropolitan area of complex terrain.

PubMed

Shmool, Jessie Lc; Michanowicz, Drew R; Cambal, Leah; Tunno, Brett; Howell, Jeffery; Gillooly, Sara; Roper, Courtney; Tripathy, Sheila; Chubb, Lauren G; Eisl, Holger M; Gorczynski, John E; Holguin, Fernando E; Shields, Kyra Naumoff; Clougherty, Jane E

2014-04-16

Characterizing intra-urban variation in air quality is important for epidemiological investigation of health outcomes and disparities. To date, however, few studies have been designed to capture spatial variation during select hours of the day, or to examine the roles of meteorology and complex terrain in shaping intra-urban exposure gradients. We designed a spatial saturation monitoring study to target local air pollution sources, and to understand the role of topography and temperature inversions on fine-scale pollution variation by systematically allocating sampling locations across gradients in key local emissions sources (vehicle traffic, industrial facilities) and topography (elevation) in the Pittsburgh area. Street-level integrated samples of fine particulate matter (PM2.5), black carbon (BC), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) were collected during morning rush and probable inversion hours (6-11 AM), during summer and winter. We hypothesized that pollution concentrations would be: 1) higher under inversion conditions, 2) exacerbated in lower-elevation areas, and 3) vary by season. During July - August 2011 and January - March 2012, we observed wide spatial and seasonal variability in pollution concentrations, exceeding the range measured at regulatory monitors. We identified elevated concentrations of multiple pollutants at lower-elevation sites, and a positive association between inversion frequency and NO2 concentration. We examined temporal adjustment methods for deriving seasonal concentration estimates, and found that the appropriate reference temporal trend differs between pollutants. Our time-stratified spatial saturation approach found some evidence for modification of inversion-concentration relationships by topography, and provided useful insights for refining and interpreting GIS-based pollution source indicators for Land Use Regression modeling.

Spatial patterns of schistosomiasis in Burkina Faso: relevance of human mobility and water resources development

NASA Astrophysics Data System (ADS)

Perez-Saez, Javier; Bertuzzo, Enrico; Frohelich, Jean-Marc; Mande, Theophile; Ceperley, Natalie; Sou, Mariam; Yacouba, Hamma; Maiga, Hamadou; Sokolow, Susanne; De Leo, Giulio; Casagrandi, Renato; Gatto, Marino; Mari, Lorenzo; Rinaldo, Andrea

2015-04-01

We study the spatial geography of schistosomiasis in the african context of Burkina Faso by means of a spatially explicit model of disease dynamics and spread. The relevance of our work lies in its ability to describe quantitatively a geographic stratification of the disease burden capable of reproducing important spatial differences, and drivers/controls of disease spread. Among the latters, we consider specifically the development and management of water resources which have been singled out empirically as an important risk factor for schistosomiasis. The model includes remotely acquired and objectively manipulated information on the distributions of population, infrastructure, elevation and climatic drivers. It also includes a general description of human mobility and addresses a first-order characterization of the ecology of the intermediate host of the parasite causing the disease based on maximum entropy learning of relevant environmenal covariates. Spatial patterns of the disease were analyzed about their disease-free equilibrium by proper extraction and mapping of suitable eigenvectors of the Jacobian matrix subsuming all stability properties of the system. Human mobility was found to be a primary control of both pathogen invasion success and of the overall distribution of disease burden. The effects of water resources development were studied by accounting for the (prior and posterior) average distances of human settlements from water bodies that may serve as suitable habitats to the intermediate host of the parasite. Water developments, in combination with human mobility, were quantitatively related to disease spread into regions previously nearly disease-free and to large-scale empirical incidence patterns. We concluded that while the model still needs refinements based on field and epidemiological evidence, the framework proposed provides a powerful tool for large-scale, long-term public health planning and management of schistosomiasis.

Socio-environmental factors and diarrheal diseases in under five-year old children in the state of Tocantins, Brazil

PubMed Central

Graepp-Fontoura, Iolanda; Santos, Floriacy Stabnow; Santos Neto, Marcelino; Tavares, Hanari Santos de Almeida; Bezerra, Maria Onice Lopes; Feitosa, Marcela de Oliveira; Neves, Adriano Figuerêdo; de Morais, Jesuane Cavalcante Melo; Nascimento, Luiz Fernando Costa

2018-01-01

Background Diarrhea is a waterborne disease that affects children, especially those under 5 years of age. The objective of this study was to identify the spatial patterns of distribution of diarrheal disease in under 5-year-old children in the State of Tocantins, Brazil, from 2008 to 2013. Methods Geoprocessing tools were used to carry out an epidemiological study, to prepare thematic maps in the TerraView 4.2.2 software based on secondary data. General indicators of the disease, presence of spatial dependence through the Global Moran’s Index (I) and the Spatial Association Index (LISA) were described. Results There were 3,015 cases of under 5-year-old children hospitalized for diarrhea, with an average annual rate (AAR) of 4.10/1,000 inhabitants (inhab.). Among the main characteristics were: increasing rates in under 1-year-old children (6.16 to 9.66/1,000 inhabitants); children aged 1 to 4 full years (63%); males (55%); 8 deaths of under one-year-old children (75%); county of Araguaína (67%); incidence in the county of Nazaré (63.97/1,000 inhab.); prevalence and incidence in the Araguaína microregion (45%, AAR 9.38/1,000 inhab.). The presence of a cluster with spatial autocorrelation was found in the Araguaína microregion, which was statistically significant (I = 0.11, p-value < 0.03), with priority of intervention (Moran Map). Conclusions There was an increase in the number of hospitalizations for diarrhea in under 5–year-old children in the state of Tocantins. The spatial analysis identified clusters of priority areas for measures of maintenance and control of diarrheal diseases. PMID:29768428

Spatial distribution and cluster analysis of risky sexual behaviours and STDs reported by Chinese adults in Guangzhou, China: a representative population-based study

PubMed Central

Chen, Wen; Zhou, Fangjing; Hall, Brian J; Wang, Yu; Latkin, Carl; Ling, Li; Tucker, Joseph D

2016-01-01

Objectives To assess associations between residences location, risky sexual behaviours and sexually transmitted diseases (STDs) among adults living in Guangzhou, China. Methods Data were obtained from 751 Chinese adults aged 18–59 years in Guangzhou, China, using stratified random sampling by using spatial epidemiological methods. Face-to-face household interviews were conducted to collect self-report data on risky sexual behaviours and diagnosed STDs. Kulldorff’s spatial scan statistic was implemented to identify and detect spatial distribution and clusters of risky sexual behaviours and STDs. The presence and location of statistically significant clusters were mapped in the study areas using ArcGIS software. Results The prevalence of self-reported risky sexual behaviours was between 5.1% and 50.0%. The self-reported lifetime prevalence of diagnosed STDs was 7.06%. Anal intercourse clustered in an area located along the border within the rural–urban continuum (p=0.001). High rate clusters for alcohol or other drugs using before sex (p=0.008) and migrants who lived in Guangzhou <1 year (p=0.007) overlapped this cluster. Excess cases for unprotected sex (p=0.031) overlapped the cluster for college students (p<0.001). Five of nine (55.6%) students who had sexual experience during the last 12 months located in the cluster of unprotected sex. Conclusions Short-term migrants and college students reported greater risky sexual behaviours. Programmes to increase safer sex within these communities to reduce the risk of STDs are warranted in Guangzhou. Spatial analysis identified geographical clusters of risky sexual behaviours, which is critical for optimising surveillance and targeting control measures for these locations in the future. PMID:26843400

A Hybrid Model for Spatially and Temporally Resolved Ozone Exposures in the Continental United States

PubMed Central

Di, Qian; Rowland, Sebastian; Koutrakis, Petros; Schwartz, Joel

2017-01-01

Ground-level ozone is an important atmospheric oxidant, which exhibits considerable spatial and temporal variability in its concentration level. Existing modeling approaches for ground-level ozone include chemical transport models, land-use regression, Kriging, and data fusion of chemical transport models with monitoring data. Each of these methods has both strengths and weaknesses. Combining those complementary approaches could improve model performance. Meanwhile, satellite-based total column ozone, combined with ozone vertical profile, is another potential input. We propose a hybrid model that integrates the above variables to achieve spatially and temporally resolved exposure assessments for ground-level ozone. We used a neural network for its capacity to model interactions and nonlinearity. Convolutional layers, which use convolution kernels to aggregate nearby information, were added to the neural network to account for spatial and temporal autocorrelation. We trained the model with AQS 8-hour daily maximum ozone in the continental United States from 2000 to 2012 and tested it with left out monitoring sites. Cross-validated R2 on the left out monitoring sites ranged from 0.74 to 0.80 (mean 0.76) for predictions on 1 km×1 km grid cells, which indicates good model performance. Model performance remains good even at low ozone concentrations. The prediction results facilitate epidemiological studies to assess the health effect of ozone in the long term and the short term. PMID:27332675

Spatial epidemiology of Escherichia coli O157:H7 in dairy cattle in relation to night roosts Of Sturnus vulgaris (European Starling) in Ohio, USA (2007-2009).

PubMed

Swirski, A L; Pearl, D L; Williams, M L; Homan, H J; Linz, G M; Cernicchiaro, N; LeJeune, J T

2014-09-01

The goal of our study was to use spatial scan statics to determine whether the night roosts of European starlings (Sturnus vulgaris) act as point sources for the dissemination of Escherichia coli O157:H7 among dairy farms. From 2007 to 2009, we collected bovine faecal samples (n = 9000) and starling gastrointestinal contents (n = 430) from 150 dairy farms in northeastern Ohio, USA. Isolates of E. coli O157:H7 recovered from these samples were subtyped using multilocus variable-number tandem repeat analysis (MLVA). Generated MLVA types were used to construct a dendrogram based on a categorical multistate coefficient and unweighted pair-group method with arithmetic mean (UPGMA). Using a focused spatial scan statistic, we identified statistically significant spatial clusters among dairy farms surrounding starling night roosts, with an increased prevalence of E. coli O157:H7-positive bovine faecal pats, increased diversity of distinguishable MLVA types and a greater number of isolates with MLVA types from bovine-starling clades versus bovine-only clades. Thus, our findings are compatible with the hypothesis that starlings have a role in the dissemination of E. coli O157:H7 among dairy farms, and further research into starling management is warranted. © 2013 Blackwell Verlag GmbH.

[Sporotrichosis and paracoccidioidomycosis in Peru: experiences in prevention and control].

PubMed

Zurita Macalupú, Susana

2014-04-01

The epidemiological picture of sporotrichosis and paracoccidioidomycosis in Peru and Latin America is sporadic, fragmented, and geographically limited, mainly due to lack of mandatory reporting and limited diagnostic coverage. However, research contributions related to understanding the interaction of these fungi, the response of the host and the environment, the use of spatial analysis that relates the distribution of these mycoses, population density and climate, contributes to the design of prevention and control strategies of these mycosis and suggest epidemiological risk maps management, based on the habitat of the fungus. This information will be used by doctors, tourists and people living in rural areas where mycoses are endemic. The aim of the paper is to present a review of the topic through research findings that contribute to the prevention and control of these mycosis.

Spatial and simultaneous representative seroprevalence of anti-Toxoplasma gondii antibodies in owners and their domiciled dogs in a major city of southern Brazil

PubMed Central

Benitez, Aline do Nascimento; Martins, Felippe Danyel Cardoso; Mareze, Marcelle; Santos, Nelson Jessé Rodrigues; Ferreira, Fernanda Pinto; Martins, Camila Marinelli; Garcia, João Luis; Mitsuka-Breganó, Regina; Freire, Roberta Lemos; Biondo, Alexander Welker

2017-01-01

Toxoplasmosis, caused by Toxoplasma gondii, has traditionally been considered an important water and foodborne protozoonosis with important public health considerations. Although felids play a well-established role as definitive hosts, canine epidemiological involvement in the parasite’s life cycle remains questionable and controversial. The increasing closeness of the human-dog bond, particularly seen in urban settings, has been recognized as a historically unprecedented worldwide movement. Sharing daily lives in the same households, dogs may be exposed to similar associated risks of T. gondii infection as their owners. Thus, epidemiological assessment of the intra-domiciled environment, especially among socio-economically different human populations, may provide novel information regarding the actual role of dogs in animal and human toxoplasmosis. Despite spatial approaches being recently used for other water and foodborne diseases, no study has been conducted on the simultaneous spatial seroprevalence of both human and animal IgG anti-T. gondii antibodies in urban areas of major cities. Accordingly, the aim of the present study was to assess the seroprevalence and associated variables of Toxoplasma infection in owners and their domiciled dogs in Londrina, southern Brazil. Human and canine seroprevalence rates and variables associated with seroprevalence were investigated through representative random sampling among 564 households, which included 597 owners and 729 dogs. Overall, statistically significant differences between the seroprevalence of human and dog anti-T. gondii antibodies were found by Immunofluorescence Antibody Testing in 248/597 (41.54%) owners and 119/729 (16.32%) dogs. Through multiple analysis, significant concomitant variables for seropositivity of household individuals (people and dogs) were determined, including public sewer service, yard cleaning frequency, and having a dirty yard. Although no statistically significant multiple logistic model was observed among owners, univariate analysis detected associations with monthly income, soil contact, and occupation. Among dogs, the absence of other dogs and the absence of a dirty yard were concomitant significantly protective associated factors. Age differences between seropositive and seronegative individuals was significant only for human beings, with the median age of negative individuals significantly higher than positive individuals. Although no spatial clusters were identified for humans or residences, a significant cluster was identified for dogs. In conclusion, characteristics of urban toxoplasmosis may include significantly higher owner seroprevalence than their owned dogs, with canine seroprevalence directly associated with having more dogs and a dirty backyard, and spatial differences in both human and dog exposures. Although not a good indicator for human foodborne diseases, dogs may be a reliable sentinel for environmental infection. Moreover, such a holistic approach may provide crucial information for more focused prevention and monitoring programs, particularly in households with multiple pets and trash-filled backyards. PMID:28732033

Spatial and simultaneous representative seroprevalence of anti-Toxoplasma gondii antibodies in owners and their domiciled dogs in a major city of southern Brazil.

PubMed

Benitez, Aline do Nascimento; Martins, Felippe Danyel Cardoso; Mareze, Marcelle; Santos, Nelson Jessé Rodrigues; Ferreira, Fernanda Pinto; Martins, Camila Marinelli; Garcia, João Luis; Mitsuka-Breganó, Regina; Freire, Roberta Lemos; Biondo, Alexander Welker; Navarro, Italmar Teodorico

2017-01-01

Toxoplasmosis, caused by Toxoplasma gondii, has traditionally been considered an important water and foodborne protozoonosis with important public health considerations. Although felids play a well-established role as definitive hosts, canine epidemiological involvement in the parasite's life cycle remains questionable and controversial. The increasing closeness of the human-dog bond, particularly seen in urban settings, has been recognized as a historically unprecedented worldwide movement. Sharing daily lives in the same households, dogs may be exposed to similar associated risks of T. gondii infection as their owners. Thus, epidemiological assessment of the intra-domiciled environment, especially among socio-economically different human populations, may provide novel information regarding the actual role of dogs in animal and human toxoplasmosis. Despite spatial approaches being recently used for other water and foodborne diseases, no study has been conducted on the simultaneous spatial seroprevalence of both human and animal IgG anti-T. gondii antibodies in urban areas of major cities. Accordingly, the aim of the present study was to assess the seroprevalence and associated variables of Toxoplasma infection in owners and their domiciled dogs in Londrina, southern Brazil. Human and canine seroprevalence rates and variables associated with seroprevalence were investigated through representative random sampling among 564 households, which included 597 owners and 729 dogs. Overall, statistically significant differences between the seroprevalence of human and dog anti-T. gondii antibodies were found by Immunofluorescence Antibody Testing in 248/597 (41.54%) owners and 119/729 (16.32%) dogs. Through multiple analysis, significant concomitant variables for seropositivity of household individuals (people and dogs) were determined, including public sewer service, yard cleaning frequency, and having a dirty yard. Although no statistically significant multiple logistic model was observed among owners, univariate analysis detected associations with monthly income, soil contact, and occupation. Among dogs, the absence of other dogs and the absence of a dirty yard were concomitant significantly protective associated factors. Age differences between seropositive and seronegative individuals was significant only for human beings, with the median age of negative individuals significantly higher than positive individuals. Although no spatial clusters were identified for humans or residences, a significant cluster was identified for dogs. In conclusion, characteristics of urban toxoplasmosis may include significantly higher owner seroprevalence than their owned dogs, with canine seroprevalence directly associated with having more dogs and a dirty backyard, and spatial differences in both human and dog exposures. Although not a good indicator for human foodborne diseases, dogs may be a reliable sentinel for environmental infection. Moreover, such a holistic approach may provide crucial information for more focused prevention and monitoring programs, particularly in households with multiple pets and trash-filled backyards.

Aircraft noise, health, and residential sorting: evidence from two quasi-experiments.

PubMed

Boes, Stefan; Nüesch, Stephan; Stillman, Steven

2013-09-01

We explore two unexpected changes in flight regulations to estimate the causal effect of aircraft noise on health. Detailed measures of noise are linked with longitudinal data on individual health outcomes based on the exact address information. Controlling for individual heterogeneity and spatial sorting into different neighborhoods, we find that aircraft noise significantly increases sleeping problems and headaches. Models that do not control for such heterogeneity and sorting substantially underestimate the negative health effects, which suggests that individuals self-select into residence based on their unobserved sensitivity to noise. Our study demonstrates that the combination of quasi-experimental variation and panel data is very powerful for identifying causal effects in epidemiological field studies. Copyright © 2013 John Wiley & Sons, Ltd.

[Syndromic surveillance in circumstances of bioterrorism threat--the essence, application abilities and superiority over a traditional epidemiological surveillance].

PubMed

Osemek, Paweł; Kocik, Janusz; Paśnik, Krzysztof

2009-12-01

This article provides a short review about trends of developing current syndromic surveillance systems. To improve methods of early detection of natural or bioterrorism-related outbreaks, it has to be established a new way of epidemiological thinking, which uses innovative real-time surveillance systems. Syndromic surveillance has been created for an early detection, to monitor the temporo-spatial spread of an outbreak, and to provide prompt data for immediate analysis and feedback to public health authorities. It supports timely decision making process for countermeasure procedures. Framework of syndromic surveillance system requires a proper electronic infrastructure to be build up. Optimal syndrome definitions and data sources for continuing specific diseases outbreak surveillance have not been determined so far. Systems of interest might enhance collaboration among clinical providers, primary care providers, emergency services, information-systems professionals and public health agencies. However economic scope of this undertakings effectively limits ability to implement it in Polish public health service right now. Besides, syndromic surveillance cannot replace traditional public health surveillance with a post-factum epidemiological investigation and laboratory analysis. It can be a useful supplement.

Leptospirosis in Mexico: Epidemiology and Potential Distribution of Human Cases

PubMed Central

Sánchez-Montes, Sokani; Espinosa-Martínez, Deborah V.; Ríos-Muñoz, César A.; Berzunza-Cruz, Miriam; Becker, Ingeborg

2015-01-01

Background Leptospirosis is widespread in Mexico, yet the potential distribution and risk of the disease remain unknown. Methodology/Principal Findings We analysed morbidity and mortality according to age and gender based on three sources of data reported by the Ministry of Health and the National Institute of Geography and Statics of Mexico, for the decade 2000–2010. A total of 1,547 cases were reported in 27 states, the majority of which were registered during the rainy season, and the most affected age group was 25–44 years old. Although leptospirosis has been reported as an occupational disease of males, analysis of morbidity in Mexico showed no male preference. A total number of 198 deaths were registered in 21 states, mainly in urban settings. Mortality was higher in males (61.1%) as compared to females (38.9%), and the case fatality ratio was also increased in males. The overall case fatality ratio in Mexico was elevated (12.8%), as compared to other countries. We additionally determined the potential disease distribution by examining the spatial epidemiology combined with spatial modeling using ecological niche modeling techniques. We identified regions where leptospirosis could be present and created a potential distribution map using bioclimatic variables derived from temperature and precipitation. Our data show that the distribution of the cases was more related to temperature (75%) than to precipitation variables. Ecological niche modeling showed predictive areas that were widely distributed in central and southern Mexico, excluding areas characterized by extreme climates. Conclusions/Significance In conclusion, an epidemiological surveillance of leptospirosis is recommended in Mexico, since 55.7% of the country has environmental conditions fulfilling the criteria that favor the presence of the disease. PMID:26207827

Epidemiology of seasonal influenza in the Middle East and North Africa regions, 2010-2016: Circulating influenza A and B viruses and spatial timing of epidemics.

PubMed

Caini, Saverio; El-Guerche Séblain, Clotilde; Ciblak, Meral A; Paget, John

2018-05-01

There is a limited knowledge regarding the epidemiology of influenza in Middle East and North Africa. We described the patterns of influenza circulation and the timing of seasonal epidemics in countries of Middle East and North Africa. We used virological surveillance data for 2010-2016 from the WHO FluNet database. In each country, we calculated the median proportion of cases that were caused by each virus type and subtype; determined the timing and amplitude of the primary and secondary peaks; and used linear regression models to test for spatial trends in the timing of epidemics. We included 70 532 influenza cases from seventeen countries. Influenza A and B accounted for a median 76.5% and 23.5% of cases in a season and were the dominant type in 86.8% and 13.2% of seasons. The proportion of influenza A cases that were subtyped was 85.9%, while only 4.4% of influenza B cases were characterized. For most countries, influenza seasonality was similar to the Northern Hemisphere, with a single large peak between January and March; exceptions were the countries in the Arabian Peninsula and Jordan, all of which showed clear secondary peaks, and some countries had an earlier primary peak (in November-December in Bahrain and Qatar). The direction of the timing of influenza activity was east to west and south to north in 2012-2013 and 2015-2016, and west to east in 2014-2015. The epidemiology of influenza is generally uniform in countries of Middle East and North Africa, with influenza B playing an important role in the seasonal disease burden. © 2018 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.

Phylodynamics of the HIV-1 CRF02_AG clade in Cameroon

PubMed Central

Faria, Nuno Rodrigues; Suchard, Marc A; Abecasis, Ana; Sousa, J. D.; Ndembi, Nicaise; Camacho, R.J.; Vandamme, Anne-Mieke; Peeters, Martine; Lemey, Philippe

2015-01-01

Evolutionary analyses have revealed an origin of pandemic HIV-1 group M in the Congo River basin in the first part of the XXth century, but the patterns of historical viral spread in or around its epicentre remain largely unexplored. Here, we combine epidemiologic and molecular sequence data to investigate the spatiotemporal patterns of the CRF02_AG clade. By explicitly integrating prevalence counts and genetic population size estimates we date the epidemic emergence of CRF02_AG at 1973.1 (1972.1, 1975.3 95% CI). To infer their phylogeographic signature at a regional scale, we analyze pol and env time-stamped sequence data from 8 countries using a Bayesian phylogeographic approach based on a discrete asymmetric model. Our data confirms a spatial origin of this clade in the Democratic Republic of Congo (DRC) and suggests that viral dissemination to Cameroon occurred at an early stage of the evolutionary history of CRF02_AG. We find considerable support for epidemiological linkage between neighbour countries. Compilation of ethnographic data suggests that well-supported viral migration was related with chance exportation events rather than by sustained human migratory flows. Finally, using sequence data from 15 locations in Cameroon, we use relaxed random walk models to explore the spatiotemporal dynamics of CRF02_AG at a finer geographical detail. Phylogeographic dispersal in continuous space reveals that at least two distinct CRF02_AG lineages are circulating in overlapping regions that are evolving at different evolutionary and diffusion rates. Altogether, by combining molecular and epidemiological data, our results provide a time scale for CRF02_AG, place its spatial root within the putative root of group-M diversity and propose a scenario for the spatiotemporal patterns of a successful HIV-1 lineage both at a regional and country-scale. PMID:21565285

Leptospirosis in Mexico: Epidemiology and Potential Distribution of Human Cases.

PubMed

Sánchez-Montes, Sokani; Espinosa-Martínez, Deborah V; Ríos-Muñoz, César A; Berzunza-Cruz, Miriam; Becker, Ingeborg

2015-01-01

Leptospirosis is widespread in Mexico, yet the potential distribution and risk of the disease remain unknown. We analysed morbidity and mortality according to age and gender based on three sources of data reported by the Ministry of Health and the National Institute of Geography and Statics of Mexico, for the decade 2000-2010. A total of 1,547 cases were reported in 27 states, the majority of which were registered during the rainy season, and the most affected age group was 25-44 years old. Although leptospirosis has been reported as an occupational disease of males, analysis of morbidity in Mexico showed no male preference. A total number of 198 deaths were registered in 21 states, mainly in urban settings. Mortality was higher in males (61.1%) as compared to females (38.9%), and the case fatality ratio was also increased in males. The overall case fatality ratio in Mexico was elevated (12.8%), as compared to other countries. We additionally determined the potential disease distribution by examining the spatial epidemiology combined with spatial modeling using ecological niche modeling techniques. We identified regions where leptospirosis could be present and created a potential distribution map using bioclimatic variables derived from temperature and precipitation. Our data show that the distribution of the cases was more related to temperature (75%) than to precipitation variables. Ecological niche modeling showed predictive areas that were widely distributed in central and southern Mexico, excluding areas characterized by extreme climates. In conclusion, an epidemiological surveillance of leptospirosis is recommended in Mexico, since 55.7% of the country has environmental conditions fulfilling the criteria that favor the presence of the disease.

Epidemiology as discourse: the politics of development institutions in the Epidemiological Profile of El Salvador

PubMed Central

Aviles, L

2001-01-01

STUDY OBJECTIVE—To determine the ways in which institutions devoted to international development influence epidemiological studies.
DESIGN—This article takes a descriptive epidemiological study of El Salvador, Epidemiological Profile, conducted in 1994 by the US Agency for International Development, as a case study. The methods include discourse analysis in order to uncover the ideological basis of the report and its characteristics as a discourse of development.
SETTING—El Salvador.
RESULTS—The Epidemiological Profile theoretical basis, the epidemiological transition theory, embodies the ethnocentrism of a "colonizer's model of the world." This report follows the logic of a discourse of development by depoliticising development, creating abnormalities, and relying on the development consulting industry. The epidemiological transition theory serves as an ideology that legitimises and dissimulates the international order.
CONCLUSIONS—Even descriptive epidemiological assessments or epidemiological profiles are imbued with theoretical assumptions shaped by the institutional setting under which epidemiological investigations are conducted.


Keywords: El Salvador; politics PMID:11160170

Spatio-temporal pattern analysis for evaluation of the spread of human infections with avian influenza A(H7N9) virus in China, 2013-2014.

PubMed

Dong, Wen; Yang, Kun; Xu, Quanli; Liu, Lin; Chen, Juan

2017-10-24

A large number (n = 460) of A(H7N9) human infections have been reported in China from March 2013 through December 2014, and H7N9 outbreaks in humans became an emerging issue for China health, which have caused numerous disease outbreaks in domestic poultry and wild bird populations, and threatened human health severely. The aims of this study were to investigate the directional trend of the epidemic and to identify the significant presence of spatial-temporal clustering of influenza A(H7N9) human cases between March 2013 and December 2014. Three distinct epidemic phases of A(H7N9) human infections were identified in this study. In each phase, standard deviational ellipse analysis was conducted to examine the directional trend of disease spreading, and retrospective space-time permutation scan statistic was then used to identify the spatio-temporal cluster patterns of H7N9 outbreaks in humans. The ever-changing location and the increasing size of the three identified standard deviational ellipses showed that the epidemic moved from east to southeast coast, and hence to some central regions, with a future epidemiological trend of continue dispersing to more central regions of China, and a few new human cases might also appear in parts of the western China. Furthermore, A(H7N9) human infections were clustering in space and time in the first two phases with five significant spatio-temporal clusters (p < 0.05), but there was no significant cluster identified in phase III. There was a new epidemiologic pattern that the decrease in significant spatio-temporal cluster of A(H7N9) human infections was accompanied with an obvious spatial expansion of the outbreaks during the study period, and identification of the spatio-temporal patterns of the epidemic can provide valuable insights for better understanding the spreading dynamics of the disease in China.

Enhancing Remotely Sensed TIR Data for Public Health Applications: Is West Nile Virus Heat-Related?

NASA Astrophysics Data System (ADS)

Weng, Q.; Liu, H.; Jiang, Y.

2014-12-01

Public health studies often require thermal infrared (TIR) images at both high temporal and spatial resolution to retrieve LST. However, currently, no single satellite sensors can deliver TIR data at both high temporal and spatial resolution. This technological limitation prevents the wide usage of remote sensing data in epidemiological studies. To solve this issue, we have developed a few image fusion techniques to generate high temporally-resolved image data. We downscaled GOES LST data to 15-minute 1-km resolution to assess community-based heat-related risk in Los Angeles County, California and simulated ASTER datasets by fusing ASTER and MODIS data to derive biophysical variables, including LST, NDVI, and normalized difference water index, to examine the effects of those environmental characteristics on WNV outbreak and dissemination. A spatio-temporal analysis of WNV outbreak and dissemination was conducted by synthesizing the remote sensing variables and mosquito surveillance data, and by focusing on WNV risk areas in July through September due to data sufficiency of mosquito pools. Moderate- and high-risk areas of WNV infections in mosquitoes were identified for five epidemiological weeks. These identified WNV-risk areas were then collocated in GIS with heat hazard, exposure, and vulnerability maps to answer the question of whether WNV is a heat related virus. The results show that elevation and built-up conditions were negatively associated with the WNV propagation, while LST positively correlated with the viral transmission. NDVI was not significantly associated with WNV transmission. San Fernando Valley was found to be the most vulnerable to mosquito infections of WNV. This research provides important insights into how high temporal resolution remote sensing imagery may be used to study time-dependant events in public health, especially in the operational surveillance and control of vector-borne, water-borne, or other epidemic diseases.

Epidemiology for the nuclear medicine technologist.

PubMed

Bolus, N E

2001-09-01

The purpose of this article is to introduce the nuclear medicine technologist to the field of epidemiology. There are many applications of epidemiology in nuclear medicine, including research studies that deal with the causes of disease or ways to prevent disease from occurring and investigating the possible effects of ionizing radiation on occupational workers and the general public. One use of an epidemiologic study is to suggest ways to reduce the occurrence of a disease. After reading this article, the nuclear medicine technologist will be familiar with: a) the history and underlying assumptions of epidemiology, b) types of epidemiologic studies, c) what is a valid statistical association for an epidemiologic study, d) proper judgment of cause and effect relationships, e) definitions of epidemiologic terms, and f) an example of a nuclear medicine research study.

Geography of breast cancer incidence according to age & birth cohorts.

PubMed

Gregorio, David I; Ford, Chandler; Samociuk, Holly

2017-06-01

Geographic variation in breast cancer incidence across Connecticut was examined according to age and birth cohort -specific groups. We assigned each of 60,937 incident breast cancer cases diagnosed in Connecticut, 1986-2009, to one of 828 census tracts around the state. Global and local spatial statistics estimated rate variation across the state according to age and birth cohorts. We found the global distribution of incidence rates across places to be more heterogeneous for younger women and later birth cohorts. Concurrently, the spatial scan identified more locations with significantly high rates that pertained to larger proportions of at-risk women within these groups. Geographic variation by age groups was more pronounced than by birth cohorts. Geographic patterns of cancer incidence exhibit differences within and across age and birth cohorts. With the continued insights from descriptive epidemiology, our capacity to effectively limit spatial disparities in cancer will improve. Copyright © 2017 Elsevier Ltd. All rights reserved.

Abrupt transition to heightened poliomyelitis epidemicity in England and Wales, 1947-1957, associated with a pronounced increase in the geographical rate of disease propagation.

PubMed

Smallman-Raynor, M R; Cliff, A D

2014-03-01

The abrupt transition to heightened poliomyelitis epidemicity in England and Wales, 1947-1957, was associated with a profound change in the spatial dynamics of the disease. Drawing on the complete record of poliomyelitis notifications in England and Wales, we use a robust method of spatial epidemiological analysis (swash-backwash model) to evaluate the geographical rate of disease propagation in successive poliomyelitis seasons, 1940-1964. Comparisons with earlier and later time periods show that the period of heightened poliomyelitis epidemicity corresponded with a sudden and pronounced increase in the spatial rate of disease propagation. This change was observed for both urban and rural areas and points to an abrupt enhancement in the propensity for the geographical spread of polioviruses. Competing theories of the epidemic emergence of poliomyelitis in England and Wales should be assessed in the light of this evidence.

Quantifying the metabolic activities of human-associated microbial communities across multiple ecological scales

PubMed Central

Maurice, Corinne Ferrier; Turnbaugh, Peter James

2013-01-01

Humans are home to complex microbial communities, whose aggregate genomes and their encoded metabolic activities are referred to as the human microbiome. Recently, researchers have begun to appreciate that different human body habitats and the activities of their resident microorganisms can be better understood in ecological terms, as a range of spatial scales encompassing single cells, guilds of microorganisms responsive to a similar substrate, microbial communities, body habitats, and host populations. However, the bulk of the work to date has focused on studies of culturable microorganisms in isolation or on DNA sequencing-based surveys of microbial diversity in small to moderately sized cohorts of individuals. Here, we discuss recent work that highlights the potential for assessing the human microbiome at a range of spatial scales, and for developing novel techniques that bridge multiple levels: for example, through the combination of single cell methods and metagenomic sequencing. These studies promise to not only provide a much-needed epidemiological and ecological context for mechanistic studies of culturable and genetically tractable microorganisms, but may also lead to the discovery of fundamental rules that govern the assembly and function of host-associated microbial communities. PMID:23550823

[Foodborne disease outbreaks surveillance in Chile].

PubMed

Olea, Andrea; Díaz, Janepsy; Fuentes, Rodrigo; Vaquero, Alejandra; García, Maritza

2012-10-01

Foodborne disease outbreaks are one of the main health problems globally, having an extensive impact on human welfare. The World Health Organization considers them as the main cause of morbidity and mortality in developing countries, and responsible for high levels of loss of productivity in developed countries. To describe the epidemiology of foodborne disease outbreaks according to data contained in an automated surveillance system. Descriptive observational study of notified outbreaks from the surveillance system, between 2005 and 2010 in Chile. The information was based on etiology, temporal and spatial distribution, and epidemiologic description of outbreaks during this period. There were 5,689 notified outbreaks. Most of them occurred during 2006 (1,106 outbreaks, rate 6.7 per 100,000 inhabitants) and 2008 (1,316 outbreaks, rate 7.9 per 100, 000 inhabitants) with an increase during summer. Fifty four percent occurred in the Metropolitan region. The group aged 15 to 44 years old, was the most affected one. Sixty four percent of the outbreaks had the food involved registered, of which fish and fishery products reached 42%. An 81% of the outbreaks did not have a precise etiologic diagnosis. Of all patients involved, 97% were outpatients, 3,2% were hospitalized patients, and 0,1% died. Only 49% of the outbreaks had information about the lack of food safety, with a 34,1% related to food handling procedures. Through the information on the epidemiology of foodborne diseases obtained by the Chilean surveillance system, appropriate control measures could be taken.

Statistical physics of vaccination

NASA Astrophysics Data System (ADS)

Wang, Zhen; Bauch, Chris T.; Bhattacharyya, Samit; d'Onofrio, Alberto; Manfredi, Piero; Perc, Matjaž; Perra, Nicola; Salathé, Marcel; Zhao, Dawei

2016-12-01

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

Spatial Dynamics of Bovine Tuberculosis in the Autonomous Community of Madrid, Spain (2010–2012)

PubMed Central

de la Cruz, Maria Luisa; Perez, Andres; Bezos, Javier; Pages, Enrique; Casal, Carmen; Carpintero, Jesus; Romero, Beatriz; Dominguez, Lucas; Barker, Christopher M.; Diaz, Rosa; Alvarez, Julio

2014-01-01

Progress in control of bovine tuberculosis (bTB) is often not uniform, usually due to the effect of one or more sometimes unknown epidemiological factors impairing the success of eradication programs. Use of spatial analysis can help to identify clusters of persistence of disease, leading to the identification of these factors thus allowing the implementation of targeted control measures, and may provide some insights of disease transmission, particularly when combined with molecular typing techniques. Here, the spatial dynamics of bTB in a high prevalence region of Spain were assessed during a three year period (2010–2012) using data from the eradication campaigns to detect clusters of positive bTB herds and of those infected with certain Mycobacterium bovis strains (characterized using spoligotyping and VNTR typing). In addition, the within-herd transmission coefficient (β) was estimated in infected herds and its spatial distribution and association with other potential outbreak and herd variables was evaluated. Significant clustering of positive herds was identified in the three years of the study in the same location (“high risk area”). Three spoligotypes (SB0339, SB0121 and SB1142) accounted for >70% of the outbreaks detected in the three years. VNTR subtyping revealed the presence of few but highly prevalent strains within the high risk area, suggesting maintained transmission in the area. The spatial autocorrelation found in the distribution of the estimated within-herd transmission coefficients in herds located within distances <14 km and the results of the spatial regression analysis, support the hypothesis of shared local factors affecting disease transmission in farms located at a close proximity. PMID:25536514

The spatial context of clinic-reported sexually transmitted infection in Hong Kong.

PubMed

Lee, Shui-Shan; Ho, King-Man; Cheung, Georgiana M T

2010-09-21

The incidence and prevalence of sexually transmitted infection (STI) in China has been on the rise in the past decade. Delineation of epidemiologic pattern is often hampered by its uneven distribution. Spatial distribution is often a neglected aspect of STI research, the description of which may enhance epidemiologic surveillance and inform service development. Over a one month-period, all first time attendees of 6 public STI clinics in Hong Kong were interviewed before clinical consultation using a standard questionnaire to assess their demographic, clinical and behavioural characteristics. A GIS (geographic information system)-based approach was adopted with mapping performed. The cases attending the clinics in different locations were profiled. A comparison was made between neighbourhood cases (patients living near a clinic) and distant cases (those farther off), by calculating the odds ratio for demographic, behavioural and geographic characteristics. Of the 1142 STI patients evaluated, the residence locations of 1029 (90.1%) could be geocoded, of which 95.6% were ethnic Chinese and 63.4% male. Geographically only about a quarter lived in the same district as the clinic. STI patients aged 55 or above were more likely to be living in the vicinity of the clinic, located in the same or adjacent tertiary planning unit (a small geographic unit below district level). A majority of patients came from locations a few kilometers from the clinic, the distance of which varies between clinics. Overall, more syphilis cases were reported in patients residing in the same or adjacent tertiary planning unit, while distant cases tended to give a higher risk of inconsistent condom use. There were otherwise no significant clinical and epidemiologic differences between neighbourhood and distant STI cases. There was no specific relationship between STI and the residence location of patients as regards their clinical and epidemiologic characteristics in the territory of Hong Kong. Older STI patients were however more inclined to attend the nearby STI clinics. Most patients have travelled a variable distance to access the STI service. The relationship between STI clinic cases and distance could be a complex issue intertwined between psychosocial characteristics and STI service coverage.

Differential Behavioral and Biochemical Responses to Caffeine in Male and Female Rats from a Validated Model of Attention Deficit and Hyperactivity Disorder.

PubMed

Nunes, Fernanda; Pochmann, Daniela; Almeida, Amanda Staldoni; Marques, Daniela Melo; Porciúncula, Lisiane de Oliveira

2018-03-20

Epidemiological studies suggest sex differences in attention deficit and hyperactivity disorder (ADHD) symptomatology. The potential benefits of caffeine have been reported in the management of ADHD, but its effects were not properly addressed with respect to sex differences. The present study examined the effects of caffeine (0.3 g/L) administered since childhood in the behavior and brain-derived neurotrophic factor (BDNF) and its related proteins in both sexes of a rat model of ADHD (spontaneously hypertensive rats-SHR). Hyperlocomotion, recognition, and spatial memory disturbances were observed in adolescent SHR rats from both sexes. However, females showed lack of habituation and worsened spatial memory. Although caffeine was effective against recognition memory impairment in both sexes, spatial memory was recovered only in female SHR rats. Besides, female SHR rats showed exacerbated hyperlocomotion after caffeine treatment. SHR rats from both sexes presented increases in the BDNF, truncated and phospho-TrkB receptors and also phospho-CREB levels in the hippocampus. Caffeine normalized BDNF in males and truncated TrkB receptor at both sexes. These findings provide insight into the potential of caffeine against fully cognitive impairment displayed by females in the ADHD model. Besides, our data revealed that caffeine intake since childhood attenuated behavioral alterations in the ADHD model associated with changes in BDNF and TrkB receptors in the hippocampus.

Preliminary assessment of cognitive impairments in canine idiopathic epilepsy.

PubMed

Winter, Joshua; Packer, Rowena Mary Anne; Volk, Holger Andreas

2018-06-02

In humans, epilepsy can induce or accelerate cognitive impairment (CI). There is emerging evidence of CI in dogs with idiopathic epilepsy (IE) from recent epidemiological studies. The aim of our study was to assess CI in dogs with IE using two tests of cognitive dysfunction designed for use in a clinical setting. Dogs with IE (n=17) were compared against controls (n=18) in their performance in two tasks; a spatial working memory task and a problem-solving task. In addition, owners completed the Canine Cognitive Dysfunction Rating (CCDR) scale for their dog. The groups did not differ statistically with respect to age and breed. Dogs with IE performed significantly worse than controls on the spatial working memory task (P = 0.016), but not on the problem solving task (P=0.683). CCDR scores were significantly higher in the IE group (P=0.016); however, no dogs reach the recommended threshold score for CCD diagnosis. Our preliminary data suggest that dogs with IE exhibit impairments in a spatial working memory task. Further research is required to explore the effect of IE on other cognitive abilities in dogs with a larger sample, characterising the age of onset, nature and progression of any impairments and the impact of anti-epileptic drugs. © British Veterinary Association (unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Spatial and temporal variability of fine particle composition and source types in five cities of Connecticut and Massachusetts.

PubMed

Lee, Hyung Joo; Gent, Janneane F; Leaderer, Brian P; Koutrakis, Petros

2011-05-01

To protect public health from PM(2.5) air pollution, it is critical to identify the source types of PM(2.5) mass and chemical components associated with higher risks of adverse health outcomes. Source apportionment modeling using Positive Matrix Factorization (PMF), was used to identify PM(2.5) source types and quantify the source contributions to PM(2.5) in five cities of Connecticut and Massachusetts. Spatial and temporal variability of PM(2.5) mass, components and source contributions were investigated. PMF analysis identified five source types: regional pollution as traced by sulfur, motor vehicle, road dust, oil combustion and sea salt. The sulfur-related regional pollution and traffic source type were major contributors to PM(2.5). Due to sparse ground-level PM(2.5) monitoring sites, current epidemiological studies are susceptible to exposure measurement errors. The higher correlations in concentrations and source contributions between different locations suggest less spatial variability, resulting in less exposure measurement errors. When concentrations and/or contributions were compared to regional averages, correlations were generally higher than between-site correlations. This suggests that for assigning exposures for health effects studies, using regional average concentrations or contributions from several PM(2.5) monitors is more reliable than using data from the nearest central monitor. Copyright © 2011 Elsevier B.V. All rights reserved.

Entomological surveillance, spatial distribution, and diversity of Culicidae (Diptera) immatures in a rural area of the Atlantic Forest biome, State of São Paulo, Brazil.

PubMed

Piovezan, Rafael; Rosa, Stéfany Larissa; Rocha, Matheus Luca; de Azevedo, Thiago Salomão; Von Zuben, Cláudio José

2013-12-01

Because of the high adaptive capacity of mosquitoes, studies that focus on transitional environments become very important, such as those in rural areas, which are considered as bridges between wild diseases and human populations of urban areas. In this study, a survey of the existing species of mosquitoes was performed in an Atlantic Forest area of the city of Santa Bárbara d'Oeste, São Paulo state, Brazil, using traps for immatures and analyzing the frequency and distribution of these insects over the sampling months. Five mosquito species were found: Aedes albopictus (the most frequent species), Aedes aegypti, Aedes fluviatilis, Culex quinquefasciatus, and Toxorhynchites theobaldi. The 4,524 eggs collected in ovitraps showed the presence of the tribe Aedini. Aedes aegypti and Ae. albopictus were identified after larval hatching in the laboratory, with different spatial distributions: the first of which coincides with the area of greatest diversity calculated using the Simpson index, while the second does not. The association of ecological analysis of spatial diversity with simple methods of data collection enables the identification of possible epidemiological risk situations and is a strategy that may be implemented to monitor ecological processes resulting from the interaction among different species of mosquitoes. © 2013 The Society for Vector Ecology.

Particulate Air Pollution and Socioeconomic Position in Rural and Urban Areas of the Northeastern United States

PubMed Central

Brochu, Paul J.; Yanosky, Jeff D.; Paciorek, Christopher J.; Schwartz, Joel; Chen, Jarvis T.; Herrick, Robert F.

2011-01-01

Objectives. Although differential exposure by socioeconomic position (SEP) to hazardous waste and lead is well demonstrated, there is less evidence for particulate air pollution (PM), which is associated with risk of death and illness. This study determined the relationship of ambient PM and SEP across several spatial scales. Methods. Geographic information system-based, spatio-temporal models were used to predict PM in the Northeastern United States. Predicted concentrations were related to census tract SEP and racial composition using generalized additive models. Results. Lower SEP was associated with small, significant increases in PM. Annual PM10 decreased between 0.09 and 0.93 micrograms per cubic meter and PM2.5 between 0.02 and 0.94 micrograms per cubic meter for interquartile range increases in income. Decrements in PM with SEP increased with spatial scale, indicating that between-city spatial gradients were greater than within-city differences. The PM–SEP relation in urban tracts was not substantially modified by racial composition. Conclusions. Lower compared with higher SEP populations were exposed to higher ambient PM in the Northeastern United States. Given the small percentage change in annual PM2.5 and PM10, SEP was not likely a major source of confounding in epidemiological studies of PM, especially those conducted within a single urban/metropolitan area. PMID:21836114

Simulation of Population-Based Commuter Exposure to NO2 Using Different Air Pollution Models

PubMed Central

Ragettli, Martina S.; Tsai, Ming-Yi; Braun-Fahrländer, Charlotte; de Nazelle, Audrey; Schindler, Christian; Ineichen, Alex; Ducret-Stich, Regina E.; Perez, Laura; Probst-Hensch, Nicole; Künzli, Nino; Phuleria, Harish C.

2014-01-01

We simulated commuter routes and long-term exposure to traffic-related air pollution during commute in a representative population sample in Basel (Switzerland), and evaluated three air pollution models with different spatial resolution for estimating commute exposures to nitrogen dioxide (NO2) as a marker of long-term exposure to traffic-related air pollution. Our approach includes spatially and temporally resolved data on actual commuter routes, travel modes and three air pollution models. Annual mean NO2 commuter exposures were similar between models. However, we found more within-city and within-subject variability in annual mean (±SD) NO2 commuter exposure with a high resolution dispersion model (40 ± 7 µg m−3, range: 21–61) than with a dispersion model with a lower resolution (39 ± 5 µg m−3; range: 24–51), and a land use regression model (41 ± 5 µg m−3; range: 24–54). Highest median cumulative exposures were calculated along motorized transport and bicycle routes, and the lowest for walking. For estimating commuter exposure within a city and being interested also in small-scale variability between roads, a model with a high resolution is recommended. For larger scale epidemiological health assessment studies, models with a coarser spatial resolution are likely sufficient, especially when study areas include suburban and rural areas. PMID:24823664

Geographical Analysis of the Distribution and Spread of Human Rabies in China from 2005 to 2011

PubMed Central

Yin, Wenwu; Yu, Hongjie; Si, Yali; Li, Jianhui; Zhou, Yuanchun; Zhou, Xiaoyan; Magalhães, Ricardo J. Soares.

2013-01-01

Background Rabies is a significant public health problem in China in that it records the second highest case incidence globally. Surveillance data on canine rabies in China is lacking and human rabies notifications can be a useful indicator of areas where animal and human rabies control could be integrated. Previous spatial epidemiological studies lacked adequate spatial resolution to inform targeted rabies control decisions. We aimed to describe the spatiotemporal distribution of human rabies and model its geographical spread to provide an evidence base to inform future integrated rabies control strategies in China. Methods We geo-referenced a total of 17,760 human rabies cases of China from 2005 to 2011. In our spatial analyses we used Gaussian kernel density analysis, average nearest neighbor distance, Spatial Temporal Density-Based Spatial Clustering of Applications with Noise and developed a model of rabies spatiotemporal spread. Findings Human rabies cases increased from 2005 to 2007 and decreased during 2008 to 2011 companying change of the spatial distribution. The ANN distance among human rabies cases increased between 2005 and 2011, and the degree of clustering of human rabies cases decreased during that period. A total 480 clusters were detected by ST-DBSCAN, 89.4% clusters initiated before 2007. Most of clusters were mainly found in South of China. The number and duration of cluster decreased significantly after 2008. Areas with the highest density of human rabies cases varied spatially each year and in some areas remained with high outbreak density for several years. Though few places have recovered from human rabies, most of affected places are still suffering from the disease. Conclusion Human rabies in mainland China is geographically clustered and its spatial extent changed during 2005 to 2011. The results provide a scientific basis for public health authorities in China to improve human rabies control and prevention program. PMID:23991098

Computer-Generated Dot Maps as an Epidemiologic Tool: Investigating an Outbreak of Toxoplasmosis

PubMed Central

Werker, Denise H.; King, Arlene S.; Marion, Stephen A.; Bell, Alison; Issac-Renton, Judith L.; Irwin, G. Stewart; Bowie, William R.

1999-01-01

We used computer-generated dot maps to examine the spatial distribution of 94 Toxoplasma gondii infections associated with an outbreak in British Columbia, Canada. The incidence among patients served by one water distribution system was 3.52 times that of patients served by other sources. Acute T. gondii infection among 3,812 pregnant women was associated with the incriminated distribution system. PMID:10603218

An epidemiological model of internet worms with hierarchical dispersal and spatial clustering of hosts.

PubMed

Hiebeler, David E; Audibert, Andrew; Strubell, Emma; Michaud, Isaac J

2017-04-07

Beginning in 2001, many instances of malicious software known as Internet worms have been using biological strategies such as hierarchical dispersal to seek out and spread to new susceptible hosts more efficiently. We measured the distribution of potentially susceptible hosts in the space of Internet addresses to determine their clustering. We have used the results to construct a full-size simulated Internet with 2 32 hosts with mean and variance of susceptible hosts chosen to match our measurements at multiple spatial scales. Epidemiological simulations of outbreaks among the roughly 2.8×10 6 susceptible hosts on this full-sized network show that local preference scanning greatly increases the chances for an infected host to locate and infect other susceptible hosts by a factor of as much as several hundred. However, once deploying this strategy, the overall success of a worm is relatively insensitive to the details of its dispersal strategy over a wide range of parameters. In addition, although using localized interactions may allow malicious software to spread more rapidly or to more hosts on average, it can also lead to increased variability in infection levels among replicate simulations. Using such dispersal strategies may therefore be a high risk, high reward strategy for the authors of such software. Copyright © 2017 Elsevier Ltd. All rights reserved.

10 CFR 602.5 - Epidemiology and Other Health Studies Financial Assistance Program.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 10 Energy 4 2013-01-01 2013-01-01 false Epidemiology and Other Health Studies Financial Assistance... AND OTHER HEALTH STUDIES FINANCIAL ASSISTANCE PROGRAM § 602.5 Epidemiology and Other Health Studies... toxic substances; (5) Epidemiology and other health studies relating to energy production, transmission...

10 CFR 602.5 - Epidemiology and Other Health Studies Financial Assistance Program.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 4 2014-01-01 2014-01-01 false Epidemiology and Other Health Studies Financial Assistance... AND OTHER HEALTH STUDIES FINANCIAL ASSISTANCE PROGRAM § 602.5 Epidemiology and Other Health Studies... toxic substances; (5) Epidemiology and other health studies relating to energy production, transmission...

10 CFR 602.5 - Epidemiology and Other Health Studies Financial Assistance Program.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 4 2012-01-01 2012-01-01 false Epidemiology and Other Health Studies Financial Assistance... AND OTHER HEALTH STUDIES FINANCIAL ASSISTANCE PROGRAM § 602.5 Epidemiology and Other Health Studies... toxic substances; (5) Epidemiology and other health studies relating to energy production, transmission...

10 CFR 602.5 - Epidemiology and Other Health Studies Financial Assistance Program.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 4 2011-01-01 2011-01-01 false Epidemiology and Other Health Studies Financial Assistance... AND OTHER HEALTH STUDIES FINANCIAL ASSISTANCE PROGRAM § 602.5 Epidemiology and Other Health Studies... toxic substances; (5) Epidemiology and other health studies relating to energy production, transmission...

Bayesian Analysis for Inference of an Emerging Epidemic: Citrus Canker in Urban Landscapes

PubMed Central

Neri, Franco M.; Cook, Alex R.; Gibson, Gavin J.; Gottwald, Tim R.; Gilligan, Christopher A.

2014-01-01

Outbreaks of infectious diseases require a rapid response from policy makers. The choice of an adequate level of response relies upon available knowledge of the spatial and temporal parameters governing pathogen spread, affecting, amongst others, the predicted severity of the epidemic. Yet, when a new pathogen is introduced into an alien environment, such information is often lacking or of no use, and epidemiological parameters must be estimated from the first observations of the epidemic. This poses a challenge to epidemiologists: how quickly can the parameters of an emerging disease be estimated? How soon can the future progress of the epidemic be reliably predicted? We investigate these issues using a unique, spatially and temporally resolved dataset for the invasion of a plant disease, Asiatic citrus canker in urban Miami. We use epidemiological models, Bayesian Markov-chain Monte Carlo, and advanced spatial statistical methods to analyse rates and extent of spread of the disease. A rich and complex epidemic behaviour is revealed. The spatial scale of spread is approximately constant over time and can be estimated rapidly with great precision (although the evidence for long-range transmission is inconclusive). In contrast, the rate of infection is characterised by strong monthly fluctuations that we associate with extreme weather events. Uninformed predictions from the early stages of the epidemic, assuming complete ignorance of the future environmental drivers, fail because of the unpredictable variability of the infection rate. Conversely, predictions improve dramatically if we assume prior knowledge of either the main environmental trend, or the main environmental events. A contrast emerges between the high detail attained by modelling in the spatiotemporal description of the epidemic and the bottleneck imposed on epidemic prediction by the limits of meteorological predictability. We argue that identifying such bottlenecks will be a fundamental step in future modelling of weather-driven epidemics. PMID:24762851

Fine-tuning the space, time, and host distribution of mycobacteria in wildlife

PubMed Central

2011-01-01

Background We describe the diversity of two kinds of mycobacteria isolates, environmental mycobacteria and Mycobacterium bovis collected from wild boar, fallow deer, red deer and cattle in Doñana National Park (DNP, Spain), analyzing their association with temporal, spatial and environmental factors. Results High diversity of environmental mycobacteria species and M. bovis typing patterns (TPs) were found. When assessing the factors underlying the presence of the most common types of both environmental mycobacteria and M. bovis TPs in DNP, we evidenced (i) host species differences in the occurrence, (ii) spatial structuration and (iii) differences in the degree of spatial association of specific types between host species. Co-infection of a single host by two M. bovis TPs occurred in all three wild ungulate species. In wild boar and red deer, isolation of one group of mycobacteria occurred more frequently in individuals not infected by the other group. While only three TPs were detected in wildlife between 1998 and 2003, up to 8 different ones were found during 2006-2007. The opposite was observed in cattle. Belonging to an M. bovis-infected social group was a significant risk factor for mycobacterial infection in red deer and wild boar, but not for fallow deer. M. bovis TPs were usually found closer to water marshland than MOTT. Conclusions The diversity of mycobacteria described herein is indicative of multiple introduction events and a complex multi-host and multi-pathogen epidemiology in DNP. Significant changes in the mycobacterial isolate community may have taken place, even in a short time period (1998 to 2007). Aspects of host social organization should be taken into account in wildlife epidemiology. Wildlife in DNP is frequently exposed to different species of non-tuberculous, environmental mycobacteria, which could interact with the immune response to pathogenic mycobacteria, although the effects are unknown. This research highlights the suitability of molecular typing for surveys at small spatial and temporal scales. PMID:21288321

Evaluating Spatial Interaction Models for Regional Mobility in Sub-Saharan Africa

PubMed Central

Wesolowski, Amy; O’Meara, Wendy Prudhomme; Eagle, Nathan; Tatem, Andrew J.; Buckee, Caroline O.

2015-01-01

Simple spatial interaction models of human mobility based on physical laws have been used extensively in the social, biological, and physical sciences, and in the study of the human dynamics underlying the spread of disease. Recent analyses of commuting patterns and travel behavior in high-income countries have led to the suggestion that these models are highly generalizable, and as a result, gravity and radiation models have become standard tools for describing population mobility dynamics for infectious disease epidemiology. Communities in Sub-Saharan Africa may not conform to these models, however; physical accessibility, availability of transport, and cost of travel between locations may be variable and severely constrained compared to high-income settings, informal labor movements rather than regular commuting patterns are often the norm, and the rise of mega-cities across the continent has important implications for travel between rural and urban areas. Here, we first review how infectious disease frameworks incorporate human mobility on different spatial scales and use anonymous mobile phone data from nearly 15 million individuals to analyze the spatiotemporal dynamics of the Kenyan population. We find that gravity and radiation models fail in systematic ways to capture human mobility measured by mobile phones; both severely overestimate the spatial spread of travel and perform poorly in rural areas, but each exhibits different characteristic patterns of failure with respect to routes and volumes of travel. Thus, infectious disease frameworks that rely on spatial interaction models are likely to misrepresent population dynamics important for the spread of disease in many African populations. PMID:26158274

What Can We Learn From Historical Trends and Distributions of Malaria? Historical Case Studies From the US, Italy, and Sri Lanka

NASA Astrophysics Data System (ADS)

Matthews, E.

2008-12-01

Malaria is currently prevalent in many countries and has been for centuries. Primary controllers of the distribution and incidence of malaria in the past have been economic, social, military, political etc. with a modest contribution from local climate variations. Studies of potential impacts of climate change on the epidemiology of diseases such as malaria have focused on the impact of changing environmental conditions on vector physiology but little attention has been paid to factors that explain historical variations in spatial and temporal distributions of the disease. This talk reports results of three historical case studies from the US, Italy and Sri Lanka that bring together a breadth of information from varied sources in order to illustrate the value of including such information in studies of disease-climate connections.

The application of epidemiology in aquatic animal health -opportunities and challenges

PubMed Central

2011-01-01

Over recent years the growth in aquaculture, accompanied by the emergence of new and transboundary diseases, has stimulated epidemiological studies of aquatic animal diseases. Great potential exists for both observational and theoretical approaches to investigate the processes driving emergence but, to date, compared to terrestrial systems, relatively few studies exist in aquatic animals. Research using risk methods has assessed routes of introduction of aquatic animal pathogens to facilitate safe trade (e.g. import risk analyses) and support biosecurity. Epidemiological studies of risk factors for disease in aquaculture (most notably Atlantic salmon farming) have effectively supported control measures. Methods developed for terrestrial livestock diseases (e.g. risk-based surveillance) could improve the capacity of aquatic animal surveillance systems to detect disease incursions and emergence. The study of disease in wild populations presents many challenges and the judicious use of theoretical models offers some solutions. Models, parameterised from observational studies of host pathogen interactions, have been used to extrapolate estimates of impacts on the individual to the population level. These have proved effective in estimating the likely impact of parasite infections on wild salmonid populations in Switzerland and Canada (where the importance of farmed salmon as a reservoir of infection was investigated). A lack of data is often the key constraint in the application of new approaches to surveillance and modelling. The need for epidemiological approaches to protect aquatic animal health will inevitably increase in the face of the combined challenges of climate change, increasing anthropogenic pressures, limited water sources and the growth in aquaculture. Table of contents 1 Introduction 4 2 The development of aquatic epidemiology 7 3 Transboundary and emerging diseases 9 3.1 Import risk analysis (IRA) 10 3.2 Aquaculture and disease emergence 11 3.3 Climate change and disease emergence 13 3.4 Outbreak investigations 13 4 Surveillance and surveys 15 4.1 Investigation of disease prevalence 15 4.2 Developments in surveillance methodology 16 4.2.1 Risk-based surveillance and scenario tree modelling 16 4.2.2 Spatial and temporal analysis 16 4.3 Test validation 17 5 Spread, establishment and impact of pathogens 18 5.1 Identifying routes of spread 18 5.1.1 Ex-ante studies of disease spread 19 5.1.2 Ex-post observational studies 21 5.2 Identifying risk factors for disease establishment 23 5.3 Assessing impact at the population level 24 5.3.1 Recording mortality 24 5.3.2 Farm health and production records 26 5.3.3 Assessing the impact of disease in wild populations 27 6 Conclusions 31 7 Competing interests 32 8 Authors' contributions 32 9 Acknowledgements 33 10 References 33 PMID:21834990

Exploring Spatial and Temporal Distribution of Cutaneous Leishmaniasis in the Americas, 2001-2011.

PubMed

Maia-Elkhoury, Ana Nilce Silveira; E Yadón, Zaida; Idali Saboyá Díaz, Martha; de Fátima de Araújo Lucena, Francisca; Gerardo Castellanos, Luis; J Sanchez-Vazquez, Manuel

2016-11-01

Cases reported in the period of 2001-2011 from 14/18 CL endemic countries were included in this study by using two spreadsheet to collect the data. Two indicators were analyzed: CL cases and incidence rate. The local regression method was used to analyze case trends and incidence rates for all the studied period, and for 2011 the spatial distribution of each indicator was analyzed by quartile and stratified into four groups. From 2001-2011, 636,683 CL cases were reported by 14 countries and with an increase of 30% of the reported cases. The average incidence rate in the Americas was 15.89/100,000 inhabitants. In 2011, 15 countries reported cases in 180 from a total of 292 units of first subnational level. The global incidence rate for all countries was 17.42 cases per 100,000 inhabitants; while in 180 administrative units at the first subnational level, the average incidence rate was 57.52/100,000 inhabitants. Nicaragua and Panama had the highest incidence but more cases occurred in Brazil and Colombia. Spatial distribution was heterogeneous for each indicator, and when analyzed in different administrative level. The results showed different distribution patterns, illustrating the limitation of the use of individual indicators and the need to classify higher-risk areas in order to prioritize the actions. This study shows the epidemiological patterns using secondary data and the importance of using multiple indicators to define and characterize smaller territorial units for surveillance and control of leishmaniasis.

Seroprevalence and spatial distribution dynamics of Yersinia pestis antibodies in dogs and cats from plague foci in the State of Ceará, Northeastern Brazil.

PubMed

Sousa, Larissa Leão Ferrer de; Alencar, Carlos Henrique Morais de; Almeida, Alzira Maria Paiva de; Cavalcanti, Luciano Pamplona de Góes

2017-01-01

In Brazil, the plague is established in several foci located mainly in the northeastern part of the country, where it alternates between active and quiescent periods. These foci in the State of Ceará have high epidemiological importance. In addition to other plague detection activities, plague areas can be monitored through serological surveys of dogs and cats (domestic carnivores), which, following feeding on plague-infected rodents, can develop mild to severe forms of the disease and produce long-lasting antibodies. This study aimed to characterize the circulation dynamics and spatial distribution of Yersinia pestis antibodies in dogs and cats in plague foci areas of Ceará. An ecological study was conducted to analyze the temporal series and spatial distribution of secondary data obtained from domestic carnivore serum surveillance in Ceará's plague areas from 1990 to 2014. Joinpoint analysis revealed that the overall trend was a reduction in antibody-positive animals. The mean proportion of antibody-positivity during the whole study period was 1.5% (3,023/203,311) for dogs, and 0.7% (426/61,135) for cats, with more than 4% antibody-positivity in dogs in 1997 and 2002. Antibody titers ranging from 1/16 to 1/64 were frequent. Despite fluctuations and a significant reduction, in recent years, there were antibody-positive animals annually throughout the study period, and the localities containing antibody-positive animals increased in number. Yersinia pestis is actively circulating in the study areas, posing a danger to the human population.

Disease Spread and Its Effect on Population Dynamics in Heterogeneous Environment

NASA Astrophysics Data System (ADS)

Upadhyay, Ranjit Kumar; Roy, Parimita

In this paper, an eco-epidemiological model in which both species diffuse along a spatial gradient has been shown to exhibit temporal chaos at a fixed point in space. The proposed model is a modification of the model recently presented by Upadhyay and Roy [2014]. The spatial interactions among the species have been represented in the form of reaction-diffusion equations. The model incorporates the intrinsic growth rate of fish population which varies linearly with the depth of water. Numerical results show that diffusion can drive otherwise stable system into aperiodic behavior with sensitivity to initial conditions. We show that spatially induced chaos plays an important role in spatial pattern formation in heterogeneous environment. Spatiotemporal distributions of species have been simulated using the diffusivity assumptions realistic for natural eco-epidemic systems. We found that in heterogeneous environment, the temporal dynamics of both the species are drastically different and show chaotic behavior. It was also found that the instability observed in the model is due to spatial heterogeneity and diffusion-driven. Cumulative death rate of predator has an appreciable effect on model dynamics as the spatial distribution of all constituent populations exhibit significant changes when this model parameter is changed and it acts as a regularizing factor.

Locally adaptive, spatially explicit projection of US population for 2030 and 2050.

PubMed

McKee, Jacob J; Rose, Amy N; Bright, Edward A; Huynh, Timmy; Bhaduri, Budhendra L

2015-02-03

Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Building on the spatial interpolation technique previously developed for high-resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically informed spatial distribution of projected population of the contiguous United States for 2030 and 2050, depicting one of many possible population futures. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection model departs from these by accounting for multiple components that affect population distribution. Modeled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the US Census's projection methodology, with the US Census's official projection as the benchmark. Applications of our model include incorporating multiple various scenario-driven events to produce a range of spatially explicit population futures for suitability modeling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.

Privacy protection versus cluster detection in spatial epidemiology.

PubMed

Olson, Karen L; Grannis, Shaun J; Mandl, Kenneth D

2006-11-01

Patient data that includes precise locations can reveal patients' identities, whereas data aggregated into administrative regions may preserve privacy and confidentiality. We investigated the effect of varying degrees of address precision (exact latitude and longitude vs the center points of zip code or census tracts) on detection of spatial clusters of cases. We simulated disease outbreaks by adding supplementary spatially clustered emergency department visits to authentic hospital emergency department syndromic surveillance data. We identified clusters with a spatial scan statistic and evaluated detection rate and accuracy. More clusters were identified, and clusters were more accurately detected, when exact locations were used. That is, these clusters contained at least half of the simulated points and involved few additional emergency department visits. These results were especially apparent when the synthetic clustered points crossed administrative boundaries and fell into multiple zip code or census tracts. The spatial cluster detection algorithm performed better when addresses were analyzed as exact locations than when they were analyzed as center points of zip code or census tracts, particularly when the clustered points crossed administrative boundaries. Use of precise addresses offers improved performance, but this practice must be weighed against privacy concerns in the establishment of public health data exchange policies.

Spatial clustering of malaria and associated risk factors during an epidemic in a highland area of western Kenya.

PubMed

Brooker, Simon; Clarke, Siân; Njagi, Joseph Kiambo; Polack, Sarah; Mugo, Benbolt; Estambale, Benson; Muchiri, Eric; Magnussen, Pascal; Cox, Jonathan

2004-07-01

The epidemiology of malaria over small areas remains poorly understood, and this is particularly true for malaria during epidemics in highland areas of Africa, where transmission intensity is low and characterized by acute within and between year variations. We report an analysis of the spatial distribution of clinical malaria during an epidemic and investigate putative risk factors. Active case surveillance was undertaken in three schools in Nandi District, Western Kenya for 10 weeks during a malaria outbreak in May-July 2002. Household surveys of cases and age-matched controls were conducted to collect information on household construction, exposure factors and socio-economic status. Household geographical location and altitude were determined using a hand-held geographical positioning system and landcover types were determined using high spatial resolution satellite sensor data. Among 129 cases identified during the surveillance, which were matched to 155 controls, we identified significant spatial clusters of malaria cases as determined using the spatial scan statistic. Conditional multiple logistic regression analysis showed that the risk of malaria was higher in children who were underweight, who lived at lower altitudes, and who lived in households where drugs were not kept at home. Copyright 2004 Blackwell Publishing Ltd

Tree spatial structure, host composition and resource availability influence mirid density or black pod prevalence in cacao agroforests in Cameroon.

PubMed

Gidoin, Cynthia; Babin, Régis; Bagny Beilhe, Leïla; Cilas, Christian; ten Hoopen, Gerben Martijn; Bieng, Marie Ange Ngo

2014-01-01

Combining crop plants with other plant species in agro-ecosystems is one way to enhance ecological pest and disease regulation mechanisms. Resource availability and microclimatic variation mechanisms affect processes related to pest and pathogen life cycles. These mechanisms are supported both by empirical research and by epidemiological models, yet their relative importance in a real complex agro-ecosystem is still not known. Our aim was thus to assess the independent effects and the relative importance of different variables related to resource availability and microclimatic variation that explain pest and disease occurrence at the plot scale in real complex agro-ecosystems. The study was conducted in cacao (Theobroma cacao) agroforests in Cameroon, where cocoa production is mainly impacted by the mirid bug, Sahlbergella singularis, and black pod disease, caused by Phytophthora megakarya. Vegetation composition and spatial structure, resource availability and pest and disease occurrence were characterized in 20 real agroforest plots. Hierarchical partitioning was used to identify the causal variables that explain mirid density and black pod prevalence. The results of this study show that cacao agroforests can be differentiated on the basis of vegetation composition and spatial structure. This original approach revealed that mirid density decreased when a minimum number of randomly distributed forest trees were present compared with the aggregated distribution of forest trees, or when forest tree density was low. Moreover, a decrease in mirid density was also related to decreased availability of sensitive tissue, independently of the effect of forest tree structure. Contrary to expectations, black pod prevalence decreased with increasing cacao tree abundance. By revealing the effects of vegetation composition and spatial structure on mirids and black pod, this study opens new perspectives for the joint agro-ecological management of cacao pests and diseases at the plot scale, through the optimization of the spatial structure and composition of the vegetation.

Tree Spatial Structure, Host Composition and Resource Availability Influence Mirid Density or Black Pod Prevalence in Cacao Agroforests in Cameroon

PubMed Central

Gidoin, Cynthia; Babin, Régis; Bagny Beilhe, Leïla; Cilas, Christian; ten Hoopen, Gerben Martijn; Bieng, Marie Ange Ngo

2014-01-01

Combining crop plants with other plant species in agro-ecosystems is one way to enhance ecological pest and disease regulation mechanisms. Resource availability and microclimatic variation mechanisms affect processes related to pest and pathogen life cycles. These mechanisms are supported both by empirical research and by epidemiological models, yet their relative importance in a real complex agro-ecosystem is still not known. Our aim was thus to assess the independent effects and the relative importance of different variables related to resource availability and microclimatic variation that explain pest and disease occurrence at the plot scale in real complex agro-ecosystems. The study was conducted in cacao (Theobroma cacao) agroforests in Cameroon, where cocoa production is mainly impacted by the mirid bug, Sahlbergella singularis, and black pod disease, caused by Phytophthora megakarya. Vegetation composition and spatial structure, resource availability and pest and disease occurrence were characterized in 20 real agroforest plots. Hierarchical partitioning was used to identify the causal variables that explain mirid density and black pod prevalence. The results of this study show that cacao agroforests can be differentiated on the basis of vegetation composition and spatial structure. This original approach revealed that mirid density decreased when a minimum number of randomly distributed forest trees were present compared with the aggregated distribution of forest trees, or when forest tree density was low. Moreover, a decrease in mirid density was also related to decreased availability of sensitive tissue, independently of the effect of forest tree structure. Contrary to expectations, black pod prevalence decreased with increasing cacao tree abundance. By revealing the effects of vegetation composition and spatial structure on mirids and black pod, this study opens new perspectives for the joint agro-ecological management of cacao pests and diseases at the plot scale, through the optimization of the spatial structure and composition of the vegetation. PMID:25313514

Epidemiology: Informing Clinical Practice and Research on Language Disorders in Children.

ERIC Educational Resources Information Center

Lubker, Bobbie Boyd; Tomblin, J. Bruce

1998-01-01

Describes the discipline of epidemiology and the application of epidemiologic methods to the study of children's language disorders. Epidemiology is described as the study of the distributions and determinants of disease, disorders, disabilities, and desirable health events in populations. Common epidemiologic research designs are discussed.…

Genetic micro-epidemiology of malaria in Papua Indonesia: Extensive P. vivax diversity and a distinct subpopulation of asymptomatic P. falciparum infections.

PubMed

Pava, Zuleima; Noviyanti, Rintis; Handayuni, Irene; Trimarsanto, Hidayat; Trianty, Leily; Burdam, Faustina H; Kenangalem, Enny; Utami, Retno A S; Tirta, Yusrifar K; Coutrier, Farah; Poespoprodjo, Jeanne R; Price, Ric N; Marfurt, Jutta; Auburn, Sarah

2017-01-01

Genetic analyses of Plasmodium have potential to inform on transmission dynamics, but few studies have evaluated this on a local spatial scale. We used microsatellite genotyping to characterise the micro-epidemiology of P. vivax and P. falciparum diversity to inform malaria control strategies in Timika, Papua Indonesia. Genotyping was undertaken on 713 sympatric P. falciparum and P. vivax isolates from a cross-sectional household survey and clinical studies conducted in Timika. Standard population genetic measures were applied, and the data was compared to published data from Kalimantan, Bangka, Sumba and West Timor. Higher diversity (HE = 0.847 vs 0.625; p = 0.017) and polyclonality (46.2% vs 16.5%, p<0.001) were observed in P. vivax versus P. falciparum. Distinct P. falciparum substructure was observed, with two subpopulations, K1 and K2. K1 was comprised solely of asymptomatic infections and displayed greater relatedness to isolates from Sumba than to K2, possibly reflecting imported infections. The results demonstrate the greater refractoriness of P. vivax versus P. falciparum to control measures, and risk of distinct parasite subpopulations persisting in the community undetected by passive surveillance. These findings highlight the need for complimentary new surveillance strategies to identify transmission patterns that cannot be detected with traditional malariometric methods.

Within-Host Evolution of Human Influenza Virus.

PubMed

Xue, Katherine S; Moncla, Louise H; Bedford, Trevor; Bloom, Jesse D

2018-03-10

The rapid global evolution of influenza virus begins with mutations that arise de novo in individual infections, but little is known about how evolution occurs within hosts. We review recent progress in understanding how and why influenza viruses evolve within human hosts. Advances in deep sequencing make it possible to measure within-host genetic diversity in both acute and chronic influenza infections. Factors like antigenic selection, antiviral treatment, tissue specificity, spatial structure, and multiplicity of infection may affect how influenza viruses evolve within human hosts. Studies of within-host evolution can contribute to our understanding of the evolutionary and epidemiological factors that shape influenza virus's global evolution. Copyright © 2018 Elsevier Ltd. All rights reserved.

Discovering network behind infectious disease outbreak

NASA Astrophysics Data System (ADS)

Maeno, Yoshiharu

2010-11-01

Stochasticity and spatial heterogeneity are of great interest recently in studying the spread of an infectious disease. The presented method solves an inverse problem to discover the effectively decisive topology of a heterogeneous network and reveal the transmission parameters which govern the stochastic spreads over the network from a dataset on an infectious disease outbreak in the early growth phase. Populations in a combination of epidemiological compartment models and a meta-population network model are described by stochastic differential equations. Probability density functions are derived from the equations and used for the maximal likelihood estimation of the topology and parameters. The method is tested with computationally synthesized datasets and the WHO dataset on the SARS outbreak.

ADHD in the Arab World: A Review of Epidemiologic Studies

ERIC Educational Resources Information Center

Farah, Lynn G.; Fayyad, John A.; Eapen, Valsamma; Cassir,Youmna; Salamoun, Mariana M.; Tabet, Caroline C.; Mneimneh, Zeina N.; Karam, Elie G.

2009-01-01

Objective: Epidemiological studies on psychiatric disorders are quite rare in the Arab World. This article reviews epidemiological studies on ADHD in all the Arab countries. Method: All epidemiological studies on ADHD conducted from 1966 through th present were reviewed. Samples were drawn from the general community, primary care clinical…

Feasibility of Assessing Public Health Impacts of Air Pollution Reduction Programs on a Local Scale: New Haven Case Study

PubMed Central

Lobdell, Danelle T.; Isakov, Vlad; Baxter, Lisa; Touma, Jawad S.; Smuts, Mary Beth; Özkaynak, Halûk

2011-01-01

Background New approaches to link health surveillance data with environmental and population exposure information are needed to examine the health benefits of risk management decisions. Objective We examined the feasibility of conducting a local assessment of the public health impacts of cumulative air pollution reduction activities from federal, state, local, and voluntary actions in the City of New Haven, Connecticut (USA). Methods Using a hybrid modeling approach that combines regional and local-scale air quality data, we estimated ambient concentrations for multiple air pollutants [e.g., PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter), NOx (nitrogen oxides)] for baseline year 2001 and projected emissions for 2010, 2020, and 2030. We assessed the feasibility of detecting health improvements in relation to reductions in air pollution for 26 different pollutant–health outcome linkages using both sample size and exploratory epidemiological simulations to further inform decision-making needs. Results Model projections suggested decreases (~ 10–60%) in pollutant concentrations, mainly attributable to decreases in pollutants from local sources between 2001 and 2010. Models indicated considerable spatial variability in the concentrations of most pollutants. Sample size analyses supported the feasibility of identifying linkages between reductions in NOx and improvements in all-cause mortality, prevalence of asthma in children and adults, and cardiovascular and respiratory hospitalizations. Conclusion Substantial reductions in air pollution (e.g., ~ 60% for NOx) are needed to detect health impacts of environmental actions using traditional epidemiological study designs in small communities like New Haven. In contrast, exploratory epidemiological simulations suggest that it may be possible to demonstrate the health impacts of PM reductions by predicting intraurban pollution gradients within New Haven using coupled models. PMID:21335318

Novel Methods in Disease Biogeography: A Case Study with Heterosporosis

PubMed Central

Escobar, Luis E.; Qiao, Huijie; Lee, Christine; Phelps, Nicholas B. D.

2017-01-01

Disease biogeography is currently a promising field to complement epidemiology, and ecological niche modeling theory and methods are a key component. Therefore, applying the concepts and tools from ecological niche modeling to disease biogeography and epidemiology will provide biologically sound and analytically robust descriptive and predictive analyses of disease distributions. As a case study, we explored the ecologically important fish disease Heterosporosis, a relatively poorly understood disease caused by the intracellular microsporidian parasite Heterosporis sutherlandae. We explored two novel ecological niche modeling methods, the minimum-volume ellipsoid (MVE) and the Marble algorithm, which were used to reconstruct the fundamental and the realized ecological niche of H. sutherlandae, respectively. Additionally, we assessed how the management of occurrence reports can impact the output of the models. Ecological niche models were able to reconstruct a proxy of the fundamental and realized niche for this aquatic parasite, identifying specific areas suitable for Heterosporosis. We found that the conceptual and methodological advances in ecological niche modeling provide accessible tools to update the current practices of spatial epidemiology. However, careful data curation and a detailed understanding of the algorithm employed are critical for a clear definition of the assumptions implicit in the modeling process and to ensure biologically sound forecasts. In this paper, we show how sensitive MVE is to the input data, while Marble algorithm may provide detailed forecasts with a minimum of parameters. We showed that exploring algorithms of different natures such as environmental clusters, climatic envelopes, and logistic regressions (e.g., Marble, MVE, and Maxent) provide different scenarios of potential distribution. Thus, no single algorithm should be used for disease mapping. Instead, different algorithms should be employed for a more informed and complete understanding of the pathogen or parasite in question. PMID:28770215

Development and application of Human Genome Epidemiology

NASA Astrophysics Data System (ADS)

Xu, Jingwen

2017-12-01

Epidemiology is a science that studies distribution of diseases and health in population and its influencing factors, it also studies how to prevent and cure disease and promote health strategies and measures. Epidemiology has developed rapidly in recent years and it is an intercross subject with various other disciplines to form a series of branch disciplines such as Genetic epidemiology, molecular epidemiology, drug epidemiology and tumor epidemiology. With the implementation and completion of Human Genome Project (HGP), Human Genome Epidemiology (HuGE) has emerged at this historic moment. In this review, the development of Human Genome Epidemiology, research content, the construction and structure of relevant network, research standards, as well as the existing results and problems are briefly outlined.

Using High-Resolution Satellite Aerosol Optical Depth To Estimate Daily PM2.5 Geographical Distribution in Mexico City.

PubMed

Just, Allan C; Wright, Robert O; Schwartz, Joel; Coull, Brent A; Baccarelli, Andrea A; Tellez-Rojo, Martha María; Moody, Emily; Wang, Yujie; Lyapustin, Alexei; Kloog, Itai

2015-07-21

Recent advances in estimating fine particle (PM2.5) ambient concentrations use daily satellite measurements of aerosol optical depth (AOD) for spatially and temporally resolved exposure estimates. Mexico City is a dense megacity that differs from other previously modeled regions in several ways: it has bright land surfaces, a distinctive climatological cycle, and an elevated semi-enclosed air basin with a unique planetary boundary layer dynamic. We extend our previous satellite methodology to the Mexico City area, a region with higher PM2.5 than most U.S. and European urban areas. Using a novel 1 km resolution AOD product from the MODIS instrument, we constructed daily predictions across the greater Mexico City area for 2004-2014. We calibrated the association of AOD to PM2.5 daily using municipal ground monitors, land use, and meteorological features. Predictions used spatial and temporal smoothing to estimate AOD when satellite data were missing. Our model performed well, resulting in an out-of-sample cross-validation R(2) of 0.724. Cross-validated root-mean-squared prediction error (RMSPE) of the model was 5.55 μg/m(3). This novel model reconstructs long- and short-term spatially resolved exposure to PM2.5 for epidemiological studies in Mexico City.

Using high-resolution satellite aerosol optical depth to estimate daily PM2.5 geographical distribution in Mexico City

PubMed Central

Just, Allan C.; Wright, Robert O.; Schwartz, Joel; Coull, Brent A.; Baccarelli, Andrea A.; Tellez-Rojo, Martha María; Moody, Emily; Wang, Yujie; Lyapustin, Alexei; Kloog, Itai

2015-01-01

Recent advances in estimating fine particle (PM2.5) ambient concentrations use daily satellite measurements of aerosol optical depth (AOD) for spatially and temporally resolved exposure estimates. Mexico City is a dense megacity that differs from other previously modeled regions in several ways: it has bright land surfaces, a distinctive climatological cycle, and an elevated semi-enclosed air basin with a unique planetary boundary layer dynamic. We extend our previous satellite methodology to the Mexico City area, a region with higher PM2.5 than most US and European urban areas. Using a novel 1 km resolution AOD product from the MODIS instrument, we constructed daily predictions across the greater Mexico City area for 2004–2014. We calibrated the association of AOD to PM2.5 daily using municipal ground monitors, land use, and meteorological features. Predictions used spatial and temporal smoothing to estimate AOD when satellite data were missing. Our model performed well, resulting in an out-of-sample cross validation R2 of 0.724. Cross-validated root mean squared prediction error (RMSPE) of the model was 5.55 μg/m3. This novel model reconstructs long- and short-term spatially resolved exposure to PM2.5 for epidemiological studies in Mexico City. PMID:26061488

Micro-epidemiology and spatial heterogeneity of P. vivax parasitaemia in riverine communities of the Peruvian Amazon: A multilevel analysis.

PubMed

Carrasco-Escobar, Gabriel; Gamboa, Dionicia; Castro, Marcia C; Bangdiwala, Shrikant I; Rodriguez, Hugo; Contreras-Mancilla, Juan; Alava, Freddy; Speybroeck, Niko; Lescano, Andres G; Vinetz, Joseph M; Rosas-Aguirre, Angel; Llanos-Cuentas, Alejandro

2017-08-14

Malaria has steadily increased in the Peruvian Amazon over the last five years. This study aimed to determine the parasite prevalence and micro-geographical heterogeneity of Plasmodium vivax parasitaemia in communities of the Peruvian Amazon. Four cross-sectional active case detection surveys were conducted between May and July 2015 in four riverine communities in Mazan district. Analysis of 2785 samples of 820 individuals nested within 154 households for Plasmodium parasitaemia was carried out using light microscopy and qPCR. The spatio-temporal distribution of Plasmodium parasitaemia, dominated by P. vivax, was shown to cluster at both household and community levels. Of enrolled individuals, 47% had at least one P. vivax parasitaemia and 10% P. falciparum, by qPCR, both of which were predominantly sub-microscopic and asymptomatic. Spatial analysis detected significant clustering in three communities. Our findings showed that communities at small-to-moderate spatial scales differed in P. vivax parasite prevalence, and multilevel Poisson regression models showed that such differences were influenced by factors such as age, education, and location of households within high-risk clusters, as well as factors linked to a local micro-geographic context, such as travel and occupation. Complex transmission patterns were found to be related to human mobility among communities in the same micro-basin.

Spatio-temporal patterns of gun violence in Syracuse, New York 2009-2015.

PubMed

Larsen, David A; Lane, Sandra; Jennings-Bey, Timothy; Haygood-El, Arnett; Brundage, Kim; Rubinstein, Robert A

2017-01-01

Gun violence in the United States of America is a large public health problem that disproportionately affects urban areas. The epidemiology of gun violence reflects various aspects of an infectious disease including spatial and temporal clustering. We examined the spatial and temporal trends of gun violence in Syracuse, New York, a city of 145,000. We used a spatial scan statistic to reveal spatio-temporal clusters of gunshots investigated and corroborated by Syracuse City Police Department for the years 2009-2015. We also examined predictors of areas with increased gun violence using a multi-level zero-inflated Poisson regression with data from the 2010 census. Two space-time clusters of gun violence were revealed in the city. Higher rates of segregation, poverty and the summer months were all associated with increased risk of gun violence. Previous gunshots in the area were associated with a 26.8% increase in the risk of gun violence. Gun violence in Syracuse, NY is both spatially and temporally stable, with some neighborhoods of the city greatly afflicted.

Spatio-temporal patterns of gun violence in Syracuse, New York 2009-2015

PubMed Central

Lane, Sandra; Jennings-Bey, Timothy; Haygood-El, Arnett; Brundage, Kim; Rubinstein, Robert A.

2017-01-01

Gun violence in the United States of America is a large public health problem that disproportionately affects urban areas. The epidemiology of gun violence reflects various aspects of an infectious disease including spatial and temporal clustering. We examined the spatial and temporal trends of gun violence in Syracuse, New York, a city of 145,000. We used a spatial scan statistic to reveal spatio-temporal clusters of gunshots investigated and corroborated by Syracuse City Police Department for the years 2009–2015. We also examined predictors of areas with increased gun violence using a multi-level zero-inflated Poisson regression with data from the 2010 census. Two space-time clusters of gun violence were revealed in the city. Higher rates of segregation, poverty and the summer months were all associated with increased risk of gun violence. Previous gunshots in the area were associated with a 26.8% increase in the risk of gun violence. Gun violence in Syracuse, NY is both spatially and temporally stable, with some neighborhoods of the city greatly afflicted. PMID:28319125

Application of GIS in public health in India: A literature-based review, analysis, and recommendations.

PubMed

Ruiz, Marilyn O'Hara; Sharma, Arun Kumar

2016-01-01

The implementation of geospatial technologies and methods for improving health has become widespread in many nations, but India's adoption of these approaches has been fairly slow. With a large population, ongoing public health challenges, and a growing economy with an emphasis on innovative technologies, the adoption of spatial approaches to disease surveillance, spatial epidemiology, and implementation of health policies in India has great potential for both success and efficacy. Through our evaluation of scientific papers selected through a structured key phrase review of the National Center for Biotechnology Information on the database PubMed, we found that current spatial approaches to health research in India are fairly descriptive in nature, but the use of more complex models and statistics is increasing. The institutional home of the authors is skewed regionally, with Delhi and South India more likely to show evidence of use. The need for scientists engaged in spatial health analysis to first digitize basic data, such as maps of road networks, hydrological features, and land use, is a strong impediment to efficiency, and their work would certainly advance more quickly without this requirement.

Integration of biomonitoring and instrumental techniques to assess the air quality in an industrial area located in the coastal of central Asturias, Spain.

PubMed

Almeida, Susana Marta; Lage, Joana; Freitas, Maria do Carmo; Pedro, Ana Isabel; Ribeiro, Tiago; Silva, Alexandra Viana; Canha, Nuno; Almeida-Silva, Marina; Sitoe, Timóteo; Dionisio, Isabel; Garcia, Sílvia; Domingues, Gonçalo; de Faria, Julia Perim; Fernández, Beatriz González; Ciaparra, Diane; Wolterbeek, Hubert T

2012-01-01

Throughout the world, epidemiological studies were established to examine the relationship between air pollution and mortality rates and adverse respiratory health effects. However, despite the years of discussion the correlation between adverse health effects and atmospheric pollution remains controversial, partly because these studies are frequently restricted to small and well-monitored areas. Monitoring air pollution is complex due to the large spatial and temporal variations of pollution phenomena, the high costs of recording instruments, and the low sampling density of a purely instrumental approach. Therefore, together with the traditional instrumental monitoring, bioindication techniques allow for the mapping of pollution effects over wide areas with a high sampling density. In this study, instrumental and biomonitoring techniques were integrated to support an epidemiological study that will be developed in an industrial area located in Gijon in the coastal of central Asturias, Spain. Three main objectives were proposed to (i) analyze temporal patterns of PM₁₀ concentrations in order to apportion emissions sources, (ii) investigate spatial patterns of lichen conductivity to identify the impact of the studied industrial area in air quality, and (iii) establish relationships amongst lichen conductivity with some site-specific characteristics. Samples of the epiphytic lichen Parmelia sulcata were transplanted in a grid of 18 by 20 km with an industrial area in the center. Lichens were exposed for a 5-mo period starting in April 2010. After exposure, lichen samples were soaked in 18-MΩ water aimed at determination of water electrical conductivity and, consequently, lichen vitality and cell damage. A marked decreasing gradient of lichens conductivity relative to distance from the emitting sources was observed. Transplants from a sampling site proximal to the industrial area reached values 10-fold higher than levels far from it. This finding showed that lichens reacted physiologically in the polluted industrial area as evidenced by increased conductivity correlated to contamination level. The integration of temporal PM₁₀ measurements and analysis of wind direction corroborated the importance of this industrialized region for air quality measurements and identified the relevance of traffic for the urban area.

The Use of Bioinformatics for Studying HIV Evolutionary and Epidemiological History in South America

PubMed Central

Bello, Gonzalo; Soares, Marcelo A.; Schrago, Carlos G.

2011-01-01

The South American human immunodeficiency virus type 1 (HIV-1) epidemic is driven by several subtypes (B, C, and F1) and circulating and unique recombinant forms derived from those subtypes. Those variants are heterogeneously distributed around the continent in a country-specific manner. Despite some inconsistencies mainly derived from sampling biases and analytical constrains, most of studies carried out in the area agreed in pointing out specificities in the evolutionary dynamics of the circulating HIV-1 lineages. In this paper, we covered the theoretical basis, and the application of bioinformatics methods to reconstruct the HIV spatial-temporal dynamics, unveiling relevant information to understand the origin, geographical dissemination and the current molecular scenario of the HIV epidemic in the continent, particularly in the countries of Southern Cone. PMID:22162803

Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing methods: a simulation study

PubMed Central

2013-01-01

Background There is a rising public and political demand for prospective cancer cluster monitoring. But there is little empirical evidence on the performance of established cluster detection tests under conditions of small and heterogeneous sample sizes and varying spatial scales, such as are the case for most existing population-based cancer registries. Therefore this simulation study aims to evaluate different cluster detection methods, implemented in the open soure environment R, in their ability to identify clusters of lung cancer using real-life data from an epidemiological cancer registry in Germany. Methods Risk surfaces were constructed with two different spatial cluster types, representing a relative risk of RR = 2.0 or of RR = 4.0, in relation to the overall background incidence of lung cancer, separately for men and women. Lung cancer cases were sampled from this risk surface as geocodes using an inhomogeneous Poisson process. The realisations of the cancer cases were analysed within small spatial (census tracts, N = 1983) and within aggregated large spatial scales (communities, N = 78). Subsequently, they were submitted to the cluster detection methods. The test accuracy for cluster location was determined in terms of detection rates (DR), false-positive (FP) rates and positive predictive values. The Bayesian smoothing models were evaluated using ROC curves. Results With moderate risk increase (RR = 2.0), local cluster tests showed better DR (for both spatial aggregation scales > 0.90) and lower FP rates (both < 0.05) than the Bayesian smoothing methods. When the cluster RR was raised four-fold, the local cluster tests showed better DR with lower FPs only for the small spatial scale. At a large spatial scale, the Bayesian smoothing methods, especially those implementing a spatial neighbourhood, showed a substantially lower FP rate than the cluster tests. However, the risk increases at this scale were mostly diluted by data aggregation. Conclusion High resolution spatial scales seem more appropriate as data base for cancer cluster testing and monitoring than the commonly used aggregated scales. We suggest the development of a two-stage approach that combines methods with high detection rates as a first-line screening with methods of higher predictive ability at the second stage. PMID:24314148

Into the environment of mosquito-borne disease: A spatial analysis of vector distribution using traditional and remotely sensed methods

NASA Astrophysics Data System (ADS)

Brown, Heidi E.

Spatially explicit information is increasingly available for infectious disease modeling. However, such information is reluctantly or inappropriately incorporated. My dissertation research uses spatially explicit data to assess relationships between landscape and mosquito species distribution and discusses challenges regarding accurate predictive risk modeling. The goal of my research is to use remotely sensed environmental information and spatial statistical methods to better understand mosquito-borne disease epidemiology for improvement of public health responses. In addition to reviewing the progress of spatial infectious disease modeling, I present four research projects. I begin by evaluating the biases in surveillance data and build up to predictive modeling of mosquito species presence. In the first study I explore how mosquito surveillance trap types influence estimations of mosquito populations. Then. I use county-based human surveillance data and landscape variables to identify risk factors for West Nile virus disease. The third study uses satellite-based vegetation indices to identify spatial variation among West Nile virus vectors in an urban area and relates the variability to virus transmission dynamics. Finally, I explore how information from three satellite sensors of differing spatial and spectral resolution can be used to identify and distinguish mosquito habitat across central Connecticut wetlands. Analyses presented here constitute improvements to the prediction of mosquito distribution and therefore identification of disease risk factors. Current methods for mosquito surveillance data collection are labor intensive and provide an extremely limited, incomplete picture of the species composition and abundance. Human surveillance data offers additional challenges with respect to reporting bias and resolution, but is nonetheless informative in identifying environmental risk factors and disease transmission dynamics. Remotely sensed imagery supports mosquito and human disease surveillance data by providing spatially explicit, line resolution information about environmental factors relevant to vector-borne disease processes. Together, surveillance and remotely sensed environmental data facilitate improved description and modeling of disease transmission. Remote sensing can be used to develop predictive maps of mosquito distribution in relation to disease risk. This has implications for increased accuracy of mosquito control efforts. The projects presented in this dissertation enhance current public health capacities by examining the applications of spatial modeling with respect to mosquito-borne disease.

Guidelines for Good Epidemiology Practices for Occupational and Environmental Epidemiologic Research. The Chemical Manufacturers Association's Epidemiology Task Group.

PubMed

1991-12-01

The Guidelines for Good Epidemiology Practices (GEPs) for Occupational and Environmental Epidemiologic Research address the conduct of studies generally undertaken to answer questions about human health in relationship to the work place or the environment. The GEPs propose minimum practices and procedures that should be considered to help ensure the quality and integrity of data used in epidemiologic research and to provide adequate documentation of the research methods. The GEPs address the process of conducting individual epidemiologic studies and do not prescribe specific research methods. The Guidelines for Good Epidemiology Practices propose minimum practices and procedures in the following areas: I. Organization and Personnel II. Facilities, Resource Commitment, and Contractors III. Protocol IV. Review and Approval V. Study Conduct VI. Communication VII. Archiving VIII. Quality Assurance Although the Guidelines for Good Epidemiology Practices will not guarantee good epidemiology, they do provide a useful framework for ensuring that all research issues are adequately addressed. This framework is proposed as a first step in improving epidemiologic research practices through adherence to sound scientific research principles. Appendices provide an overview of standard operating procedures, a glossary of terms used in the Guidelines, and suggested references on occupational epidemiology methods.

Geographical Information Systems risk assessment models for zoonotic fascioliasis in the South American Andes region.

PubMed

Fuentes, M V; Sainz-Elipe, S; Nieto, P; Malone, J B; Mas-Coma, S

2005-03-01

The WHO recognises Fasciola hepatica to be an important human health problem. The Andean countries of Peru, Bolivia and Chile are those most severely affected by this distomatosis, though areas of Ecuador, Colombia and Venezuela are also affected. As part of a multidisciplinary project, we present results of use of a Geographical Information Systems (GIS) forecast model to conduct an epidemiological analysis of human and animal fasciolosis in the central part of the Andes mountains. The GIS approach enabled us to develop a spatial and temporal epidemiological model to map the disease in the areas studied and to classify transmission risk into low, moderate and high risk areas so that areas requiring the implementation of control activities can be identified. Current results are available on a local scale for: (1) the northern Bolivian Altiplano, (2) Puno in the Peruvian Altiplano, (3) the Cajamarca and Mantaro Peruvian valleys, and (4) the Ecuadorian provinces of Azuay, Cotopaxi and Imbabura. Analysis of results demonstrated the validity of a forecast model that combines use of climatic data to calculate of forecast indices with remote sensing data, through the classification of Normalized Difference Vegetation Index (NDVI) maps.

Spatial analysis and characteristics of pig farming in Thailand.

PubMed

Thanapongtharm, Weerapong; Linard, Catherine; Chinson, Pornpiroon; Kasemsuwan, Suwicha; Visser, Marjolein; Gaughan, Andrea E; Epprech, Michael; Robinson, Timothy P; Gilbert, Marius

2016-10-06

In Thailand, pig production intensified significantly during the last decade, with many economic, epidemiological and environmental implications. Strategies toward more sustainable future developments are currently investigated, and these could be informed by a detailed assessment of the main trends in the pig sector, and on how different production systems are geographically distributed. This study had two main objectives. First, we aimed to describe the main trends and geographic patterns of pig production systems in Thailand in terms of pig type (native, breeding, and fattening pigs), farm scales (smallholder and large-scale farming systems) and type of farming systems (farrow-to-finish, nursery, and finishing systems) based on a very detailed 2010 census. Second, we aimed to study the statistical spatial association between these different types of pig farming distribution and a set of spatial variables describing access to feed and markets. Over the last decades, pig population gradually increased, with a continuously increasing number of pigs per holder, suggesting a continuing intensification of the sector. The different pig-production systems showed very contrasted geographical distributions. The spatial distribution of large-scale pig farms corresponds with that of commercial pig breeds, and spatial analysis conducted using Random Forest distribution models indicated that these were concentrated in lowland urban or peri-urban areas, close to means of transportation, facilitating supply to major markets such as provincial capitals and the Bangkok Metropolitan region. Conversely the smallholders were distributed throughout the country, with higher densities located in highland, remote, and rural areas, where they supply local rural markets. A limitation of the study was that pig farming systems were defined from the number of animals per farm, resulting in their possible misclassification, but this should have a limited impact on the main patterns revealed by the analysis. The very contrasted distribution of different pig production systems present opportunities for future regionalization of pig production. More specifically, the detailed geographical analysis of the different production systems will be used to spatially-inform planning decisions for pig farming accounting for the specific health, environment and economical implications of the different pig production systems.

A spatial approach for the epidemiology of antibiotic use and resistance in community-based studies: the emergence of urban clusters of Escherichia coli quinolone resistance in Sao Paulo, Brasil

PubMed Central

2011-01-01

Background Population antimicrobial use may influence resistance emergence. Resistance is an ecological phenomenon due to potential transmissibility. We investigated spatial and temporal patterns of ciprofloxacin (CIP) population consumption related to E. coli resistance emergence and dissemination in a major Brazilian city. A total of 4,372 urinary tract infection E. coli cases, with 723 CIP resistant, were identified in 2002 from two outpatient centres. Cases were address geocoded in a digital map. Raw CIP consumption data was transformed into usage density in DDDs by CIP selling points influence zones determination. A stochastic model coupled with a Geographical Information System was applied for relating resistance and usage density and for detecting city areas of high/low resistance risk. Results E. coli CIP resistant cluster emergence was detected and significantly related to usage density at a level of 5 to 9 CIP DDDs. There were clustered hot-spots and a significant global spatial variation in the residual resistance risk after allowing for usage density. Conclusions There were clustered hot-spots and a significant global spatial variation in the residual resistance risk after allowing for usage density. The usage density of 5-9 CIP DDDs per 1,000 inhabitants within the same influence zone was the resistance triggering level. This level led to E. coli resistance clustering, proving that individual resistance emergence and dissemination was affected by antimicrobial population consumption. PMID:21356088

Spatiotemporal analysis of dengue fever in Nepal from 2010 to 2014.

PubMed

Acharya, Bipin Kumar; Cao, ChunXiang; Lakes, Tobia; Chen, Wei; Naeem, Shahid

2016-08-22

Due to recent emergence, dengue is becoming one of the major public health problems in Nepal. The numbers of reported dengue cases in general and the area with reported dengue cases are both continuously increasing in recent years. However, spatiotemporal patterns and clusters of dengue have not been investigated yet. This study aims to fill this gap by analyzing spatiotemporal patterns based on monthly surveillance data aggregated at district. Dengue cases from 2010 to 2014 at district level were collected from the Nepal government's health and mapping agencies respectively. GeoDa software was used to map crude incidence, excess hazard and spatially smoothed incidence. Cluster analysis was performed in SaTScan software to explore spatiotemporal clusters of dengue during the above-mentioned time period. Spatiotemporal distribution of dengue fever in Nepal from 2010 to 2014 was mapped at district level in terms of crude incidence, excess risk and spatially smoothed incidence. Results show that the distribution of dengue fever was not random but clustered in space and time. Chitwan district was identified as the most likely cluster and Jhapa district was the first secondary cluster in both spatial and spatiotemporal scan. July to September of 2010 was identified as a significant temporal cluster. This study assessed and mapped for the first time the spatiotemporal pattern of dengue fever in Nepal. Two districts namely Chitwan and Jhapa were found highly affected by dengue fever. The current study also demonstrated the importance of geospatial approach in epidemiological research. The initial result on dengue patterns and risk of this study may assist institutions and policy makers to develop better preventive strategies.

A multilevel analysis of the relationship between neighborhood social disorder and depressive symptoms: Evidence from the South African National Income Dynamics Study

PubMed Central

Tomita, Andrew; Labys, Charlotte A.; Burns, Jonathan K

2015-01-01

The apartheid regime that governed South Africa from 1948 – 1994 established spatial segregation that is understood to have contributed to the magnitude of neighborhood social disorder in the post-apartheid era. Although a number of neighborhood social disorder characteristics, such as perceived violence and crime in the community, are prominent issues in South Africa, the extent to which these perceived spatial attributes are linked to depression is unknown at the population-level. Multilevel modeling of data from the second wave of the South African National Income Dynamics Study (SA-NIDS) was utilized to examine the relationship between depressive symptomatology and neighborhood social disorder as indicated by the perceived frequency of violent, criminal and illicit activities in the community. Depressive symptomatology was assessed using the 10-item version of the Center for Epidemiologic Studies Depression Scale. A cut off score of ten or higher was used to indicate the presence of significant depressive symptomatology. Results showed that perception of neighborhood social disorder was independently associated with significant levels of depressive symptomatology. Gender, race/ethnicity, perceived health status, and education were significant for individual-level covariates of depression. Community intervention strategies that reduce the risk of neighborhood disorganization and emphasize positive social norms in the neighborhood are warranted. Taking into account the residential de-racialization of a country transitioning from apartheid to non-racial democracy, a longitudinal spatial study design assessing the dynamics between depression and the aforementioned perceptions of neighborhood attributes may also be warranted. PMID:25642654

Performances of different global positioning system devices for time-location tracking in air pollution epidemiological studies.

PubMed

Wu, Jun; Jiang, Chengsheng; Liu, Zhen; Houston, Douglas; Jaimes, Guillermo; McConnell, Rob

2010-11-23

People's time-location patterns are important in air pollution exposure assessment because pollution levels may vary considerably by location. A growing number of studies are using global positioning systems (GPS) to track people's time-location patterns. Many portable GPS units that archive location are commercially available at a cost that makes their use feasible for epidemiological studies. We evaluated the performance of five portable GPS data loggers and two GPS cell phones by examining positional accuracy in typical locations (indoor, outdoor, in-vehicle) and factors that influence satellite reception (building material, building type), acquisition time (cold and warm start), battery life, and adequacy of memory for data storage. We examined stationary locations (eg, indoor, outdoor) and mobile environments (eg, walking, traveling by vehicle or bus) and compared GPS locations to highly-resolved US Geological Survey (USGS) and Digital Orthophoto Quarter Quadrangle (DOQQ) maps. The battery life of our tested instruments ranged from <9 hours to 48 hours. The acquisition of location time after startup ranged from a few seconds to >20 minutes and varied significantly by building structure type and by cold or warm start. No GPS device was found to have consistently superior performance with regard to spatial accuracy and signal loss. At fixed outdoor locations, 65%-95% of GPS points fell within 20-m of the corresponding DOQQ locations for all the devices. At fixed indoor locations, 50%-80% of GPS points fell within 20-m of the corresponding DOQQ locations for all the devices except one. Most of the GPS devices performed well during commuting on a freeway, with >80% of points within 10-m of the DOQQ route, but the performance was significantly impacted by surrounding structures on surface streets in highly urbanized areas. All the tested GPS devices had limitations, but we identified several devices which showed promising performance for tracking subjects' time location patterns in epidemiological studies.

Performances of Different Global Positioning System Devices for Time-Location Tracking in Air Pollution Epidemiological Studies

PubMed Central

Wu, Jun; Jiang, Chengsheng; Liu, Zhen; Houston, Douglas; Jaimes, Guillermo; McConnell, Rob

2010-01-01

Background: People’s time-location patterns are important in air pollution exposure assessment because pollution levels may vary considerably by location. A growing number of studies are using global positioning systems (GPS) to track people’s time-location patterns. Many portable GPS units that archive location are commercially available at a cost that makes their use feasible for epidemiological studies. Methods: We evaluated the performance of five portable GPS data loggers and two GPS cell phones by examining positional accuracy in typical locations (indoor, outdoor, in-vehicle) and factors that influence satellite reception (building material, building type), acquisition time (cold and warm start), battery life, and adequacy of memory for data storage. We examined stationary locations (eg, indoor, outdoor) and mobile environments (eg, walking, traveling by vehicle or bus) and compared GPS locations to highly-resolved US Geological Survey (USGS) and Digital Orthophoto Quarter Quadrangle (DOQQ) maps. Results: The battery life of our tested instruments ranged from <9 hours to 48 hours. The acquisition of location time after startup ranged from a few seconds to >20 minutes and varied significantly by building structure type and by cold or warm start. No GPS device was found to have consistently superior performance with regard to spatial accuracy and signal loss. At fixed outdoor locations, 65%–95% of GPS points fell within 20-m of the corresponding DOQQ locations for all the devices. At fixed indoor locations, 50%–80% of GPS points fell within 20-m of the corresponding DOQQ locations for all the devices except one. Most of the GPS devices performed well during commuting on a freeway, with >80% of points within 10-m of the DOQQ route, but the performance was significantly impacted by surrounding structures on surface streets in highly urbanized areas. Conclusions: All the tested GPS devices had limitations, but we identified several devices which showed promising performance for tracking subjects’ time location patterns in epidemiological studies. PMID:21151593

Retrospective analysis of Bluetongue farm risk profile definition, based on biology, farm management practices and climatic data.

PubMed

Cappai, Stefano; Loi, Federica; Coccollone, Annamaria; Contu, Marino; Capece, Paolo; Fiori, Michele; Canu, Simona; Foxi, Cipriano; Rolesu, Sandro

2018-07-01

Bluetongue (BT) is a vector-borne disease transmitted by species of Culicoides midges (Diptera: Ceratopogonidae). Many studies have contributed to clarifying various aspects of its aetiology, epidemiology and vector dynamic; however, BT remains a disease of epidemiological and economic importance that affects ruminants worldwide. Since 2000, the Sardinia region has been the most affected area of the Mediterranean basin. The region is characterised by wide pastoral areas for sheep and represents the most likely candidate region for the study of Bluetongue virus (BTV) distribution and prevalence in Italy. Furthermore, specific information on the farm level and epidemiological studies needs to be provided to increase the knowledge on the disease's spread and to provide valid mitigation strategies in Sardinia. This study conducted a punctual investigation into the spatial patterns of BTV transmission to define a risk profile for all Sardinian farmsby using a logistic multilevel mixed model that take into account agro-meteorological aspects, as well as farm characteristics and management. Data about animal density (i.e. sheep, goats and cattle), vaccination, previous outbreaks, altitude, land use, rainfall, evapotranspiration, water surface, and farm management practices (i.e. use of repellents, treatment against insect vectors, storage of animals in shelter overnight, cleaning, presence of mud and manure) were collected for 12,277 farms for the years 2011-2015. The logistic multilevel mixed model showed the fundamental role of climatic factors in disease development and the protective role of good management, vaccination, outbreak in the previous year and altitude. Regional BTV risk maps were developed, based on the predictor values of logistic model results, and updated every 10 days. These maps were used to identify, 20 days in advance, the areas at highest risk. The risk farm profile, as defined by the model, would provide specific information about the role of each factor for all Sardinian institutions involved in devising BT prevention and control strategies. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

A Critical Assessment of Officially Reported Chagas Disease Surveillance Data in Mexico

PubMed Central

Shelly, Ellen M.; Acuna-Soto, Rodolfo; Ernst, Kacey C.; Sterling, Charles R.

2016-01-01

Objective Chagas disease, a disease caused by Trypanosoma cruzi, disproportionately affects poor people throughout Latin America. In Mexico, assessments of officially reported burden have not been previously reported. To evaluate discontinuity between surveillance data and data from other sources, we used data from the Mexican Ministry of Health to describe the distribution of reported Chagas disease over time in Mexico and compare it with estimates from the literature. Methods We summarized age and sex differences for Chagas cases and mortality for 1995–2013 and 1982–2010, respectively. We examined the spatial distribution of Chagas disease over time with respect to disease burden. We further compared officially reported figures with estimates from the literature. Results Among 6,494 officially reported cases, rates of Chagas disease were highest in adults aged 25–44 years (47.3%). Mortality was highest in adults aged ≥45 years (423/495, 85.5%). The data indicated increasing temporal trends for incidence and mortality. The greatest burden occurred in southern states, with increasing spatial distribution over time. Fewer than 900 cases and 40 deaths were officially reported annually, in contrast to estimates from the literature of approximately 69,000 new cases and 25,000 deaths annually. Conclusion While increasing trends in officially reported data have been observed, large discrepancies in case estimates compromise our understanding of Chagas disease epidemiology. Reported cases based on current practices are not enough to correctly assess the Chagas disease burden and spatial distribution in Mexico. Understanding the true epidemiology of this disease will lead to more focused and successful control and prevention strategies to decrease disease burden. PMID:26843671

Strengthening the Reporting of Observational Studies in Epidemiology—Nutritional Epidemiology (STROBE-nut): An Extension of the STROBE Statement

PubMed Central

Hawwash, Dana; Ocké, Marga C.; Berg, Christina; Forsum, Elisabet; Sonestedt, Emily; Wirfält, Elisabet; Åkesson, Agneta; Kolsteren, Patrick; Byrnes, Graham; De Keyzer, Willem; Van Camp, John; Slimani, Nadia; Cevallos, Myriam; Egger, Matthias; Huybrechts, Inge

2016-01-01

Background Concerns have been raised about the quality of reporting in nutritional epidemiology. Research reporting guidelines such as the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement can improve quality of reporting in observational studies. Herein, we propose recommendations for reporting nutritional epidemiology and dietary assessment research by extending the STROBE statement into Strengthening the Reporting of Observational Studies in Epidemiology—Nutritional Epidemiology (STROBE-nut). Methods and Findings Recommendations for the reporting of nutritional epidemiology and dietary assessment research were developed following a systematic and consultative process, coordinated by a multidisciplinary group of 21 experts. Consensus on reporting guidelines was reached through a three-round Delphi consultation process with 53 external experts. In total, 24 recommendations for nutritional epidemiology were added to the STROBE checklist. Conclusion When used appropriately, reporting guidelines for nutritional epidemiology can contribute to improve reporting of observational studies with a focus on diet and health. PMID:27270749

Spatial and temporal associations of road traffic noise and air pollution in London: Implications for epidemiological studies.

PubMed

Fecht, Daniela; Hansell, Anna L; Morley, David; Dajnak, David; Vienneau, Danielle; Beevers, Sean; Toledano, Mireille B; Kelly, Frank J; Anderson, H Ross; Gulliver, John

2016-03-01

Road traffic gives rise to noise and air pollution exposures, both of which are associated with adverse health effects especially for cardiovascular disease, but mechanisms may differ. Understanding the variability in correlations between these pollutants is essential to understand better their separate and joint effects on human health. We explored associations between modelled noise and air pollutants using different spatial units and area characteristics in London in 2003-2010. We modelled annual average exposures to road traffic noise (LAeq,24h, Lden, LAeq,16h, Lnight) for ~190,000 postcode centroids in London using the UK Calculation of Road Traffic Noise (CRTN) method. We used a dispersion model (KCLurban) to model nitrogen dioxide, nitrogen oxide, ozone, total and the traffic-only component of particulate matter ≤2.5μm and ≤10μm. We analysed noise and air pollution correlations at the postcode level (~50 people), postcodes stratified by London Boroughs (~240,000 people), neighbourhoods (Lower layer Super Output Areas) (~1600 people), 1km grid squares, air pollution tertiles, 50m, 100m and 200m in distance from major roads and by deprivation tertiles. Across all London postcodes, we observed overall moderate correlations between modelled noise and air pollution that were stable over time (Spearman's rho range: |0.34-0.55|). Correlations, however, varied considerably depending on the spatial unit: largest ranges were seen in neighbourhoods and 1km grid squares (both Spearman's rho range: |0.01-0.87|) and was less for Boroughs (Spearman's rho range: |0.21-0.78|). There was little difference in correlations between exposure tertiles, distance from road or deprivation tertiles. Associations between noise and air pollution at the relevant geographical unit of analysis need to be carefully considered in any epidemiological analysis, in particular in complex urban areas. Low correlations near roads, however, suggest that independent effects of road noise and traffic-related air pollution can be reliably determined within London. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Air pollution, inflammation and preterm birth in Mexico City: study design and methods.

PubMed

O'Neill, Marie S; Osornio-Vargas, Alvaro; Buxton, Miatta A; Sánchez, Brisa N; Rojas-Bracho, Leonora; Castillo-Castrejon, Marisol; Mordhukovich, Irina B; Brown, Daniel G; Vadillo-Ortega, Felipe

2013-03-15

Preterm birth is one of the leading causes of perinatal mortality and is associated with long-term adverse health consequences for surviving infants. Preterm birth rates are rising worldwide, and no effective means for prevention currently exists. Air pollution exposure may be a significant cause of prematurity, but many published studies lack the individual, clinical data needed to elucidate possible biological mechanisms mediating these epidemiological associations. This paper presents the design of a prospective study now underway to evaluate those mechanisms in a cohort of pregnant women residing in Mexico City. We address how air quality may act together with other factors to induce systemic inflammation and influence the duration of pregnancy. Data collection includes: biomarkers relevant to inflammation in cervico-vaginal exudate and peripheral blood, along with full clinical information, pro-inflammatory cytokine gene polymorphisms and air pollution data to evaluate spatial and temporal variability in air pollution exposure. Samples are collected on a monthly basis and participants are followed for the duration of pregnancy. The data will be used to evaluate whether ambient air pollution is associated with preterm birth, controlling for other risk factors. We will evaluate which time windows during pregnancy are most influential in the air pollution and preterm birth association. In addition, the epidemiological study will be complemented with a parallel toxicology invitro study, in which monocytic cells will be exposed to air particle samples to evaluate the expression of biomarkers of inflammation. Copyright © 2012 Elsevier B.V. All rights reserved.

Air pollution, inflammation and preterm birth in Mexico City: Study design and methods

PubMed Central

O’Neill, Marie S.; Osornio-Vargas, Alvaro; Buxton, Miatta A.; Sanchez, Brisa N.; Rojas-Bracho, Leonora; Castillo-Castrejon, Marisol; Mordhukovich, Irina B.; Brown, Daniel G.; Vadillo-Ortega, Felipe

2012-01-01

Preterm birth is one of the leading causes of perinatal mortality and is associated with long-term adverse health consequences for surviving infants. Preterm birth rates are rising worldwide, and no effective means for prevention currently exists. Air pollution exposure may be a significant cause of prematurity, but many published studies lack the individual, clinical data needed to elucidate possible biological mechanisms mediating these epidemiological associations. This paper presents the design of a prospective study now underway to evaluate those mechanisms in a cohort of pregnant women residing in Mexico City. We address how air quality may act together with other factors to induce systemic inflammation and influence the duration of pregnancy. Data collection includes: biomarkers relevant to inflammation in cervico-vaginal exudate and peripheral blood, along with full clinical information, pro-inflammatory cytokine gene polymorphisms and air pollution data to evaluate spatial and temporal variability in air pollution exposure. Samples are collected on a monthly basis and participants are followed for the duration of pregnancy. The data will be used to evaluate whether ambient air pollution is associated with preterm birth, controlling for other risk factors. We will evaluate which time windows during pregnancy are most influential in the air pollution and preterm birth association. In addition, the epidemiological study will be complemented with a parallel toxicology invitro study, in which monocytic cells will be exposed to air particle samples to evaluate the expression of biomarkers of inflammation. PMID:23177781

Circulation of a Meaban-Like Virus in Yellow-Legged Gulls and Seabird Ticks in the Western Mediterranean Basin

PubMed Central

Cerdà-Cuéllar, Marta; Lecollinet, Sylvie; Pearce-Duvet, Jessica; Busquets, Núria; García-Bocanegra, Ignacio; Pagès, Nonito; Vittecoq, Marion; Hammouda, Abdessalem; Samraoui, Boudjéma; Garnier, Romain; Ramos, Raül; Selmi, Slaheddine; González-Solís, Jacob; Jourdain, Elsa; Boulinier, Thierry

2014-01-01

In recent years, a number of zoonotic flaviviruses have emerged worldwide, and wild birds serve as their major reservoirs. Epidemiological surveys of bird populations at various geographical scales can clarify key aspects of the eco-epidemiology of these viruses. In this study, we aimed at exploring the presence of flaviviruses in the western Mediterranean by sampling breeding populations of the yellow-legged gull (Larus michahellis), a widely distributed, anthropophilic, and abundant seabird species. For 3 years, we sampled eggs from 19 breeding colonies in Spain, France, Algeria, and Tunisia. First, ELISAs were used to determine if the eggs contained antibodies against flaviviruses. Second, neutralization assays were used to identify the specific flaviviruses present. Finally, for colonies in which ELISA-positive eggs had been found, chick serum samples and potential vectors, culicid mosquitoes and soft ticks (Ornithodoros maritimus), were collected and analyzed using serology and PCR, respectively. The prevalence of flavivirus-specific antibodies in eggs was highly spatially heterogeneous. In northeastern Spain, on the Medes Islands and in the nearby village of L'Escala, 56% of eggs had antibodies against the flavivirus envelope protein, but were negative for neutralizing antibodies against three common flaviviruses: West Nile, Usutu, and tick-borne encephalitis viruses. Furthermore, little evidence of past flavivirus exposure was obtained for the other colonies. A subset of the Ornithodoros ticks from Medes screened for flaviviral RNA tested positive for a virus whose NS5 gene was 95% similar to that of Meaban virus, a flavivirus previously isolated from ticks of Larus argentatus in western France. All ELISA-positive samples subsequently tested positive for Meaban virus neutralizing antibodies. This study shows that gulls in the western Mediterranean Basin are exposed to a tick-borne Meaban-like virus, which underscores the need of exploring the spatial and temporal distribution of this flavivirus as well as its potential pathogenicity for animals and humans. PMID:24625959

Agent Based Modeling: Fine-Scale Spatio-Temporal Analysis of Pertussis

NASA Astrophysics Data System (ADS)

Mills, D. A.

2017-10-01

In epidemiology, spatial and temporal variables are used to compute vaccination efficacy and effectiveness. The chosen resolution and scale of a spatial or spatio-temporal analysis will affect the results. When calculating vaccination efficacy, for example, a simple environment that offers various ideal outcomes is often modeled using coarse scale data aggregated on an annual basis. In contrast to the inadequacy of this aggregated method, this research uses agent based modeling of fine-scale neighborhood data centered around the interactions of infants in daycare and their families to demonstrate an accurate reflection of vaccination capabilities. Despite being able to prevent major symptoms, recent studies suggest that acellular Pertussis does not prevent the colonization and transmission of Bordetella Pertussis bacteria. After vaccination, a treated individual becomes a potential asymptomatic carrier of the Pertussis bacteria, rather than an immune individual. Agent based modeling enables the measurable depiction of asymptomatic carriers that are otherwise unaccounted for when calculating vaccination efficacy and effectiveness. Using empirical data from a Florida Pertussis outbreak case study, the results of this model demonstrate that asymptomatic carriers bias the calculated vaccination efficacy and reveal a need for reconsidering current methods that are widely used for calculating vaccination efficacy and effectiveness.

Filaria monitoring visualization system: a geographical information system-based application to manage lymphatic filariasis in Andhra Pradesh, India.

PubMed

Upadhyayula, Suryanaryana Murty; Mutheneni, Srinivasa Rao; Kumaraswamy, Sriram; Kadiri, Madhusudhan Rao; Pabbisetty, Sampath Kumar; Yellepeddi, Venkata Suryanarayana Murthy

2012-05-01

Among various public health diseases, filariasis constitutes a major public health problem in India, wherein an estimated 553.7 million people are at risk of infection. The aim of this article is to present a spatial mapping and analysis of filariasis data over a 3-year period (2004-2007) from Karimnagar, Chittoor, East and West Godavari districts of Andhra Pradesh, India. The data include epidemiological and entomological studies (i.e., infection rate, infectivity rate, mosquito per man hour, and microfilaria rate). These parameters were customized on Geographical Information System (GIS) platform and developed filaria monitoring visualization system (FMVS) for identifying the endemic/risk areas of filariasis among these four districts. GIS map for filariasis transmission from the study areas was created and stratified into different spatial entities like low, medium, and high risk zones. On the basis of the data and FMVS maps, it was demonstrated that filariasis remained unevenly distributed within the districts. Balancing the intervention coverage in different villages with overall mass drug administration and continued promotion of the proper use of control measures are necessary for further reduction of filarial cases in these districts.

Geographic profiling as a novel spatial tool for targeting infectious disease control.

PubMed

Le Comber, Steven C; Rossmo, D Kim; Hassan, Ali N; Fuller, Douglas O; Beier, John C

2011-05-18

Geographic profiling is a statistical tool originally developed in criminology to prioritise large lists of suspects in cases of serial crime. Here, we use two data sets--one historical and one modern--to show how it can be used to locate the sources of infectious disease. First, we re-analyse data from a classic epidemiological study, the 1854 London cholera outbreak. Using 321 disease sites as input, we evaluate the locations of 13 neighbourhood water pumps. The Broad Street pump--the outbreak's source--ranks first, situated in the top 0.2% of the geoprofile. We extend our study with an analysis of reported malaria cases in Cairo, Egypt, using 139 disease case locations to rank 59 mosquitogenic local water sources, seven of which tested positive for the vector Anopheles sergentii. Geographic profiling ranks six of these seven sites in positions 1-6, all in the top 2% of the geoprofile. In both analyses the method outperformed other measures of spatial central tendency. We suggest that geographic profiling could form a useful component of integrated control strategies relating to a wide variety of infectious diseases, since evidence-based targeting of interventions is more efficient, environmentally friendly and cost-effective than untargeted intervention.

An agent-based approach for modeling dynamics of contagious disease spread

PubMed Central

Perez, Liliana; Dragicevic, Suzana

2009-01-01

Background The propagation of communicable diseases through a population is an inherent spatial and temporal process of great importance for modern society. For this reason a spatially explicit epidemiologic model of infectious disease is proposed for a greater understanding of the disease's spatial diffusion through a network of human contacts. Objective The objective of this study is to develop an agent-based modelling approach the integrates geographic information systems (GIS) to simulate the spread of a communicable disease in an urban environment, as a result of individuals' interactions in a geospatial context. Methods The methodology for simulating spatiotemporal dynamics of communicable disease propagation is presented and the model is implemented using measles outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by agents associated to places where they interact with other agents. They are endowed with mobility, through a transportation network allowing them to move between places within the urban environment, in order to represent the spatial heterogeneity and the complexity involved in infectious diseases diffusion. The model is implemented on georeferenced land use dataset from Metro Vancouver and makes use of census data sets from Statistics Canada for the municipality of Burnaby, BC, Canada study site. Results The results provide insights into the application of the model to calculate ratios of susceptible/infected in specific time frames and urban environments, due to its ability to depict the disease progression based on individuals' interactions. It is demonstrated that the dynamic spatial interactions within the population lead to high numbers of exposed individuals who perform stationary activities in areas after they have finished commuting. As a result, the sick individuals are concentrated in geographical locations like schools and universities. Conclusion The GIS-agent based model designed for this study can be easily customized to study the disease spread dynamics of any other communicable disease by simply adjusting the modeled disease timeline and/or the infection model and modifying the transmission process. This type of simulations can help to improve comprehension of disease spread dynamics and to take better steps towards the prevention and control of an epidemic outbreak. PMID:19656403

[How to write high-quality epidemiological research paper Ⅵ. Strengthening the Reporting of Observational Studies in Epidemiology-Nutritional Epidemiology (STROBE-nut)].

PubMed

Ding, C Y; Cao, Y; Yang, C; Sun, F; Zhan, S Y

2017-01-10

Concerns have been raised about the reporting quality in nutritional epidemiology. Therefore, strengthening the reporting of observational studies in epidemiology-nutritional epidemiology (STROBE-nut) has been proposed by extending the STROBE statement to include additional recommendations on issues related to nutritional epidemiology and dietary assessment, aiming to provide more specific guidelines on how to report observational research in the field. This paper presents a brief introduction to STROBE-nut and also an explanation of the key points in the additional items, with an example illustrating the application of the checklist.

Estimating the Delay between Host Infection and Disease (Incubation Period) and Assessing Its Significance to the Epidemiology of Plant Diseases

PubMed Central

Leclerc, Melen; Doré, Thierry; Gilligan, Christopher A.; Lucas, Philippe; Filipe, João A. N.

2014-01-01

Knowledge of the incubation period of infectious diseases (time between host infection and expression of disease symptoms) is crucial to our epidemiological understanding and the design of appropriate prevention and control policies. Plant diseases cause substantial damage to agricultural and arboricultural systems, but there is still very little information about how the incubation period varies within host populations. In this paper, we focus on the incubation period of soilborne plant pathogens, which are difficult to detect as they spread and infect the hosts underground and above-ground symptoms occur considerably later. We conducted experiments on Rhizoctonia solani in sugar beet, as an example patho-system, and used modelling approaches to estimate the incubation period distribution and demonstrate the impact of differing estimations on our epidemiological understanding of plant diseases. We present measurements of the incubation period obtained in field conditions, fit alternative probability models to the data, and show that the incubation period distribution changes with host age. By simulating spatially-explicit epidemiological models with different incubation-period distributions, we study the conditions for a significant time lag between epidemics of cryptic infection and the associated epidemics of symptomatic disease. We examine the sensitivity of this lag to differing distributional assumptions about the incubation period (i.e. exponential versus Gamma). We demonstrate that accurate information about the incubation period distribution of a pathosystem can be critical in assessing the true scale of pathogen invasion behind early disease symptoms in the field; likewise, it can be central to model-based prediction of epidemic risk and evaluation of disease management strategies. Our results highlight that reliance on observation of disease symptoms can cause significant delay in detection of soil-borne pathogen epidemics and mislead practitioners and epidemiologists about the timing, extent, and viability of disease control measures for limiting economic loss. PMID:24466153

Estimating the delay between host infection and disease (incubation period) and assessing its significance to the epidemiology of plant diseases.

PubMed

Leclerc, Melen; Doré, Thierry; Gilligan, Christopher A; Lucas, Philippe; Filipe, João A N

2014-01-01

Knowledge of the incubation period of infectious diseases (time between host infection and expression of disease symptoms) is crucial to our epidemiological understanding and the design of appropriate prevention and control policies. Plant diseases cause substantial damage to agricultural and arboricultural systems, but there is still very little information about how the incubation period varies within host populations. In this paper, we focus on the incubation period of soilborne plant pathogens, which are difficult to detect as they spread and infect the hosts underground and above-ground symptoms occur considerably later. We conducted experiments on Rhizoctonia solani in sugar beet, as an example patho-system, and used modelling approaches to estimate the incubation period distribution and demonstrate the impact of differing estimations on our epidemiological understanding of plant diseases. We present measurements of the incubation period obtained in field conditions, fit alternative probability models to the data, and show that the incubation period distribution changes with host age. By simulating spatially-explicit epidemiological models with different incubation-period distributions, we study the conditions for a significant time lag between epidemics of cryptic infection and the associated epidemics of symptomatic disease. We examine the sensitivity of this lag to differing distributional assumptions about the incubation period (i.e. exponential versus Gamma). We demonstrate that accurate information about the incubation period distribution of a pathosystem can be critical in assessing the true scale of pathogen invasion behind early disease symptoms in the field; likewise, it can be central to model-based prediction of epidemic risk and evaluation of disease management strategies. Our results highlight that reliance on observation of disease symptoms can cause significant delay in detection of soil-borne pathogen epidemics and mislead practitioners and epidemiologists about the timing, extent, and viability of disease control measures for limiting economic loss.

Advanced brain aging: relationship with epidemiologic and genetic risk factors, and overlap with Alzheimer disease atrophy patterns.

PubMed

Habes, M; Janowitz, D; Erus, G; Toledo, J B; Resnick, S M; Doshi, J; Van der Auwera, S; Wittfeld, K; Hegenscheid, K; Hosten, N; Biffar, R; Homuth, G; Völzke, H; Grabe, H J; Hoffmann, W; Davatzikos, C

2016-04-05

We systematically compared structural imaging patterns of advanced brain aging (ABA) in the general-population, herein defined as significant deviation from typical BA to those found in Alzheimer disease (AD). The hypothesis that ABA would show different patterns of structural change compared with those found in AD was tested via advanced pattern analysis methods. In particular, magnetic resonance images of 2705 participants from the Study of Health in Pomerania (aged 20-90 years) were analyzed using an index that captures aging atrophy patterns (Spatial Pattern of Atrophy for Recognition of BA (SPARE-BA)), and an index previously shown to capture atrophy patterns found in clinical AD (Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease (SPARE-AD)). We studied the association between these indices and risk factors, including an AD polygenic risk score. Finally, we compared the ABA-associated atrophy with typical AD-like patterns. We observed that SPARE-BA had significant association with: smoking (P<0.05), anti-hypertensive (P<0.05), anti-diabetic drug use (men P<0.05, women P=0.06) and waist circumference for the male cohort (P<0.05), after adjusting for age. Subjects with ABA had spatially extensive gray matter loss in the frontal, parietal and temporal lobes (false-discovery-rate-corrected q<0.001). ABA patterns of atrophy were partially overlapping with, but notably deviating from those typically found in AD. Subjects with ABA had higher SPARE-AD values; largely due to the partial spatial overlap of associated patterns in temporal regions. The AD polygenic risk score was significantly associated with SPARE-AD but not with SPARE-BA. Our findings suggest that ABA is likely characterized by pathophysiologic mechanisms that are distinct from, or only partially overlapping with those of AD.

Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia

PubMed Central

Midekisa, Alemayehu; Senay, Gabriel B; Wimberly, Michael C

2014-01-01

Malaria is a major global public health problem, particularly in Sub-Saharan Africa. The spatial heterogeneity of malaria can be affected by factors such as hydrological processes, physiography, and land cover patterns. Tropical wetlands, for example, are important hydrological features that can serve as mosquito breeding habitats. Mapping and monitoring of wetlands using satellite remote sensing can thus help to target interventions aimed at reducing malaria transmission. The objective of this study was to map wetlands and other major land cover types in the Amhara region of Ethiopia and to analyze district-level associations of malaria and wetlands across the region. We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM+ imagery, respectively. The model that integrated data from both sensors yielded more accurate land cover classification than single-sensor models. The resulting map of wetlands and other major land cover classes had an overall accuracy of 93.5%. Topographic indices and subpixel level fractional cover indices contributed most strongly to the land cover classification. Further, we found strong spatial associations of percent area of wetlands with malaria cases at the district level across the dry, wet, and fall seasons. Overall, our study provided the most extensive map of wetlands for the Amhara region and documented spatiotemporal associations of wetlands and malaria risk at a broad regional level. These findings can assist public health personnel in developing strategies to effectively control and eliminate malaria in the region. Key Points Remote sensing produced an accurate wetland map for the Ethiopian highlands Wetlands were associated with spatial variability in malaria risk Mapping and monitoring wetlands can improve malaria spatial decision support PMID:25653462

Wetlands and Malaria in the Amazon: Guidelines for the Use of Synthetic Aperture Radar Remote-Sensing

PubMed Central

Catry, Thibault; Li, Zhichao; Roux, Emmanuel; Herbreteau, Vincent; Dessay, Nadine

2018-01-01

The prevention and control of mosquito-borne diseases, such as malaria, are important health issues in tropical areas. Malaria transmission is a multi-scale process strongly controlled by environmental factors, and the use of remote-sensing data is suitable for the characterization of its spatial and temporal dynamics. Synthetic aperture radar (SAR) is well-adapted to tropical areas, since it is capable of imaging independent of light and weather conditions. In this study, we highlight the contribution of SAR sensors in the assessment of the relationship between vectors, malaria and the environment in the Amazon region. More specifically, we focus on the SAR-based characterization of potential breeding sites of mosquito larvae, such as man-made water collections and natural wetlands, providing guidelines for the use of SAR capabilities and techniques in order to optimize vector control and malaria surveillance. In light of these guidelines, we propose a framework for the production of spatialized indicators and malaria risk maps based on the combination of SAR, entomological and epidemiological data to support malaria risk prevention and control actions in the field. PMID:29518988

Wetlands and Malaria in the Amazon: Guidelines for the Use of Synthetic Aperture Radar Remote-Sensing.

PubMed

Catry, Thibault; Li, Zhichao; Roux, Emmanuel; Herbreteau, Vincent; Gurgel, Helen; Mangeas, Morgan; Seyler, Frédérique; Dessay, Nadine

2018-03-07

The prevention and control of mosquito-borne diseases, such as malaria, are important health issues in tropical areas. Malaria transmission is a multi-scale process strongly controlled by environmental factors, and the use of remote-sensing data is suitable for the characterization of its spatial and temporal dynamics. Synthetic aperture radar (SAR) is well-adapted to tropical areas, since it is capable of imaging independent of light and weather conditions. In this study, we highlight the contribution of SAR sensors in the assessment of the relationship between vectors, malaria and the environment in the Amazon region. More specifically, we focus on the SAR-based characterization of potential breeding sites of mosquito larvae, such as man-made water collections and natural wetlands, providing guidelines for the use of SAR capabilities and techniques in order to optimize vector control and malaria surveillance. In light of these guidelines, we propose a framework for the production of spatialized indicators and malaria risk maps based on the combination of SAR, entomological and epidemiological data to support malaria risk prevention and control actions in the field.

Dengue infection.

PubMed

Guzman, Maria G; Gubler, Duane J; Izquierdo, Alienys; Martinez, Eric; Halstead, Scott B

2016-08-18

Dengue is widespread throughout the tropics and local spatial variation in dengue virus transmission is strongly influenced by rainfall, temperature, urbanization and distribution of the principal mosquito vector Aedes aegypti. Currently, endemic dengue virus transmission is reported in the Eastern Mediterranean, American, South-East Asian, Western Pacific and African regions, whereas sporadic local transmission has been reported in Europe and the United States as the result of virus introduction to areas where Ae. aegypti and Aedes albopictus, a secondary vector, occur. The global burden of the disease is not well known, but its epidemiological patterns are alarming for both human health and the global economy. Dengue has been identified as a disease of the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. According to the WHO, dengue control is technically feasible with coordinated international technical and financial support for national programmes. This Primer provides a general overview on dengue, covering epidemiology, control, disease mechanisms, diagnosis, treatment and research priorities.

Geophysical variables and behavior: LIII. Epidemiological considerations for incidence of cancer and depression in areas of frequent UFO reports.

PubMed

Persinger, M A

1988-12-01

Luminous phenomena and anomalous physical forces have been hypothesized to be generated by focal tectonic strain fields that precede earthquakes. If these geophysical processes exist, then their spatial and temporal density should be greatest during periods of protracted, localized UFO reports; they might be used as dosimetric indicators. Contemporary epidemiological data concerning the health risks of power frequency electromagnetic fields and radon gas levels (expected correlates of certain tectonic strain fields), suggest that increased incidence (odds ratios greater 1:3) of brain tumors and leukemia should be evident within "flap" areas. In addition the frequency of variants of temporal lobe lability, psychological depression and posttraumatic stress should be significantly elevated. UFO field investigators, because they have repeated, intermittent close proximity to these fields, are considered to be a particularly high risk population for these disorders.

Geophysical variables and behavior: LIII. Epidemiological considerations for incidence of cancer and depression in areas of frequent UFO reports

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

Persinger, M.A.

Luminous phenomena and anomalous physical forces have been hypothesized to be generated by focal tectonic strain fields that precede earthquakes. If these geophysical processes exist, then their spatial and temporal density should be greatest during periods of protracted, localized UFO reports; they might be used as dosimetric indicators. Contemporary epidemiological data concerning the health risks of power frequency electromagnetic fields and radon gas levels (expected correlates of certain tectonic strain fields), suggest that increased incidence (odds ratios greater 1:3) of brain tumors and leukemia should be evident within flap areas. In addition the frequency of variants of temporal lobe lability,more » psychological depression and posttraumatic stress should be significantly elevated. UFO field investigators, because they have repeated, intermittent close proximity to these fields, are considered to be a particularly high risk population for these disorders. 22 references.« less

Landscape Epidemiology of Tularemia Outbreaks in Sweden

PubMed Central

Svensson, Kerstin; Bäck, Erik; Eliasson, Henrik; Berglund, Lennart; Granberg, Malin; Karlsson, Linda; Larsson, Pär; Forsman, Mats

2009-01-01

Summer outbreaks of tularemia that occurred from 1995 through 2005 in 2 locations in Sweden affected 441 persons. We performed an epidemiologic investigation of these outbreaks using a novel strategy, involving high-resolution genotyping of Francisella tularensis isolates obtained from 136 patients (using 18 genetic markers developed from 6 F. tularensis genome sequences) and interviews with the patients. Strong spatial associations were found between F. tularensis subpopulations and the places of disease transmission; infection by some subpopulations occurred within areas as small as 2 km2, indicating unidentified environmental point sources of tularemia. In both locations, disease clusters were associated with recreational areas beside water, and genetic subpopulations were present throughout the tularemia season and persisted over years. High-resolution genotyping in combination with patients’ statements about geographic places of disease transmission provided valuable indications of likely sources of infection and the causal genotypes during these tularemia outbreaks. PMID:19961673

Snippets from the past: the evolution of Wade Hampton Frost's epidemiology as viewed from the American Journal of Hygiene/Epidemiology.

PubMed

Morabia, Alfredo

2013-10-01

Wade Hampton Frost, who was a Professor of Epidemiology at Johns Hopkins University from 1919 to 1938, spurred the development of epidemiologic methods. His 6 publications in the American Journal of Hygiene, which later became the American Journal of Epidemiology, comprise a 1928 Cutter lecture on a theory of epidemics, a survey-based study of tonsillectomy and immunity to Corynebacterium diphtheriae (1931), 2 papers from a longitudinal study of the incidence of minor respiratory diseases (1933 and 1935), an attack rate ratio analysis of the decline of diphtheria in Baltimore (1936), and a 1936 lecture on the age, time, and cohort analysis of tuberculosis mortality. These 6 American Journal of Hygiene /American Journal of Epidemiology papers attest that Frost's personal evolution mirrored that of the emerging "early" epidemiology: The scope of epidemiology extended beyond the study of epidemics of acute infectious diseases, and rigorous comparative study designs and their associated quantitative methods came to light.

Snippets From the Past: The Evolution of Wade Hampton Frost's Epidemiology as Viewed From the American Journal of Hygiene/Epidemiology

PubMed Central

Morabia, Alfredo

2013-01-01

Wade Hampton Frost, who was a Professor of Epidemiology at Johns Hopkins University from 1919 to 1938, spurred the development of epidemiologic methods. His 6 publications in the American Journal of Hygiene, which later became the American Journal of Epidemiology, comprise a 1928 Cutter lecture on a theory of epidemics, a survey-based study of tonsillectomy and immunity to Corynebacterium diphtheriae (1931), 2 papers from a longitudinal study of the incidence of minor respiratory diseases (1933 and 1935), an attack rate ratio analysis of the decline of diphtheria in Baltimore (1936), and a 1936 lecture on the age, time, and cohort analysis of tuberculosis mortality. These 6 American Journal of Hygiene /American Journal of Epidemiology papers attest that Frost's personal evolution mirrored that of the emerging “early” epidemiology: The scope of epidemiology extended beyond the study of epidemics of acute infectious diseases, and rigorous comparative study designs and their associated quantitative methods came to light. PMID:24022889

Deriving meteorological variables across Africa for the study and control of vector-borne disease: a comparison of remote sensing and spatial interpolation of climate

PubMed Central

Hay, S. I.; Lennon, J. J.

2012-01-01

Summary This paper presents the results of an investigation into the utility of remote sensing (RS) using meteorological satellites sensors and spatial interpolation (SI) of data from meteorological stations, for the prediction of spatial variation in monthly climate across continental Africa in 1990. Information from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration’s (NOAA) polar-orbiting meteorological satellites was used to estimate land surface temperature (LST) and atmospheric moisture. Cold cloud duration (CCD) data derived from the High Resolution Radiometer (HRR) on-board the European Meteorological Satellite programme’s (EUMETSAT) Meteosat satellite series were also used as a RS proxy measurement of rainfall. Temperature, atmospheric moisture and rainfall surfaces were independently derived from SI of measurements from the World Meteorological Organization (WMO) member stations of Africa. These meteorological station data were then used to test the accuracy of each methodology, so that the appropriateness of the two techniques for epidemiological research could be compared. SI was a more accurate predictor of temperature, whereas RS provided a better surrogate for rainfall; both were equally accurate at predicting atmospheric moisture. The implications of these results for mapping short and long-term climate change and hence their potential for the study and control of disease vectors are considered. Taking into account logistic and analytical problems, there were no clear conclusions regarding the optimality of either technique, but there was considerable potential for synergy. PMID:10203175

Deriving meteorological variables across Africa for the study and control of vector-borne disease: a comparison of remote sensing and spatial interpolation of climate.

PubMed

Hay, S I; Lennon, J J

1999-01-01

This paper presents the results of an investigation into the utility of remote sensing (RS) using meteorological satellites sensors and spatial interpolation (SI) of data from meteorological stations, for the prediction of spatial variation in monthly climate across continental Africa in 1990. Information from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration's (NOAA) polar-orbiting meteorological satellites was used to estimate land surface temperature (LST) and atmospheric moisture. Cold cloud duration (CCD) data derived from the High Resolution Radiometer (HRR) on-board the European Meteorological Satellite programme's (EUMETSAT) Meteosat satellite series were also used as a RS proxy measurement of rainfall. Temperature, atmospheric moisture and rainfall surfaces were independently derived from SI of measurements from the World Meteorological Organization (WMO) member stations of Africa. These meteorological station data were then used to test the accuracy of each methodology, so that the appropriateness of the two techniques for epidemiological research could be compared. SI was a more accurate predictor of temperature, whereas RS provided a better surrogate for rainfall; both were equally accurate at predicting atmospheric moisture. The implications of these results for mapping short and long-term climate change and hence their potential for the study and control of disease vectors are considered. Taking into account logistic and analytical problems, there were no clear conclusions regarding the optimality of either technique, but there was considerable potential for synergy.

Spatial distribution of Batrachochytrium dendrobatidis in South American caecilians.

PubMed

Lambertini, Carolina; Becker, C Guilherme; Bardier, Cecilia; da Silva Leite, Domingos; Toledo, Luís Felipe

2017-04-20

The amphibian-killing fungus Batrachochytrium dendrobatidis (Bd) is linked to population declines in anurans and salamanders globally. To date, however, few studies have attempted to screen Bd in live caecilians; Bd-positive caecilians have only been reported in Africa and French Guiana. Here, we performed a retrospective survey of museum preserved specimens to (1) describe spatial patterns of Bd infection in Gymnophiona across South America and (2) test whether areas of low climatic suitability for Bd in anurans predict Bd spatial epidemiology in caecilians. We used quantitative PCR to detect Bd in preserved caecilians collected over a 109 yr period, and performed autologistic regressions to test the effect of bioclimatic metrics of temperature and precipitation, vegetation density, and elevation on the likelihood of Bd occurrence. We detected an overall Bd prevalence of 12.4%, with positive samples spanning the Uruguayan savanna, Brazil's Atlantic Forest, and the Amazon basin. Our autologistic models detected a strong effect of macroclimate, a weaker effect of vegetation density, and no effect of elevation on the likelihood of Bd occurrence. Although most of our Bd-positive records overlapped with reported areas of high climatic suitability for the fungus in the Neotropics, many of our new Bd-positive samples extend far into areas of poor suitability for Bd in anurans. Our results highlight an important gap in the study of amphibian chytridiomycosis: the potential negative impact of Bd on Neotropical caecilians and the hypothetical role of caecilians as Bd reservoirs.

Repeated cognitive stimulation alleviates memory impairments in an Alzheimer's disease mouse model.

PubMed

Martinez-Coria, Hilda; Yeung, Stephen T; Ager, Rahasson R; Rodriguez-Ortiz, Carlos J; Baglietto-Vargas, David; LaFerla, Frank M

2015-08-01

Alzheimer's disease is a neurodegenerative disease associated with progressive memory and cognitive decline. Previous studies have identified the benefits of cognitive enrichment on reducing disease pathology. Additionally, epidemiological and clinical data suggest that repeated exercise, and cognitive and social enrichment, can improve and/or delay the cognitive deficiencies associated with aging and neurodegenerative diseases. In the present study, 3xTg-AD mice were exposed to a rigorous training routine beginning at 3 months of age, which consisted of repeated training in the Morris water maze spatial recognition task every 3 months, ending at 18 months of age. At the conclusion of the final Morris water maze training session, animals subsequently underwent testing in another hippocampus-dependent spatial task, the Barnes maze task, and on the more cortical-dependent novel object recognition memory task. Our data show that periodic cognitive enrichment throughout aging, via multiple learning episodes in the Morris water maze task, can improve the memory performance of aged 3xTg-AD mice in a separate spatial recognition task, and in a preference memory task, when compared to naïve aged matched 3xTg-AD mice. Furthermore, we observed that the cognitive enrichment properties of Morris water maze exposer, was detectable in repeatedly trained animals as early as 6 months of age. These findings suggest early repeated cognitive enrichment can mitigate the diverse cognitive deficits observed in Alzheimer's disease. Published by Elsevier Inc.

Meeting Report: Atmospheric Pollution and Human Reproduction

PubMed Central

Slama, Rémy; Darrow, Lyndsey; Parker, Jennifer; Woodruff, Tracey J.; Strickland, Matthew; Nieuwenhuijsen, Mark; Glinianaia, Svetlana; Hoggatt, Katherine J.; Kannan, Srimathi; Hurley, Fintan; Kalinka, Jaroslaw; Šrám, Radim; Brauer, Michael; Wilhelm, Michelle; Heinrich, Joachim; Ritz, Beate

2008-01-01

Background There is a growing body of epidemiologic literature reporting associations between atmospheric pollutants and reproductive outcomes, particularly birth weight and gestational duration. Objectives The objectives of our international workshop were to discuss the current evidence, to identify the strengths and weaknesses of published epidemiologic studies, and to suggest future directions for research. Discussion Participants identified promising exposure assessment tools, including exposure models with fine spatial and temporal resolution that take into account time–activity patterns. More knowledge on factors correlated with exposure to air pollution, such as other environmental pollutants with similar temporal variations, and assessment of nutritional factors possibly influencing birth outcomes would help evaluate importance of residual confounding. Participants proposed a list of points to report in future publications on this topic to facilitate research syntheses. Nested case–control studies analyzed using two-phase statistical techniques and development of cohorts with extensive information on pregnancy behaviors and biological samples are promising study designs. Issues related to the identification of critical exposure windows and potential biological mechanisms through which air pollutants may lead to intrauterine growth restriction and premature birth were reviewed. Conclusions To make progress, this research field needs input from toxicology, exposure assessment, and clinical research, especially to aid in the identification and exposure assessment of feto-toxic agents in ambient air, in the development of early markers of adverse reproductive outcomes, and of relevant biological pathways. In particular, additional research using animal models would help better delineate the biological mechanisms underpinning the associations reported in human studies. PMID:18560536

Meeting report: atmospheric pollution and human reproduction.

PubMed

Slama, Rémy; Darrow, Lyndsey; Parker, Jennifer; Woodruff, Tracey J; Strickland, Matthew; Nieuwenhuijsen, Mark; Glinianaia, Svetlana; Hoggatt, Katherine J; Kannan, Srimathi; Hurley, Fintan; Kalinka, Jaroslaw; Srám, Radim; Brauer, Michael; Wilhelm, Michelle; Heinrich, Joachim; Ritz, Beate

2008-06-01

There is a growing body of epidemiologic literature reporting associations between atmospheric pollutants and reproductive outcomes, particularly birth weight and gestational duration. The objectives of our international workshop were to discuss the current evidence, to identify the strengths and weaknesses of published epidemiologic studies, and to suggest future directions for research. Participants identified promising exposure assessment tools, including exposure models with fine spatial and temporal resolution that take into account time-activity patterns. More knowledge on factors correlated with exposure to air pollution, such as other environmental pollutants with similar temporal variations, and assessment of nutritional factors possibly influencing birth outcomes would help evaluate importance of residual confounding. Participants proposed a list of points to report in future publications on this topic to facilitate research syntheses. Nested case-control studies analyzed using two-phase statistical techniques and development of cohorts with extensive information on pregnancy behaviors and biological samples are promising study designs. Issues related to the identification of critical exposure windows and potential biological mechanisms through which air pollutants may lead to intrauterine growth restriction and premature birth were reviewed. To make progress, this research field needs input from toxicology, exposure assessment, and clinical research, especially to aid in the identification and exposure assessment of feto-toxic agents in ambient air, in the development of early markers of adverse reproductive outcomes, and of relevant biological pathways. In particular, additional research using animal models would help better delineate the biological mechanisms underpinning the associations reported in human studies.

Locally-Adaptive, Spatially-Explicit Projection of U.S. Population for 2030 and 2050

DOE PAGES

McKee, Jacob J.; Rose, Amy N.; Bright, Eddie A.; ...

2015-02-03

Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Moreover, knowing the spatial distribution of future population allows for increased preparation in the event of an emergency. Building on the spatial interpolation technique previously developed for high resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically-informed spatial distribution of the projected population of the contiguous U.S. for 2030 and 2050. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection modelmore » departs from these by accounting for multiple components that affect population distribution. Modelled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the U.S. Census s projection methodology with the U.S. Census s official projection as the benchmark. Applications of our model include, but are not limited to, suitability modelling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.« less

Locally-Adaptive, Spatially-Explicit Projection of U.S. Population for 2030 and 2050

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

McKee, Jacob J.; Rose, Amy N.; Bright, Eddie A.

Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Moreover, knowing the spatial distribution of future population allows for increased preparation in the event of an emergency. Building on the spatial interpolation technique previously developed for high resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically-informed spatial distribution of the projected population of the contiguous U.S. for 2030 and 2050. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection modelmore » departs from these by accounting for multiple components that affect population distribution. Modelled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the U.S. Census s projection methodology with the U.S. Census s official projection as the benchmark. Applications of our model include, but are not limited to, suitability modelling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.« less

The Applications of Model-Based Geostatistics in Helminth Epidemiology and Control

PubMed Central

Magalhães, Ricardo J. Soares; Clements, Archie C.A.; Patil, Anand P.; Gething, Peter W.; Brooker, Simon

2011-01-01

Funding agencies are dedicating substantial resources to tackle helminth infections. Reliable maps of the distribution of helminth infection can assist these efforts by targeting control resources to areas of greatest need. The ability to define the distribution of infection at regional, national and subnational levels has been enhanced greatly by the increased availability of good quality survey data and the use of model-based geostatistics (MBG), enabling spatial prediction in unsampled locations. A major advantage of MBG risk mapping approaches is that they provide a flexible statistical platform for handling and representing different sources of uncertainty, providing plausible and robust information on the spatial distribution of infections to inform the design and implementation of control programmes. Focussing on schistosomiasis and soil-transmitted helminthiasis, with additional examples for lymphatic filariasis and onchocerciasis, we review the progress made to date with the application of MBG tools in large-scale, real-world control programmes and propose a general framework for their application to inform integrative spatial planning of helminth disease control programmes. PMID:21295680

Ecologic Factors Associated with West Nile Virus Transmission, Northeastern United States

PubMed Central

Brown, Heidi E.; Childs, James E.; Diuk-Wasser, Maria A.

2008-01-01

Since 1999, West Nile virus (WNV) disease has affected the northeastern United States. To describe the spatial epidemiology and identify risk factors for disease incidence, we analyzed 8 years (1999–2006) of county-based human WNV disease surveillance data. Among the 56.6 million residents in 8 northeastern states sharing primary enzootic vectors, we found 977 cases. We controlled for population density and potential bias from surveillance and spatial proximity. Analyses demonstrated significant spatial spreading from 1999 through 2004 (p<0.01, r2 = 0.16). A significant trend was apparent among increasingly urban counties; county quartiles with the least (<38%) forest cover had 4.4-fold greater odds (95% confidence interval [CI] 1.4–13.2, p = 0.01) of having above-median disease incidence (>0.75 cases/100,000 residents) than counties with the most (>70%) forest cover. These results quantify urbanization as a risk factor for WNV disease incidence and are consistent with knowledge of vector species in this area. PMID:18826816

Microenvironment Tracker (MicroTrac) | Science Inventory ...

EPA Pesticide Factsheets

Epidemiologic studies have shown associations between air pollution concentrations measured at central-site ambient monitors and adverse health outcomes. Using central-site concentrations as exposure surrogates, however, can lead to exposure errors due to time spent in various indoor and outdoor microenvironments (ME) with pollutant concentrations that can be substantially different from central-site concentrations. These exposure errors can introduce bias and incorrect confidence intervals in health effect estimates, which diminish the power of such studies to establish correct conclusions about the exposure and health effects association. The significance of this issue was highlighted in the National Research Council (NRC) Report “Research Priorities for Airborne Particulate Matter”, which recommends that EPA address exposure error in health studies. To address this limitation, we developed MicroTrac, an automated classification model that estimates time of day and duration spent in eight ME (indoors and outdoors at home, work, school; inside vehicles; other locations) from personal global positioning system (GPS) data and geocoded boundaries of buildings (e.g., home, work, school). MicroTrac has several innovative design features: (1) using GPS signal quality to account for GPS signal loss inside certain buildings, (2) spatial buffering of building boundaries to account for the spatial inaccuracy of the GPS device, and (3) temporal buffering of GPS positi

Assessing temporally and spatially resolved PM 2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements

NASA Astrophysics Data System (ADS)

Kloog, Itai; Koutrakis, Petros; Coull, Brent A.; Lee, Hyung Joo; Schwartz, Joel

2011-11-01

Land use regression (LUR) models provide good estimates of spatially resolved long-term exposures, but are poor at capturing short term exposures. Satellite-derived Aerosol Optical Depth (AOD) measurements have the potential to provide spatio-temporally resolved predictions of both long and short term exposures, but previous studies have generally showed relatively low predictive power. Our objective was to extend our previous work on day-specific calibrations of AOD data using ground PM 2.5 measurements by incorporating commonly used LUR variables and meteorological variables, thus benefiting from both the spatial resolution from the LUR models and the spatio-temporal resolution from the satellite models. Later we use spatial smoothing to predict PM 2.5 concentrations for day/locations with missing AOD measures. We used mixed models with random slopes for day to calibrate AOD data for 2000-2008 across New-England with monitored PM 2.5 measurements. We then used a generalized additive mixed model with spatial smoothing to estimate PM 2.5 in location-day pairs with missing AOD, using regional measured PM 2.5, AOD values in neighboring cells, and land use. Finally, local (100 m) land use terms were used to model the difference between grid cell prediction and monitored value to capture very local traffic particles. Out-of-sample ten-fold cross-validation was used to quantify the accuracy of our predictions. For days with available AOD data we found high out-of-sample R2 (mean out-of-sample R2 = 0.830, year to year variation 0.725-0.904). For days without AOD values, our model performance was also excellent (mean out-of-sample R2 = 0.810, year to year variation 0.692-0.887). Importantly, these R2 are for daily, rather than monthly or yearly, values. Our model allows one to assess short term and long-term human exposures in order to investigate both the acute and chronic effects of ambient particles, respectively.

Evaluating Multipollutant Exposure and Urban Air Quality: Pollutant Interrelationships, Neighborhood Variability, and Nitrogen Dioxide as a Proxy Pollutant

PubMed Central

Levy, Ilan; Mihele, Cristian; Lu, Gang; Narayan, Julie; Brook, Jeffrey R.

2013-01-01

Background: Although urban air pollution is a complex mix containing multiple constituents, studies of the health effects of long-term exposure often focus on a single pollutant as a proxy for the entire mixture. A better understanding of the component pollutant concentrations and interrelationships would be useful in epidemiological studies that exploit spatial differences in exposure by clarifying the extent to which measures of individual pollutants, particularly nitrogen dioxide (NO2), represent spatial patterns in the multipollutant mixture. Objectives: We examined air pollutant concentrations and interrelationships at the intraurban scale to obtain insight into the nature of the urban mixture of air pollutants. Methods: Mobile measurements of 23 air pollutants were taken systematically at high resolution in Montreal, Quebec, Canada, over 34 days in the winter, summer, and autumn of 2009. Results: We observed variability in pollution levels and in the statistical correlations between different pollutants according to season and neighborhood. Nitrogen oxide species (nitric oxide, NO2, nitrogen oxides, and total oxidized nitrogen species) had the highest overall spatial correlations with the suite of pollutants measured. Ultrafine particles and hydrocarbon-like organic aerosol concentration, a derived measure used as a specific indicator of traffic particles, also had very high correlations. Conclusions: Our findings indicate that the multipollutant mix varies considerably throughout the city, both in time and in space, and thus, no single pollutant would be a perfect proxy measure for the entire mix under all circumstances. However, based on overall average spatial correlations with the suite of pollutants measured, nitrogen oxide species appeared to be the best available indicators of spatial variation in exposure to the outdoor urban air pollutant mixture. Citation: Levy I, Mihele C, Lu G, Narayan J, Brook JR. 2014. Evaluating multipollutant exposure and urban air quality: pollutant interrelationships, neighborhood variability, and nitrogen dioxide as a proxy pollutant. Environ Health Perspect 122:65–72; http://dx.doi.org/10.1289/ehp.1306518 PMID:24225648

The limitations of opportunistic epidemiology, pseudopod epidemiology.

PubMed

Kuller, Lewis H

2016-10-01

Epidemiology has been remarkably successful in the past in identifying the important agents of disease, the impact of the environment, both physical and social, and interrelationship with host susceptibility (genomics). Many of the advances in improving the health of individuals and populations have been the result of epidemiology studies that have identified the specific "agents" of disease and application of public health and preventive medicine. In recent years, large longitudinal studies have dominated epidemiology research, especially long incubation period chronic diseases. The initial hypotheses in these studies have been expanded by vertical extension studies using newer technologies to measure independent variables, vertical pseudopods, and additional studies of other diseases, horizontal pseudopods, of the original longitudinal study. Host susceptibility, i.e. genomics, has also become a prominent component of these longitudinal studies. The critical question addressed in this paper is whether these "pseudopod" epidemiology approaches have enhanced public health or generated a large number of studies of little impact.

Elevation and cholera: an epidemiological spatial analysis of the cholera epidemic in Harare, Zimbabwe, 2008-2009

PubMed Central

2012-01-01

Background In highly populated African urban areas where access to clean water is a challenge, water source contamination is one of the most cited risk factors in a cholera epidemic. During the rainy season, where there is either no sewage disposal or working sewer system, runoff of rains follows the slopes and gets into the lower parts of towns where shallow wells could easily become contaminated by excretes. In cholera endemic areas, spatial information about topographical elevation could help to guide preventive interventions. This study aims to analyze the association between topographic elevation and the distribution of cholera cases in Harare during the cholera epidemic in 2008 and 2009. Methods We developed an ecological study using secondary data. First, we described attack rates by suburb and then calculated rate ratios using whole Harare as reference. We illustrated the average elevation and cholera cases by suburbs using geographical information. Finally, we estimated a generalized linear mixed model (under the assumption of a Poisson distribution) with an Empirical Bayesian approach to model the relation between the risk of cholera and the elevation in meters in Harare. We used a random intercept to allow for spatial correlation of neighboring suburbs. Results This study identifies a spatial pattern of the distribution of cholera cases in the Harare epidemic, characterized by a lower cholera risk in the highest elevation suburbs of Harare. The generalized linear mixed model showed that for each 100 meters of increase in the topographical elevation, the cholera risk was 30% lower with a rate ratio of 0.70 (95% confidence interval=0.66-0.76). Sensitivity analysis confirmed the risk reduction with an overall estimate of the rate ratio between 20% and 40%. Conclusion This study highlights the importance of considering topographical elevation as a geographical and environmental risk factor in order to plan cholera preventive activities linked with water and sanitation in endemic areas. Furthermore, elevation information, among other risk factors, could help to spatially orientate cholera control interventions during an epidemic. PMID:22708576

Implications of different approaches for characterizing ambient air pollutant concentrations within the urban airshed for time-series studies and health benefits analyses.

PubMed

Strickland, Matthew J; Darrow, Lyndsey A; Mulholland, James A; Klein, Mitchel; Flanders, W Dana; Winquist, Andrea; Tolbert, Paige E

2011-05-11

In time-series studies of the health effects of urban air pollutants, decisions must be made about how to characterize pollutant levels within the airshed. Emergency department visits for pediatric asthma exacerbations were collected from Atlanta hospitals. Concentrations of carbon monoxide, nitrogen dioxide, ozone, sulfur dioxide, particulate matter less than 10 microns in diameter (PM10), particulate matter less than 2.5 microns in diameter (PM2.5), and the PM2.5 components elemental carbon, organic carbon, and sulfate were obtained from networks of ambient air quality monitors. For each pollutant we created three different daily metrics. For one metric we used the measurements from a centrally-located monitor; for the second we averaged measurements across the network of monitors; and for the third we estimated the population-weighted average concentration using an isotropic spatial model. Rate ratios for each of the metrics were estimated from time-series models. For pollutants with relatively homogeneous spatial distributions we observed only small differences in the rate ratio across the three metrics. Conversely, for spatially heterogeneous pollutants we observed larger differences in the rate ratios. For a given pollutant, the strength of evidence for an association (i.e., chi-square statistics) tended to be similar across metrics. Given that the chi-square statistics were similar across the metrics, the differences in the rate ratios for the spatially heterogeneous pollutants may seem like a relatively small issue. However, these differences are important for health benefits analyses, where results from epidemiological studies on the health effects of pollutants (per unit change in concentration) are used to predict the health impacts of a reduction in pollutant concentrations. We discuss the relative merits of the different metrics as they pertain to time-series studies and health benefits analyses.

Using NASA Remotely Sensed Data to Help Characterize Environmental Risk Factors for National Public Health Applications

NASA Technical Reports Server (NTRS)

Al-Hamdan, Mohammad; Crosson, William; Economou, Sigrid; Estes, Maurice, Jr.; Estes, Sue; Hemmings, Sarah; Kent, Shia; Quattrochi, Dale; Wade, Gina; McClure, Leslie

2011-01-01

NASA Marshall Space Flight Center is collaborating with the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) National Center for Public Health Informatics to address issues of environmental health and enhance public health decision making by utilizing NASA remotely sensed data and products. The objectives of this study are to develop high-quality spatial data sets of environmental variables, link these with public health data from a national cohort study, and deliver the linked data sets and associated analyses to local, state and federal end-user groups. Three daily environmental data sets will be developed for the conterminous U.S. on different spatial resolutions for the period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid utilizing the US Environmental Protection Agency (EPA) ground observations and NASA's MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Solar Insolation (SI) using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental data sets will be linked with public health data from the UAB REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline and other health outcomes. These environmental datasets and public health linkage analyses will be disseminated to end-users for decision making through the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system.

10-year spatial and temporal trends of PM2.5 concentrations in the southeastern US estimated using high-resolution satellite data

PubMed Central

Hu, X.; Waller, L. A.; Lyapustin, A.; Wang, Y.; Liu, Y.

2017-01-01

Long-term PM2.5 exposure has been associated with various adverse health outcomes. However, most ground monitors are located in urban areas, leading to a potentially biased representation of true regional PM2.5 levels. To facilitate epidemiological studies, accurate estimates of the spatiotemporally continuous distribution of PM2.5 concentrations are important. Satellite-retrieved aerosol optical depth (AOD) has been increasingly used for PM2.5 concentration estimation due to its comprehensive spatial coverage. Nevertheless, previous studies indicated that an inherent disadvantage of many AOD products is their coarse spatial resolution. For instance, the available spatial resolutions of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) AOD products are 10 and 17.6 km, respectively. In this paper, a new AOD product with 1 km spatial resolution retrieved by the multi-angle implementation of atmospheric correction (MAIAC) algorithm based on MODIS measurements was used. A two-stage model was developed to account for both spatial and temporal variability in the PM2.5–AOD relationship by incorporating the MAIAC AOD, meteorological fields, and land use variables as predictors. Our study area is in the southeastern US centered at the Atlanta metro area, and data from 2001 to 2010 were collected from various sources. The model was fitted annually, and we obtained model fitting R2 ranging from 0.71 to 0.85, mean prediction error (MPE) from 1.73 to 2.50 μg m−3, and root mean squared prediction error (RMSPE) from 2.75 to 4.10 μg m−3. In addition, we found cross-validation R2 ranging from 0.62 to 0.78, MPE from 2.00 to 3.01 μgm−3, and RMSPE from 3.12 to 5.00 μgm−3, indicating a good agreement between the estimated and observed values. Spatial trends showed that high PM2.5 levels occurred in urban areas and along major highways, while low concentrations appeared in rural or mountainous areas. Our time-series analysis showed that, for the 10-year study period, the PM2.5 levels in the southeastern US have decreased by ∼20 %. The annual decrease has been relatively steady from 2001 to 2007 and from 2008 to 2010 while a significant drop occurred between 2007 and 2008. An observed increase in PM2.5 levels in year 2005 is attributed to elevated sulfate concentrations in the study area in warm months of 2005. PMID:28966656

Privacy Protection Versus Cluster Detection in Spatial Epidemiology

PubMed Central

Olson, Karen L.; Grannis, Shaun J.; Mandl, Kenneth D.

2006-01-01

Objectives. Patient data that includes precise locations can reveal patients’ identities, whereas data aggregated into administrative regions may preserve privacy and confidentiality. We investigated the effect of varying degrees of address precision (exact latitude and longitude vs the center points of zip code or census tracts) on detection of spatial clusters of cases. Methods. We simulated disease outbreaks by adding supplementary spatially clustered emergency department visits to authentic hospital emergency department syndromic surveillance data. We identified clusters with a spatial scan statistic and evaluated detection rate and accuracy. Results. More clusters were identified, and clusters were more accurately detected, when exact locations were used. That is, these clusters contained at least half of the simulated points and involved few additional emergency department visits. These results were especially apparent when the synthetic clustered points crossed administrative boundaries and fell into multiple zip code or census tracts. Conclusions. The spatial cluster detection algorithm performed better when addresses were analyzed as exact locations than when they were analyzed as center points of zip code or census tracts, particularly when the clustered points crossed administrative boundaries. Use of precise addresses offers improved performance, but this practice must be weighed against privacy concerns in the establishment of public health data exchange policies. PMID:17018828

Spatial analysis of lettuce downy mildew using geostatistics and geographic information systems.

PubMed

Wu, B M; van Bruggen, A H; Subbarao, K V; Pennings, G G

2001-02-01

ABSTRACT The epidemiology of lettuce downy mildew has been investigated extensively in coastal California. However, the spatial patterns of the disease and the distance that Bremia lactucae spores can be transported have not been determined. During 1995 to 1998, we conducted several field- and valley-scale surveys to determine spatial patterns of this disease in the Salinas valley. Geostatistical analyses of the survey data at both scales showed that the influence range of downy mildew incidence at one location on incidence at other locations was between 80 and 3,000 m. A linear relationship was detected between semivariance and lag distance at the field scale, although no single statistical model could fit the semi-variograms at the valley scale. Spatial interpolation by the inverse distance weighting method with a power of 2 resulted in plausible estimates of incidence throughout the valley. Cluster analysis in geographic information systems on the interpolated disease incidence from different dates demonstrated that the Salinas valley could be divided into two areas, north and south of Salinas City, with high and low disease pressure, respectively. Seasonal and spatial trends along the valley suggested that the distinction between the downy mildew conducive and nonconducive areas might be determined by environmental factors.

Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?

PubMed

Chen, Shi; Ilany, Amiyaal; White, Brad J; Sanderson, Michael W; Lanzas, Cristina

2015-01-01

Animal social network is the key to understand many ecological and epidemiological processes. We used real-time location system (RTLS) to accurately track cattle position, analyze their proximity networks, and tested the hypothesis of temporal stationarity and spatial homogeneity in these networks during different daily time periods and in different areas of the pen. The network structure was analyzed using global network characteristics (network density), subgroup clustering (modularity), triadic property (transitivity), and dyadic interactions (correlation coefficient from a quadratic assignment procedure) at hourly level. We demonstrated substantial spatial-temporal heterogeneity in these networks and potential link between indirect animal-environment contact and direct animal-animal contact. But such heterogeneity diminished if data were collected at lower spatial (aggregated at entire pen level) or temporal (aggregated at daily level) resolution. The network structure (described by the characteristics such as density, modularity, transitivity, etc.) also changed substantially at different time and locations. There were certain time (feeding) and location (hay) that the proximity network structures were more consistent based on the dyadic interaction analysis. These results reveal new insights for animal network structure and spatial-temporal dynamics, provide more accurate descriptions of animal social networks, and allow more accurate modeling of multiple (both direct and indirect) disease transmission pathways.

Los Alamos National Laboratory: A guide to records series supporting epidemiologic studies conducted for the Department of Energy

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

NONE

1997-01-01

The purpose of this guide is to describe each series of records that pertains to the epidemiologic studies conducted by the Epidemiology Section of the Occupational Medicine Group (ESH-2) at the Department of Energy`s (DOE) Los Alamos National Laboratory (LANL) in Los Alamos, New Mexico. The records described in this guide relate to occupational studies performed by the Epidemiology Section, including those pertaining to workers at LANL, Mound Plant, Oak Ridge Reservation, Pantex Plant, Rocky Flats Plant, and Savannah River Site. Also included are descriptions of other health-related records generated or collected by the Epidemiology Section and a small setmore » of records collected by the Industrial Hygiene and Safety Group. This guide is not designed to describe the universe of records generated by LANL which may be used for epidemiologic studies of the LANL work force. History Associates Incorporated (HAI) prepared this guide as part of its work as the support services contractor for DOE`s Epidemiologic Records Inventory Project. This introduction briefly describes the Epidemiologic Records Inventory Project, HAI`s role in the project, the history of LANL the history and functions of LANL`s Health Division and Epidemiology Section, and the various epidemiologic studies performed by the Epidemiology Section. It provides information on the methodology that HAI used to inventory and describe records housed in the offices of the LANL Epidemiology Section in Technical Area 59 and at the LANL Records Center. Other topics include the methodology used to produce the guide, the arrangement of the detailed record series descriptions, and information concerning access to records repositories.« less

Dementia-specific risks of scabies: Retrospective epidemiologic analysis of an unveiled nosocomial outbreak in Japan from 1989–90

PubMed Central

Tsutsumi, Masae; Nishiura, Hiroshi; Kobayashi, Toshio

2005-01-01

Background Although senile dementia patients in long-term care facilities are at leading risk of scabies, the epidemiologic characteristics of this disease have yet to be fully clarified. This study documents the findings of a ward-scale nosocomial outbreak in western Japan from 1989–90, for which permission to publish was only recently obtained. Methods A retrospective epidemiologic study was performed to identify specific risk factors of scabies among patients with dementia. Analyses were based on a review of medical and nursing records. All inpatients in the affected ward at the time of the outbreak were included in the study. Observational and analytical approaches were employed to assess the findings. Results Twenty of 65 inpatients in the ward met the case definition of scabies. The outbreak lasted for almost 10 months and as a result, the spatial distribution of infections showed no localized patterns in the latter phase of the outbreak. The duration of illness significantly decreased after initiation of control measures (P = 0.0067). Movement without assistance (Odds Ratio [OR] = 11.3; 95% Confidence Interval [CI]: 2.9, 44.8) and moving beyond the room (but within the ward) (OR = 4.1; 95% CI: 1.4, 12.5) were significantly associated with infection, while types of room (Western or Japanese) and sleeping arrangement (on beds or futons laid directly on the floor) appeared not to be risk factors. Conclusion Univariate analysis demonstrated the importance of patients' behaviours during daily activities in controlling scabies among senile dementia patients. The findings also support previous evidence that catching scabies from fomites is far less common. Moreover, since cognitive disorders make it difficult for individuals to communicate and understand the implications of risky contacts as well as treatment method, and given the non-specific nature of individual contacts that are often unpredictable, real-time observations might help improve control practices. PMID:16225694

Becoming the Framingham Study 1947–1950

PubMed Central

Oppenheimer, Gerald M.

2005-01-01

In the epidemiological imagination, the Framingham Heart Study has attained iconic status, both as the prototype of the cohort study and as a result of its scientific success. When the Public Health Service launched the study in 1947, epidemiological knowledge of coronary heart disease was poor, and epidemiology primarily involved the study of infectious disease. In constructing their investigation, Framingham’s initiators had to invent new approaches to epidemiological research. These scientific goals were heavily influenced by the contending institutional and personal interests buffeting the study. The study passed through vicissitudes and stages during its earliest years as its organizers grappled to define its relationship to medicine, epidemiology, and the local community. PMID:15798116

[Eco-epidemiology: towards epidemiology of complexity].

PubMed

Bizouarn, Philippe

2016-05-01

In order to solve public health problems posed by the epidemiology of risk factors centered on the individual and neglecting the causal processes linking the risk factors with the health outcomes, Mervyn Susser proposed a multilevel epidemiology called eco-epidemiology, addressing the interdependence of individuals and their connection with molecular, individual, societal, environmental levels of organization participating in the causal disease processes. The aim of this epidemiology is to integrate more than a level of organization in design, analysis and interpretation of health problems. After presenting the main criticisms of risk-factor epidemiology focused on the individual, we will try to show how eco-epidemiology and its development could help to understand the need for a broader and integrative epidemiology, in which studies designed to identify risk factors would be balanced by studies designed to answer other questions equally vital to public health. © 2016 médecine/sciences – Inserm.

Spatial variability of the effect of air pollution on term birth weight: evaluating influential factors using Bayesian hierarchical models.

PubMed

Li, Lianfa; Laurent, Olivier; Wu, Jun

2016-02-05

Epidemiological studies suggest that air pollution is adversely associated with pregnancy outcomes. Such associations may be modified by spatially-varying factors including socio-demographic characteristics, land-use patterns and unaccounted exposures. Yet, few studies have systematically investigated the impact of these factors on spatial variability of the air pollution's effects. This study aimed to examine spatial variability of the effects of air pollution on term birth weight across Census tracts and the influence of tract-level factors on such variability. We obtained over 900,000 birth records from 2001 to 2008 in Los Angeles County, California, USA. Air pollution exposure was modeled at individual level for nitrogen dioxide (NO2) and nitrogen oxides (NOx) using spatiotemporal models. Two-stage Bayesian hierarchical non-linear models were developed to (1) quantify the associations between air pollution exposure and term birth weight within each tract; and (2) examine the socio-demographic, land-use, and exposure-related factors contributing to the between-tract variability of the associations between air pollution and term birth weight. Higher air pollution exposure was associated with lower term birth weight (average posterior effects: -14.7 (95 % CI: -19.8, -9.7) g per 10 ppb increment in NO2 and -6.9 (95 % CI: -12.9, -0.9) g per 10 ppb increment in NOx). The variation of the association across Census tracts was significantly influenced by the tract-level socio-demographic, exposure-related and land-use factors. Our models captured the complex non-linear relationship between these factors and the associations between air pollution and term birth weight: we observed the thresholds from which the influence of the tract-level factors was markedly exacerbated or attenuated. Exacerbating factors might reflect additional exposure to environmental insults or lower socio-economic status with higher vulnerability, whereas attenuating factors might indicate reduced exposure or higher socioeconomic status with lower vulnerability. Our Bayesian models effectively combined a priori knowledge with training data to infer the posterior association of air pollution with term birth weight and to evaluate the influence of the tract-level factors on spatial variability of such association. This study contributes new findings about non-linear influences of socio-demographic factors, land-use patterns, and unaccounted exposures on spatial variability of the effects of air pollution.

Spatio-temporal modelling of residential exposure to particulate matter and gaseous pollutants for the Heinz Nixdorf Recall Cohort

NASA Astrophysics Data System (ADS)

Nonnemacher, Michael; Jakobs, Hermann; Viehmann, Anja; Vanberg, Irene; Kessler, Christoph; Moebus, Susanne; Möhlenkamp, Stefan; Erbel, Raimund; Hoffmann, Barbara; Memmesheimer, Michael

2014-07-01

For the simultaneous analysis of short- and long-term effects of air pollution in the Heinz Nixdorf Recall Cohort a sophisticated exposure modelling was performed. The dispersion and chemistry transport model EURAD (European Air Pollution Dispersion) was used for the estimation of hourly concentrations of a number of pollutants for a horizontal grid with a cell size of 1 km² covering the whole study area (three large adjacent cities in a highly urbanized region in Western Germany) for the years 2000-2003 and 2006-2008. For each 1 km² cell we estimated the mean concentration by calculating daily means from the hourly concentrations modelled by the EURAD process. The modelled concentrations showed an overall tendency to decrease from 2001 to 2008 whereas the trend in the single grid cells and study period was inhomogeneous. Participant-related exposure slightly increased from 2001 to 2003 followed by a decrease from 2006 to 2008. The exposure modelling enables a very flexible exposure assessment compared to conventional modelling approaches which either use central monitoring or temporally static spatial contrasts. The modelling allows the calculation of an average exposure concentration for any place and time within the study region and study period with a high spatial and temporal resolution. This is important for the assessment of short-, medium and long-term effects of air pollution on human health in epidemiological studies.

Genetic micro-epidemiology of malaria in Papua Indonesia: Extensive P. vivax diversity and a distinct subpopulation of asymptomatic P. falciparum infections

PubMed Central

Pava, Zuleima; Noviyanti, Rintis; Handayuni, Irene; Trimarsanto, Hidayat; Trianty, Leily; Burdam, Faustina H.; Kenangalem, Enny; Utami, Retno A. S.; Tirta, Yusrifar K.; Coutrier, Farah; Poespoprodjo, Jeanne R.; Price, Ric N.; Marfurt, Jutta

2017-01-01

Background Genetic analyses of Plasmodium have potential to inform on transmission dynamics, but few studies have evaluated this on a local spatial scale. We used microsatellite genotyping to characterise the micro-epidemiology of P. vivax and P. falciparum diversity to inform malaria control strategies in Timika, Papua Indonesia. Methods Genotyping was undertaken on 713 sympatric P. falciparum and P. vivax isolates from a cross-sectional household survey and clinical studies conducted in Timika. Standard population genetic measures were applied, and the data was compared to published data from Kalimantan, Bangka, Sumba and West Timor. Results Higher diversity (HE = 0.847 vs 0.625; p = 0.017) and polyclonality (46.2% vs 16.5%, p<0.001) were observed in P. vivax versus P. falciparum. Distinct P. falciparum substructure was observed, with two subpopulations, K1 and K2. K1 was comprised solely of asymptomatic infections and displayed greater relatedness to isolates from Sumba than to K2, possibly reflecting imported infections. Conclusions The results demonstrate the greater refractoriness of P. vivax versus P. falciparum to control measures, and risk of distinct parasite subpopulations persisting in the community undetected by passive surveillance. These findings highlight the need for complimentary new surveillance strategies to identify transmission patterns that cannot be detected with traditional malariometric methods. PMID:28498860

The epidemiology of Plasmodium vivax and Plasmodium falciparum malaria in China, 2004-2012: from intensified control to elimination.

PubMed

Zhang, Qian; Lai, Shengjie; Zheng, Canjun; Zhang, Honglong; Zhou, Sheng; Hu, Wenbiao; Clements, Archie C A; Zhou, Xiao-Nong; Yang, Weizhong; Hay, Simon I; Yu, Hongjie; Li, Zhongjie

2014-11-03

In China, the national malaria elimination programme has been operating since 2010. This study aimed to explore the epidemiological changes in patterns of malaria in China from intensified control to elimination stages. Data on nationwide malaria cases from 2004 to 2012 were extracted from the Chinese national malaria surveillance system. The secular trend, gender and age features, seasonality, and spatial distribution by Plasmodium species were analysed. In total, 238,443 malaria cases were reported, and the proportion of Plasmodium falciparum increased drastically from <10% before 2010 to 55.2% in 2012. From 2004 to 2006, malaria showed a significantly increasing trend and with the highest incidence peak in 2006 (4.6/100,000), while from 2007 onwards, malaria decreased sharply to only 0.18/100,000 in 2012. Males and young age groups became the predominantly affected population. The areas affected by Plasmodium vivax malaria shrunk, while areas affected by P. falciparum malaria expanded from 294 counties in 2004 to 600 counties in 2012. This study demonstrated that malaria has decreased dramatically in the last five years, especially since the Chinese government launched a malaria elimination programme in 2010, and areas with reported falciparum malaria cases have expanded over recent years. These findings suggest that elimination efforts should be improved to meet these changes, so as to achieve the nationwide malaria elimination goal in China in 2020.

Spatial and temporal patterns of chronic wasting disease: Fine-scale mapping of a wildlife epidemic in Wisconsin

USGS Publications Warehouse

Osnas, E.E.; Heisey, D.M.; Rolley, R.E.; Samuel, M.D.

2009-01-01

Emerging infectious diseases threaten wildlife populations and human health. Understanding the spatial distributions of these new diseases is important for disease management and policy makers; however, the data are complicated by heterogeneities across host classes, sampling variance, sampling biases, and the space-time epidemic process. Ignoring these issues can lead to false conclusions or obscure important patterns in the data, such as spatial variation in disease prevalence. Here, we applied hierarchical Bayesian disease mapping methods to account for risk factors and to estimate spatial and temporal patterns of infection by chronic wasting disease (CWD) in white-tailed deer (Odocoileus virginianus) of Wisconsin, USA. We found significant heterogeneities for infection due to age, sex, and spatial location. Infection probability increased with age for all young deer, increased with age faster for young males, and then declined for some older animals, as expected from disease-associated mortality and age-related changes in infection risk. We found that disease prevalence was clustered in a central location, as expected under a simple spatial epidemic process where disease prevalence should increase with time and expand spatially. However, we could not detect any consistent temporal or spatiotemporal trends in CWD prevalence. Estimates of the temporal trend indicated that prevalence may have decreased or increased with nearly equal posterior probability, and the model without temporal or spatiotemporal effects was nearly equivalent to models with these effects based on deviance information criteria. For maximum interpretability of the role of location as a disease risk factor, we used the technique of direct standardization for prevalence mapping, which we develop and describe. These mapping results allow disease management actions to be employed with reference to the estimated spatial distribution of the disease and to those host classes most at risk. Future wildlife epidemiology studies should employ hierarchical Bayesian methods to smooth estimated quantities across space and time, account for heterogeneities, and then report disease rates based on an appropriate standardization. ?? 2009 by the Ecological Society of America.

Genotyping of Mycobacterium tuberculosis: application in epidemiologic studies

PubMed Central

Kato-Maeda, Midori; Metcalfe, John Z.; Flores, Laura

2014-01-01

Genotyping is used to track specific isolates of Mycobacterium tuberculosis in a community. It has been successfully used in epidemiologic research (termed ‘molecular epidemiology’) to study the transmission dynamics of TB. In this article, we review the genetic markers used in molecular epidemiologic studies including the use of whole-genome sequencing technology. We also review the public health application of molecular epidemiologic tools. PMID:21366420

Examination of Different Exposure Metrics in an Epidemiological Study

EPA Science Inventory

Epidemiological studies of air pollution have traditionally relied upon measurements of ambient concentration from central-site monitoring stations as surrogates of population exposures. However, depending on the epidemiological study design, this approach may introduce exposure...

Improving the Linkages between Air Pollution Epidemiology and Quantitative Risk Assessment

PubMed Central

Bell, Michelle L.; Walker, Katy; Hubbell, Bryan

2011-01-01

Background: Air pollution epidemiology plays an integral role in both identifying the hazards of air pollution as well as supplying the risk coefficients that are used in quantitative risk assessments. Evidence from both epidemiology and risk assessments has historically supported critical environmental policy decisions. The extent to which risk assessors can properly specify a quantitative risk assessment and characterize key sources of uncertainty depends in part on the availability, and clarity, of data and assumptions in the epidemiological studies. Objectives: We discuss the interests shared by air pollution epidemiology and risk assessment communities in ensuring that the findings of epidemiological studies are appropriately characterized and applied correctly in risk assessments. We highlight the key input parameters for risk assessments and consider how modest changes in the characterization of these data might enable more accurate risk assessments that better represent the findings of epidemiological studies. Discussion: We argue that more complete information regarding the methodological choices and input data used in epidemiological studies would support more accurate risk assessments—to the benefit of both disciplines. In particular, we suggest including additional details regarding air quality, demographic, and health data, as well as certain types of data-rich graphics. Conclusions: Relatively modest changes to the data reported in epidemiological studies will improve the quality of risk assessments and help prevent the misinterpretation and mischaracterization of the results of epidemiological studies. Such changes may also benefit epidemiologists undertaking meta-analyses. We suggest workshops as a way to improve the dialogue between the two communities. PMID:21816702

Spatio-temporal modeling with GIS and remote sensing for schistosomiasis control in Sichuan, China

NASA Astrophysics Data System (ADS)

Xu, Bing

Schistosomiasis is a water-borne parasitic disease endemic in tropical and subtropical areas. Its transmission requires certain kind of snail as the intermediate host. Some efforts have been made to mapping snail habitats with remote sensing and schistosomiasis transmission modeling. However, the modeling is limited to isolated residential groups and does not include spatial interaction among those groups. Remotely sensed data are only used in snail habitat classification, not in estimation of snail abundance that is an important parameter in schistosomiasis transmission modeling. This research overcomes the above two problems using innovative geographic information system (GIS) and remote sensing technology. A mountainous environment near Xichang, China, is chosen as the test site. Environmental and epidemiological data are stored in a GIS to support modeling. Snail abundance is estimated from land-cover and land-use fractions derived from high spatial resolution IKONOS satellite data. Spatial interaction is determined in consideration of neighborhoods, group areas, relative slopes among groups, and natural barriers. Land-cover and land-use information extracted from 4 m high resolution IKONOS data is used as reference in scaling up to the regional level. The scale-up is done with coarser resolution satellite data including Landsat Thematic Mapper (TM), EO-1 Advanced Land Imager (ALI) and Hyperion data all at 30 m resolution. Snail abundance is estimated by regressing snail survey data with land-cover and land-use fractions. An R2 of 0.87 is obtained between the average snail density predicted and that surveyed at the group level. With such a model, a snail density map is generated for all residential groups in the study area. A spatio-temporal model of schistosomiasis transmission is finally built to incorporate the spatial interaction caused by miracidia and cercaria migration. Comparing the model results with and without spatial interaction has revealed a number of advantages of the spatio-temporal model. Particularly, with the inclusion of spatial interaction, more effective control of schistosomiasis transmission over the whole study area can be achieved.