Read by QxMD icon Read

Spatial and Spatio-temporal Epidemiology

Pablo Ariel Martinez, Mayane Alves Andrade, Claudio Juan Bidau
The temporal pattern of co-occurrence of human beings and venomous species (scorpions, spiders, snakes) is changing. Thus, the temporal pattern of areas with risk of accidents with such species tends to become dynamic in time. We analyze the areas of occurrence of species of Tityus in Argentina and assess the impact of global climate change on their area of distribution by the construction of risk maps. Using data of occurrence of the species and climatic variables, we constructed models of species distribution (SMDs) under current and future climatic conditions...
June 2018: Spatial and Spatio-temporal Epidemiology
Murali Krishna Enduri, Shivakumar Jolad
Dengue is a vector borne disease transmitted to humans by Aedes aegypti mosquitoes carrying virus of different serotypes. Dengue exhibits complex spatial and temporal dynamics, influenced by various biological, human and environmental factors. In this work, we study the dengue spread for a single serotype (DENV-1) including statistical models of human mobility with exponential step length distribution, by using reaction-diffusion equations and Stochastic Cellular Automata (SCA) approach. We analyze the spatial and temporal spreading of the disease using parameters from field studies...
June 2018: Spatial and Spatio-temporal Epidemiology
Gigliana Maria Sobral Cavalcante, Ítalo de Macedo Bernardino, Lorena Marques da Nóbrega, Raquel Conceição Ferreira, Efigênia Ferreira E Ferreira, Sérgio d'Avila
The aim of study was to describe trends in physical violence among Brazilian victims and investigate spatial vulnerability of the location of victim's residences. This study performed an ecological-level longitudinal analysis, examining violence rates over 4 years. Cases of 4795 victims of physical aggression attended at a Center of Legal Medicine were investigated. Trend analysis was used to evaluate the data, with the creation of polynomial regression models (p < 0.05). Violence rates showed significant temporal variations according to sociodemographic characteristics of victims (p < 0...
June 2018: Spatial and Spatio-temporal Epidemiology
Aparna Lal, Jonathan Marshall, Jackie Benschop, Aleisha Brock, Simon Hales, Michael G Baker, Nigel P French
Spatio-temporal disease patterns can provide clues to etiological pathways, but can be complex to model. Using a flexible Bayesian hierarchical framework, we identify previously undetected space-time clusters and environmental and socio-demographic risk factors for reported giardiasis and cryptosporidiosis at the New Zealand small area level. For giardiasis, there was no seasonal pattern in outbreak probability and an inverse association with density of dairy cattle (β^1  = -0.09, Incidence Risk Ratio (IRR) 0...
June 2018: Spatial and Spatio-temporal Epidemiology
C Edson Utazi, Emmanuel O Afuecheta, C Christopher Nnanatu
Model-based approaches for the analysis of areal count data are commonplace in spatiotemporal analysis. In Bayesian hierarchical models, a latent process is incorporated in the mean function to account for dependence in space and time. Typically, the latent process is modelled using a conditional autoregressive (CAR) prior. The aim of this paper is to offer an alternative approach to CAR-based priors for modelling the latent process. The proposed approach is based on a spatiotemporal generalization of a latent process Poisson regression model developed in a time series setting...
June 2018: Spatial and Spatio-temporal Epidemiology
Mehran Rostami, Mohammad Karamouzian, Ardeshir Khosravi, Shahab Rezaeian
AIM: We aimed to compare the fatal drug overdose rates in Iran in 2006 and 2011. METHODS: This analysis was performed based on data on fatal drug overdose cases from the Iranian death registration system. The crude and adjusted rates per 100,000 populations for geographical regions stratified by gender and age groups were calculated using the 2006 and 2011 census of Iranian population. Annual percentage change was calculated to examine annual changes of fatal drug overdose rates across different regions...
June 2018: Spatial and Spatio-temporal Epidemiology
Rachel Carroll, Shanshan Zhao
In Bayesian disease mapping, spatial random effects are used to account for confounding in the data so that reasonable estimates for the fixed effects can be obtained. Typically, the spatial random effects are mapped and qualitative comments are made related to an increase or decrease in risk for certain areas. The approach outlined here illustrates how a quantitative secondary assessment can be applied to make more useful and applicable inference related to these spatial random effects. We are able to recover important but unmeasured or unincluded risk factors via a secondary model fit...
June 2018: Spatial and Spatio-temporal Epidemiology
Yan Kestens, Benoit Thierry, Martine Shareck, Madeleine Steinmetz-Wood, Basile Chaix
BACKGROUND: Accounting for daily mobility allows assessment of multiple exposure to environments. This study compares spatial data obtained (i) from an interactive map-based questionnaire on regular activity locations (VERITAS) and (ii) from GPS tracking. METHODS: 234 participants of the RECORD GPS Study completed the VERITAS questionnaire and wore a GPS tracker for 7 days. Analyses illustrate the spatial match between both datasets. RESULTS: For half of the sample, 85...
June 2018: Spatial and Spatio-temporal Epidemiology
Marie Denis, Benoît Cochard, Indra Syahputra, Hubert de Franqueville, Sébastien Tisné
In the field of epidemiology, studies are often focused on mapping diseases in relation to time and space. Hierarchical modeling is a common flexible and effective tool for modeling problems related to disease spread. In the context of oil palm plantations infected by the fungal pathogen Ganoderma boninense, we propose and compare two spatio-temporal hierarchical Bayesian models addressing the lack of information on propagation modes and transmission vectors. We investigate two alternative process models to study the unobserved mechanism driving the infection process...
February 2018: Spatial and Spatio-temporal Epidemiology
Rajib Paul, Chae Young Lim, Amy B Curtis, Tapabrata Maiti, Kathleen M Baker, Libertie B Mantilla, Elizabeth L MacQuillan
The purpose of this study is to identify regions with diabetes health-service shortage. American Diabetes Association (ADA)-accredited diabetes self-management education (DSME) is recommended for all those with diabetes. In this study, we focus on demographic patterns and geographic regionalization of the disease by including accessibility and availability of diabetes education resources as a critical component in understanding and confronting differences in diabetes prevalence, as well as addressing regional or sub-regional differences in awareness, treatment and control...
February 2018: Spatial and Spatio-temporal Epidemiology
Hui Luan, Jane Law, Martin Lysy
Neighborhood restaurant environment (NRE) plays a vital role in shaping residents' eating behaviors. While NRE 'healthfulness' is a multi-facet concept, most studies evaluate it based only on restaurant type, thus largely ignoring variations of in-restaurant features. In the few studies that do account for such features, healthfulness scores are simply averaged over accessible restaurants, thereby concealing any uncertainty that attributed to neighborhoods' size or spatial correlation. To address these limitations, this paper presents a Bayesian Spatial Factor Analysis for assessing NRE healthfulness in the city of Kitchener, Canada...
February 2018: Spatial and Spatio-temporal Epidemiology
Grant D Brown, Aaron T Porter, Jacob J Oleson, Jessica A Hinman
Approximate Bayesia n Computation (ABC) provides an attractive approach to estimation in complex Bayesian inferential problems for which evaluation of the kernel of the posterior distribution is impossible or computationally expensive. These highly parallelizable techniques have been successfully applied to many fields, particularly in cases where more traditional approaches such as Markov chain Monte Carlo (MCMC) are impractical. In this work, we demonstrate the application of approximate Bayesian inference to spatially heterogeneous Susceptible-Exposed-Infectious-Removed (SEIR) stochastic epidemic models...
February 2018: Spatial and Spatio-temporal Epidemiology
Weidong Gu, Felicita Medalla, Robert M Hoekstra
The National Antimicrobial Resistance Monitoring System (NARMS) at the Centers for Disease Control and Prevention tracks resistance among Salmonella infections. The annual number of Salmonella isolates of a particular serotype from states may be small, making direct estimation of resistance proportions unreliable. We developed a Bayesian hierarchical model to improve estimation by borrowing strength from relevant sampling units. We illustrate the models with different specifications of spatio-temporal interaction using 2004-2013 NARMS data for ceftriaxone-resistant Salmonella serotype Heidelberg...
February 2018: Spatial and Spatio-temporal Epidemiology
Benjamin D Hallowell, Sara W Robb, Kristina W Kintziger
As HIV-seropositive individuals live longer, they are more likely to acquire conditions seen in the general population. Excluding AIDS-defining malignancies, HIV-seropositive individuals are more likely to develop cancer than individuals in the general population. In order to better inform future screening and prevention efforts in this population, we compared the geographic distribution and location characteristics of HIV-seropositive and HIV-seronegative cancer cases in South Carolina (SC). To do this we obtained linked HIV and cancer data from the SC enhanced HIV/AIDS Reporting System and Central Cancer Registry...
February 2018: Spatial and Spatio-temporal Epidemiology
Nushrat Nazia, Mohammad Ali, Md Jakariya, Quamrun Nahar, Mohammad Yunus, Michael Emch
We identify high risk clusters and measure their persistence in time and analyze spatial and population drivers of small area incidence over time. The geographically linked population and cholera surveillance data in Matlab, Bangladesh for a 10-year period were used. Individual level data were aggregated by local 250 × 250 m communities. A retrospective space-time scan statistic was applied to detect high risk clusters. Generalized estimating equations were used to identify risk factors for cholera. We identified 10 high risk clusters, the largest of which was in the southern part of the study area where a smaller river flows into a large river...
February 2018: Spatial and Spatio-temporal Epidemiology
Susanna M Cramb, Paula Moraga, Kerrie L Mengersen, Peter D Baade
Interpreting changes over time in small-area variation in cancer survival, in light of changes in cancer incidence, aids understanding progress in cancer control, yet few space-time analyses have considered both measures. Bayesian space-time hierarchical models were applied to Queensland Cancer Registry data to examine geographical changes in cancer incidence and relative survival over time for the five most common cancers (colorectal, melanoma, lung, breast, prostate) diagnosed during 1997-2004 and 2005-2012 across 516 Queensland residential small-areas...
November 2017: Spatial and Spatio-temporal Epidemiology
Paula Moraga
During last years, public health surveillance has been facilitated by the existence of several packages implementing statistical methods for the analysis of spatial and spatio-temporal disease data. However, these methods are still inaccesible for many researchers lacking the adequate programming skills to effectively use the required software. In this paper we present SpatialEpiApp, a Shiny web application that integrate two of the most common approaches in health surveillance: disease mapping and detection of clusters...
November 2017: Spatial and Spatio-temporal Epidemiology
Daniel Rainham, Patrick Brown, Tara Sampalli
Multiple chemical sensitivity (MCS) is a chronic condition characterized by recurring and severe symptoms triggered by exposures to low levels of toxicants or anthropogenic pollution. This study investigated the spatial structure of MCS incidence and estimated the contribution of socio-economic deprivation to variations in rates of MCS at the community level in Nova Scotia, Canada. Patient data were used to calculate cumulative incidence rate ratios for treated multiple chemical sensitivities. Poisson regression with a spatially autoregressive random effect was employed to assess spatial variation in MCS...
November 2017: Spatial and Spatio-temporal Epidemiology
Yimer Wasihun Kifle, Niel Hens, Christel Faes
This paper formulates and compares a general class of spatiotemporal models for univariate space-time geostatistical data. The implementation of stochastic partial differential equation (SPDE) approach combined with integrated nested Laplace approximation into the R-INLA package makes it computationally feasible to use spatiotemporal models. However, the impact of specifying models with and without space-time interaction is unclear. We formulate an extensive class of additive and coupled spatiotemporal SPDE models and investigate the distinction between them by (1) Extending their temporal effect, allowing a random walk process in time, (2) varying the spatial correlation function and (3) running a simulation study to assess the effect of misspecifying the spatial and temporal models, and to assess the generalizability of our results to a higher number of locations...
November 2017: Spatial and Spatio-temporal Epidemiology
Sara E Benjamin Neelon, Thomas Burgoine, John A Gallis, Pablo Monsivais
BACKGROUND: we assessed manager perceptions of food security and obesity in young children attending nurseries across England, assessing spatial differences by area-level deprivation. METHODS: we conducted an adjusted multinomial logistic regression and an adjusted geographically weighted logistic regression examining the odds of a manager perceiving obesity, food insecurity, or both as a problem among children in care measured via a mailed survey. RESULTS: 851 (54...
November 2017: Spatial and Spatio-temporal Epidemiology
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"