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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
Mehreteab Aregay, Andrew B Lawson, Christel Faes, Russell S Kirby, Rachel Carroll, Kevin Watjou
In spatial epidemiology, data are often arrayed hierarchically. The classification of individuals into smaller units, which in turn are grouped into larger units, can induce contextual effects. On the other hand, a scaling effect can occur due to the aggregation of data from smaller units into larger units. In this paper, we propose a shared multilevel model to address the contextual effects. In addition, we consider a shared multiscale model to adjust for both scale and contextual effects simultaneously. We also study convolution and independent multiscale models, which are special cases of shared multilevel and shared multiscale models, respectively...
August 2017: Spatial and Spatio-temporal Epidemiology
Francisco Laguna, María Eugenia Grillet, José R León, Carenne Ludeña
The influence of climatic variables on the dynamics of human malaria has been widely highlighted. Also, it is known that this mosquito-borne infection varies in space and time. However, when the data is spatially incomplete most popular spatio-temporal methods of analysis cannot be applied directly. In this paper, we develop a two step methodology to model the spatio-temporal dependence of malaria incidence on local rainfall, temperature, and humidity as well as the regional sea surface temperatures (SST) in the northern coast of Venezuela...
August 2017: Spatial and Spatio-temporal Epidemiology
Mehran Rostami, Younes Mohammadi, Abdollah Jalilian, Bashir Nazparvar
Substance use disorder is one of the main mental health problems in Iran. In this paper, the six-monthly counts of deaths due to substance abuse in Iran at provincial level between March 21, 2005, and March 20, 2014, were modeled using a log-Gaussian Cox point process model. By assuming population density as the exposure variable, the considered model incorporated known and unknown influential factors in order to describe spatio-temporal variations in the relative risk of substance abuse mortality. We found evidence of spatial heterogeneity and inequality by gender in deaths related to substance abuse across Iran...
August 2017: Spatial and Spatio-temporal Epidemiology
Ayodeji O Olarinmoye, Johnson F Ojo, Ayotunde J Fasunla, Olayinka O Ishola, Fahnboah G Dakinah, Charles K Mulbah, Khalid Al-Hezaimi, Babasola O Olugasa
We developed time trend model, determined treatment outcome and estimated annual human deaths among dog bite victims (DBVs) from 2010 to 2013 in Monrovia, Liberia. Data obtained from clinic records included victim's age, gender and site of bite marks, site name of residence of rabies-exposed patients, promptness of care sought, initial treatment and post-exposure-prophylaxis (PEP) compliance. We computed DBV time-trend plot, seasonal index and year 2014 case forecast. Associated annual human death (AHD) was estimated using a standardized decision tree model...
August 2017: Spatial and Spatio-temporal Epidemiology
Tayyab Ikram Shah, Stephan Milosavljevic, Brenna Bath
This research is focused on methodological challenges and considerations associated with the estimation of the geographical aspects of access to healthcare with a focus on rural and remote areas. With the assumption that GIS-based accessibility measures for rural healthcare services will vary across geographic units of analysis and estimation techniques, which could influence the interpretation of spatial access to rural healthcare services. Estimations of geographical accessibility depend on variations of the following three parameters: 1) quality of input data; 2) accessibility method; and 3) geographical area...
June 2017: Spatial and Spatio-temporal Epidemiology
Sidrah Hafeez, Muhammad Amin, Bilal Ahmed Munir
BACKGROUND: Dengue is identified as serious vector born infectious disease by WHO, threating around 2.5 billion people around the globe. Pakistan is facing dengue epidemic since 1994 but 2010 and 2011 dengue outbreaks were worst. During 2011 dengue outbreak 22,562 cases were reported and 363 died due to this fatal infection in Pakistan. In this study, Lahore District was chosen as it was severely affected in 2011 dengue outbreak with 14,000 reported cases and 300 deaths. There is no vaccine developed yet for the disease control, so only effective early warning, prevention and control measures can reduce the potential disease risk...
June 2017: Spatial and Spatio-temporal Epidemiology
Diba Khan, Lauren M Rossen, Brady E Hamilton, Yulei He, Rong Wei, Erin Dienes
Teen birth rates have evidenced a significant decline in the United States over the past few decades. Most of the states in the US have mirrored this national decline, though some reports have illustrated substantial variation in the magnitude of these decreases across the U.S. Importantly, geographic variation at the county level has largely not been explored. We used National Vital Statistics Births data and Hierarchical Bayesian space-time interaction models to produce smoothed estimates of teen birth rates at the county level from 2003-2012...
June 2017: Spatial and Spatio-temporal Epidemiology
Roghieh Ramezankhani, Arezoo Hosseini, Nooshin Sajjadi, Mostafa Khoshabi, Azra Ramezankhani
OBJECTIVES: This study was designed to determine the environmental factors associated with cutaneous leishmaniasis (CL) in Isfahan Province, using spatial analysis. METHODS: Data of monthly CL incidence from 2010 to 2013, climate and environmental factors including: temperature, humidity, rainfall, wind speed, normalized difference vegetation index (NDVI), altitude and population density across the Isfahan's cities was used to perform spatial analysis by ordinary least square (OLS) regression and geographically weighted regression (GWR)...
June 2017: Spatial and Spatio-temporal Epidemiology
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