Spatial epidemiology is increasingly being used to assess health risks associated with environmental hazards. Risk patterns tend to have both a temporal and a spatial component; thus, spatial epidemiology must combine methods from epidemiology, statistics, and geographic information science. Recent statistical advances in spatial epidemiology include the use of smoothing in risk maps to create an interpretable risk surface, the extension of spatial models to incorporate the time dimension, and the combination of individual- and area-level information. Advances in geographic information systems and the growing availability of modeling packages have led to an improvement in exposure assessment. Techniques drawn from geographic information science are being developed to enable the visualization of uncertainty and ensure more meaningful inferences are made from data. When public health concerns related to the environment arise, it is essential to address such anxieties appropriately and in a timely manner. Tools designed to facilitate the investigation process are being developed, although the availability of complete and clean health data, and appropriate exposure data often remain limiting factors.
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http://dx.doi.org/10.1289/ehp.10816 | DOI Listing |
Epidemiol Serv Saude
January 2025
Universidade do Estado do Rio de Janeiro, Departamento de Epidemiologia, Rio de Janeiro, RJ, Brazil.
Objective: To describe the mortality profile and analyze the spatiotemporal distribution of COVID-19 mortality among international migrants residing in Brazil from 2020 to 2022.
Methods: This is a descriptive and ecological cross-sectional study using secondary data. Absolute and relative frequencies of the sociodemographic profile and mortality coefficients (MCs) were analyzed.
Sleep Epidemiol
December 2024
Socio-Spatial Determinants of Health (SSDH) Laboratory, Population and Community Health Sciences Branch, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland.
Introduction: Research suggests that perceived neighborhood social environments (PNSE) may contribute to gender and race/ethnicity-based sleep disparities. Our study aimed to examine associations between PNSE factors and adolescents' sleep patterns. As a secondary aim, we examined how gender and race/ethnic groups might moderate these associations.
View Article and Find Full Text PDFHand, foot and mouth disease (HFMD) is a major public health issue in Hubei Province; however, research on the direct and indirect effects of factors affecting HFMD is limited. This study employed structural equation modeling (SEM) and geographically weighted regression (GWR) to investigate the various impacts and spatial variations in the factors influencing the HFMD epidemic in Hubei Province from 2016 to 2018. The results indicated that (1) with respect to the direct effects, the number of primary school students had the greatest positive direct effect on the number of HFMD cases, with a coefficient of 0.
View Article and Find Full Text PDFJ Environ Manage
January 2025
Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.
Background: Air pollution is a major public health threat globally. Health studies, regulatory actions, and policy evaluations typically rely on air pollutant concentrations from single exposure models, assuming accurate estimations and ignoring related uncertainty. We developed a modeling framework, bneR, to apply the Bayesian Nonparametric Ensemble (BNE) prediction model that combines existing exposure models as inputs to provide air pollution estimates and their spatio-temporal uncertainty.
View Article and Find Full Text PDFPLoS One
January 2025
QUT Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.
Background: Spatial data are often aggregated by area to protect the confidentiality of individuals and aid the calculation of pertinent risks and rates. However, the analysis of spatially aggregated data is susceptible to the modifiable areal unit problem (MAUP), which arises when inference varies with boundary or aggregation changes. While the impact of the MAUP has been examined previously, typically these studies have focused on well-populated areas.
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