Spatiotemporal Approaches to Assess the Association of Environmental Risk Factors With Cardiovascular Diseases: A Scoping Review

Cardiovascular diseases (CVDs) remain a leading cause of mortality globally, with environmental risk factors playing a significant role in their prevalence. This review aims to critically evaluate the current methodologies employed in spatiotemporal analyses of CVDs and provides recommendations to enhance the accuracy and practical application of these models. A systematic search of the literature was conducted using Scopus, PubMed, and Embase databases. Studies were selected based on their use of spatiotemporal models to assess the relationship between environmental factors and CVDs. We evaluated the methodological quality of included studies using the Spatial Methodology Appraisal of Research Tool (SMART). Significant challenges were noted, including the need for higher spatial resolution data sets and improved methods for addressing the modifiable areal and temporal unit problems and ecological bias. Additionally, the visualization of spatiotemporal data remains underutilized and underdeveloped, limiting the practical utility of the findings. We also discuss combining parameters to form an indicator that better represents environmental conditions, as well as cases where ground, satellite, or modeled data products are suitable. These recommendations could extend to other acquired chronic diseases and their relationship with environmental risk factors to improve the utility of spatiotemporal models. While spatiotemporal modeling holds considerable promise in understanding and mitigating CVD risks associated with environmental factors, appropriate data selection, addressing methodological pitfalls and reporting spatial and temporal model outcomes are necessary to enhance their reliability and impact.

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Publication Details

Journal
GeoHealth
Published
2026-01-01
DOI
https://doi.org/10.1029/2024gh001268
Primary Topic
Health, Environment, Cognitive Aging
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatiotemporal Approaches to Assess the Association of Environmental Risk Factors With Cardiovascular Diseases: A Scoping Review

Vishal Singh, Susanna Cramb, Wenbiao Hu, Javier Cortes‐Ramirez et al.
GeoHealth
Health, Environment, Cognitive Aging
article

Spatiotemporal Approaches to Assess the Association of Environmental Risk Factors With Cardiovascular Diseases: A Scoping Review

Vishal Singh, Susanna Cramb, Wenbiao Hu, Javier Cortes‐Ramirez, Jialu Wang
article en

Abstract

Cardiovascular diseases (CVDs) remain a leading cause of mortality globally, with environmental risk factors playing a significant role in their prevalence. This review aims to critically evaluate the current methodologies employed in spatiotemporal analyses of CVDs and provides recommendations to enhance the accuracy and practical application of these models. A systematic search of the literature was conducted using Scopus, PubMed, and Embase databases. Studies were selected based on their use of spatiotemporal models to assess the relationship between environmental factors and CVDs. We evaluated the methodological quality of included studies using the Spatial Methodology Appraisal of Research Tool (SMART). Significant challenges were noted, including the need for higher spatial resolution data sets and improved methods for addressing the modifiable areal and temporal unit problems and ecological bias. Additionally, the visualization of spatiotemporal data remains underutilized and underdeveloped, limiting the practical utility of the findings. We also discuss combining parameters to form an indicator that better represents environmental conditions, as well as cases where ground, satellite, or modeled data products are suitable. These recommendations could extend to other acquired chronic diseases and their relationship with environmental risk factors to improve the utility of spatiotemporal models. While spatiotemporal modeling holds considerable promise in understanding and mitigating CVD risks associated with environmental factors, appropriate data selection, addressing methodological pitfalls and reporting spatial and temporal model outcomes are necessary to enhance their reliability and impact.

GeoHealthVol. 10(1)
Queensland University of Technology (AU)
Queensland University of Technology
Good health and well-being
Openalex Percentile: Top 96%
Health, Environment, Cognitive Aging
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