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Spatiotemporal Approaches to Assess the Association of Environmental Risk Factors With Cardiovascular Diseases: A Scoping Review
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Spatiotemporal Approaches to Assess the Association of Environmental Risk Factors With Cardiovascular Diseases: A Scoping Review
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Journal Article
Spatiotemporal Approaches to Assess the Association of Environmental Risk Factors With Cardiovascular Diseases: A Scoping Review
2026
Overview
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. Plain Language Summary Cardiovascular diseases (CVDs), like heart attacks and strokes, are major causes of death worldwide, with environmental factors such as air pollution and temperature linked to their occurrence. The occurrence of CVDs and environmental factors are closely linked to the geographical location, as well as changes over time. This review looks at how researchers are using models that track changes over space and time to study these links. Our review highlights key challenges in these models, such as the need for more precise data on where people live and better methods to account for the way different time periods and regions are grouped. We also found that tools for visualizing this data are often underdeveloped, making it harder for researchers and policymakers to apply the findings in real‐world settings. We provide recommendations on choosing the best data sources to reflect environmental conditions accurately and combining several factors into one indicator to better represent environmental risks. These recommendations could improve the way we model and understand how CVDs and environmental factors are connected, benefiting research into other chronic diseases as well. By enhancing the data and methods used in these models, we can better understand and ultimately reduce CVD risks related to environmental factors. Key Points Spatiotemporal models provide a robust understanding of the relationship between health effects and environmental risk factors Current applications require improvements in spatial and temporal resolution of data sets to reduce generalization of exposure levels Advanced visualization tools are needed to interpret spatiotemporal data for improving their utility in a public health setting
Publisher
John Wiley & Sons, Inc,Wiley,American Geophysical Union (AGU)
Subject
/ Atmospheric Composition and Structure
/ Climate and Interannual Variability
/ Climate Change and Variability
/ Data Analysis: Algorithms and Implementation
/ Data Presentation and Visualization
/ Disaster Risk Analysis and Assessment
/ Earthquake Ground Motions and Engineering Seismology
/ Geospatial Data Applications for Environmental Justice
/ Hydrological Cycles and Budgets
/ Keywords
/ Land/Atmosphere Interactions
/ Marine Geology and Geophysics
/ Modeling
/ Nonlinear Waves, Shock Waves, Solitons
/ Ocean influence of Earth rotation
/ Ocean Monitoring with Geodetic Techniques
/ Ocean/Atmosphere Interactions
/ Ocean/Earth/atmosphere/hydrosphere/cryosphere interactions
/ Oceanic
/ Oceans
/ Remote Sensing and Electromagnetic Processes
/ Review
/ Risk
/ Sea Level: Variations and Mean
/ Spatial Analysis and Representation
/ spatiotemporal data visualization
