ArticleFrontiers in public health2026
Geospatial modeling of environmental and socioeconomic drivers of health outcomes.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objectives: This study aimed to compute the spatially varying associations between seven chronic health conditions-high blood pressure, asthma, chronic obstructive pulmonary disease (COPD), stroke, cancer, diabetes, and frequent mental distress-and key environmental, built-environment, and socioeconomic determinants at the fine, census-tract scale in Harris County, Texas, USA. Methods: The study uses three methods: Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) to develop association models using 2022 data. The study further validates the developed models using data from 2020 to 2024. Results: MGWR outperformed both OLS and GWR for every health outcome, increasing the adjusted R Conclusion: By allowing each determinant to act at its own spatial scale, MGWR produced a transferable framework for identifying vulnerable communities and estimating chronic disease burden where health data are sparse or missing. The fine-resolution, census-tract results can help guide place-based public health interventions toward the eastern and southeastern areas of Harris County where poverty, air pollution, and chronic disease prevalence coincide, supporting more efficient and targeted resource allocation.
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