Geospatial modeling of environmental and socioeconomic drivers of health outcomes.

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 R2 (for example, from 0.57 to 0.75 for blood pressure and from 0.58 to 0.70 for cancer) and reducing the corrected Akaike Information Criterion (AICc) by several hundred points. Poverty and particulate matter (PM2.5) were the most consistent predictors, with the strongest positive coefficients concentrated in central, eastern, and southeastern tracts near industrial corridors and in lower-income communities. Different determinants operated at distinct spatial scales, and re-estimating the framework for 2020, 2021, 2023, and 2024 reproduced the 2022 patterns (|ΔR2| ≤ 0.09), confirming the stability of the models. These fine-resolution results identify specific high-burden neighborhoods that global models may overlook.

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.