How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data

TL;DR

This paper introduces an interpretable machine learning framework using GAMs and MGWR to analyze environmental and sociodemographic factors affecting health outcomes across locations, revealing key predictors and regional variations.

Contribution

It develops a novel interpretable modeling approach leveraging new spatial-temporal data to identify and analyze health-related predictors across different regions and outcomes.

Findings

NO2 identified as a global predictor for multiple health conditions

Regional variations in predictors like air pollution and solar radiation

COVID-19 influenced local predictor importance

Abstract

Health outcomes depend on complex environmental and sociodemographic factors whose effects change over location and time. Only recently has fine-grained spatial and temporal data become available to study these effects, namely the MEDSAT dataset of English health, environmental, and sociodemographic information. Leveraging this new resource, we use a variety of variable importance techniques to robustly identify the most informative predictors across multiple health outcomes. We then develop an interpretable machine learning framework based on Generalized Additive Models (GAMs) and Multiscale Geographically Weighted Regression (MGWR) to analyze both local and global spatial dependencies of each variable on various health outcomes. Our findings identify NO2 as a global predictor for asthma, hypertension, and anxiety, alongside other outcome-specific predictors related to occupation, marriage, and vegetation. Regional analyses reveal local variations with air pollution and solar radiation, with notable shifts during COVID. This comprehensive approach provides actionable insights for addressing health disparities, and advocates for the integration of interpretable machine learning in public health.