GPS-Health: A Novel Analytic Infrastructure for Capturing, Visualizing, and Analyzing Multi-Level, Multi-Domain Geographically Distributed Social Determinants of Health
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Background
Health disparities across a range of conditions and outcomes exist across the life course and are driven by the uneven geographic distribution of multidimensional social determinants of health (SDOH). Previous multidimensional measures of SDOH (e.g. Area Deprivation Index, Social Vulnerability Index, Social Deprivation Index) collapse multiple measures into a single summary value applied to everyone living within a predefined map unit, engendering construct and internal validity issues.
Methods
We present a new SDOH data approach: the Geographic Patterns of Social Determinants of Health (GPS-Health). We use a theoretical framework weaving together kyriarchy, intersectionality, and structural violence to select SDOH domains that can elucidate how individuals experience multidimensional spatial distributions of SDOH. We apply the approach to Maryland.
Results
Our dataset includes 2,369,365 property parcels, from which we calculate distances to 8 types of SDOH exact locations.
Discussion
GPS-Health will aid in the understanding of how the SDOH influence individual health outcomes.
