Estimation of Area Deprivation Index Rankings in South Dakota Using Spatiotemporal Change of Support Method

Social determinants of health (SDOH) are estimated to drive nearly 80% of health outcomes, making the accurate measurement of neighborhood disadvantage essential for equitable public health policy and resource allocation. The Area Deprivation Index (ADI) serves as a critical multidimensional proxy for SDOH; however, its utility is frequently hindered by a “change of support” problem, where the geographic resolution of available data (census block groups) does not align with the scales required for policy implementation, such as ZIP Code Tabulation Areas (ZCTAs). This mismatch can lead to biased inferences and the misidentification of high-risk communities. This study employs a spatiotemporal change of support (STCOS) model to bridge these scales, specifically addressing the challenge that ADI rankings lack the known variance measures required for robust statistical modeling. We propose and evaluate several variance estimation techniques, including multiple linear regression and finite mixture models, to generate reliable, uncertainty-aware ADI estimates for South Dakota at the ZCTA level. Our findings demonstrate that these refined estimates can successfully identify areas of elevated social risk, even in regions with missing data. By providing a statistically rigorous framework for harmonizing mismatched health data, this research offers a vital tool for decision-makers to support health equity initiatives, such as the Centers for Medicare & Medicaid Services Accountable Care Organization Realizing Equity, Access, and Community Health Model, known as the ACO REACH Model, and to ensure that interventions and resources are precisely directed toward the most underserved populations.