Abstract P322: Mapping Geographic Variation in Hypertension Prevalence in United States Congressional Districts Using Spatial Modeling

Introduction: Using spatial methods to explore drivers of hypertension (HTN) risks, commonalities, and differences across geographical areas can be assessed to inform future public health research and advocacy efforts. Objectives: To examine ecological associations between social determinants of health (SDoH), space, and HTN across congressional districts (CDs) in effect for the 118th US Congress. Methods: We obtained aggregate district-level data from Congressional District Health Dashboard on health behaviors, exposures, and outcomes for US CDs in 2019. Primary outcome was HTN prevalence, defined as proportion of adults who self-reported diagnosed HTN. Exposure variables were SDoH: health outcomes (diabetes, obesity, frequent physical and mental distress), health behaviors (physical activity, binge drinking, smoking), clinical care (routine checkups), and physical environment (air pollution). We fitted a hierarchical Bayesian Besag-York-Mollié model using the integrated nested Laplace approximation (INLA) method, which allowed the estimation of spatial autocorrelation of HTN prevalence at CDs level, accounting for SDoH. Results: Across 421 CDs, we found a variation in the modeled linear proportion of 83% across the CDs in the United States after adjusting for known HTN risk factors at the CDs level. A notable degree of variation in HTN prevalence exists within specific CDs, independent of recognized risk factors ( Figure ). These unmeasured variables act as confounding elements, and districts with darker shading indicate increased HTN attributed to the district, while lighter, red-shaded districts exhibit a reduced district-specific proportion of HTN. Conclusions: Our findings show significant geographic variation of HTN beyond known risk factors which underscores the significance of adopting a spatial perspective to understand HTN prevalence. It emphasizes the necessity for tailored public health interventions that consider the unique geographic context of each CDs.