Abstract 4371126: ZIP Code Stratification of Social Determinants of Health and Cardiovascular Disease Burden

Introduction: ZIP code is one of the strongest predictors of cardiometabolic health, often surpassing genetic and clinical factors. Regional disparities in areas such as environment and socioeconomic status manifest as compromised SDoH, increasing cardiometabolic risk. While prior studies have correlated ZIP-level deprivation with outcomes, few have gathered patient-reported SDoH and paired it with ICD-10-defined conditions. This study aimed to quantify SDoH infractions by ZIP and evaluate their association with cardiometabolic disease. Methods: In this dual-center study, patients completed a 13-item SDoH questionnaire adapted from the CMS Accountable Health Communities Health-Related Social Needs (AHC HRSN) tool through a hard copy or QR-linked digital form following informed consent. Domains included housing, food insecurity, transportation, safety, utilities, employment, education, finances, physical activity, substance use, mental health, disability, and social support. Utilizing ICD-10 documentation, an EMR review identified diagnoses of HTN, DM, HLD, ASCVD, overweight status, and obesity. Data analysis was stratified by ZIP and SDoH status, with the latter defined as ≥1 infraction. Results: Eighty-one patients completed the SDoH questionnaire across 56 unique ZIP codes, with 58.9% (n = 47) having ≥1 SDoH infraction. The most frequent domains were financial strain (25.9%), physical activity problems (24.7%), and mental health concerns (24.7%). SDoH burden varied, with the highest-burden ZIP reporting 14 infractions (Figure 1). HTN, DM, and obesity prevalence were 58.7%, 26.1%, and 37.0% among those with ≥1 SDoH infraction versus 40.4%, 10.6%, and 21.3% in those without. Odds ratios were 3.62 (p = 0.009), 3.88 (p = 0.033), and 3.00 (p = 0.038), respectively. HLD, ASCVD, or overweight status saw no significant differences (Figure 2). ZIPs with ≥1 SDoH infraction had significantly lower median incomes than those without ($62,964 vs. $93,443; p = 0.045, Cd = 0.79), supporting geographic clustering of social risk in lower-income areas (Figure 3). Conclusion: This study supports ZIP-based stratification of SDoH and cardiometabolic risk. Integrating SDoH screening may inform patient-centered care, equity efforts, and local planning. Mapping SDoH types may also serve as a needs assessment tool for policy development.