1573 Background: Health disparities arise from the confluence of medical and non-medical factors that affect measures of prevention, treatment, and outcomes. One crucial preventative measure is mammography rate. Neighborhood-level analysis using tools such as the CDC’s Social Vulnerability Index (SVI) may identify correlates of disparity in mammography access and utilization. Such investigation may reveal drivers of disparity in communities with lower screening. Methods: Self-reported mammography rates among women aged 50-74 were obtained from the CDC’s 2018 PLACES dataset at the Census Tract level and merged with the CDC’s 2018 SVI release. Data were available for 72,075 Census Tracts across all states and Washington, D.C. National univariate and multivariate regressions were performed between mammography rates and percentiles for SVI percentiles. State-specific multivariate regressions were performed for each state's Census Tracts. Results: In the national analysis, significant correlations with non-negligible effect size (correlation coefficient > 0.20 or < -0.20) were found between mammography rate and the following: Theme 2 - Household Composition & Disability (R = -0.22, p < 0.0001), Mobile Homes (R = -0.36, p < 0.0001), Civilians with a Disability (R = -0.24, p < 0.0001), and Minority Status (R = 0.25, p < 0.0001). The national multivariate model achieved Multiple R of 0.54 and Significance F < 0.001. The variables with the most negative associations (lower mammography rates with increased vulnerability) were Income (coefficient = -4.05, p < 0.001) and Speaks English Less than Well (coefficient = -2.33, p < 0.001). Across the state multivariate models, mean Multiple R was 0.70 (95% CI 0.67-0.73), with Significance F < 0.05 in all states (Table). The variables with the most negative mean coefficients across models were Income (-3.20) and No High School Diploma (-2.98); the most positive was Minority status (6.11). Conclusions: Neighborhood-level social determinants correlate meaningfully with mammography rates. Although lower income, limited English proficiency, and lower educational attainment are most strongly associated with lower mammography, the effect sizes vary across geographies, and additional factors have meaningful influence in certain states. The stronger performance of most state-specific models than the national model reinforces this finding. It demonstrates the need for further analysis at the level of state and within narrower geographies. Ultimately, targeted interventions should address those disparities that are most relevant to specific neighborhoods. [Table: see text]