Unraveling disparities in county-level dementia diagnosis prevalence across the United States.

INTRODUCTION: We aimed to identify demographic, socioeconomic, and environmental factors contributing to county-level variation in dementia diagnosis prevalence across the United States. METHODS: Using 2020 Dementia Data Hub data covering 61.5 million Medicare beneficiaries, we modeled the top tertile of dementia diagnosis prevalence across 3076 counties. Forty-one county-level variables were evaluated using logistic regression and region-specific models. RESULTS: Top-tertile counties averaged 1986 dementia cases per 100,000 residents; 43.8% were in the South. The main model included seven predictors: higher diabetes prevalence, uninsured rate, fast-food access, White race prevalence, smaller household size, smoking rate, and elevation (area under the curve [AUC]: 0.84; 95% confidence interval: 0.82 to 0.85). Region-specific models improved accuracy (AUCs 0.83 to 0.89). DISCUSSION: Dementia diagnosis prevalence varies widely across the United States and can be predicted with high accuracy using a small set of regionally adaptable variables. Region-specific modeling may help policymakers identify high-burden communities, tailor prevention strategies, and monitor the impact of targeted interventions over time. HIGHLIGHTS: Six models tested predictors of county-level dementia diagnosis prevalence. Backward selection was the main model: seven variables, high accuracy (AUC = 0.84). Higher diabetes, uninsured rates, White race prevalence, and fast food linked to more dementia diagnoses. Larger households, higher elevation, and more smokers linked to less dementia diagnoses. Some predictors (e.g., elevation) likely act as proxies; collinearity limits causality.