From Medicare Diagnosis to Underlying Dementia Prevalence: Bridging Models for Local Estimation

Thomas M. Menino Convention & Exhibition Center 

Dementia is one of the most pressing public health challenges in the United States, yet reliable geographically disaggregated prevalence estimates remain limited. Estimates by age, sex, and small geographic area are essential for planning, resource allocation, and targeted surveillance, but existing data sources are insufficient to measure dementia prevalence reliably below broad national or regional levels.
In this study, we estimate dementia prevalence at the state and county levels by age group, 65–79 and 80+, and sex by integrating information from the Health and Retirement Study, Medicare administrative records, population data, and other publicly available auxiliary sources. Health and Retirement Study data linked to Medicare records support estimation of the relationship between survey-based dementia classification and Medicare diagnosis codes, but the sample size limits direct subnational inference. Medicare-derived administrative data support granular estimation but capture diagnosed dementia rather than true dementia prevalence and may be affected by differential diagnosis and coding patterns across populations.
We address these challenges using extensions of continuation-ratio models, introduced to the small area estimation literature by Slud, Franco, and Hall (2024) and Rein, Franco, et al. (2024), fit within a hierarchical Bayesian framework. The proposed bridging models use the ordered structure of dementia classification and diagnosis states observed in the linked Health and Retirement Study data, calibrate to local Medicare-based diagnosed dementia estimates, and borrow strength across areas using auxiliary administrative and population covariates. The resulting estimates distinguish diagnosed dementia from underlying dementia burden and provide calibrated small area prevalence estimates for policy-relevant geographic and demographic subgroups.

Small Area Estimation (SAE)

data integration

public health

dementia

survey statistics