Abstract
BACKGROUND: Alzheimer's disease (AD) carries a high societal burden inequitably distributed across demographic groups. Using real-world electronic health record (EHR) data with accurate population identification, we examine demographic differences and potentially modifiable drivers of AD decline. METHODS: Leveraging EHR data (1994-2022) from two large independent healthcare systems, we applied an unsupervised phenotyping algorithm to predict AD diagnosis and validated using gold-standard chart-reviewed and registry-derived diagnosis labels. Among patients with ≥24 months of EHR data not living in nursing homes pre-AD diagnosis, we estimated the time-to-decline (nursing home admission, death) in healthcare system-specific covariate-adjusted competing risk survival analyses stratified by demographic groups. We then performed covariate-adjusted fixed-effects meta-analyses using inverse variance weighting. RESULTS: The algorithm demonstrates robust performance in identifying AD populations across healthcare systems and demographic groups (AUROC score range: 0.835-0.923). Of the 29,262 AD patients in both healthcare systems (61% women, 90% non-Hispanic White, 79.52 ± 9.39 years of age at AD diagnosis), 49% transition to nursing homes and 52% die during follow-up. In covariate-adjusted fixed-effects meta-analysis, women have higher nursing home admission risk (HR [95% CI] = 1.061 [1.024-1.100], p = 1.203×10(-3)) but lower death risk (HR [95% CI] = 0.856 [0.811-0.904], p = 2.434×10(-8)) than men. Non-Hispanic White individuals have similar nursing home risk (HR [95% CI] = 1.006 [0.952-1.063], p = 8.306×10(-1)) but higher death risk (HR [95% CI] = 1.376 [1.245-1.521], p = 4.084×10(-10)) than racial and ethnic minorities. Older age at AD diagnosis and greater comorbidity burden increase both nursing home admission and death risk. CONCLUSIONS: We provide real-world evidence of drivers of demographic differences in AD decline that could inform individual clinical management and public health policies.