LAUSR

BACKGROUND Disparities research in dementia is limited by lack of large, diverse, and representative samples with systematic dementia ascertainment. Algorithmic diagnosis of dementia offers a cost-effective alternate approach. Prior work in the nationally-representative Health and Retirement Study (HRS) has demonstrated that existing algorithms are ill-suited for racial/ethnic disparities work given differences in sensitivity and specificity by race/ethnicity. METHODS We implemented traditional and machine learning methods to identify an improved algorithm that (a) had ≤5 percentage point difference in sensitivity and specificity across racial/ethnic groups, (b) achieved ≥80% overall accuracy across racial/ethnic groups, and (c) achieved ≥75% sensitivity and ≥90% specificity overall. Final recommendations were based on robustness, accuracy of estimated race/ethnicity-specific prevalence and prevalence ratios compared to those using in-person diagnoses, and ease of use. RESULTS We identified six algorithms that met our pre-specified criteria. Our three recommended algorithms achieved ≤3 percentage point difference in sensitivity and ≤5 percentage point difference in specificity across racial/ethnic groups, as well as 77%-83% sensitivity, 92-94% specificity, and 90-92% accuracy overall in analyses designed to emulate out-of-sample performance. Pairwise prevalence ratios between non-Hispanic whites, non-Hispanic blacks, and Hispanics estimated by application of these algorithms are within 1% to 10% of prevalence ratios estimated based on in-person diagnoses. CONCLUSIONS We believe these algorithms will be of immense value to dementia researchers interested in racial/ethnic disparities. Our process can be replicated to allow minimally biasing algorithmic classification of dementia for other purposes.