|Title||Development of Algorithmic Dementia Ascertainment for Racial/Ethnic Disparities Research in the US Health and Retirement Study.|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||Gianattasio, KZ, Ciarleglio, A, Power, MC|
|Keywords||Algorithms, Alzheimer’s disease, Dementia, Disparities, Machine learning, Measurement|
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 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: (1) had ≤5 percentage point difference in sensitivity and specificity across racial/ethnic groups; (2) achieved ≥80% overall accuracy across racial/ethnic groups; and (3) 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 prespecified 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%-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.
|PubMed Central ID||PMC6888863|
|Grant List||K01 MH113850 / MH / NIMH NIH HHS / United States |
R03 AG055485 / AG / NIA NIH HHS / United States
U01 AG009740 / AG / NIA NIH HHS / United States