@article {11242, title = {Development of Algorithmic Dementia Ascertainment for Racial/Ethnic Disparities Research in the US Health and Retirement Study.}, journal = {Epidemiology}, volume = {31}, year = {2020}, pages = {126-133}, abstract = {

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.

}, keywords = {Algorithms, Alzheimer{\textquoteright}s disease, Dementia, Disparities, Machine learning, Measurement}, issn = {1531-5487}, doi = {10.1097/EDE.0000000000001101}, author = {Kan Z Gianattasio and Ciarleglio, Adam and Melinda C Power} }