Introduction: Cognitive screening measures often lack sensitivity and are hampered by inequities across ethnoracial groups. A multitrait multimethod (MTMM) classification may attenuate these shortcomings.
Methods: A sample of 7227 participants across the diagnostic spectrum were selected from the National Alzheimer's Coordinating Center cohort. Random forest ensemble methods were used to predict diagnosis across the sample and within Black American (n = 1025) and non-Hispanic White groups (n = 5263) based on: (1) a demographically corrected Montreal Cognitive Assessment (MoCA), (2) MoCA and Functional Assessment Questionnaire (FAQ), (3) MoCA and FAQ with demographic correction.
Results: The MTMM approach with demographic correction had the highest diagnostic accuracy for the cognitively unimpaired (area under curve [AUC] [95% confidence interval (CI)]): 0.906 [0.892, 0.920]) and mild cognitive impairment (AUC: 0.835 [0.810, 0.860]) groups and reduced racial disparities.
Discussion: With further validation, the MTMM approach combining cognitive screening and functional status assessment may serve to improve diagnostic accuracy and extend opportunities for early intervention with greater equity.
Keywords: Functional Activities Questionnaire (FAQ); Montreal Cognitive Assessment (MoCA); cognitive screening; diagnostic accuracy; random forest.
© 2021 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.