Diagnosing Mild Cognitive Impairment Among Racially Diverse Older Adults: Comparison of Consensus, Actuarial, and Statistical Methods

Article type: Research Article

Authors: Devlin, Kathryn N.^a | Brennan, Laura^b | Saad, Laura^c | Giovannetti, Tania^d | Hamilton, Roy H.^{e; f} | Wolk, David A.^{e; f} | Xie, Sharon X.^{e; g} | Mechanic-Hamilton, Dawn^{e; f; *}

Affiliations: [a] Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, USA | [b] Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA, USA | [c] Department of Psychology, Rutgers University, New Brunswick, NJ, USA | [d] Department of Psychology, Temple University, Philadelphia, PA, USA | [e] Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA, USA | [f] Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA | [g] Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA

Correspondence: [*] Correspondence to: Dawn Mechanic-Hamilton, Penn Memory Center, 3400 Civic Center Boulevard, Philadelphia, PA 19104, USA. Tel.: +215 662 4516; E-mail: [email protected].

Abstract: Background:Actuarial and statistical methods have been proposed as alternatives to conventional methods of diagnosing mild cognitive impairment (MCI), with the aim of enhancing diagnostic and prognostic validity, but have not been compared in racially diverse samples. Objective:We compared the agreement of consensus, actuarial, and statistical MCI diagnostic methods, and their relationship to race and prognostic indicators, among diverse older adults. Methods:Participants (N = 354; M age = 71; 68% White, 29% Black) were diagnosed with MCI or normal cognition (NC) according to clinical consensus, actuarial neuropsychological criteria (Jak/Bondi), and latent class analysis (LCA). We examined associations with race/ethnicity, longitudinal cognitive and functional change, and incident dementia. Results:MCI rates by consensus, actuarial criteria, and LCA were 44%, 53%, and 41%, respectively. LCA identified three MCI subtypes (memory; memory/language; memory/executive) and two NC classes (low normal; high normal). Diagnostic agreement was substantial, but agreement of the actuarial method with consensus and LCA was weaker than the agreement between consensus and LCA. Among cases classified as MCI by actuarial criteria only, Black participants were over-represented, and outcomes were generally similar to those of NC participants. Consensus diagnoses best predicted longitudinal outcomes overall, whereas actuarial diagnoses best predicted longitudinal functional change among Black participants. Conclusion:Consensus diagnoses optimize specificity in predicting dementia, but among Black older adults, actuarial diagnoses may be more sensitive to early signs of decline. Results highlight the need for cross-cultural validity in MCI diagnosis and should be explored in community- and population-based samples.

Keywords: Actuarial analysis, Alzheimer’s disease, cognitive aging, early diagnosis, latent class analysis, memory disorders, mild cognitive impairment, minority health, race factors, statistical models

DOI: 10.3233/JAD-210455

Journal: Journal of Alzheimer's Disease, vol. 85, no. 2, pp. 627-644, 2022