Classification statistics of the Montreal Cognitive Assessment (MoCA): Are we interpreting the MoCA correctly?

• Type of Material: Article
• Authors: Lauren N Ratcliffe, Mercer University; Taylor McDonald, Mercer University; Brittany Robinson, Mercer University; John R Sass, Cognitive Rehabilitation of Georgia; Kelsey C Hewitt, Emory University; David W Loring, Emory University
• Language: English
• Date: 2022-06-06
• Publisher: TAYLOR & FRANCIS INC
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2024Informa UK Limited
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1080/13854046.2022.2086487
• Title of Journal or Parent Work: CLINICAL NEUROPSYCHOLOGIST
• Volume: 37
• Issue: 3
• Start Page: 562
• End Page: 576
• Grant/Funding Information: The NACC database is funded by NIA/NIH Grant U24 AG072122. NACC data are contributed by the NIA-funded ADRCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG005131 (PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD).
• Keywords: MoCA; RACIAL DISPARITIES; AFRICAN-AMERICANS; IMPAIRMENT; ALZHEIMERS-DISEASE; Psychology, Clinical; Life Sciences & Biomedicine; Social Sciences; POPULATION; Psychology; Science & Technology; Cognitive aging; VALIDATION; cognitive screening; VERSION; Clinical Neurology; ACADEMY; DEMENTIA; disparities; Neurosciences & Neurology; race; PERFORMANCE; diagnosis and classification
• Research Categories: Psychology, Clinical; Biology, Neuroscience; Health Sciences, Medicine and Surgery

Abstract

Objective: The Montreal Cognitive Assessment (MoCA) is a common cognitive screener for detecting mild cognitive impairment (MCI). However, previously suggested cutoff scores of 26/30 and above is often criticized and lacks racial diversity. The purpose of this study is to investigate the potential influence of race on MoCA classification cutoff score accuracy. Method: Data were obtained from the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set and yielded 4,758 total participants. Participants were predominately White (82.8%) and female (61.7%) with a mean age of 69.3 years (SD = 10.3) and education level of 16.3 years (SD = 2.6). Based on NACC’s classification, participants were either cognitively normal (n = 3,650) or MCI (n = 1,108). Results: Sensitivity and specificity analyses revealed that when using the cutoff score of ≤26/30, the MoCA correctly classified 73.2% of White cognitively normal participants and 83.1% of White MCI participants. In contrast, this criterion correctly classified 40.5% of Black cognitively normal participants and 90.8% of Black MCI participants. Our sample was highly educated; therefore, we did not observe significant differences in scores when accounting for education across race. Classification statistics are presented. Conclusions: Black participants were misclassified at a higher rate than White participants when applying the ≤26/30 cutoff score. We suggest cutoff scores of ≤25/30 be applied to White persons and ≤22/30 for Black persons. These findings highlight the need for racially stratified population-based norms given the high misclassification of Black participants without such adjustment.