Background: Brief, global assessments such as the Montreal Cognitive Assessment (MoCA) are widely used in primary care for assessing cognition in older adults. Like other neuropsychological instruments, lower formal education can influence MoCA interpretation. Methods: Data from 2 large studies of cognitive aging were used—Alzheimer’s Disease Neuroimaging Initiative (ADNI) and National Alzheimer’s Coordinating Center (NACC). Both use comprehensive examinations to determine cognitive status and have brain amyloid status for many participants. Mixed models were used to account for random variation due to data source. Results: Cognitively intact participants with lower education (≤12 years) were more likely than those with higher education (>12 years) to be classified as potentially impaired using the MoCA cutoff of <26 (P < .01). Backwards selection revealed 4 MoCA items significantly associated with education (cube copy, serial subtraction, phonemic fluency, abstraction). Subtracting these items scores yielded an alternative MoCA score with a maximum of 24 and a cutoff of ≤19 for classifying participants with mild cognitive impairment. Using the alternative MoCA score and cutoff, among cognitively intact participants, both education groups were similarly likely to be classified as potentially impaired (P > .67). Conclusions: The alternative MoCA score neutralized the effects of formal education. Although further research is needed, this alternative score offers a simple procedure for interpreting MoCAs administered to older adults with ≤12 years education. These educational effects also highlight that the MoCA is part of the assessment process—not a singular diagnostic test—and a comprehensive workup is necessary to accurately diagnose cognitive impairments.
|Number of pages||15|
|Journal||Journal of the American Board of Family Medicine|
|State||Published - Nov 2022|
Bibliographical noteFunding 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).
© 2022 American Board of Family Medicine. All rights reserved.
- Clinical Medicine
- Cognitive Aging
- Montreal Cognitive Assessment
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health
- Family Practice