Detection of Mild Cognitive Impairment Among Community-Dwelling African Americans Using the Montreal Cognitive Assessment

Heidi C. Rossetti, Emily E. Smith, Linda S. Hynan, Laura H. Lacritz, C. Munro Cullum, Aaron Van Wright, Myron F. Weiner

Research output: Contribution to journalArticle

Abstract

OBJECTIVE: To establish a cut score for the Montreal Cognitive Assessment (MoCA) that distinguishes mild cognitive impairment (MCI) from normal cognition (NC) in a community-based African American (AA) sample. METHODS: A total of 135 AA participants, from a larger aging study, diagnosed MCI (n = 90) or NC (n = 45) via consensus diagnosis using clinical history, Clinical Dementia Rating score, and comprehensive neuropsychological testing. Logistic regression models utilized sex, education, age, and MoCA score to predict MCI versus NC. Receiver operating characteristic (ROC) curve analysis determined a cut score to distinguish MCI from NC based on optimal sensitivity, specificity, diagnostic accuracy, and greatest perpendicular distance above the identity line. ROC results were compared with previously published MoCA cut scores. RESULTS: The MCI group was slightly older (MMCI = 64.76[5.87], MNC = 62.33[6.76]; p = .033) and less educated (MMCI = 13.07[2.37], MNC = 14.36[2.51]; p = .004) and had lower MoCA scores (MMCI=21.26[3.85], MNC = 25.47[2.13]; p < .001) than the NC group. Demographics were non-significant in regression models. The area under the curve (AUC) was significant (MoCA = .83, p < .01) and an optimal cut score of <24 maximized sensitivity (72%), specificity (84%), and provided 76% diagnostic accuracy. In comparison, the traditional cut score of <26 had higher sensitivity (84%), similar accuracy (76%), but much lower specificity (58%). CONCLUSIONS: This study provides a MoCA cut score to help differentiate persons with MCI from NC in a community-dwelling AA sample. A cut score of <24 reduces the likelihood of misclassifying normal AA individuals as impaired than the traditional cut score. This study underscores the importance of culturally appropriate norms to optimize the utility of commonly used cognitive screening measures.

Original languageEnglish (US)
Pages (from-to)809-813
Number of pages5
JournalArchives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists
Volume34
Issue number6
DOIs
StatePublished - Aug 28 2019

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Independent Living
African Americans
Cognition
ROC Curve
Logistic Models
Sensitivity and Specificity
Sex Education
Area Under Curve
Dementia
Cognitive Dysfunction
Demography

Keywords

  • Cross-cultural/minority
  • Mild cognitive impairment
  • Norms/normative studies

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Clinical Psychology
  • Psychiatry and Mental health

Cite this

@article{0a7228c934fc4f94a60c2d75f5d6da32,
title = "Detection of Mild Cognitive Impairment Among Community-Dwelling African Americans Using the Montreal Cognitive Assessment",
abstract = "OBJECTIVE: To establish a cut score for the Montreal Cognitive Assessment (MoCA) that distinguishes mild cognitive impairment (MCI) from normal cognition (NC) in a community-based African American (AA) sample. METHODS: A total of 135 AA participants, from a larger aging study, diagnosed MCI (n = 90) or NC (n = 45) via consensus diagnosis using clinical history, Clinical Dementia Rating score, and comprehensive neuropsychological testing. Logistic regression models utilized sex, education, age, and MoCA score to predict MCI versus NC. Receiver operating characteristic (ROC) curve analysis determined a cut score to distinguish MCI from NC based on optimal sensitivity, specificity, diagnostic accuracy, and greatest perpendicular distance above the identity line. ROC results were compared with previously published MoCA cut scores. RESULTS: The MCI group was slightly older (MMCI = 64.76[5.87], MNC = 62.33[6.76]; p = .033) and less educated (MMCI = 13.07[2.37], MNC = 14.36[2.51]; p = .004) and had lower MoCA scores (MMCI=21.26[3.85], MNC = 25.47[2.13]; p < .001) than the NC group. Demographics were non-significant in regression models. The area under the curve (AUC) was significant (MoCA = .83, p < .01) and an optimal cut score of <24 maximized sensitivity (72{\%}), specificity (84{\%}), and provided 76{\%} diagnostic accuracy. In comparison, the traditional cut score of <26 had higher sensitivity (84{\%}), similar accuracy (76{\%}), but much lower specificity (58{\%}). CONCLUSIONS: This study provides a MoCA cut score to help differentiate persons with MCI from NC in a community-dwelling AA sample. A cut score of <24 reduces the likelihood of misclassifying normal AA individuals as impaired than the traditional cut score. This study underscores the importance of culturally appropriate norms to optimize the utility of commonly used cognitive screening measures.",
keywords = "Cross-cultural/minority, Mild cognitive impairment, Norms/normative studies",
author = "Rossetti, {Heidi C.} and Smith, {Emily E.} and Hynan, {Linda S.} and Lacritz, {Laura H.} and Cullum, {C. Munro} and {Van Wright}, Aaron and Weiner, {Myron F.}",
year = "2019",
month = "8",
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doi = "10.1093/arclin/acy091",
language = "English (US)",
volume = "34",
pages = "809--813",
journal = "Archives of Clinical Neuropsychology",
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TY - JOUR

T1 - Detection of Mild Cognitive Impairment Among Community-Dwelling African Americans Using the Montreal Cognitive Assessment

AU - Rossetti, Heidi C.

AU - Smith, Emily E.

AU - Hynan, Linda S.

AU - Lacritz, Laura H.

AU - Cullum, C. Munro

AU - Van Wright, Aaron

AU - Weiner, Myron F.

PY - 2019/8/28

Y1 - 2019/8/28

N2 - OBJECTIVE: To establish a cut score for the Montreal Cognitive Assessment (MoCA) that distinguishes mild cognitive impairment (MCI) from normal cognition (NC) in a community-based African American (AA) sample. METHODS: A total of 135 AA participants, from a larger aging study, diagnosed MCI (n = 90) or NC (n = 45) via consensus diagnosis using clinical history, Clinical Dementia Rating score, and comprehensive neuropsychological testing. Logistic regression models utilized sex, education, age, and MoCA score to predict MCI versus NC. Receiver operating characteristic (ROC) curve analysis determined a cut score to distinguish MCI from NC based on optimal sensitivity, specificity, diagnostic accuracy, and greatest perpendicular distance above the identity line. ROC results were compared with previously published MoCA cut scores. RESULTS: The MCI group was slightly older (MMCI = 64.76[5.87], MNC = 62.33[6.76]; p = .033) and less educated (MMCI = 13.07[2.37], MNC = 14.36[2.51]; p = .004) and had lower MoCA scores (MMCI=21.26[3.85], MNC = 25.47[2.13]; p < .001) than the NC group. Demographics were non-significant in regression models. The area under the curve (AUC) was significant (MoCA = .83, p < .01) and an optimal cut score of <24 maximized sensitivity (72%), specificity (84%), and provided 76% diagnostic accuracy. In comparison, the traditional cut score of <26 had higher sensitivity (84%), similar accuracy (76%), but much lower specificity (58%). CONCLUSIONS: This study provides a MoCA cut score to help differentiate persons with MCI from NC in a community-dwelling AA sample. A cut score of <24 reduces the likelihood of misclassifying normal AA individuals as impaired than the traditional cut score. This study underscores the importance of culturally appropriate norms to optimize the utility of commonly used cognitive screening measures.

AB - OBJECTIVE: To establish a cut score for the Montreal Cognitive Assessment (MoCA) that distinguishes mild cognitive impairment (MCI) from normal cognition (NC) in a community-based African American (AA) sample. METHODS: A total of 135 AA participants, from a larger aging study, diagnosed MCI (n = 90) or NC (n = 45) via consensus diagnosis using clinical history, Clinical Dementia Rating score, and comprehensive neuropsychological testing. Logistic regression models utilized sex, education, age, and MoCA score to predict MCI versus NC. Receiver operating characteristic (ROC) curve analysis determined a cut score to distinguish MCI from NC based on optimal sensitivity, specificity, diagnostic accuracy, and greatest perpendicular distance above the identity line. ROC results were compared with previously published MoCA cut scores. RESULTS: The MCI group was slightly older (MMCI = 64.76[5.87], MNC = 62.33[6.76]; p = .033) and less educated (MMCI = 13.07[2.37], MNC = 14.36[2.51]; p = .004) and had lower MoCA scores (MMCI=21.26[3.85], MNC = 25.47[2.13]; p < .001) than the NC group. Demographics were non-significant in regression models. The area under the curve (AUC) was significant (MoCA = .83, p < .01) and an optimal cut score of <24 maximized sensitivity (72%), specificity (84%), and provided 76% diagnostic accuracy. In comparison, the traditional cut score of <26 had higher sensitivity (84%), similar accuracy (76%), but much lower specificity (58%). CONCLUSIONS: This study provides a MoCA cut score to help differentiate persons with MCI from NC in a community-dwelling AA sample. A cut score of <24 reduces the likelihood of misclassifying normal AA individuals as impaired than the traditional cut score. This study underscores the importance of culturally appropriate norms to optimize the utility of commonly used cognitive screening measures.

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