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

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 (M-MCI = 64.76[5.87], M-NC = 62.33[6.76]; p = .033) and less educated (M-MCI = 13.07[2.37], M-NC = 14.36[2.51]; p = .004) and had lower MoCA scores (M-MCI = 21.26[3.85], M-NC = 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.