Brief Methodological Reports
Diagnostic Validity of Age and Education Corrections for the Mini-Mental State Examination in Older African Americans
Address correspondence to Otto Pedraza, Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL 32224. E-mail: email@example.com
To investigate whether demographic (age and education) adjustments for the Mini-Mental State Examination (MMSE) attenuate mean score discrepancies between African-American and Caucasian adults and whether demographically adjusted MMSE scores improve the diagnostic classification accuracy of dementia in African-American adults over unadjusted MMSE scores.
Community-dwelling adults participating in the Mayo Clinic Alzheimer's Disease Patient Registry and Alzheimer's Disease Research Center.
Three thousand two hundred fifty-four adults (2,819 Caucasian, 435 African American) aged 60 and older.
MMSE score at study entry.
African-American adults had significantly lower unadjusted MMSE scores (23.0 ± 7.4) than Caucasian adults (25.3 ± 5.4). This discrepancy persisted despite adjustment of MMSE scores for age and years of education using established regression weights or newly derived weights. Controlling for dementia severity at baseline and adjusting MMSE scores for age and quality of education attenuated this discrepancy. In African-American adults, an age- and education-adjusted MMSE cut score of 23/24 provided optimal dementia classification accuracy, but this represented only a modest improvement over an unadjusted MMSE cut score of 22/23. The posterior probability of dementia in African-American adults is presented for various unadjusted MMSE cut scores and prevalence rates of dementia.
Age, dementia severity at study entry, and quality of educational experience are important explanatory factors in understanding the existing discrepancies in MMSE performance between Caucasian and African-American adults. These findings support the use of unadjusted MMSE scores when screening older African Americans for dementia, with an unadjusted MMSE cut score of 22/23 yielding optimal classification accuracy.