Predictors of dementia misclassification when using brief cognitive assessments

Neurol Clin Pract. 2019 Apr;9(2):109-117. doi: 10.1212/CPJ.0000000000000566.


Background: Brief cognitive assessments can result in false-positive and false-negative dementia misclassification. We aimed to identify predictors of misclassification by 3 brief cognitive assessments; the Mini-Mental State Examination (MMSE), Memory Impairment Screen (MIS) and animal naming (AN).

Methods: Participants were 824 older adults in the population-based US Aging, Demographics and Memory Study with adjudicated dementia diagnosis (DSM-III-R and DSM-IV criteria) as the reference standard. Predictors of false-negative, false-positive and overall misclassification by the MMSE (cut-point <24), MIS (cut-point <5) and AN (cut-point <9) were analysed separately in multivariate bootstrapped fractional polynomial regression models. Twenty-two candidate predictors included sociodemographics, dementia risk factors and potential sources of test bias.

Results: Misclassification by at least one assessment occurred in 301 (35.7%) participants, whereas only 14 (1.7%) were misclassified by all 3 assessments. There were different patterns of predictors for misclassification by each assessment. Years of education predicts higher false-negatives (odds ratio [OR] 1.23, 95% confidence interval [95% CI] 1.07-1.40) and lower false-positives (OR 0.77, 95% CI 0.70-0.83) by the MMSE. Nursing home residency predicts lower false-negatives (OR 0.15, 95% CI 0.03-0.63) and higher false-positives (OR 4.85, 95% CI 1.27-18.45) by AN. Across the assessments, false-negatives were most consistently predicted by absence of informant-rated poor memory. False-positives were most consistently predicted by age, nursing home residency and non-Caucasian ethnicity (all p < 0.05 in at least 2 models). The only consistent predictor of overall misclassification across all assessments was absence of informant-rated poor memory.

Conclusions: Dementia is often misclassified when using brief cognitive assessments, largely due to test specific biases.