Comprehensive Demographic Correction Improves Sensitivity and Reduces Bias in Cognitive Assessment

Background. Scores on neuropsychological assessments are typically corrected for the influences of age, education, and gender (AEG). However, other demographic factors, such as crystallized ability and race/ethnicity, independently affect test performance. As a result, standard scores systematically over- or under-classify impairment in patients whose demographic profile differs from that of the reference population. Methods. We developed a Comprehensive (C-) model scoring algorithm that added vocabulary, age-squared, race/ethnicity, Latino background, a coarse socioeconomic status proxy, computer use, and daily prescription medications to the standard AEG predictor pool. The model was devel