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, age², race/ethnicity, Latino background, a coarse socioeconomic status proxy, computer use, and daily prescription medications to the standard AEG predictor pool. The model was developed using data from 1,914 community-dwelling adults assessed with the California Cognitive Assessment Battery (CCAB; Woods et al., 2024). For each of 118 individual cognitive measures, stability-selection LASSO identified robust predictors in 300 random 80/20 splits retained at ≥ 80% frequency and then estimated mean coefficients and confidence intervals in 1,000 bootstrap OLS samples. Cross-sample frozen-coefficient validation was used to evaluate scoring model generalization in two subgroups: Group 1 (n = 1,033, older, first enrolled cohort) and Group 2 (n = 881, a recently recruited younger cohort).

Results

Stability selection retained a mean of 2.81 predictors per measure (range 1-6). Compared to the AEG model, the C-model approximately doubled variance explained (r² = 0.50 vs 0.25; mean across cognitive domains r² = 0.32 vs 0.18) and outperformed AEG in 98.8% of individual measures with non-trivial demographic signal. Racial disparities in MCI classification (the bottom-7th-percentile) were substantially reduced: Black-vs-White ratios fell from 5.6 (AEG) to 1.8 (C). Conversely, sensitivity was improved in individuals with elevated premorbid function: MCI classification ratios in low-vs-high vocabulary quartiles fell from 11.3 to 2.1. AIC favored the C-model in 88.1% of measures (mean ΔAIC = −167), ruling out overfitting. Frozen-coefficient validation preserved the C-model’s r² advantage in every cognitive domain.

Conclusions

By correcting scores for race, premorbid cognitive functioning (vocabulary), and other demographic predictors, the C-model explains substantially more variance than the AEG model, reduces racial bias, and increases sensitivity to cognitive decline in high-functioning participants. C and AEG models can be used in parallel: model concordance increases diagnostic confidence, while disagreement carries diagnostic information.

Highlights

• We developed a Comprehensive (C-) model for scoring California Cognitive Assessment Battery (CCAB) tests that supplements standard age + education + gender (AEG) demographic corrections with additional predictors including vocabulary, age², race/ethnicity, Latino background, socioeconomic status (SES), computer use, and daily medications.

• To avoid model overfitting, significant predictors were identified with stability-selection LASSO, resulting in a mean C-model retention of 2.81 (range 1-6) predictors per individual test score.

• In 1,914 community-dwelling adults assessed with CCAB, the C-model approximately doubled explained variance for overall performance (OMNI) scores when compared with the AEG model (r² = 0.50 vs 0.25), and accounted for more variance than the AEG model in 98.8% of measures with non-trivial demographic signal. Cross-sample validation with two demographically distinct cohorts showed that the C-model’s r² advantage was preserved in every cognitive domain (Δr² variation < ±0.020 across fits), supporting generalizability.

• The C-model reduced demographic classification disparities in classifying mild cognitive impairment (MCI, bottom 7% of participants) and normalized MCI detection across different levels of premorbid cognitive reserve: Black-vs-White MCI-classification ratios fell from 5.6 (AEG) to 1.8 (C), and low-vs-high vocabulary performance ratios fell from 11.3 to 2.1.

• C-model scores are orthogonal to vocabulary; comparisons with premorbid crystallized intelligence are incorporated directly in the scoring algorithm, eliminating the need for post-hoc comparisons.

• The C-model is best used in parallel with AEG scoring: concordance between models increases diagnostic confidence, while disagreement provides additional diagnostic information.