Robust demographically-adjusted normative data for the Montreal Cognitive Assessment (MoCA): Results from the systolic blood pressure intervention trial

Academic Article

Abstract

  • Objectives: To generate robust, demographically-adjusted regression-based norms for the Montreal Cognitive Assessment (MoCA) using a large sample of diverse older US adults. Methods: Baseline MoCA scores were examined for participants in the Systolic Blood Pressure Intervention Trial (SPRINT). A robust, cognitively-normal sample was drawn from individuals not subsequently adjudicated with cognitive impairment through 4 years of follow-up. Multivariable Beta-Binomial regression was used to model the association of demographic variables with MoCA performance and to create demographically-stratified normative tables. Results: Participants’ (N = 5,338) mean age was 66.9 ± 8.8 years, with 35.7% female, 63.1% White, 27.4% Black, 9.5% Hispanic, and 44.5% with a college or graduate education. A large proportion scored below published MoCA cutoffs: 61.4% scored below 26 and 29.2% scored below 23. A disproportionate number falling below these cutoffs were Black, Hispanic, did not graduate from college, or were ≥75 years of age. Multivariable modeling identified education, race/ethnicity, age, and sex as significant predictors of MoCA scores (p<.001), with the best fitting model explaining 24.4% of the variance. Model-based predictions of median MoCA scores were generally 1 to 2 points lower for Black and Hispanic participants across combinations of age, sex, and education. Demographically-stratified norm-tables based on regression modeling are provided to facilitate clinical use, along with our raw data. Conclusion: By using regression-based strategies that more fully account for demographic variables, we provide robust, demographically-adjusted metrics to improve cognitive screening with the MoCA in diverse older adults.
  • Published In

    Digital Object Identifier (doi)

    Author List

  • Sachs BC; Chelune GJ; Rapp SR; Couto AM; Willard JJ; Williamson JD; Sink KM; Coker LH; Gaussoin SA; Gure TR