Montefiore Einstein Robust Geriatric Normative Project: Robust Age- and Regression-Based Demographic Norms for the Repeatable Battery for Neuropsychological Status and Select Neuropsychological Tests in Older Adults.

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This study introduces the Montefiore Einstein Robust Geriatric Normative Project (MERGER-NP), which provides robust normative data for older adults on the Repeatable Battery for Neuropsychological Status (RBANS) and other select neuropsychological tests. Age-stratified and regression-based demographic norms were derived from a robust sample of older adults (n = 420, mean age = 75.55, 68% female). The study included assessments using the RBANS, Trail Making Test, Digit-Symbol Coding, a 15-item version of the Boston Naming Test, and verbal fluency tests, along with word reading measures. Regression-based norms were generated by analyzing predictors of test performance, integrating demographic variables and measurable social determinants of health (SDOH), specifically word reading ability and occupational attainment. Normative data include convenient look-up tables for the RBANS and other tests. Findings indicate that word reading measures significantly predict neuropsychological performance, accounting for up to 41% of the variance when included with demographic variables. Notably, our analyses revealed that race often did not contribute unique variance when controlling for reading ability. Additionally, occupation was identified as a significant predictor of test performance, with Job Zone scores retained in approximately 60% of regression models. The MERGER-NP enhances existing normative data by integrating robust norms with regression-based methods, facilitating more precise assessments for older adults. The findings underscore the utility of including SDOH such as reading ability and occupation into normative approaches, with important implications for improving diagnostic accuracy and patient care in clinical settings. Future research should explore the generalizability of these norms to more diverse populations.