Updated demographically adjusted norms for the Brief Visuospatial Memory Test-revised and Hopkins Verbal Learning Test-revised in Spanish-speakers from the U.S.-Mexico border region: The NP-NUMBRS project

Abstract

Objective

We generated demographically adjusted norms for the Brief Visuospatial Memory Test-revised (BVMT-R) and the Hopkins Verbal Learning Test-revised (HVLT-R) for Spanish-speakers from the U.S.-Mexico border region as part of a larger normative project. Methods: Healthy native Spanish-speakers (n = 203; Age: 19–60 years; Education: 0–20 years, 59% women) living in Arizona (n = 63) and California (n = 140) completed the BVMT-R and the HVLT-R as part of the larger Neuropsychological Norms for the U.S.-Mexico Border Region in Spanish (NP-NUMBRS) project. Raw scores were converted to T-scores utilizing fractional polynomial equations, which considered linear and non-linear effects of demographic variables (age, education, sex). To demonstrate the benefit of employing our population-specific norms, we computed the proportion of our participants whose test performance fell below one standard deviation (T-score < 40) when applying published norms from non-Hispanic English-speakers, compared to the base rate derived from the new normative sample. Results: The resulting demographically adjusted T-scores showed the expected psychometric properties and corrected the misclassification in rates of impairment that were obtained when applying norms based on the English-speaking sample. Unexpectedly, participants in Arizona obtained slightly lower HVLT-R T-scores than those in California. This site effect was not explained by available sociodemographic or language factors. Supplementary formulas were computed adjusting for site in addition to demographics. Conclusions: These updated norms improve accuracy in identification of learning and memory impairment among Spanish-speaking adults living in the U.S.-Mexico border region. It will be important to generate additional data for elders, as the present norms are only applicable to adults age 60 and younger.

Keywords:

• Spanish-speaking
• test norms
• demographic corrections
• learning
• memory

Disclosure statement

There were no financial interests or benefits arising from the direct application of the current research.

Notes

1 From Cherner, Marquine et al. (2021): A bootstrap (K = 1000) method was used to sample with replacement from the data and an MFP model was fitted each time. The frequency of the original polynomial in the bootstrap samples was assessed separately for each numeric predictor (age and education). In addition, the bootstrap procedure was used to generate “bagged” estimate of the MFP curve (and its 95% confidence boundaries), which was then compared to the curve fitted on the original data. Both assessments were done to see if the curve obtained by the norming procedure is really the best fitting curve or a curve obtained by chance or abnormalities in the data (e.g., outliers). Secondly, we tested to see if the application of normative formulas resulted in T-scores free of demographic effects. Associations between T-scores and demographic characteristics were tested with the t-test for two independent samples (sex effect) and with Pearson’s correlation test (age and, separately, education effects). The norming procedure was considered successful in removing demographic effects if the p-values for the above associations were greater than 0.2. In the third step of sensitivity analysis, normative formulas were applied to hypothetical data to test the results of extrapolation; application of formulas to a person with a combination of test scores and demographic characteristics not seen in the normative data. All extreme results, i.e., T-scores that are extremely low or extremely high (65 SD), were investigated to see if they were due to an unusual and unlikely combination of parameters (e.g., poor test performance in a younger person with college education) or due to, possibly, a poor model fit. All computations and statistical test procedures were performed using R software (R Core Team, 2018) and “MFP” R package.

Additional information

Funding

This work was supported by grants from the National Institutes of Health (P30MH62512, R01MH57266, K23MH105297, P30AG059299, U01AG052564-01) and the University of California San Diego Hispanic Center of Excellence (HRSA D34HP31027).