Abstract:
|
The Medicare Bayesian Improved Surname Geocoding (MBISG) method estimates a vector of six race/ethnicity probabilities (White, Black, Hispanic, Asian, AI/AN, and multiracial) for individuals based on surname, address, and an imperfect administrative race/ethnicity variable using Bayes' rule. Recent work substantially improved the performance of the method by (a) allowing the association of SS data with race/ethnicity to vary by age, (b) newly addressing compound surnames, (c) incorporating additional data elements, and (d) embedding the Bayesian core within a multinomial regression framework, resulting in MBISG version 2.0. Here we present the results of validation work using self-reported race/ethnicity from US Census data as a gold standard, including applications to estimating racial/ethnic disparities in quality of care.
|