Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity. (3rd December 2018) - Haas, Ann; Elliott, Marc N.; Dembosky, Jacob W.; Adams, John L.; Wilson‐Frederick, Shondelle M.; Mallett, Joshua S.; Gaillot, Sarah; Haffer, Samuel C.; Haviland, Amelia M.
Abstract : Objective: To improve an existing method, Medicare Bayesian Improved Surname Geocoding (MBISG) 1.0 that augments the Centers for Medicare & Medicaid Services' (CMS) administrative measure of race/ethnicity with surname and geographic data to estimate race/ethnicity. Data Sources/Study Setting: Data from 284 627 respondents to the 2014 Medicare CAHPS survey. Study Design: We compared performance (cross‐validated Pearson correlation of estimates and self‐reported race/ethnicity) for several alternative models predicting self‐reported race/ethnicity in cross‐sectional observational data to assess accuracy of estimates, resulting in MBISG 2.0. MBISG 2.0 adds to MBISG 1.0 first name, demographic, and coverage predictors of race/ethnicity and uses a more flexible data aggregation framework. Data Collection/Extraction Methods: We linked survey‐reported race/ethnicity to CMS administrative and US census data. Principal Findings: MBISG 2.0 removed 25‐39 percent of the remaining MBISG 1.0 error for Hispanics, Whites, and Asian/Pacific Islanders (API), and 9 percent for Blacks, resulting in correlations of 0.88 to 0.95 with self‐reported race/ethnicity for these groups. Conclusions: MBISG 2.0 represents a substantial improvement over MBISG 1.0 and the use of CMS administrative data on race/ethnicity alone. MBISG 2.0 is used in CMS' public reporting of Medicare Advantage contract HEDIS measures stratified by race/ethnicity for Hispanics, Whites, API, and Blacks.