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.

On average, beneficiaries with ESRD report patient experiences that are at least as positive as non-ESRD beneficiaries. However, black and less educated patients with ESRD reported worse experiences than other ESRD patients. Stratified reporting of patient experience by race/ethnicity or education in patients with ESRD can be used to monitor this disparity. Physician choice and confidence and trust in physicians may be particularly important for patients with ESRD.

Background: Researchers are increasingly interested in measuring race/ethnicity, but some survey respondents skip race/ethnicity items. Objectives: The main objectives of this study were to investigate the extent to which racial/ethnic groups differ in skipping race/ethnicity survey items, the degree to which this reflects reluctance to disclose race/ethnicity, and the utility of imputing missing race/ethnicity. Research Design: We applied a previously developed method for imputing race/ethnicity from administrative data (Medicare Bayesian Improved Surname and Geocoding 2.0) to data from a national survey where race/ethnicity was usually self-reported, but was sometimes missing. A linear mixed-effects regression model predicted the probability of self-reporting race/ethnicity from imputed racial/ethnic probabilities. Subjects: In total, 508,497 Medicare beneficiaries responding to the 2013–2014 Medicare Consumer Assessment of Healthcare Providers and Systems surveys were included in this study. Measures: Self-reported race/ethnicity and estimated racial/ethnic probabilities. Results: Black beneficiaries were most likely to not self-report their race/ethnicity (6.6%), followed by Hispanic (4.7%) and Asian/Pacific Islander (4.7%) beneficiaries. Non-Hispanic whites were the least likely to skip these items (3.2%). The 3.7% overall rate of missingness is similar to adjacent demographic items. General patterns of item missingness rather than a specific reluctance to disclose race/ethnicity appears to explain the elevated rate of missing race/ethnicity among Asian/Pacific Islander and Hispanic beneficiaries and most but not all among Black beneficiaries. Adding imputed cases to the data set did not substantially alter the estimated overall racial/ethnic distribution, but it did modestly increase sample size and statistical power. Conclusions: It may be worthwhile to impute race/ethnicity when this information is unavailable in survey data sets due to item nonresponse, especially when missingness is high.

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Similarities in CAHPS and HEDIS disparities across measures might reflect common structural factors, such as language services or provider incentives, affecting several measures simultaneously. Health plan structural changes might reduce disparities across multiple measures.