Consistency in Self-Reported Race-and-Ethnicity Over Time: Implications for Improving the Accuracy of Imputations and Making the Best Use of Self-Report.

Background: Medicare Bayesian Improved Surname and Geocoding (MBISG), which augments an imperfect race-and-ethnicity administrative variable to estimate probabilities that people would self-identify as being in each of 6 mutually exclusive racial-and-ethnic groups, performs very well for Asian American and Native Hawaiian/Pacific Islander (AA&NHPI), Black, Hispanic, and White race-and-ethnicity, somewhat less well for American Indian/Alaska Native (AI/AN), and much less well for Multiracial race-and-ethnicity.

Objectives: To assess whether temporal inconsistency of self-reported race-and-ethnicity might limit improvements in approaches like MBISG.

Methods: Using the Medicare Health Outcomes Survey (HOS) baseline (2013-2018) and 2-year follow-up data (2015-2020), we evaluate the consistency of self-reported race-and-ethnicity coded 2 ways: the 6 mutually exclusive MBISG categories and individual endorsements of each racial-and-ethnic group.

Comparison of Alternative Approaches to Using Race-and-Ethnicity Data in Estimating Differences in Health Care and Social Determinants of Health.

Objective: The objective of this study was to compare 2 approaches for representing self-reported race-and-ethnicity, additive modeling (AM), in which every race or ethnicity a person endorses counts toward measurement of that category, and a commonly used mutually exclusive categorization (MEC) approach. The benchmark was a gold-standard, but often impractical approach that analyzes all combinations of race-and-ethnicity as distinct groups.

Methods: Data came from 313,739 respondents to the 2021 Medicare Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys who self-reported race-and-ethnicity.