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.

Factors associated with self-reports of limitations in activities of daily living among Medicare Fee-for-Service recipients.

Background: Physical function is an important indicator of physical health and predicts mortality. This study identified characteristics associated with limitations in Medicare recipients' activities of daily living.

Methods: 2019 Consumer Assessment of Healthcare Providers and Systems Fee-for-Service Medicare Survey data: 79,725 respondents (34% response rate) who were 65 and older and 53% female; 7% Black, 5% Hispanic, 4% Asian American, Native Hawaiian, or other Pacific Islander, 2% Multiracial, 1% American Indian/Alaskan Native; 35% with high school education or less.