The source matters: Agreement and accuracy of race and ethnicity codes in Medicare administrativeand assessment data

Abstract

Background: Errors in racial and ethnic classification of Medicare beneficiaries limit health services research on minority health and health disparities among priority populations, including American Indians and Alaskan Natives.Objective: To compare the agreement and accuracy of three sources of race and ethnicity information contained in the Medicare data warehouse: 1) the Enrollment Database (EDB) which originate from Social Security data; 2) the Research Triangle Institute (RTI) imputed data based on name and geography; and 3) self-reported race and ethnicity data collected during routine home health care assessments as part of the Outcome and Assessment Information Set (OASIS).Subjects: Medicare beneficiaries over the age of 18 who received home health care in 2015 (N = 4,243,090). Measures: Percent agreement, sensitivity, specificity, positive predictive value, and Cohen’s kappa coefficient. Results: Compared to self-reported race/ethnicity data from OASIS, the RTI race code is more accurate than the EDB race code. Non-Hispanic whites and blacks were correctly classified by the RTI race code with 97% accuracy. However, more than half of American Indians/Alaskan Natives, one-fourth of Asian American/Pacific Islanders, and nearly one-tenth of Hispanics were misclassified by the RTI race code. Misclassification of race/ethnicity occurred less often for men, compared to women. Discussion: These findings highlight the strengths and limitations of using race/ethnicity classifications contained in Medicare administrative data. Health services and policy researchers should consider using self-identified race/ethnicity information to augment administrative datasources. This is especially important for research that aims to include Asian Americans/Pacific Islanders and American Indians/Alaskan Natives.