Validity of Race and Ethnicity Codes in Medicare Administrative Data Compared With Gold-standard Self-reported Race Collected During Routine Home Health Care Visits

Abstract

Background: Misclassification of Medicare beneficiaries’ race/ethnicity in administrative data sources is frequently overlooked and a limitation in health disparities research. Objective: To compare the validity of 2 race/ethnicity variables found in Medicare administrative data [enrollment database (EDB) and Research Triangle Institute (RTI) race] against a gold-standard source also available in the Medicare data warehouse: the self-reported race/ethnicity variable on the home health 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 κ coefficient. Results: The EDB and RTI race variable have high validity for black race and low validity for American Indian/Alaskan Native race. Although the RTI race variable has better validity than the EDB race variable for other races, κ values suggest room for future improvements in classification of whites (0.90), Hispanics (0.87), Asian/Pacific Islanders (0.77), and American Indian/Alaskan Natives (0.44). Discussion: The status quo of using “good-enough for government” race/ethnicity variables contained in Medicare administrative data for minority health disparities research can be improved through the use of self-reported race/ethnicity data, available in the Medicare data warehouse. Health services and policy researchers should critically examine the source of race/ethnicity variables used in minority health and health disparities research. Future work to improve the accuracy of Medicare beneficiaries’ race/ethnicity data should incorporate and augment the self-reported race/ethnicity data contained in assessment and survey data, available within the Medicare data warehouse.

Racial Disparities in Post-Acute Home Health Care Referral and Utilization among Older Adults with Diabetes

“…Home health care use during the 120 days prior to index hospitalization was calculated from OASIS assessments (2014–2015). Self-reported race/ethnicity data from OASIS assessments (2013–2016) augmented the imputed Research Triangle Institute (RTI) race variable in the MBSF, minimizing misclassification errors and instances of other/unknown race [ 32 ].…”

Racial/Ethnic Disparities in Interhospital Transfer for Conditions With a Mortality Benefit to Transfer Among Patients With Medicare

“…Specifically, there are known inaccuracies in coding for Hispanic ethnicity in Medicare data. 19 , 40 , 41 A study comparing 2010 Medicare race/ethnicity data with self-reported survey data found that only one-third of patients were correctly identified as Hispanic. 40 However, we have no reason to suspect differential misclassification of race by transfer status; therefore, we do not expect systematic bias, only bias toward the null.…”

“…The primary independent variable of interest was race/ethnicity, as categorized in this data set by the Research Triangle Institute race code (for White, Black, Hispanic, or Other), which uses administrative data rather than self-report. 19 We use the term Hispanic throughout the manuscript because this is how this ethnicity is reported by CMS. 19 We focused a priori on Black and Hispanic patients because these groups are traditionally underserved in the US health care system.…”

“… 19 We use the term Hispanic throughout the manuscript because this is how this ethnicity is reported by CMS. 19 We focused a priori on Black and Hispanic patients because these groups are traditionally underserved in the US health care system. We use the term disparity generically to refer to differences in health care metrics between population subgroups.…”

Conceptualizing and Counting Discretionary Utilization in the Final 100 Days of Life: A Scoping Review

“…Prospective studies were more likely to examine race differences in DIALs, perhaps because data on race are more likely to be missing from clinical or administrative data sets. 123 Based on the data synthesized here, we offer five prioritized recommendations to improve research guiding the development of EoL metrics (e.g., DIALs) that could be used by the public (including patients and caregivers), clinicians, researchers, hospitals, health systems, payers, governments, and nongovernmental organizations.…”