Beyond black and white: mapping misclassification of Medicare beneficiaries race and ethnicity
Open accessAbstract
The Centers for Medicare and Medicaid Services administrative data contains two variables that are used for research and evaluation of health disparities: the enrollment database (EDB) beneficiary race code and the Research Triangle Institute (RTI) race code. The objective of this article is to examine state-level variation in racial/ethnic misclassification of EDB and RTI race codes compared with self-reported data collected during home health care. The study population included 4,231,370 Medicare beneficiaries who utilized home health care services in 2015. We found substantial variation between states in Medicare administrative data misclassification of self-identified Hispanic, Asian American/Pacific Islander, and American Indian/Alaska Native beneficiaries. Caution should be used when interpreting state-level health care disparities and minority health outcomes based on existing race variables contained in Medicare data sets. Self-reported race/ethnicity data collected during routine care of Medicare beneficiaries may be used to improve the accuracy of minority health and health disparities reporting and research.
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Four studies found that Asian patients were more likely to be missing information identifying their race than white patients.
“Four of these studies also found that Asian patients were more likely to be missing information identifying their race than white patients.24,30,33,36”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [24]
The Results section ('Misclassification of Asians and Pacific Islander beneficiaries'; page unavailable) reports substantial misclassification of Asian American/Pacific Islander Medicare beneficiaries, and the Discussion says Asians are under-identified in Medicare administrative data. The paper does not establish that their race information was missing, rather than incorrectly classified, or give the claimed missing-information comparison with White patients. (LLM, conf=0.83)
Experimental — accuracy not yet measuredMisclassification bias was noted as a problem or potential problem in 18 of the 26 (69%) articles about the plausibility of race/ethnicity data.
“Misclassification bias, that is, incorrect assignment, was noted as a problem or a potential problem in 18 of the 26 (69%) articles about the plausibility of race/ethnicity data.11,13,23–38”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [24]
Fourteen studies reported that Hispanic patients were more likely to be misclassified, either that information about their ethnicity was missing or they had been mistakenly grouped into the "Other" category.
“Fourteen studies reported that Hispanic patients were more likely to be misclassified, either that information about their ethnicity was missing or they had been mistakenly grouped into the “Other” category.11,23–25,29,30,32,33,36–41”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [24]
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- Authors
- Grafova, I. B., & Jarrín, O. F.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- School of Nursing, Rutgers, The State University of New Jersey, Newark, NJ, USA
- Venue
- Medical Care Research and Review
- Publisher
- SAGE Publications
- Published
- 2020-07-07
- Volume
- 78
- Issue
- 5
- Pages
- 616-626
- ISSN
- 1077-5587 (ISSN-L)
- 1552-6801
- PMC
- PMC8602956
- OpenAlex
- W3042066551
- Cited by
- 42
- References
- 43
- Citations by year
- 2026: 6 · 2025: 3 · 2024: 3 · 2023: 11 · 2022: 13 · 2021: 6
- FWCI
- 6.59
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Medical Care Research and Review (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Rutgers University Community Repository (Rutgers University) (submittedVersion)
- Funders
- Agency for Healthcare Research and Quality (R00-HS022406)
- National Institute on Aging (P30-AG0059304)
- National Center for Advancing Translational Sciences (UL1-TR003017)
- Topics
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Race, Genetics, and Society
- Social Sciences › Health › Health disparities and outcomes
- Keywords
- racial misclassification, Medicare beneficiaries, race and ethnicity, home health care, self-reported race, state-level variation, health disparities, minority health
- MeSH terms
- Aged, Ethnicity, Medicare, Hispanic or Latino, Humans, United States, Centers for Medicare and Medicaid Services, U.S., Healthcare Disparities
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