Race and ethnicity: comparing medical records to self-reports
Open accessAbstract
Understanding and eliminating health disparities requires accurate data on race/ethnicity. To assess the quality of race/ethnicity data, we compared medical record classifications to self-report of a study of prophylactic mastectomy. A total of 788 women had race/ethnicity from both sources; 69.9% were 55 years of age or older, 38.3% were at least college graduates, and 67.8% were married or living with someone. There were 817 race/thnicity classifications for the 788 women, of which 758 (92.3%) were identical in the medical record and self-report. Sensitivity and positive predictive value were high (86.7%-97.2%) for whites, Asians, and blacks and moderate (64.0% and 68.1%) for Latinas. However, only one of 18 Native Americans was correctly identified in her medical record. Our results indicate that even if the overall accuracy of medical record classifications for race/ethnicity is high, such a finding may obscure substantial inaccuracies in the recording for racial/ethnic minorities, especially Latinas and Native Americans.
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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 [32]
The Results and Table 2 (page not supplied) support a higher misclassification rate for Latinas: medical records correctly identified 32 of 50 self-reported Latinas. But Table 2 records 17 as white and one as Asian; it does not support the claimed explanation that their ethnicity was missing or that they were grouped as “Other.” This paper is one study, not a report of fourteen studies. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSix studies found disproportionately high rates of misclassification for American Indians in comparison to other racial/ethnic groups; most often, these patients were misidentified as white.
“Six studies found disproportionately high rates of misclassification for American Indians in comparison to other racial/ethnic groups; most often, these patients were misidentified as white.27,28,30,32,41,42”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [32]
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Agreement between administrative data and patients’ self-reports of race/ethnicity
Kressin, N. R., Chang, B.-H., Hendricks, A., & Kazis, L. E. (2003) · American Journal of Public Health · doi:10.2105/ajph.93.10.1734 · Cited by 234 · see citing works
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Klinger, E. V., Carlini, S. V., Gonzalez, I., Hubert, S. S., Linder, J. A., Rigotti, N. A., Kontos, E. Z., Park, E. R., Marinacci, L. X., & Haas, J. S. (2015) · Journal of General Internal Medicine · doi:10.1007/s11606-014-3102-8 · Cited by 234 · see citing works
The quality of social determinants data in the electronic health record: a systematic review
Cook, L. A., Sachs, J., & Weiskopf, N. G. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab199 · Cited by 153 · see citing works
Derose, S. F., Contreras, R., Coleman, K. J., Koebnick, C., & Jacobsen, S. J. (2013) · Medical Care Research and Review · doi:10.1177/1077558712466293 · Cited by 102 · see citing works
Smith, N., Iyer, R. L., Langer-Gould, A., Getahun, D. T., Strickland, D., Jacobsen, S. J., Chen, W., Derose, S. F., & Koebnick, C. (2010) · BMC Health Services Research · doi:10.1186/1472-6963-10-316 · Cited by 62 · see citing works
“This partially explains the lower sensitivity observed in our study compared to other studies which excluded non-classified individuals from their study population [14,15].”
BackgroundResultInfluentialSartin, E. B., Metzger, K. B., Pfeiffer, M. R., Myers, R. K., & Curry, A. E. (2021) · Traffic Injury Prevention · doi:10.1080/15389588.2021.1955109 · Cited by 14 · see citing works
- Authors
- West, C. N.
- Affiliations
- Group Health Cooperative (US)
- Corresponding author
- West, C. N.
- Venue
- Journal of the National Cancer Institute Monographs
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-11-01
- Volume
- 2005
- Issue
- 35
- Pages
- 72-74
- ISSN
- 1052-6773 (ISSN-L)
- OpenAlex
- W2111391008
- Cited by
- 64
- References
- 2
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 7 · 2023: 7 · 2022: 5 · 2021: 4 · 2019: 1 · 2018: 4 · 2017: 1 · 2016: 2 · 2015: 2 · 2014: 5 · 2013: 4 · 2012: 2
- FWCI
- 3.52
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JNCI Monographs (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r01ca090323-02)
- National Cancer Institute (5U19 CA79689, U19CA079689, CA079689, 1R01 CA090323)
- 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
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- prophylactic mastectomy, health disparities, racial and ethnic minorities, Hispanic women, Native Americans, sensitivity and positive predictive value, data accuracy
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Breast Neoplasms, Ethnicity, Female, Humans, Medical Records, Middle Aged, Sensitivity and Specificity, United States, Case-Control Studies, Racial Groups
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