Inconsistencies between self-reported ethnicity and ethnicity recorded in a health maintenance organization
Abstract
Purpose Information on patient ethnicity in hospital admissions databases is often used in epidemiologic and health services research. However, the extent of consistency of these data with self-reported ethnicity is not well studied, particularly for specific Asian subgroups. We examined agreement between ethnicity in records of a sample of members of five Northern California Kaiser Permanente medical centers with self-reported ethnicity. Methods Subjects were 3168 cases and 2413 controls aged 45 years and older from a study of fractures. Ethnicity recorded in the Kaiser admissions database (primarily inpatient) was compared with self-reported ethnicity from the study interviews. Results Among study subjects with available Kaiser ethnicity, sensitivities and positive predictive values of the Kaiser classification were high among blacks (0.95 for both measures) and whites (0.98 and 0.94, respectively), slightly lower among Asians (0.88 and 0.95, respectively), and considerably lower among Hispanics (0.55 and 0.81, respectively) and American Indians (0.47 and 0.50, respectively). Among Asian subgroups, the proportion classified as Asian was high among Chinese (0.94) and Japanese (0.99) but lower among Filipinos (0.79) and other Asians (0.74). Among the 228 (4%) subjects who self-identified with multiple ethnicities, 13 of 18 white + Hispanic subjects were classified as being white, and of the 77 subjects identifying as part American Indian, only one was classified as being Amer…
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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.
“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 [41]
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- Authors
- Gomez, S. L., Kelsey, J. L., Glaser, S. L., Lee, M. M., & Sidney, S.
- Affiliations
- Cancer Prevention Institute of California (US)
- Corresponding author
- Gomez, Scarlett L.
- Venue
- Annals of Epidemiology
- Publisher
- Elsevier BV
- Published
- 2005-01
- Volume
- 15
- Issue
- 1
- Pages
- 71-79
- ISSN
- 1047-2797 (ISSN-L)
- OpenAlex
- W2068658201
- Cited by
- 86
- References
- 0
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 3 · 2022: 3 · 2021: 4 · 2020: 4 · 2019: 4 · 2018: 3 · 2017: 7 · 2016: 2 · 2015: 3 · 2014: 7 · 2013: 4 · 2012: 3
- FWCI
- 15.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r01ar042421-05)
- National Cancer Institute (T32CA066521, 1T32CA66521)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01 AR 42421)
- 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 › Sociology and Political Science › Survey Methodology and Nonresponse
- Keywords
- self-reported ethnicity, hospital admission data, data quality, Asian subgroups, Hispanic origin, Kaiser Permanente, fracture, health maintenance organizations
- MeSH terms
- California, Documentation, Ethnicity, Health Maintenance Organizations, Hospital Information Systems, Hospitals, Group Practice, Humans, Medical Records, Medical Records Department, Hospital, Self Disclosure, Organizational Policy, Databases, Factual, Fractures, Bone
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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