Health plan administrative records versus birth certificate records: quality of race and ethnicity information in children
Open accessAbstract
Background: To understand racial and ethnic disparities in health care utilization and their potential underlying causes, valid information on race and ethnicity is necessary. However, the validity of pediatric race and ethnicity information in administrative records from large integrated health care systems using electronic medical records is largely unknown. Methods: Information on race and ethnicity of 325,810 children born between 1998-2008 was extracted from health plan administrative records and compared to birth certificate records. Positive predictive values (PPV) were calculated for correct classification of race and ethnicity in administrative records compared to birth certificate records. Results: Misclassification of ethnicity and race in administrative records occurred in 23.1% and 33.6% children, respectively; the majority due to missing ethnicity (48.3%) and race (40.9%) information. Misclassification was most common in children of minority groups. PPV for White, Black, Asian/Pacific Islander, American Indian/Alaskan Native, multiple and other was 89.3%, 86.6%, 73.8%, 18.2%, 51.8% and 1.2%, respectively. PPV for Hispanic ethnicity was 95.6%. Racial and ethnic information improved with increasing number of medical visits. Subgroup analyses comparing racial classification between non-Hispanics and Hispanics showed White, Black and Asian race was more accurate among non-Hispanics than Hispanics. Conclusions: In children, race and ethnicity information from administrative records has significant limitations in accurately identifying small minority groups. These results suggest that the quality of racial information obtained from administrative records may benefit from additional supplementation by birth certificate data.
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Misclassification 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 [26]
Multiple studies recommended comparing and linking data across an individual's record to fill in or correct missing data fields, either to increase completeness and/or to check the accuracy of implausible data.
“This method compares and links data across an individual’s record in order to fill in or correct missing data fields and was recommended by several studies as a way to either increase completeness26,28,49,60,62,64,72 and/or check the accuracy of implausible data.13,26,28”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [26]
Smith et al. recommended supplementing race/ethnicity data from the EHR with birth certificate data to increase completeness and plausibility.
“For example, Smith et al.26 recommended supplementing race/ethnicity data from the EHR with birth certificate data to increase completeness and plausibility.”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as Smith et al. [26]
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Menda, N., & Edwards, E. (2023) · Clinics of Perinatology · doi:10.1016/j.clp.2023.01.010 · Cited by 3 · see citing works
Lopes, S. S., Shi, L., Sivaraj, L., Truong, K., Rolke, L., Heavner, S. F., & Basco, W. (2022) · Academic pediatrics · doi:10.1016/j.acap.2022.02.021 · Cited by 3 · see citing works
Racial Differences in Hospitalizations Due to Injuries in South Dakota Children and Adolescents
Bai, W., & Specker, B. (2019) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-019-00611-x · Cited by 3
“records and hospital discharge data may be as high as 50% and misclassification may be more of an issue among children of minority groups [6, 7, 29].”
BackgroundNwachukwu, B. U., Schairer, W., Pan, T. J., Widmann, R., Blanco, J., Green, D., Lyman, S., & Dodwell, E. (2017) · Journal of pediatric orthopedics · doi:10.1097/bpo.0000000000000915 · Cited by 2
Ogundiran, T. J., & Gigli, K. H. (2024) · Journal of Neonatal Nursing · doi:10.1016/j.jnn.2024.11.007 · Cited by 1 · see citing works
Creating Data Systems to Promote Health Equity
Tjoeng, Y. L., Mazwi, M., Goodwin, A., McCradden, M. D., & Chan, T. (2025) · PEDIATRICS · doi:10.1542/peds.2025-070739k
Recurrence Risk of Preterm Birth in Successive Pregnancies Based on Its Subtypes
Smith, I., Fassett, M., Sacks, D. A., Khadka, N., Mensah, N. A., Peltier, M. R., Chiu, V. Y., Xie, F.-G., Shi, J.-X. M., & Getahun, D. (2024) · American Journal of Perinatology · doi:10.1055/a-2552-9058
“…the second pregnancies that are chosen a priori, based on previously documented associations between the confounding factors and the outcome variable or biological plausibility (but excluding those that could be in the causal pathway); these included maternal age ( < 20, 20 – 29, 30 – 34, and (cid”
Self-citationManagement, Utilization, and Outcomes of Preterm Labor in an Integrated Health Care System
Getahun, D., Sacks, D. A., Shi, J.-X. M., Xie, F.-G., Khadka, N., Chiu, V. Y., Mensah, N. A., Avila, C. C., Yeh, M., Kawatkar, A., et al. (2024) · American Journal of Perinatology · doi:10.1055/s-0044-1786545
Self-citationCracking the Administrative Database Code to Risk Stratify Congenital Heart Disease Surgery
Lopez, K. N., & Morris, S. (2022) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.11.032
Kim, J. H. (2012) · Jornal de Pediatria · doi:10.1016/j.jpeds.2012.05.020
- Authors
- Smith, N., Iyer, R. L., Langer-Gould, A., Getahun, D. T., Strickland, D., Jacobsen, S. J., Chen, W., Derose, S. F., & Koebnick, C.
- Affiliations
- Kaiser Permanente (US)
- Corresponding author
- Koebnick, Corinna
- ORCID
- Venue
- BMC Health Services Research
- Published
- 2010-11-23
- Volume
- 10
- Issue
- 1
- Pages
- 316-316
- ISSN
- 1472-6963 (ISSN-L)
- PMC
- PMC3001430
- OpenAlex
- W1996503617
- Cited by
- 62
- References
- 27
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 4 · 2022: 6 · 2021: 7 · 2020: 4 · 2019: 2 · 2018: 5 · 2017: 1 · 2016: 5 · 2015: 1 · 2014: 4 · 2013: 7 · 2012: 8
- FWCI
- 10.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Health Services Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Kaiser Permanente
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases (R21DK085395)
- Topics
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Race, Genetics, and Society
- Keywords
- racial misclassification, children, Hispanic origin, minority groups, positive predictive value, electronic health records
- MeSH terms
- Birth Certificates, California, Child, Child, Preschool, Delivery of Health Care, Ethnicity, Female, Health Planning, Humans, Infant, Infant, Newborn, Male, Predictive Value of Tests, Quality Control, Confidence Intervals, Odds Ratio, Medical Records Systems, Computerized, Racial Groups, Healthcare Disparities
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