Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity
Open accessAbstract
Objective: To improve an existing method, Medicare Bayesian Improved Surname Geocoding ( MBISG ) 1.0 that augments the Centers for Medicare & Medicaid Services’ ( CMS ) administrative measure of race/ethnicity with surname and geographic data to estimate race/ethnicity. Data Sources/Study Setting: Data from 284 627 respondents to the 2014 Medicare CAHPS survey. Study Design: We compared performance (cross‐validated Pearson correlation of estimates and self‐reported race/ethnicity) for several alternative models predicting self‐reported race/ethnicity in cross‐sectional observational data to assess accuracy of estimates, resulting in MBISG 2.0. MBISG 2.0 adds to MBISG 1.0 first name, demographic, and coverage predictors of race/ethnicity and uses a more flexible data aggregation framework. Data Collection/Extraction Methods: We linked survey‐reported race/ethnicity to CMS administrative and US census data. Principal Findings: MBISG 2.0 removed 25‐39 percent of the remaining MBISG 1.0 error for Hispanics, Whites, and Asian/Pacific Islanders ( API ), and 9 percent for Blacks, resulting in correlations of 0.88 to 0.95 with self‐reported race/ethnicity for these groups. Conclusions: MBISG 2.0 represents a substantial improvement over MBISG 1.0 and the use of CMS administrative data on race/ethnicity alone. MBISG 2.0 is used in CMS ’ public reporting of Medicare Advantage contract HEDIS measures stratified by race/ethnicity for Hispanics, Whites, API , and Blacks.
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The most widely researched imputation method was Bayesian Improved Surname Geocoding (BISG).
“The most widely researched imputation method was Bayesian Improved Surname Geocoding (BISG).22,45,46,50,65”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [45]
The passage labeled Abstract (page not provided) says BISG has been validated for imputing race/ethnicity in a variety of populations and cites several studies. That supports BISG as a researched imputation method, but the paper does not compare how extensively it has been researched with other methods or establish that it was the most widely researched. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSeveral studies looked at the use of imputation to increase the completeness of datasets and avoid casewise deletion.
“Several studies looked at the use of imputation, also referred to as indirect estimation, to increase the completeness of datasets and avoid casewise deletion.14,22,30,45,46,50,61,65,67–70”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [45]
The Conclusion (page unavailable in our copy) says the study supports indirect methods for reporting race/ethnicity when Medicare administrative data are unavailable or improperly reported. That supports using imputation to address incomplete data, but the paper does not say it avoids casewise deletion; its Methods section says respondents missing self-reported race/ethnicity were omitted from the validation sample. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTen of these (66%) identified that the incomplete data led to validity issues, most commonly that these data were not missing at random and had the potential to introduce bias.
“Ten of these (66%) identified that the incomplete data led to validity issues, most commonly that these data were not missing at random and had the potential to introduced bias.12,34,43–50”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [45]
The paper says in its introductory discussion that systematic race/ethnicity misclassification leads to biased disparity estimates. Section 2.3.1 also says missing-data quality might correlate with race/ethnicity, but it does not establish that the missing data were not at random. The “ten (66%)” figure is the citing authors’ synthesis, not a finding attributed to this individual paper. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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Cites
A leisurely look at the bootstrap, the jackknife, and cross-validation
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Flagged first, then by citations
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Cited by
Sorted by citations
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
Incentivizing excellent care to at-risk groups with a health equity summary score
Agniel, D., Martino, S. C., Burkhart, Q., Hambarsoomian, K., Orr, N., Beckett, M. K., James, C., Scholle, S. H., Wilson-Frederick, S., Ng, J., et al. (2021) · Journal of General Internal Medicine · doi:10.1007/s11606-019-05473-x · Cited by 36 · see citing works
Self-citation
- Authors
- Haas, A., Elliott, M. N., Dembosky, J. W., Adams, J. L., Wilson‐Frederick, S. M., Mallett, J. S., Gaillot, S., Haffer, S. C., & Haviland, A. M.
- Affiliations
- RAND Corporation (US)
- Kaiser Permanente (US)
- Centers for Medicare and Medicaid Services (US)
- United States Equal Employment Opportunity Commission (US)
- Corresponding author
- Elliott, Marc N.
- Venue
- Health Services Research
- Publisher
- Wiley
- Published
- 2018-12-03
- Volume
- 54
- Issue
- 1
- Pages
- 13-23
- ISSN
- 0017-9124 (ISSN-L)
- 1475-6773
- PMC
- PMC6338295
- OpenAlex
- W2902201369
- Cited by
- 53
- References
- 3
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 10 · 2023: 8 · 2022: 16 · 2021: 4 · 2020: 2 · 2019: 4
- FWCI
- 3.14
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Health Services Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Centers for Medicare and Medicaid Services (GS‐10F‐0275P/HHSM‐500‐2014‐00399G)
- Topics
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Keywords
- Medicare, HEDIS measures, Medicare Advantage, racial stratification, Hispanic population, administrative data, racial misclassification
- MeSH terms
- Bayes Theorem, Cross-Sectional Studies, Ethnicity, Female, Medicare, Health Services Accessibility, Humans, Male, United States, Healthcare Disparities
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