ArticleHealth services research2019
Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity.
Article in Health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- The quality of social determinants data in the electronic health record: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
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- Handling Missing Race and Ethnicity in an EHR-Based Study Through Integration of Individual Measures and Neighborhood Sociodemographic and Socioeconomic Measures.Microorganisms · 2026Article
- Fair positive unlabeled learning for predicting undiagnosed Alzheimer's disease in diverse electronic health records.NPJ digital medicine · 2025Article
- Evaluating Methods for Imputing Race and Ethnicity in Electronic Health Record Data.Health services research · 2025Article
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- Medicare transitional care management services' association with readmissions and mortality.Health affairs scholar · 2024Article
- Comparing HEDIS performance of Dual Eligible Special Needs Plans with other coverage types for dually eligible people.Health affairs scholar · 2024Article
- Methods for retrospectively improving race/ethnicity data quality: a scoping review.Epidemiologic reviews · 2023Article
- Misracialization of Indigenous people in population health and mortality studies: a scoping review to establish promising practices.Epidemiologic reviews · 2023Article
- Assessing the validity of race and ethnicity coding in administrative Medicare data for reporting outcomes among Medicare advantage beneficiaries from 2015 to 2017.Health services research · 2023Article
- Income-related disparities in Medicare advantage behavioral health care quality.Health services research · 2023Article
- Racial and ethnic disparities in COVID-19 booster vaccination among U.S. older adults differ by geographic region and Medicare enrollment.Frontiers in public health · 2023Article
- Real-world Application of Racial and Ethnic Imputation and Cohort Balancing Techniques to Deliver Equitable Clinical Trial Recruitment.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Describing and assessing a new method of approximating categorical individual-level income using community-level income from the census (weighting by income probabilities).Health services research · 2022Article
- Implications of missingness in self-reported data for estimating racial and ethnic disparities in Medicaid quality measures.Health services research · 2022Article
- The association of strained ICU capacity with hospital patient racial and ethnic composition and federal relief during the COVID-19 pandemic.Health services research · 2022Article
- Distinguishing neighborhood and individual social risk factors in health care.Health services research · 2022Article
- Trends in Missing Race and Ethnicity Information After Imputation in HealthCare.gov Marketplace Enrollment Data, 2015-2021.JAMA network open · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
objectiveTo 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
settingData from 284 627 respondents to the 2014 Medicare CAHPS survey. STUDY
designWe 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
methodsWe linked survey-reported race/ethnicity to CMS administrative and US census data. PRINCIPAL
findingsMBISG 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.
conclusionsMBISG 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.