ArticleHealth services research2019
Imputing race and ethnic information in administrative health data.
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 12 papers.
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
12 citing papers in PubMed.
- A New Playbook: State-Driven Solutions for Resilient Health Data.The Milbank quarterly · 2026Article
- Standardization and accuracy of race and ethnicity data: Equity implications for medical AI.PLOS digital health · 2025Article
- Assessing the utility of healthcare claims data to determine potential health impacts of PFAS exposure with public drinking water.Environmental epidemiology (Philadelphia, Pa.) · 2025Article
- Recommendations to improve race identification in health records: A rapid scoping review.PloS one · 2025Article
- Multiple Imputation of Missing Race/Ethnicity Information in the National Assisted Reproductive Technology Surveillance System.Journal of women's health (2002) · 2024Article
- Methods for retrospectively improving race/ethnicity data quality: a scoping review.Epidemiologic reviews · 2023Article
- Disaggregating Latino nativity in equity research using electronic health records.Health services research · 2023Article
- Addressing health disparities using multiply imputed injury surveillance data.International journal for equity in health · 2023Article
- The association of prescription opioid use with suicide attempts: An analysis of statewide medical claims data.PloS one · 2022Article
- Race, Ethnicity, and 60-Day Outcomes After Hospitalization With COVID-19.Journal of the American Medical Directors Association · 2021Article
- Variation in Reporting of the Race and Ethnicity of COVID-19 Cases and Deaths Across US States: April 12, 2020, and November 9, 2020.American journal of public health · 2021Article
- Imputing race and ethnic information in administrative health data.Health services research · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectiveTo improve on existing methods to infer race/ethnicity in health care data through an analysis of birth records from Connecticut. DATA SOURCE: A total of 162 467 Connecticut birth records from 2009 to 2013. STUDY
designWe developed a logistic model to predict race/ethnicity using data from US Census and patient-level information. Model performance was tested and compared to previous studies. Five performance measures were used for comparison. PRINCIPAL
findingsOur full model correctly classifies 81 percent of subjects and shows improvement over extant methods. We achieved substantially improved sensitivity in predicting black race.
conclusionsPredictive models using Census information and patients' demographic characteristics can be used to accurately populate race/ethnicity information in health care databases, enhancing opportunities to investigate and address disparities in access to, utilization of, and outcomes of care.
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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.