ArticleEpidemiologic reviews2023
Methods for retrospectively improving race/ethnicity data quality: a scoping review.
Article in Epidemiologic reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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Who cites it
11 citing papers in PubMed.
- Linking "Big" Geospatial and Health Data: Implications for Research in Environmental Epidemiology.Environmental health perspectives · 2026Article
- A novel surname-based methodology to improve the disaggregation of Asian American ethnic subgroups in patients with ovarian cancer.Gynecologic oncology reports · 2026Article
- Evaluating the impact of discordant and missing demographic information on population health assessments using linked electronic health records and Census Bureau microdata.PLOS digital health · 2026Article
- Overall and Avoidable Healthcare Utilization among Heterogeneous Hispanic/Latino Ethnic Groups with Cognitive Impairment in the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA).Journal of racial and ethnic health disparities · 2026Article
- Decreasing missingness in race and ethnicity data by inclusion of preferred language for mapping to aggregate categories.Health affairs scholar · 2025Article
- Evaluating Methods for Imputing Race and Ethnicity in Electronic Health Record Data.Health services research · 2025Article
- Racial Misclassification of American Indian and Alaska Native People in the Electronic Medical Record: An Unexpected Hurdle in a Retrospective Medical Record Cohort Study.Journal of medical Internet research · 2025Article
- Enabling disaggregation of Asian American subgroups: a dataset of Wikidata names for disparity estimation.Scientific data · 2025Article
- Addressing discretization-induced bias in demographic prediction.PNAS nexus · 2025Article
- Cardiac Arrest Mortality and Disposition Patterns in United States Emergency Departments.Journal of clinical medicine · 2024Article
- IDEAL: A Community-Academic-Governmental Collaboration Toward Improving Evidence-Based Data Collection on Race and Ethnicity.Preventing chronic disease · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Improving race and ethnicity (hereafter, race/ethnicity) data quality is imperative to ensure underserved populations are represented in data sets used to identify health disparities and inform health care policy. We performed a scoping review of methods that retrospectively improve race/ethnicity classification in secondary data sets. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searches were conducted in the MEDLINE, Embase, and Web of Science Core Collection databases in July 2022. A total of 2 441 abstracts were dually screened, 453 full-text articles were reviewed, and 120 articles were included. Study characteristics were extracted and described in a narrative analysis. Six main method types for improving race/ethnicity data were identified: expert review (n = 9; 8%), name lists (n = 27, 23%), name algorithms (n = 55, 46%), machine learning (n = 14, 12%), data linkage (n = 9, 8%), and other (n = 6, 5%). The main racial/ethnic groups targeted for classification were Asian (n = 56, 47%) and White (n = 51, 43%). Some form of validation evaluation was included in 86 articles (72%). We discuss the strengths and limitations of different method types and potential harms of identified methods. Innovative methods are needed to better identify racial/ethnic subgroups and further validation studies. Accurately collecting and reporting disaggregated data by race/ethnicity are critical to address the systematic missingness of relevant demographic data that can erroneously guide policymaking and hinder the effectiveness of health care practices and intervention.
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Registered trials
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