ArticleJournal of the American Medical Informatics Association : JAMIA2016
Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals.
Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses 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
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- The quality of social determinants data in the electronic health record: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- ABO phenotype and death in critically ill patients with COVID-19.British journal of haematology · 2020Trial
- Race and Ethnicity Data in the Electronic Health Records: New Insights Through Comparison with American Community Survey Microdata.Journal of racial and ethnic health disparities · 2026Article
- Statistical Accuracy of Administratively Recorded Race/Ethnicity in the Military Health System and Race/Ethnicity Ascertained via Questionnaire.Journal of racial and ethnic health disparities · 2026Article
- Evaluating Methods for Imputing Race and Ethnicity in Electronic Health Record Data.Health services research · 2025Article
- Disparities in Cancer Mortality among Disaggregated Asian American Subpopulations, 2018-2021.Journal of racial and ethnic health disparities · 2025Article
- Discrepancies in Aggregate Patient Data between Two Sources with Data Originating from the Same Electronic Health Record: A Case Study.Applied clinical informatics · 2025Article
- Enhancing Imputation Accuracy for Catch-All Missing Data Mechanisms with DFBETAS and Leverage.Research in statistics (Philadelphia, Pa.) · 2025Article
- Assessing the capture of sociodemographic information in electronic medical records to inform clinical decision making.PloS one · 2025Article
- Systematically assessing the quality of dental electronic health record data for an investigation into oral health care disparities.Journal of public health dentistry · 2024Article
- Using a Health Information Exchange to Characterize Changes in HIV Viral Load Suppression and Disparities During the COVID-19 Pandemic in New York City.Open forum infectious diseases · 2023Article
- Assessing the accuracy of electronic health record gender identity and REaL data at an academic medical center.BMC health services research · 2023Article
- Cancer Epidemiology in the Northeastern United States (2013-2017).Cancer research communications · 2023Article
- Epidemiology of Sarcoidosis in U.S. Veterans from 2003 to 2019.Annals of the American Thoracic Society · 2023Article
- Gaps in the Identification of Child Race and Ethnicity in a Pediatric Emergency Department.The western journal of emergency medicine · 2023Article
- Race and Ethnicity in Pulmonary Function Test Interpretation: An Official American Thoracic Society Statement.American journal of respiratory and critical care medicine · 2023Article
- A Scoping Review of Approaches to Improving Quality of Data Relating to Health Inequalities.International journal of environmental research and public health · 2022Article
- Modeling the Potential Impact of Missing Race and Ethnicity Data in Infectious Disease Surveillance Systems on Disparity Measures: Scenario Analysis of Different Imputation Strategies.JMIR public health and surveillance · 2022Article
- Race Versus Social Determinants of Health in COVID-19 Hospitalization Prediction.American journal of preventive medicine · 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
3 authors.
Funding
Abstract
backgroundMeasurement of patient race/ethnicity in electronic health records is mandated and important for tracking health disparities.
objectiveCharacterize the quality of race/ethnicity data collection efforts.
methodsFor all cancer patients diagnosed (2007-2010) at two hospitals, we extracted demographic data from five sources: 1) a university hospital cancer registry, 2) a university electronic medical record (EMR), 3) a community hospital cancer registry, 4) a community EMR, and 5) a joint clinical research registry. The patients whose data we examined (N = 17 834) contributed 41 025 entries (range: 2-5 per patient across sources), and the source comparisons generated 1-10 unique pairs per patient. We used generalized estimating equations, chi-squares tests, and kappas estimates to assess data availability and agreement.
resultsCompared to sex and insurance status, race/ethnicity information was significantly less likely to be available (χ(2 )> 8043, P < .001), with variation across sources (χ(2 )> 10 589, P < .001). The university EMR had a high prevalence of "Unknown" values. Aggregate kappa estimates across the sources was 0.45 (95% confidence interval, 0.45-0.45; N = 31 276 unique pairs), but improved in sensitivity analyses that excluded the university EMR source (κ = 0.89). Race/ethnicity data were in complete agreement for only 6988 patients (39.2%). Pairs with a "Black" data value in one of the sources had the highest agreement (95.3%), whereas pairs with an "Other" value exhibited the lowest agreement across sources (11.1%). DISCUSSION: Our findings suggest that high-quality race/ethnicity data are attainable. Many of the "errors" in race/ethnicity data are caused by missing or "Unknown" data values.
conclusionsTo facilitate transparent reporting of healthcare delivery outcomes by race/ethnicity, healthcare systems need to monitor and enforce race/ethnicity data collection standards.
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.