ArticleDemography2024
Assessing Electronic Health Records for Describing Racial and Ethnic Health Disparities: A Research Note.
Article in Demography, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- 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
- Linking national primary care electronic health records to individual records from the U.S. Census Bureau's American Community Survey: evaluating the likelihood of linkage based on patient health.Journal of the American Medical Informatics Association : JAMIA · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The use of data derived from electronic health records (EHRs) to describe racial and ethnic health disparities is increasingly common, but there are challenges. While the number of patients covered by EHRs can be quite large, such patients may not be representative of a source population. One way to evaluate the extent of this limitation is by linking EHRs to an external source, in this case with the American Community Survey (ACS). Relying on a stratified random sample of about 200,000 patient records from a large, public, integrated health delivery system in North Carolina (2016-2019), we assess linkages to restricted ACS microdata (2001-2017) by race and ethnicity to understand the strengths and weaknesses of EHR-derived data for describing disparities. The results in this research note suggest that Black-White comparisons will benefit from standard adjustments (e.g., weighting procedures) but that misestimation of health disparities may arise for Hispanic patients because of differential coverage rates for this group.
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.