Assessing Electronic Health Records for Describing Racial and Ethnic Health Disparities

A Research Note

Bibliographic Data

ID10969871
AuthorsAubrey Limburg (0000-0002-0437-913X, U.S. Census Bureau, Suitland, MD, USA, corresponding author), Jordan Young (0009-0001-0672-3723, University of North Carolina at Chapel Hill), Tanya S Carey (0000-0001-6539-5021, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA), Paul Roman Chelminski (0000-0002-1447-1044, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA), Victoria M Udalova (0000-0003-2239-1439, U.S. Census Bureau, Suitland, MD, USA), Barbara Entwisle (0000-0003-0131-1022, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA)
Year2024
Volume61
Issue5
Pages1325-1338
Publication date2024-10-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueDemography (JOURNAL)
Journal identifiersISSN: 0070-3370 • E-ISSN: 1533-7790
PublisherDuke University Press (PUBLISHER • US)
DOI10.1215/00703370-11582088
PMID39330990
OpenAlexW4402902143
LanguageEN
Citations received2
References cited21

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

Actuarial science · American Community Survey · Business · Census · Environmental health · Ethnic group · Family medicine · Health care · Health equity · Health records · Microdata (statistics · Political science · Population · Population stratification · Public health · Food Security and Health in Diverse Populations · Healthcare Policy and Management · Medicine · Nursing · Primary Care and Health Outcomes · Gerontology