Using advanced racial and ethnic identity demographics to improve surveillance of work-related conditions in an occupational clinic setting

Andre G. Montoya-Barthelemy, Karyn Leniek, Emily Bannister, Marcus Rushing, Fozia A. Abrar, Tobias E. Baumann, Madeleine Manly, Jonathan Wilhelm, Ashley Niece, Scott Riester, Hyun Kim, Jonathan Sellman, Jay Desai, Paul J. Anderson, Ralph S. Bovard, Nicolas P. Pronk, Zeke J. McKinney

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Background: Although racial and ethnic identities are associated with a multitude of disparate medical outcomes, surveillance of these subpopulations in the occupational clinic setting could benefit enormously from a more detailed and nuanced recognition of racial and ethnic identity. Methods: The research group designed a brief questionnaire to capture several dimensions of this identity and collected data from patients seen for work-related conditions in four occupational medicine clinics from May 2019 through March 2020. Responses were used to calculate the sensitivity and specificity of extant racial/ethnic identity data within our electronic health records system, and were compared to participants' self-reported industry and occupation, coded according to North American Industry Classification System and Standard Occupational Classification System listings. Results: Our questionnaire permitted collection of data that defined our patients' specific racial/ethnic identity with far greater detail, identified patients with multiple ethnic identities, and elicited their preferred language. Response rate was excellent (94.2%, n = 773). Non-White participants frequently selected a racial/ethnic subcategory (78.1%–92.2%). Using our race/ethnicity data as a referent, the electronic health record (EHR) had a high specificity (>87.1%), widely variable sensitivity (11.8%–82.2%), and poorer response rates (75.1% for race, 82.5% for ethnicity, as compared to 93.8% with our questionnaire). Additional analyses revealed some industries and occupations disproportionately populated by patients of particular racial/ethnic identities. Conclusions: Our project demonstrates the usefulness of a questionnaire which more effectively identifies racial/ethnic subpopulations in an occupational medicine clinic, permitting far more detailed characterization of their occupations, industries, and diagnoses.

Original languageEnglish (US)
Pages (from-to)357-370
Number of pages14
JournalAmerican Journal of Industrial Medicine
Issue number5
StatePublished - May 2022

Bibliographical note

Funding Information:
This project was funded by the HealthPartners Institute, through the Regions Hospital Red Fund, St Paul, Minnesota (grant sponsor: Fozia Abrar, MD, MPH).

Publisher Copyright:
© 2022 The Authors. American Journal of Industrial Medicine Published by Wiley Periodicals LLC


  • SOC system
  • clinical surveillance
  • electronic health record
  • occupational coding
  • occupational health
  • racial and ethnic disparities
  • Ethnicity
  • United States
  • Humans
  • Occupations

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't


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