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

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 r...

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Autores principales: Montoya‐Barthelemy, Andre G., Leniek, Karyn, Bannister, Emily, Rushing, Marcus, Abrar, Fozia A., Baumann, Tobias E., Manly, Madeleine, Wilhelm, Jonathan, Niece, Ashley, Riester, Scott, Kim, Hyun, Sellman, Jonathan, Desai, Jay, Anderson, Paul J., Bovard, Ralph S., Pronk, Nicolas P., McKinney, Zeke J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9314926/
https://www.ncbi.nlm.nih.gov/pubmed/35235683
http://dx.doi.org/10.1002/ajim.23332
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author Montoya‐Barthelemy, Andre G.
Leniek, Karyn
Bannister, Emily
Rushing, Marcus
Abrar, Fozia A.
Baumann, Tobias E.
Manly, Madeleine
Wilhelm, Jonathan
Niece, Ashley
Riester, Scott
Kim, Hyun
Sellman, Jonathan
Desai, Jay
Anderson, Paul J.
Bovard, Ralph S.
Pronk, Nicolas P.
McKinney, Zeke J.
author_facet Montoya‐Barthelemy, Andre G.
Leniek, Karyn
Bannister, Emily
Rushing, Marcus
Abrar, Fozia A.
Baumann, Tobias E.
Manly, Madeleine
Wilhelm, Jonathan
Niece, Ashley
Riester, Scott
Kim, Hyun
Sellman, Jonathan
Desai, Jay
Anderson, Paul J.
Bovard, Ralph S.
Pronk, Nicolas P.
McKinney, Zeke J.
author_sort Montoya‐Barthelemy, Andre G.
collection PubMed
description 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.
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spelling pubmed-93149262022-07-30 Using advanced racial and ethnic identity demographics to improve surveillance of work‐related conditions in an occupational clinic setting Montoya‐Barthelemy, Andre G. Leniek, Karyn Bannister, Emily Rushing, Marcus Abrar, Fozia A. Baumann, Tobias E. Manly, Madeleine Wilhelm, Jonathan Niece, Ashley Riester, Scott Kim, Hyun Sellman, Jonathan Desai, Jay Anderson, Paul J. Bovard, Ralph S. Pronk, Nicolas P. McKinney, Zeke J. Am J Ind Med Research Articles 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. John Wiley and Sons Inc. 2022-03-02 2022-05 /pmc/articles/PMC9314926/ /pubmed/35235683 http://dx.doi.org/10.1002/ajim.23332 Text en © 2022 The Authors. American Journal of Industrial Medicine Published by Wiley Periodicals LLC https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Montoya‐Barthelemy, Andre G.
Leniek, Karyn
Bannister, Emily
Rushing, Marcus
Abrar, Fozia A.
Baumann, Tobias E.
Manly, Madeleine
Wilhelm, Jonathan
Niece, Ashley
Riester, Scott
Kim, Hyun
Sellman, Jonathan
Desai, Jay
Anderson, Paul J.
Bovard, Ralph S.
Pronk, Nicolas P.
McKinney, Zeke J.
Using advanced racial and ethnic identity demographics to improve surveillance of work‐related conditions in an occupational clinic setting
title Using advanced racial and ethnic identity demographics to improve surveillance of work‐related conditions in an occupational clinic setting
title_full Using advanced racial and ethnic identity demographics to improve surveillance of work‐related conditions in an occupational clinic setting
title_fullStr Using advanced racial and ethnic identity demographics to improve surveillance of work‐related conditions in an occupational clinic setting
title_full_unstemmed Using advanced racial and ethnic identity demographics to improve surveillance of work‐related conditions in an occupational clinic setting
title_short Using advanced racial and ethnic identity demographics to improve surveillance of work‐related conditions in an occupational clinic setting
title_sort using advanced racial and ethnic identity demographics to improve surveillance of work‐related conditions in an occupational clinic setting
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9314926/
https://www.ncbi.nlm.nih.gov/pubmed/35235683
http://dx.doi.org/10.1002/ajim.23332
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