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Socioeconomic predictors of COVID-19-related health disparities among United States workers: A structural equation modeling study

The COVID-19 pandemic has disproportionately impacted the physical and mental health, and the economic stability, of specific population subgroups in different ways, deepening existing disparities. Essential workers have faced the greatest risk of exposure to COVID-19; women have been burdened by ca...

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Autores principales: Capasso, Ariadna, Kim, Sooyoung, Ali, Shahmir H., Jones, Abbey M., DiClemente, Ralph J., Tozan, Yesim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021756/
https://www.ncbi.nlm.nih.gov/pubmed/36962121
http://dx.doi.org/10.1371/journal.pgph.0000117
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author Capasso, Ariadna
Kim, Sooyoung
Ali, Shahmir H.
Jones, Abbey M.
DiClemente, Ralph J.
Tozan, Yesim
author_facet Capasso, Ariadna
Kim, Sooyoung
Ali, Shahmir H.
Jones, Abbey M.
DiClemente, Ralph J.
Tozan, Yesim
author_sort Capasso, Ariadna
collection PubMed
description The COVID-19 pandemic has disproportionately impacted the physical and mental health, and the economic stability, of specific population subgroups in different ways, deepening existing disparities. Essential workers have faced the greatest risk of exposure to COVID-19; women have been burdened by caretaking responsibilities; and rural residents have experienced healthcare access barriers. Each of these factors did not occur on their own. While most research has so far focused on individual factors related to COVID-19 disparities, few have explored the complex relationships between the multiple components of COVID-19 vulnerabilities. Using structural equation modeling on a sample of United States (U.S.) workers (N = 2800), we aimed to 1) identify factor clusters that make up specific COVID-19 vulnerabilities, and 2) explore how these vulnerabilities affected specific subgroups, specifically essential workers, women and rural residents. We identified 3 COVID-19 vulnerabilities: financial, mental health, and healthcare access; 9 out of 10 respondents experienced one; 15% reported all three. Essential workers [standardized coefficient (β) = 0.23; unstandardized coefficient (B) = 0.21, 95% CI = 0.17, 0.24] and rural residents (β = 0.13; B = 0.12, 95% CI = 0.09, 0.16) experienced more financial vulnerability than non-essential workers and non-rural residents, respectively. Women (β = 0.22; B = 0.65, 95% CI = 0.65, 0.74) experienced worse mental health than men; whereas essential workers reported better mental health (β = -0.08; B = -0.25, 95% CI = -0.38, -0.13) than other workers. Rural residents (β = 0.09; B = 0.15, 95% CI = 0.07, 0.24) experienced more healthcare access barriers than non-rural residents. Findings highlight how interrelated financial, mental health, and healthcare access vulnerabilities contribute to the disproportionate COVID-19-related burden among U.S. workers. Policies to secure employment conditions, including fixed income and paid sick leave, are urgently needed to mitigate pandemic-associated disparities.
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spelling pubmed-100217562023-03-17 Socioeconomic predictors of COVID-19-related health disparities among United States workers: A structural equation modeling study Capasso, Ariadna Kim, Sooyoung Ali, Shahmir H. Jones, Abbey M. DiClemente, Ralph J. Tozan, Yesim PLOS Glob Public Health Research Article The COVID-19 pandemic has disproportionately impacted the physical and mental health, and the economic stability, of specific population subgroups in different ways, deepening existing disparities. Essential workers have faced the greatest risk of exposure to COVID-19; women have been burdened by caretaking responsibilities; and rural residents have experienced healthcare access barriers. Each of these factors did not occur on their own. While most research has so far focused on individual factors related to COVID-19 disparities, few have explored the complex relationships between the multiple components of COVID-19 vulnerabilities. Using structural equation modeling on a sample of United States (U.S.) workers (N = 2800), we aimed to 1) identify factor clusters that make up specific COVID-19 vulnerabilities, and 2) explore how these vulnerabilities affected specific subgroups, specifically essential workers, women and rural residents. We identified 3 COVID-19 vulnerabilities: financial, mental health, and healthcare access; 9 out of 10 respondents experienced one; 15% reported all three. Essential workers [standardized coefficient (β) = 0.23; unstandardized coefficient (B) = 0.21, 95% CI = 0.17, 0.24] and rural residents (β = 0.13; B = 0.12, 95% CI = 0.09, 0.16) experienced more financial vulnerability than non-essential workers and non-rural residents, respectively. Women (β = 0.22; B = 0.65, 95% CI = 0.65, 0.74) experienced worse mental health than men; whereas essential workers reported better mental health (β = -0.08; B = -0.25, 95% CI = -0.38, -0.13) than other workers. Rural residents (β = 0.09; B = 0.15, 95% CI = 0.07, 0.24) experienced more healthcare access barriers than non-rural residents. Findings highlight how interrelated financial, mental health, and healthcare access vulnerabilities contribute to the disproportionate COVID-19-related burden among U.S. workers. Policies to secure employment conditions, including fixed income and paid sick leave, are urgently needed to mitigate pandemic-associated disparities. Public Library of Science 2022-02-09 /pmc/articles/PMC10021756/ /pubmed/36962121 http://dx.doi.org/10.1371/journal.pgph.0000117 Text en © 2022 Capasso et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Capasso, Ariadna
Kim, Sooyoung
Ali, Shahmir H.
Jones, Abbey M.
DiClemente, Ralph J.
Tozan, Yesim
Socioeconomic predictors of COVID-19-related health disparities among United States workers: A structural equation modeling study
title Socioeconomic predictors of COVID-19-related health disparities among United States workers: A structural equation modeling study
title_full Socioeconomic predictors of COVID-19-related health disparities among United States workers: A structural equation modeling study
title_fullStr Socioeconomic predictors of COVID-19-related health disparities among United States workers: A structural equation modeling study
title_full_unstemmed Socioeconomic predictors of COVID-19-related health disparities among United States workers: A structural equation modeling study
title_short Socioeconomic predictors of COVID-19-related health disparities among United States workers: A structural equation modeling study
title_sort socioeconomic predictors of covid-19-related health disparities among united states workers: a structural equation modeling study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021756/
https://www.ncbi.nlm.nih.gov/pubmed/36962121
http://dx.doi.org/10.1371/journal.pgph.0000117
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