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Occupational differences in the prevalence and severity of long-COVID: analysis of the Coronavirus (COVID-19) Infection Survey
OBJECTIVES: To establish whether prevalence and severity of long-COVID symptoms vary by industry and occupation. METHODS: We used Office for National Statistics COVID-19 Infection Survey (CIS) data (February 2021–April 2022) of working-age participants (16–65 years). Exposures were industry, occupat...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615205/ https://www.ncbi.nlm.nih.gov/pubmed/37770179 http://dx.doi.org/10.1136/oemed-2023-108930 |
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author | Kromydas, Theocharis Demou, Evangelia Edge, Rhiannon Gittins, Matthew Katikireddi, Srinivasa Vittal Pearce, Neil van Tongeren, Martie Wilkinson, Jack Rhodes, Sarah |
author_facet | Kromydas, Theocharis Demou, Evangelia Edge, Rhiannon Gittins, Matthew Katikireddi, Srinivasa Vittal Pearce, Neil van Tongeren, Martie Wilkinson, Jack Rhodes, Sarah |
author_sort | Kromydas, Theocharis |
collection | PubMed |
description | OBJECTIVES: To establish whether prevalence and severity of long-COVID symptoms vary by industry and occupation. METHODS: We used Office for National Statistics COVID-19 Infection Survey (CIS) data (February 2021–April 2022) of working-age participants (16–65 years). Exposures were industry, occupation and major Standard Occupational Classification (SOC) group. Outcomes were self-reported: (1) long-COVID symptoms and (2) reduced function due to long-COVID. Binary (outcome 1) and ordered (outcome 2) logistic regression were used to estimate odds ratios (OR)and prevalence (marginal means). RESULTS: Public facing industries, including teaching and education, social care, healthcare, civil service, retail and transport industries and occupations, had the highest likelihood of long-COVID. By major SOC group, those in caring, leisure and other services (OR 1.44, 95% CIs 1.38 to 1.52) had substantially elevated odds than average. For almost all exposures, the pattern of ORs for long-COVID symptoms followed SARS-CoV-2 infections, except for professional occupations (eg, some healthcare, education, scientific occupations) (infection: OR<1; long-COVID: OR>1). The probability of reporting long-COVID for industry ranged from 7.7% (financial services) to 11.6% (teaching and education); whereas the prevalence of reduced function by ‘a lot’ ranged from 17.1% (arts, entertainment and recreation) to 22%–23% (teaching and education and armed forces) and to 27% (not working). CONCLUSIONS: The risk and prevalence of long-COVID differs across industries and occupations. Generally, it appears that likelihood of developing long-COVID symptoms follows likelihood of SARS-CoV-2 infection, except for professional occupations. These findings highlight sectors and occupations where further research is needed to understand the occupational factors resulting in long-COVID. |
format | Online Article Text |
id | pubmed-7615205 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-76152052023-10-13 Occupational differences in the prevalence and severity of long-COVID: analysis of the Coronavirus (COVID-19) Infection Survey Kromydas, Theocharis Demou, Evangelia Edge, Rhiannon Gittins, Matthew Katikireddi, Srinivasa Vittal Pearce, Neil van Tongeren, Martie Wilkinson, Jack Rhodes, Sarah Occup Environ Med Workplace OBJECTIVES: To establish whether prevalence and severity of long-COVID symptoms vary by industry and occupation. METHODS: We used Office for National Statistics COVID-19 Infection Survey (CIS) data (February 2021–April 2022) of working-age participants (16–65 years). Exposures were industry, occupation and major Standard Occupational Classification (SOC) group. Outcomes were self-reported: (1) long-COVID symptoms and (2) reduced function due to long-COVID. Binary (outcome 1) and ordered (outcome 2) logistic regression were used to estimate odds ratios (OR)and prevalence (marginal means). RESULTS: Public facing industries, including teaching and education, social care, healthcare, civil service, retail and transport industries and occupations, had the highest likelihood of long-COVID. By major SOC group, those in caring, leisure and other services (OR 1.44, 95% CIs 1.38 to 1.52) had substantially elevated odds than average. For almost all exposures, the pattern of ORs for long-COVID symptoms followed SARS-CoV-2 infections, except for professional occupations (eg, some healthcare, education, scientific occupations) (infection: OR<1; long-COVID: OR>1). The probability of reporting long-COVID for industry ranged from 7.7% (financial services) to 11.6% (teaching and education); whereas the prevalence of reduced function by ‘a lot’ ranged from 17.1% (arts, entertainment and recreation) to 22%–23% (teaching and education and armed forces) and to 27% (not working). CONCLUSIONS: The risk and prevalence of long-COVID differs across industries and occupations. Generally, it appears that likelihood of developing long-COVID symptoms follows likelihood of SARS-CoV-2 infection, except for professional occupations. These findings highlight sectors and occupations where further research is needed to understand the occupational factors resulting in long-COVID. BMJ Publishing Group 2023-10 2023-09-28 /pmc/articles/PMC7615205/ /pubmed/37770179 http://dx.doi.org/10.1136/oemed-2023-108930 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Workplace Kromydas, Theocharis Demou, Evangelia Edge, Rhiannon Gittins, Matthew Katikireddi, Srinivasa Vittal Pearce, Neil van Tongeren, Martie Wilkinson, Jack Rhodes, Sarah Occupational differences in the prevalence and severity of long-COVID: analysis of the Coronavirus (COVID-19) Infection Survey |
title | Occupational differences in the prevalence and severity of long-COVID: analysis of the Coronavirus (COVID-19) Infection Survey |
title_full | Occupational differences in the prevalence and severity of long-COVID: analysis of the Coronavirus (COVID-19) Infection Survey |
title_fullStr | Occupational differences in the prevalence and severity of long-COVID: analysis of the Coronavirus (COVID-19) Infection Survey |
title_full_unstemmed | Occupational differences in the prevalence and severity of long-COVID: analysis of the Coronavirus (COVID-19) Infection Survey |
title_short | Occupational differences in the prevalence and severity of long-COVID: analysis of the Coronavirus (COVID-19) Infection Survey |
title_sort | occupational differences in the prevalence and severity of long-covid: analysis of the coronavirus (covid-19) infection survey |
topic | Workplace |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615205/ https://www.ncbi.nlm.nih.gov/pubmed/37770179 http://dx.doi.org/10.1136/oemed-2023-108930 |
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