Occupational differences in the prevalence and severity of long-COVID: analysis of the Coronavirus (COVID-19) Infection Survey

Theocharis Kromydas; Evangelia Demou; Rhiannon Edge; Matthew Gittins; Srinivasa Vittal Katikireddi; Neil Pearce; Martie van Tongeren; Jack Wilkinson; Sarah Rhodes

doi:10.1136/oemed-2023-108930

Occupational differences in the prevalence and severity of long-COVID: analysis of the Coronavirus (COVID-19) Infection Survey

Occup Environ Med. 2023 Oct;80(10):545-552. doi: 10.1136/oemed-2023-108930. Epub 2023 Sep 28.

Authors

Affiliations

¹ MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, UK theocharis.kromydas@glasgow.ac.uk.
² MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, UK.
³ Lancaster Medical School, Lancaster University, Lancaster, UK.
⁴ Centre for Biostatistics, The University of Manchester, Manchester, UK.
⁵ Faculty of Public Health, London School of Hygiene & Tropical Medicine, London, UK.
⁶ Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK.
⁷ Centre for Occupational and Environmental Health, The University of Manchester, Manchester, UK.
⁸ Thomas Ashton Institute for Risk and Regulatory Research, The University of Manchester, Manchester, UK.

Abstract

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.

Keywords: COVID-19; Epidemiology; Longitudinal studies; Materials, exposures or occupational groups; Occupational Health.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Adult
Aged
COVID-19* / epidemiology
Humans
Middle Aged
Occupations
Post-Acute COVID-19 Syndrome
Prevalence
SARS-CoV-2
Young Adult

Abstract

Publication types

MeSH terms

Grants and funding