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J Sleep Res ; 30(5): e13326, 2021 10.
Article in English | MEDLINE | ID: covidwho-1122181

ABSTRACT

Despite the strong evidence on circadian rhythm disruption in shift workers and consequent increased vulnerability for infection, longitudinal association between shift work and COVID-19 infection is unexplored. In this study, data from UK Biobank participants who were tested for COVID-19 infection (16 March to 7 September 2020) were used to explore the link between shift work and COVID-19 infection. Using the baseline occupational information, participants were categorised as non-shift workers, day shift workers, mixed shift workers and night shift workers. Multivariable regression models were used to assess the association between shift work and COVID-19 infection. Among the 18,221 participants (9.4% positive cases), 11.2% were health workers, and 16.4% were involved in shift-work-based jobs. Ethnic minorities (18%) and people in night-shift-based jobs (18.1%) had a significantly higher prevalence of COVID-19 infection than others. Adjusted logistics regression model suggest that, compared with their counterparts, people employed in a night-shift-based job were 1.85-fold (95% CI: 1.42-2.41) more likely to have COVID-19 infection. Sensitivity analysis focusing on people working in a non-healthcare setting suggests that people in shift-work-based jobs had 1.81-fold (95% CI: 1.04%-3.18%) higher odds of COVID-19 infection than their counterparts. Shift workers, particularly night shift workers, irrespective of their occupational group, seem to be at high risk of COVID-19 infection. If similar results are obtained from other studies, then it would mandate to revisit the criteria for defining high-risk groups for COVID-19 and implementing appropriate interventions to protect people in shift-based jobs.


Subject(s)
COVID-19 , Shift Work Schedule , Biological Specimen Banks , COVID-19/epidemiology , Cohort Studies , Female , Humans , Male , Middle Aged , Risk Assessment , Shift Work Schedule/adverse effects , United Kingdom/epidemiology
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