Shift workers are at increased risk of severe COVID-19 compared with day workers: Results from the international COVID sleep study (ICOSS) of 7141 workers
Bibliographic record
Abstract
The present study had two main aims. First, to investigate whether shift/night workers had a higher prevalence and severity of COVID-19 compared with day workers. Second, to investigate whether people regularly working in face-to-face settings during the pandemic exhibited a higher prevalence and severity of COVID-19 compared with those having no need to be in close contact with others at work. Data consisted of 7141 workers from 15 countries and four continents who participated in the International COVID Sleep Study-II (ICOSS-II) between May and December 2021. The associations between work status and a positive COVID-19 test and several indications of disease severity were tested with chi-square tests and logistic regressions adjusted for relevant confounders. In addition, statistical analyses were conducted for the associations between face-to-face work and COVID-19 status. Results showed that shift/night work was not associated with an increased risk of COVID-19 compared to day work. Still, shift/night workers reported higher odds for moderate to life-threatening COVID-19 (adjusted odds ratio (aOR) = 2.71, 95%-confidence interval = 1.23-5.95) and need for hospital care (aOR = 5.66, 1.89-16.95). Face-to-face work was associated with an increased risk of COVID-19 (aOR = 1.55, 1.12-2.14) but not with higher disease severity. In conclusion, shift/night work was not associated with an increased risk of COVID-19, but when infected, shift/night workers reported more severe disease. Impaired sleep and circadian disruption commonly seen among shift/night workers may be mediating factors. Working face-to-face increased the risk of COVID-19, likely due to increased exposure to the virus. However, face-to-face work was not associated with increased disease severity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".