SARS-CoV-2 seroprevalence in health care workers from 10 hospitals in Quebec, Canada: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has disproportionately affected health care workers. We sought to estimate SARS-CoV-2 seroprevalence among hospital health care workers in Quebec, Canada, after the first wave of the pandemic and to explore factors associated with SARS-CoV-2 seropositivity. METHODS: Between July 6 and Sept. 24, 2020, we enrolled health care workers from 10 hospitals, including 8 from a region with a high incidence of COVID-19 (the Montréal area) and 2 from low-incidence regions of Quebec. Eligible health care workers were physicians, nurses, orderlies and cleaning staff working in 4 types of care units (emergency department, intensive care unit, COVID-19 inpatient unit and non-COVID-19 inpatient unit). Participants completed a questionnaire and underwent SARS-CoV-2 serology testing. We identified factors independently associated with higher seroprevalence. RESULTS: Among 2056 enrolled health care workers, 241 (11.7%) had positive SARS-CoV-2 serology. Of these, 171 (71.0%) had been previously diagnosed with COVID-19. Seroprevalence varied among hospitals, from 2.4% to 3.7% in low-incidence regions to 17.9% to 32.0% in hospitals with outbreaks involving 5 or more health care workers. Higher seroprevalence was associated with working in a hospital where outbreaks occurred (adjusted prevalence ratio 4.16, 95% confidence interval [CI] 2.63-6.57), being a nurse or nursing assistant (adjusted prevalence ratio 1.34, 95% CI 1.03-1.74) or an orderly (adjusted prevalence ratio 1.49, 95% CI 1.12-1.97), and Black or Hispanic ethnicity (adjusted prevalence ratio 1.41, 95% CI 1.13-1.76). Lower seroprevalence was associated with working in the intensive care unit (adjusted prevalence ratio 0.47, 95% CI 0.30-0.71) or the emergency department (adjusted prevalence ratio 0.61, 95% CI 0.39-0.98). INTERPRETATION: Health care workers in Quebec hospitals were at high risk of SARS-CoV-2 infection, particularly in outbreak settings. More work is needed to better understand SARS-CoV-2 transmission dynamics in health care settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".