In-hospital outcomes of SARS-CoV-2-infected health care workers in the COVID-19 pandemic first wave, Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: Health care workers (HCW), particularly immigrants and ethnic minorities are at increased risk for SARS-CoV-2 infection. Outcomes during a COVID-19 associated hospitalization are not well described among HCW. We aimed to describe the characteristics of HCW admitted with COVID-19 including immigrant status and ethnicity and the associated risk factors for Intensive Care unit (ICU) admission and death. METHODS: Adults with laboratory-confirmed community-acquired COVID-19 hospitalized from March 1 to June 30, 2020, at four tertiary-care hospitals in Montréal, Canada were included. Demographics, comorbidities, occupation, immigration status, country of birth, ethnicity, workplace exposures, and hospital outcomes (ICU admission and death) were obtained through a chart review and phone survey. A Fine and Gray competing risk proportional hazards model was used to estimate the risk of ICU admission among HCW stratified by immigrant status and region of birth. RESULTS: Among 1104 included persons, 150 (14%) were HCW, with a phone survey participation rate of 68%. HCWs were younger (50 vs 64 years; p<0.001), more likely to be female (61% vs 41%; p<0.001), migrants (68% vs 55%; p<0.01), non-White (65% vs 41%; p<0.001) and healthier (mean Charlson Comorbidity Index of 0.3 vs 1.2; p<0.001) compared to non-HCW. They were as likely to be admitted to the ICU (28% vs 31%; p = 0.40) but were less likely to die (4% vs. 17%; p<0.001). Immigrant HCW accounted for 68% of all HCW cases and, compared to Canadian HCW, were more likely to be personal support workers (PSW) (54% vs. 33%, p<0.01), to be Black (58% vs 4%) and to work in a Residential Care Facility (RCF) (59% vs 33%; p = 0.05). Most HCW believed that they were exposed at work, 55% did not always have access to personal protective equipment (PPE) and 40% did not receive COVID-19-specific Infection Control (IPAC) training. CONCLUSION: Immigrant HCW were particularly exposed to COVID-19 infection in the first wave of the pandemic in Quebec. Despite being young and healthy, one third of all HCW required ICU admission, highlighting the importance of preventing workplace transmission through strong infection prevention and control measures, including high COVID-19 vaccination coverage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".