Risk of SARS-CoV-2 infection before and after the Omicron wave in a cohort of healthcare workers in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Longitudinal healthcare worker (HCW) cohorts throughout the COVID-19 pandemic provide a unique opportunity to study the relative contributions of various exposures to infection risk over time. This study aimed to examine how demographic, health, occupational, household and community factors influenced the SARS-CoV-2 infection risk in a cohort of HCWs in Southeastern Ontario, Canada, during the early pandemic and the Omicron waves. We compared the contribution of these factors to infection risk and explored the implications for future epidemic preparedness and the protection of HCWs. METHODS: We conducted a longitudinal analysis using data from a cohort of HCWs recruited from one acute care hospital and four long-term care homes. The analysis was divided into two periods: the initial phase of the pandemic (period #1) and the first three Omicron waves (period #2). We employed Poisson regression for period #1 and Cox regression for period #2 to examine associations of demographic factors (age, sex, ethnicity, migration status, income insufficiency), health factors (chronic conditions, smoking history, SARS-CoV-2 vaccination status), household factors (exposure to COVID-19), occupational factors (work role, exposure to COVID-19 patients, personal protective equipment access, aerosol-generating procedures) and community exposures (use of masks, distance, hand-washing) with SARS-CoV-2 infection. RESULTS: At period #1, 17/208 (8.2%) HCWs reported having had SARS-CoV-2 infection. At period #2, 65/167 (38.3%) reported at least one SARS-CoV-2 infection. In period #1, factors associated with increased risk of infection included working in a long-term care home, exposure to more COVID-19-positive patients, working as a nurse or therapist, and inadequate use of personal protective equipment. In period #2, the hazard of infection was higher among HCWs who had COVID-19-infected children at home, whereas the use of protective measures in the community (maintaining social distance, mask-wearing) and receiving a vaccine booster were associated with reduced risk. Providing care to COVID-19 patients was not associated with the risk of acquiring SARS-CoV-2 infection at period #2. CONCLUSIONS: During the Omicron wave, community and household exposures, but not occupational exposure to COVID-19 cases, were the primary factors contributing to infection risk in HCWs. This contrasts with the early waves of the pandemic where occupational exposures played a significant role. These findings may be explained by the effectiveness of institutional interventions in reducing the risk of SARS-CoV-2 transmission in healthcare settings, alongside the failure of community-level interventions to mitigate risk during the Omicron period.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".