Epidemiology of COVID-19 Among Healthcare Workers In Ontario, Canada During The First Pandemic Wave
Bibliographic record
Abstract
Aim and Objectives: This study aims to describe and compare COVID-19 cases among healthcare workers, long-term care residents, and the general population in Ontario, Canada, considering baseline characteristics, trends over time, and socioeconomic status. Methods: This study used test-confirmed COVID-19 case reports between March 13th, 2020 to June 15th, 2020, reported by Ontario’s Public Health Units to the Ontario Ministry of Health Public Health Case and Contact Management Solution (CCM). Cases were stratified into three sub-populations based on risk group characteristics identified in CCM data: healthcare workers, long-term care residents, and the general population. The residential postal codes of the cases reported to CCM were linked to area-level socioeconomic characteristics of material deprivation from the Ontario Marginalization Index (ON-MARG). Demographic characteristics and case outcomes were captured in CCM data for each case. Results: COVID-19 cases among healthcare workers were more concentrated between working ages of 20–59 and in females, compared to the general population and long-term care cases. Additionally, hospitalization and mortality were low among healthcare workers compared to the other sub-populations. Over time, COVID-19 cases decreased among healthcare workers. For both healthcare workers and the general population, more cases were observed in areas of high material deprivation, and this disparity between high- and low- income areas increased over time. Conclusion: Healthcare workers are a known high-risk group for COVID-19. For the surveillance of this disease, it is important to understand how they compare to other population groups regarding infection, hospitalization, and mortality. Our analysis shows clear socioeconomic gradients in the distribution of the disease. Thus, focusing our efforts on identifying and testing healthcare workers that work or live in lower socioeconomic areas would benefit the residents and workers in these areas and support the ongoing COVID-19 response.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".