Risk factors for infection with SARS-CoV-2 in a cohort of Canadian healthcare workers: 2020–2023
Bibliographic record
Abstract
Determining the factors that impact the risk for infection with SARS-CoV-2 is a priority as the virus continues to infect people worldwide. The objective was to determine the effectiveness of vaccines and other factors associated with infection among Canadian healthcare workers (HCWs) followed from 15 June 2020 to 1 December 2023. We also investigate the association between antibodies to SARS-CoV-2 and subsequent infections with SARS-CoV-2. Of the 2474 eligible participants, 2133 (86%) were female, 33% were nurses, the median age was 41 years, and 99.3% had received at least two doses of COVID-19 vaccine by 31 December 2021. The incidence of SARS-CoV-2 was 0.91 per 1000 person-days. Prior to the circulation of the Omicron variants, vaccine effectiveness (VE) was estimated at 85% (95% CI 1, 98) for participants who received the primary series of vaccine. During the Omicron period, relative adjusted VE was 43% (95% CI 29, 54), 56% (95% CI 42, 67), and 46% (95% CI 24, 62) for 3, 4, and ≥ 5 doses compared with those who received primary series after adjusting for previous infection and other covariates. Exposure to infected household members, coworkers, or friends in the previous 14 days were risk factor for infection, while contact with an infected patient was not statistically significant. Participants with higher levels of immunoglobulin G (IgG) anti-receptor binding domain (RBD) antibodies had lower rates of infection than those with the lowest levels. COVID-19 vaccines remained effective throughout the follow-up of this cohort of highly vaccinated HCWs. IgG anti-RBD antibody levels may be useful as correlates of protection for issues such as vaccine development and testing. There remains a need to increase the awareness among HCWs about the risk of contracting SARS-CoV-2 from contacts at a variety of venues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".