Risk Factors for Infection with SARS-CoV-2; A Prospective Cohort of Teachers and Education Workers, 2021–2023
Bibliographic record
Abstract
Objectives: to determine factors associated with rates of SARS-CoV-2 infection and to determine whether anti-receptor-binding domain (RBD) IgG levels were associated with the rates of infectionDesign: prospective cohort of 34 months’ duration (February 2021 to December 2023)Setting: teachers and education workers working ≥8 hours per week in the Canadian province of OntarioParticipants: 3155 education workers were eligible for the risk factor analysis; 2977 for the serological analysisPrimary outcome measure: SARS-CoV-2 infection, symptomatic or asymptomaticResults: 1909 SARS-CoV-2 infections were reported (0.93 per 1000 participant-days); the highest incidence occurred during the period dominated by the Omicron BA.2 variant (2.01/1000 participant-days). After a median of 740 (interquartile range 361, 971) days of follow-up, participants who received three (adjusted hazard ratio (aHR) 0.24; 95% CI 0.11, 0.50; p<0.001), four (aHR 0.14; 95% CI 0.07, 0.30; p<0.001), or five or more (aHR 0.14; 95% CI 0.06, 0.30; p<0.001) doses of COVID-19 vaccines had lower rates of infection than participants who received <2 doses. Likewise, those who had immunity from a previous infection had lower rates of infection (aHR 0.05; 95% CI 0.04, 0.07) than those without. Participants 30 years of age or older had a lower rate of infection than participants 20-29 years old and those with Caucasian/European ancestry had a lower rate of infection (aHR 0.83; 95% CI 0.71, 0.96; p=0.01) than others. Also, compared to participants without known contact with an infected person, those in close contact with an infected household member (aHR 1.54; 95% CI 1.39, 1.71; p<0.001), coworker or student (aHR 1.22; 95% CI 1.04, 1.42, p=0.012), or individuals from more than one setting (aHR 1.45; 95% CI 1.29, 1.63; p<0.001) had higher rates of infection. Blood samples with anti-RBD antibody levels in the highest quintile (≥5850 BAU/mL) were associated with a lower rate of subsequent infection (aHR 0.40; 95% CI 0.23, 0.72) compared to samples with RBD levels below the threshold of detection. Conclusions: COVID-19 vaccines continued to provide protection against infection with SARS-CoV-2 through December 2023. Infection following exposure to people with SARS-CoV-2 occurred in a variety of venues indicating the need to practice intervention strategies when the potential for transmission is high.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".