Single-dose mRNA vaccine effectiveness against SARS-CoV-2 in healthcare workers extending 16 weeks post-vaccination: a test-negative design from Quebec, Canada
Bibliographic record
Abstract
ABSTRACT Introduction In Canada, first and second doses of mRNA vaccines against SARS-CoV-2 were uniquely spaced 16 weeks apart, but the duration of single-dose protection remains uncertain. We estimated one- and two-dose mRNA vaccine effectiveness (VE) among healthcare workers (HCWs) in Quebec, Canada including protection against varying outcome severity, variants of concern (VOC), and the stability of single-dose protection out to 16 weeks post-vaccination. Methods A test-negative design compared vaccination among SARS-CoV-2 test-positive and weekly-matched (10:1), randomly-sampled, test-negative HCWs using linked surveillance and immunization databases. Vaccine status was defined by one dose ≥14 days or two doses ≥7 days before illness onset or specimen collection. Adjusted VE was estimated by conditional logistic regression. Results Primary analysis included 5,316 cases and 53,160 controls. Single-dose VE was 70% (95%CI: 68-73) against SARS-CoV-2 infection, 73% (95%CI: 71-75) against COVID-19 illness and 97% (95%CI: 92-99) against associated hospitalization. Two-dose VE was 86% (95%CI: 81-90) and 93% (95%CI: 89-95), respectively, with no associated hospitalizations. VE was higher for non-VOC than VOC (73% Alpha) among single-dose (77%, 95%CI: 73-81 versus 63%, 95%CI: 57-67) but not two-dose recipients (87%, 95%CI: 57-96 versus 94%, 95%CI: 89-96). Across 16 weeks, no decline in single-dose VE was observed with appropriate stratification based upon prioritized vaccination determined by higher versus lower likelihood of direct patient contact. Conclusion One mRNA vaccine dose provided substantial and sustained protection to HCWs extending at least four months post-vaccination. In circumstances of vaccine shortage, delaying the second dose may be a pertinent public health strategy to consider.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".