Effectiveness of Coronavirus Disease 2019 Vaccines Against Hospitalization and Death in Canada: A Multiprovincial, Test-Negative Design Study
Bibliographic record
Abstract
BACKGROUND: A major goal of coronavirus disease 2019 (COVID-19) vaccination is to prevent severe outcomes (hospitalizations and deaths). We estimated the effectiveness of messenger RNA (mRNA) and ChAdOx1 COVID-19 vaccines against severe outcomes in 4 Canadian provinces between December 2020 and September 2021. METHODS: We conducted this multiprovincial, retrospective, test-negative study among community-dwelling adults aged ≥18 years in Ontario, Quebec, British Columbia, and Manitoba using linked provincial databases and a common study protocol. Multivariable logistic regression was used to estimate province-specific vaccine effectiveness against COVID-19 hospitalization and/or death. Estimates were pooled using random-effects models. RESULTS: We included 2 508 296 tested participants, with 31 776 COVID-19 hospitalizations and 5842 deaths. Vaccine effectiveness was 83% after a first dose and 98% after a second dose against both hospitalization and death (separately). Against severe outcomes, effectiveness was 87% (95% confidence interval [CI], 71%-94%) ≥84 days after a first dose of mRNA vaccine, increasing to 98% (95% CI, 96%-99%) ≥112 days after a second dose. Vaccine effectiveness against severe outcomes for ChAdOx1 was 88% (95% CI, 75%-94%) ≥56 days after a first dose, increasing to 97% (95% CI, 91%-99%) ≥56 days after a second dose. Lower 1-dose effectiveness was observed for adults aged ≥80 years and those with comorbidities, but effectiveness became comparable after a second dose. Two doses of vaccines provided very high protection for both homologous and heterologous schedules and against Alpha, Gamma, and Delta variants. CONCLUSIONS: Two doses of mRNA or ChAdOx1 vaccine provide excellent protection against severe outcomes.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".