Effectiveness of mRNA COVID-19 monovalent and bivalent vaccine booster doses against Omicron severe outcomes among adults aged ≥50 years in Ontario, Canada
Bibliographic record
Abstract
ABSTRACT Objective We estimated the effectiveness of booster doses of monovalent and bivalent mRNA COVID-19 vaccines against Omicron-associated severe outcomes among adults aged ≥50 years in Ontario, Canada. Methods We used a test-negative design to estimate vaccine effectiveness (VE), with unvaccinated adults as the comparator, against hospitalization or death among SARS-CoV-2-tested adults aged ≥50 years between June 19, 2022 and January 28, 2023 stratified by time since vaccination. We explored VE by vaccine product (Moderna Spikevax ® monovalent; Pfizer-BioNTech Comirnaty ® monovalent; Moderna Spikevax ® BA.1 bivalent; Pfizer-BioNTech Comirnaty ® BA.4/BA.5 bivalent). Results We included 3,755 Omicron cases and 14,338 test-negative controls. For the Moderna and Pfizer-BioNTech monovalent vaccines, VE 7-29 days after vaccination was 85% (95% confidence interval [CI], 72-92%) and 88% (95%CI, 82-92%), respectively, and was 82% (95%CI, 76-87%) and 82% (95%CI, 77-86%) 90-119 days after vaccination. For the Moderna BA.1 bivalent vaccine, VE was 86% (95%CI, 82-90%) 7-29 days after vaccination and was 76% (95%CI, 66-83%) 90-119 days after vaccination. For the Pfizer-BioNTech BA.4/BA.5 bivalent vaccine, VE 7-29 days after vaccination was 83% (95%CI, 77-88%) and was 81% (95%CI 72-87%) 60-89 days after vaccination. Conclusions Booster doses of monovalent and bivalent mRNA COVID-19 vaccines provided similar, strong initial protection against severe outcomes in community-dwelling adults aged ≥50 years in Ontario. Nonetheless, uncertainty remains around waning protection of these vaccines.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".