Effectiveness of mRNA COVID-19 vaccine booster doses against Omicron severe outcomes
Bibliographic record
Abstract
ABSTRACT Background To inform planning for further booster doses of COVID-19 vaccines, we estimated the effectiveness of monovalent mRNA vaccines against Omicron-associated severe outcomes in adults over time. Methods We used a test-negative design and multivariable logistic regression to estimate vaccine effectiveness (VE; 2, 3, or 4 doses compared to unvaccinated individuals) and marginal effectiveness (3 or 4 doses compared to 2 doses) against Omicron-associated hospitalization or death among community-dwelling adults aged ≥50 years who were tested for SARS-CoV-2 between January 2, 2022 and October 1, 2022 in Ontario, Canada, stratified by age group and time since vaccination. We also compared VE during periods of Omicron BA.1/BA.2 and BA.4/BA.5 sublineage predominance. Results We included 11,160 cases of Omicron-associated severe outcomes and 62,880 test-negative symptomatic controls. Depending on the age group, compared to unvaccinated individuals, VE was 91-98% 7-59 days after a third dose, waned to 76-87% after ≥240 days, was restored to 92-97% 7-59 days after a fourth dose, and waned to 86-89% after ≥120 days. Trends in marginal effectiveness were consistent with VE estimates. VE was lower during the BA.4/BA.5-predominant period compared to the BA.1/BA.2-predominant period based on the same intervals since vaccination. Conclusion Our findings suggest that 1 or 2 booster doses of monovalent mRNA COVID-19 vaccines initially restored very strong protection against Omicron-associated severe outcomes in all age groups, but VE subsequently declined over time with some age-related differences, and particularly so during a period of BA.4/BA.5 predominance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".