COVID-19 Vaccine Effectiveness Against Omicron Infection and Hospitalization
Bibliographic record
Abstract
OBJECTIVES: This study aimed to provide real-world evidence on coronavirus disease 2019 vaccine effectiveness (VE) against symptomatic infection and severe outcomes caused by Omicron in children aged 5 to 11 years. METHODS: We used the test-negative study design and linked provincial databases to estimate BNT162b2 vaccine effectiveness against symptomatic infection and severe outcomes caused by Omicron in children aged 5 to 11 years between January 2 and August 27, 2022 in Ontario. We used multivariable logistic regression to estimate VE by time since the latest dose, compared with unvaccinated children, and we evaluated VE by dosing interval. RESULTS: We included 6284 test-positive cases and 8389 test-negative controls. VE against symptomatic infection declined from 24% (95% confidence interval [CI], 8% to 36%) 14 to 29 days after a first dose and 66% (95% CI, 60% to 71%) 7 to 29 days after 2 doses. VE was higher for children with dosing intervals of ≥56 days (57% [95% CI, 51% to 62%]) than 15 to 27 days (12% [95% CI, -11% to 30%]) and 28 to 41 days (38% [95% CI, 28% to 47%]), but appeared to wane over time for all dosing interval groups. VE against severe outcomes was 94% (95% CI, 57% to 99%) 7 to 29 days after 2 doses and declined to 57% (95%CI, -20% to 85%) after ≥120 days. CONCLUSIONS: In children aged 5 to 11 years, 2 doses of BNT162b2 provide moderate protection against symptomatic Omicron infection within 4 months of vaccination and good protection against severe outcomes. Protection wanes more rapidly for infection than severe outcomes. Overall, longer dosing intervals confer higher protection against symptomatic infection, however protection decreases and becomes similar to shorter dosing interval starting 90 days after vaccination.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".