COVID-19 vaccine effectiveness against severe omicron-related outcomes in children aged 5 to 11 years in Ontario: A Canadian immunization research network (CIRN) study
Bibliographic record
Abstract
Understanding how the efficacy of COVID-19 vaccines translates from clinical trials to real-world settings is critical to inform evolving vaccination policies. The objective of this study was to assess COVID-19 vaccine effectiveness (VE) against severe COVID-19-related outcomes in children aged 5–11 years, including COVID-19-related hospital admissions and multisystem inflammatory syndrome in children (MIS-C). We conducted a retrospective, population-based cohort study using linked health administrative data in the first year following the emergence of the Omicron variant (January 2 to December 31, 2022) in Ontario, Canada. Baseline differences between subgroups of interest were compared using standardized differences. We used multivariable Cox proportional hazard regression models to estimate VE by time since last vaccine dose by treating vaccination as a time-varying exposure, compared to unvaccinated children. We included a total of 1,058,740 children, of which 583,867 (55.1 %) had received at least one vaccine dose by the end of the study period. In total, there were 185 COVID-19-related hospital admissions and 39 cases of MIS-C. The rate of COVID-19-related admission was substantially higher in children with an underlying comorbid condition compared to children who were previously healthy (adjusted hazard ratio [aHR] = 4.77, 95 %CI, 3.56–6.38). VE against COVID-19-related admission ranged from 93 % (95 %CI, 52–99 %) 7–29 days after a second dose to 63 % (95 %CI; 41–77 %) ≥120 days after a second dose. There was no statistically significant difference in the rate of MIS-C in children who received at least one dose of the vaccine compared to unvaccinated children (aHR = 0.71; 95 %CI, 0.38–1.34). We found that, for children aged 5–11 years, VE against COVID-19-related hospitalization was high in the first four months after a second dose. Children with comorbid conditions were found to be at much higher risk of COVID-19-related severe outcomes and thus may benefit most from COVID-19 vaccination. • In children who received two or more doses of COVID-19 vaccines, vaccination was effective in reducing the risk of Omicron-related hospitalization; • There was no significant difference in the risk of MIS-C in children who received at least one dose of the vaccine compared to unvaccinated children; • Children with an underlying comorbidity were at much higher risk of Omicron-related hospitalization and may benefit most from being up-to-date for COVID-19 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".