Vaccine Effectiveness Against Hospitalization Among Adolescent and Pediatric SARS-CoV-2 Cases in Ontario, Canada
Bibliographic record
Abstract
Background: Vaccines against SARS-CoV-2 have been shown to reduce risk of infection, as well as severe disease among those with breakthrough infection, in adults. The latter effect is particularly important as Immune evasion by Omicron variants appears to have made vaccines less effective for prevention of infection. There is currently little available information on the protection conferred by vaccination against severe illness due to SARS-CoV-2 in children. Methods: To minimize confounding by changing vaccination practices and dominant circulating viral variants, we performed an age- and time-matched nested case-control design. Reported SARS-CoV-2 case records in Ontario children and adolescents aged 4 to 17 were linked to vaccination records. We used multivariable logistic regression to estimate the effectiveness of one and two vaccine doses against hospitalization. Results: We identified 130 hospitalized SARS-CoV-2 cases and 1,300 non-hospitalized, age- and time-matched controls, with disease onset between May 28, 2021 and January 9, 2022. One vaccine dose was shown to be 34% effective against hospitalization among SARS-CoV-2 cases (aOR = 0.66 [95% CI: 0.34, 1.21]). In contrast, two doses were 56% (aOR = 0.44 [95% CI: 0.23, 0.83]) effective at preventing hospitalization among SARS-CoV-2 cases. Exploratory instrumental variable analyses, and calculation of E-values, suggested that these effects are unlikely to be explained by unmeasured confounding. Conclusions: Even with immune evasion by SARS-CoV-2 variants, two vaccine doses continue to provide protection against hospitalization among adolescent and pediatric SARS-CoV-2 cases, even when the vaccines do not prevent infection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".