Vaccine effectiveness against laboratory-confirmed influenza hospitalizations among young children during the 2010-11 to 2013-14 influenza seasons in Ontario, Canada
Bibliographic record
Abstract
Uncertainty remains regarding the magnitude of effectiveness of influenza vaccines for preventing serious outcomes, especially among young children. We estimated vaccine effectiveness (VE) against laboratory-confirmed influenza hospitalizations among children aged 6-59 months. We used the test-negative design in hospitalized children in Ontario, Canada during the 2010-11 to 2013-14 influenza seasons. We used logistic regression models adjusted for age, season, and time within season to calculate VE estimates by vaccination status (full vs. partial), age group, and influenza season. We also assessed VE incorporating prior history of influenza vaccination. We included specimens from 9,982 patient hospitalization episodes over four seasons, with 12.8% testing positive for influenza. We observed variation in VE by vaccination status, age group, and influenza season. For the four seasons combined, VE was 60% (95%CI, 44%-72%) for full vaccination and 39% (95%CI, 17%-56%) for partial vaccination. VE for full vaccination was 67% (95%CI, 48%-79%) for children aged 24-59 months, 48% (95%CI, 12%-69%) for children aged 6-23 months, 77% (95%CI, 47%-90%) for 2010-11, 59% (95%CI, 13%-81%) for 2011-12, 33% (95%CI, -18% to 62%) for 2012-13, and 72% (95%CI, 42%-86%) for 2013-14. VE in children aged 24-59 months appeared similar between those vaccinated in both the current and previous seasons and those vaccinated in the current season only, with the exception of 2012-13, when VE was lower for those vaccinated in the current season only. Influenza vaccination is effective in preventing pediatric laboratory-confirmed influenza hospitalizations during most seasons.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".