Population effects of influenza vaccination in children and adolescents: Systematic review
Bibliographic record
Abstract
OBJECTIVES: To investigate indirect vaccine effectiveness (indirVE) of vaccination of children and adolescents with seasonal influenza vaccines against influenza-related outcomes occurring in other population groups. METHODS: We performed a systematic review of studies (randomized and non-randomized) on indirVE of vaccination of participants aged 6 months-17 years with tri- or quadrivalent seasonal influenza vaccines against influenza (lab-confirmed; non-lab-confirmed) occurring in contacts of vaccinated persons or members of the wider community (last search: 17th March 2024). GRADE certainty of evidence (CoE) was evaluated (PROSPERO: CRD42024546400). RESULTS: We identified 28 studies (5 randomized; 23 non-randomized). In community-based studies, indirect protection against laboratory-confirmed influenza (LCI) ranged from -38 % [95 % CI: -574 to 72] to 61 % [95 % CI: 8-83] (very low CoE). In household-based settings, indirVE against LCI varied between -151.2 % [95 % CI: -1194.6 to 51.3] and 39.4 % [95 % CI: 7.4 to 60.3] (very low CoE). In school-based settings, highly variable indirect effects were observed on LCI, hospitalization, emergency department visits and school/work absenteeism (very low CoE). CONCLUSIONS: There is no clear evidence of indirect effects from influenza vaccination in children. While plausible, effect size is uncertain and varies by study design, population, and vaccine type. Stronger indirect effects appeared only when direct VE was high.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".