Real-world effectiveness of live attenuated influenza vaccines (LAIV) and inactivated influenza vaccines (IIV) in children from 2003 to 2023: a systematic literature review and network meta-analysis
Bibliographic record
Abstract
Circulating influenza strains, vaccine effectiveness (VE), and vaccine recommendations vary over time. A systematic literature review (SLR), random effects meta-analysis (REMA), and network meta-analysis (NMA) estimated absolute VE (aVE) and relative VE (rVE) of LAIV and IIV in children/adolescents from initial LAIV approval in 2003. Northern Hemisphere studies (2003–2023) with children ≤19 yrs were included. A modified Newcastle-Ottawa Scale assessed risk-of-bias. REMA estimated aVE and three-node NMA (LAIV-IIV-unvaccinated) estimated rVE over three periods: 2003–04 to 2008–09 (pre-2009 A(H1N1) pandemic); 2010–11 to 2016–17 (post-2009 pandemic); 2017–18 to 2022–23 (post-LAIV strain-selection optimization). 109 studies included. aVE of LAIV and IIV against any influenza was similar (~50%) in each period. Effectiveness of LAIV vs. IIV against influenza types/subtypes was comparable except: (1) greater effectiveness with IIV for A(H1N1) in 2010–11 to 2016–17 (rVE –46% [95% CI: –57, –33]); (2) greater effectiveness with LAIV for influenza B in 2017–18 to 2022–23 (rVE 196% [95% CI: 73, 406]). In 2017–18 to 2022–23, effectiveness of LAIV and IIV against A(H1N1) was similar (rVE 10% [95% CI: −35, 87]). LAIV and IIV have demonstrated comparable effectiveness against any influenza in children.
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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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".