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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.002 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".