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Indirect Effectiveness of Influenza Vaccination: A Systematic Review of Cluster-randomized Controlled Trials

2025· review· en· W4410271450 on OpenAlexaffabout
A. C. Gilmore, I. Galbreath, Lorna Hobbs, A. Larocco, Lindsay Friedman, Bryna Warshawsky, Jethro C.C. Kwong, Kathleen M. Neuzil, Justin R. Ortiz

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsPublic Health Agency of CanadaPublic Health Ontario
Fundersnot available
KeywordsMedicineVaccinationRandomized controlled trialCluster (spacecraft)MEDLINEIntensive care medicineVirologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Rationale: Indirect vaccine effectiveness (IVE) is the measure of protection conferred from vaccinated individuals to others who are susceptible to infection. Conceptually, by vaccinating sufficient numbers of persons against influenza and reducing primary infections and/or contagiousness, the risk of unvaccinated persons developing influenza decreases. To inform public health expectations, we conducted a systematic review of cluster-randomized influenza vaccination trials designed to measure IVE. Methods: We searched MEDLINE and EMBASE for relevant articles from inception through September 23, 2024. We identified cluster-randomized, controlled trials published in English that measured IVE against laboratory-confirmed influenza illness (LCI) outcomes in humans and reported overall IVE as a percentage or provided sufficient data to calculate it. We then extracted and collated article data to analyze studies and report both indirect and direct vaccine effectiveness. Results: We identified four relevant publications, describing seven separate influenza vaccine trials: one article from Canada described a single trial conducted in 2008-09, two articles from Senegal described three separate trials conducted from 2008-11, and one article from India described three separate trials conducted from 2009-12. Among the seven trials, four (57%) measured significant direct vaccine effectiveness ranging from 25.6% (95% CI 6.8% to 40.6%) to 74.2% (95% CI 57.8% to 84.3%). Two (29%) of the trials measured significant IVE ranging from 38.1% (95% CI 7.4% to 58.6%) to 61% (95% CI 8%, 83%), while five (71%) did not find significant evidence of IVE (with 95% CI crossing the null). Each of the five studies without significant IVE reported mismatch between a vaccine strain and a circulating viral strain, including one study conducted during the emergence of the 2009 pandemic influenza A (H1N1). Three studies that measured significant direct vaccine effectiveness failed to measure significant IVE. Conclusions: Our study indicates that seasonal influenza vaccination programs may not necessarily confer measurable indirect protections to unvaccinated populations. Among the seven published cluster-randomized influenza vaccination trials, five failed to detect any significant IVE, with each implicating vaccine mismatch against circulating viruses as contributing to the findings. Furthermore, among the four studies that measured significant overall direct influenza vaccine effectiveness, three did not measure significant IVE. In the future, we will expand our analysis to include observational research studies as well as measures of strain-specific IVE. While currently available influenza vaccines are moderately effective at preventing influenza illness among vaccinated persons, they may be inconsistent at conferring indirect protections.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.049
metaresearch head score (Gemma)0.179
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.049
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.179
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0190.017
Bibliometrics0.0130.012
Science and technology studies0.0010.002
Scholarly communication0.0050.004
Open science0.0040.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.085
GPT teacher head0.480
Teacher spread0.395 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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