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Record W2564903874 · doi:10.1371/journal.pone.0167281

Effect of Influenza Vaccination of Children on Infection Rate in Hutterite Communities: Follow-Up Study of a Randomized Trial

2016· article· en· W2564903874 on OpenAlexaffabout
Biao Wang, Margaret L. Russell, Lorraine Moss, Kevin Fonseca, David J. D. Earn, Fred Y. Aoki, Gregory Horsman, Paul Van Caeseele, Khami Chokani, Mark Vooght, Lorne A. Babiuk, Richard J. Webby, Stephen D. Walter, Mark Loeb

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of AlbertaSaskatchewan HealthSaskatchewan Disease Control LaboratoryUniversity of ManitobaProvincial Laboratory of Public HealthPrince Albert Grand CouncilUniversity of CalgaryMcMaster University
FundersSanofi PasteurSanofi
KeywordsMedicineVaccinationRandomized controlled trialInfluenza vaccinePandemicInfluenza seasonOutbreakInfluenza A virusCluster (spacecraft)DemographyInternal medicineImmunologyVirologyCoronavirus disease 2019 (COVID-19)VirusDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: An earlier cluster randomized controlled trial (RCT) of Hutterite colonies had shown that if more than 80% of children and adolescents were immunized with influenza vaccine there was a statistically significant reduction in laboratory-confirmed influenza among all unimmunized community members. We assessed the impact of this intervention for two additional influenza seasonal periods. METHODS: Follow-up data for two influenza seasonal periods of a cluster randomized trial involving 1053 Canadian children and adolescents aged 36 months to 15 years in Season 2 and 1014 in Season 3 who received the study vaccine, and 2805 community members in Season 2 and 2840 in Season 3 who did not receive the study vaccine. Follow-up for Season 2 began November 18, 2009 and ended April 25, 2010 while Season 3 extended from December 6, 2010 and ended May 27, 2011. Children were randomly assigned in a blinded manner according to community membership to receive either inactivated trivalent influenza vaccine or hepatitis A. The primary outcome was confirmed influenza A and B infection using RT-PCR assay. Due to the outbreak of 2009 H1N1 pandemic, data in Season 2 were excluded for analysis. RESULTS: For an analysis of the combined Season 1 and Season 3 data, among non-recipients (i.e., participants who did not receive study vaccines), 66 of the 2794 (2.4%) participants in the influenza vaccine colonies and 121 of the 2301 (5.3%) participants in the hepatitis A colonies had influenza confirmed by RT-PCR, for a protective effectiveness of 60% (95% CI, 6% to 83%; P = 0.04); among all study participants (i.e., including both those who received study vaccine and those who did not), 125 of the 3806 (3.3%) in the influenza vaccine colonies and 239 of the 3243 (7.4%) in the hepatitis A colonies had influenza confirmed by RT-PCR, for a protective effectiveness of 63% (95% CI, 5% to 85%; P = 0.04). CONCLUSION: Immunizing children and adolescents with inactivated influenza vaccine can offer a protective effect among unimmunized community members for influenza A and B together when considered over multiple years of seasonal influenza. TRIAL REGISTRATION: Clinicaltrials.gov NCT00877396.

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)0.010
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.083
GPT teacher head0.364
Teacher spread0.281 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations36
Published2016
Admission routes2
Has abstractyes

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