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Record W4386588860 · doi:10.1016/j.vaccine.2023.08.070

Vaccine Effectiveness of non-adjuvanted and adjuvanted trivalent inactivated influenza vaccines in the prevention of influenza-related hospitalization in older adults: A pooled analysis from the Serious Outcomes Surveillance (SOS) Network of the Canadian Immunization Research Network (CIRN)

2023· article· en· W4386588860 on OpenAlexafffundabout
Henrique Pott, Melissa K. Andrew, Zachary Shaffelburg, Michaela Nichols, Lingyun Ye, May ElSherif, Todd F. Hatchette, Jason J. LeBlanc, Ardith Ambrose, Guy Boivin, William Bowie, Jennie Johnstone, Kevin Katz, Philippe Lagacé‐Wiens, Mark Loeb, Anne McCarthy, Allison McGeer, André Poirier, Jeff Powis, David Richardson, Makeda Semret, Stephanie Smith, Daniel Smyth, Grant Stiver, Sylvie Trottier, Louis Valiquette, Duncan Webster, Shelly McNeil

Bibliographic record

VenueVaccine · 2023
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsHorizon Health NetworkCentre hospitalier universitaire de QuébecWilliam Osler Health SystemMount Sinai HospitalMoncton HospitalCentres Intégré Universitaires de Santé et de Services SociauxUniversity of Alberta HospitalOttawa HospitalNorth York General HospitalMcMaster UniversityAlberta Hospital EdmontonUniversity of British ColumbiaSt. Boniface HospitalMcGill UniversityPublic Health Agency of CanadaUniversité LavalUniversité de SherbrookeToronto East General HospitalDalhousie University
FundersCanadian Institutes of Health Research
KeywordsMedicineInfluenza vaccineVirologyVaccinationImmunologyIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Influenza vaccines prevent influenza-related morbidity and mortality; however, suboptimal vaccine effectiveness (VE) of non-adjuvanted trivalent inactivated influenza vaccine (naTIV) or quadrivalent formulations in older adults prompted the use of enhanced products such as adjuvanted TIV (aTIV). Here, the VE of aTIV is compared to naTIV for preventing influenza-associated hospitalization among older adults. METHODS: A test-negative design study was used with pooled data from the 2012 to 2015 influenza seasons. An inverse probability of treatment (IPT)-weighted logistic regression estimated the Odds Ratio (OR) for laboratory-confirmed influenza-associated hospitalization. VE was calculated as (1-OR)*100% with accompanying 95% confidence intervals (CI). RESULTS: Of 7,101 adults aged ≥ 65, 3,364 received naTIV and 526 received aTIV. The overall VE against influenza hospitalization was 45.9% (95% CI: 40.2%-51.1%) for naTIV and 53.5% (42.8%-62.3%) for aTIV. No statistically significant differences in VE were found between aTIV and naTIV by age group or influenza season, though a trend favoring aTIV over naTIV was noted. Frailty may have impacted VE in aTIV recipients compared to those receiving naTIV, according to an exploratory analysis; VE adjusted by frailty was 59.1% (49.6%-66.8%) for aTIV and 44.8% (39.1%-50.0%) for naTIV. The overall relative VE of aTIV to naTIV against laboratory-confirmed influenza hospital admission was 25% (OR 0.75; 0.61-0.92), demonstrating statistically significant benefit favoring aTIV. CONCLUSIONS: Adjusting for frailty, aTIV showed statistically significantly better protection than naTIV against influenza-associated hospitalizations in older adults. In future studies, it is important to consider frailty as a significant confounder of VE.

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)0.017
metaresearch head score (Gemma)0.017
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: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.031
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.353
Teacher spread0.329 · 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
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

Citations15
Published2023
Admission routes3
Has abstractyes

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