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Record W4389204859 · doi:10.1101/2023.11.29.23299123

Comparing trivalent and quadrivalent seasonal influenza vaccine efficacy in persons 60 years of age and older: A systematic review and network meta-analysis

2023· review· en· W4389204859 on OpenAlexafffundabout
Areti Angeliki Veroniki, Sai Surabi Thirugnanasampanthar, Menelaos Konstantinidis, Jasmeen Dourka, Marco Ghassemi, Dipika Neupane, Paul A. Khan, Vera Nincic, Margarita Corry, Reid Robson, Amanda Parker, Charlene Soobiah, Angela Sinilaité, Paméla Doyon-Plourde, Anabel Gil, Winnie Siu, Nasheed Moqueet, Adrienne Stevens, Kelly English, Iván D. Flórez, Juan José Yepes-Núñez, Brian Hutton, Matthew Muller, Lorenzo Moja, Sharon E. Straus, Andrea C. Tricco

Bibliographic record

VenuemedRxiv · 2023
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsPublic Health OntarioCanada Research ChairsOttawa HospitalUniversity of OttawaPublic Health Agency of CanadaMcMaster UniversityUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineMeta-analysisOdds ratioRandomized controlled trialConfidence intervalPlaceboIncidence (geometry)MEDLINEInfluenza vaccineInternal medicineSystematic reviewAdverse effectPediatricsVaccinationAlternative medicineImmunology

Abstract

fetched live from OpenAlex

ABSTRACT Objectives To compare the efficacy of influenza vaccines of any valency for adults 60 years and older. Methods Design Systematic review with network meta-analysis (NMA) Information sources MEDLINE, EMBASE, JBI Evidence-Based Practice (EBP) Database, PsycINFO, and Cochrane Evidence Based Medicine database from inception to June 20, 2022. Eligibility criteria Randomized controlled trials (RCTs) including older adults (≥60 years old) receiving an influenza vaccine licensed in Canada or the United States (versus placebo, no vaccine, or any other licensed vaccine), at any dose. Outcome measures Primary outcomes: Laboratory-confirmed influenza (LCI) and influenza-like illness (ILI). Secondary outcomes: number of vascular adverse events, hospitalization for acute respiratory infection (ARI) and ILI, inpatient hospitalization, emergency room (ER) visit for ILI, outpatient visit, and mortality, among others. Data extraction, risk of bias (ROB), and certainty of evidence assessment Two reviewers screened, abstracted, and appraised articles (Cochrane ROB 2 tool) independently. We assessed certainty of findings using CINeMA and GRADE approaches. Data synthesis We performed random-effects meta-analysis and NMA, and estimated odds ratios (ORs) for dichotomous outcomes and incidence rate ratios (IRRs) for count outcomes along with corresponding 95% confidence intervals (95%CI) and prediction intervals. Results We included 41 RCTs and 15 companion reports comprising eight vaccine types and 206,032 participants. Vaccines prevented LCI compared with placebo, with high-dose trivalent (IIV3-HD) (NMA, nine RCTs, 52,202 participants, OR 0.23, 95%CI [0.11 to 0.51], low certainty of evidence) and RIV (OR 0.25, 95%CI [0.08 to 0.73], low certainty of evidence) among the most efficacious vaccines. Standard dose trivalent inactivated influenza vaccine (IIV3-SD) prevented ILI compared with placebo, but the result was imprecise (meta-analysis, two RCTs, 854 participants, OR 0.39, 95%CI [0.15 to 1.02], low certainty of evidence). Any high dose (HD) prevented ILI compared with placebo (NMA, nine RCTs, 65,658 participants, OR 0.38, 95%CI [0.15 to 0.93]). Adjuvanted quadrivalent inactivated influenza vaccine (IIV4-Adj) was associated with the least vascular adverse events (NMA: eight RCTs, 57,677 participants, IRR 0.18, 95%CI [0.07 to 0.43], very low certainty of evidence). RIV on all-cause mortality was comparable to placebo (NMA: 20 RCTs, 140,577 participants, OR 1.01, 95%CI [0.23 to 4.49], low certainty of evidence). Conclusions This systematic review demonstrated high efficacy associated with IIV3-HD and RIV vaccines in protecting elderly persons against LCI, and RIV vaccine minimizing all-cause mortality when compared with other vaccines. However, differences in efficacy between these vaccines remain uncertain with very low to moderate certainty of evidence. Funding Canadian Institutes of Health Research Drug Safety and Effectiveness Network (No. DMC – 166263) Systematic review registration PROSPERO CRD42020177357 SUMMARY BOX What is already known on this topic Seasonal influenza vaccination of older adults (≥60 years old) is an important societal, cost-effective means of reducing morbidity and mortality. A multitude of licensed seasonal influenza vaccines for older adults are available in a variety of formulations (such as IIV3, IIV4; prepared in standard and high doses; with and without an adjuvant) relying on production methods including those based on embryonated chicken eggs, or mammalian cell cultures and comprising seasonally selected viral strains or recombinant constructs. Lack of high-quality analysis of randomized control trial (RCT) data pertaining to influenza vaccine production and composition poses challenges for public health clinicians and policy makers who are tasked with making evidence-based decisions regarding recommendations about choosing optimally efficacious and safe influenza vaccines for older adults. What this study adds This systematic review and network meta-analysis of RCT data found that recombinant influenza vaccines (RIV) are among the most effective (lowest odds of laboratory-confirmed influenza [LCI]) and safest (lowest odds of all-cause mortality) of any licensed influenza vaccine type administered to older adults. How this study might affect research, practice or policy Our review points to a potential safety concern regarding increased odds of all-cause mortality associated with older adults receiving adjuvanted influenza vaccines (IIV3-adj and IIV4-adj).

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)0.025
metaresearch head score (Gemma)0.070
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: Review · Consensus signal: Review
Teacher disagreement score0.027
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.070
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0270.045
Bibliometrics0.0110.009
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.328
GPT teacher head0.450
Teacher spread0.121 · 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".

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Citations0
Published2023
Admission routes3
Has abstractyes

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