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Record W4378516755 · doi:10.3390/vaccines11050933

Cost-Effectiveness of the Use of Adjuvanted Quadrivalent Seasonal Influenza Vaccine in Older Adults in Ireland

2023· article· en· W4378516755 on OpenAlexaff
Van Hung Nguyen, Mansoor Ashraf, Joaquin Mould-Quevedo

Bibliographic record

VenueVaccines · 2023
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsSNC-Lavalin (Canada)
FundersSeqirus
KeywordsMedicineInfluenza vaccineCost effectivenessCost-effectiveness analysisSeasonal influenzaPopulationDemographyVaccinationImmunologyEnvironmental healthInternal medicineCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

BACKGROUND: Enhanced vaccines (e.g., containing adjuvants) have shown increased immunogenicity and effectiveness in older adults, who often respond sub-optimally to conventional influenza vaccines. In this study, we evaluated the cost-effectiveness of an inactivated, seasonal, MF59-adjuvanted quadrivalent influenza vaccine (aQIV) for use in adults ≥ 65 years in Ireland. METHODS: A published dynamic influenza model incorporating social contact, population immunity, and epidemiological data was used to assess the cost-effectiveness of aQIV in adults ≥ 65 years of age compared with a non-adjuvanted QIV. Sensitivity analysis was performed for influenza incidence, relative vaccine effectiveness, excess mortality, and the impact on bed occupancy from co-circulating influenza and COVID-19. RESULTS: The use of aQIV resulted in discounted incremental cost-effectiveness ratios (ICERs) of EUR 2420/quality-adjusted life years (QALYs) and EUR 12,970/QALY from societal and payer perspectives, respectively, both of which are below the cost-effectiveness threshold of EUR 45,000/QALY. Sensitivity analysis showed that aQIV was effective in most scenarios, except when relative vaccine effectiveness compared to QIV was below 3%, and resulted in a modest reduction in excess bed occupancy. CONCLUSION: The use of aQIV for adults ≥ 65 years old in Ireland was shown to be highly cost-effective from both payer and societal perspectives.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.134
GPT teacher head0.393
Teacher spread0.259 · 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 teacher head, 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

Citations11
Published2023
Admission routes1
Has abstractyes

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