Modelling the Economic Impact of lnfluenza Vaccine Programs with the Cell-Based Quadrivalent Influenza Vaccine and Adjuvanted Trivalent Influenza Vaccine in Canada
Bibliographic record
Abstract
In Canada, approximately 12,000 people annually are hospitalized with influenza. While vaccination is the most effective method for reducing the burden of seasonal influenza, the propagation of vaccine virus strains in eggs can result in egg adaption, resulting in reduced antigenic similarity to circulating strains and thus lower vaccine effectiveness (VE). Cell-based propagation methods avoid these alterations and therefore may be more effective than egg-propagation vaccines. We evaluated three different scenarios: (1) egg-based quadrivalent influenza vaccine (QIVe) for individuals <65 years and adjuvanted trivalent influenza vaccine (aTIV) for ≥65 years; (2) QIVe (<65 years) and high-dose QIV (HD −; QIV; ≥65 years); and (3) cell-based derived QIV (QIVc; <65 years) and aTIV (≥65 years) compared with a baseline scenario of QIVe for all age groups. Modelling was performed using a dynamic age-structured SEIR model, which assessed each strain individually using data from the 2012−2019 seasons. Probabilistic sensitivity analysis assessed the robustness of the results with respect to variation in absolute VE, relative VE, number of egg-adapted seasons, and economic parameters. QIVe + aTIV was cost-saving compared with the baseline scenario (QIVe for all), and QIVe + HD − QIV was not cost-effective in the majority of simulations, reflecting the high acquisition cost of HD − QIV. Overall, while the incremental benefits may vary by influenza season, QIVc + aTIV resulted in the greatest reductions in cases, hospitalizations, and mortality, and was cost-effective (ICER < CAD 50,000) in all simulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".