Cost-effectiveness analysis of intranasal live attenuated vaccine (LAIV) versus injectable inactivated influenza vaccine (TIV) for Canadian children and adolescents
Bibliographic record
Abstract
BACKGROUND: Influenza affects all age groups and is common in children. Between 15% and 42% of preschool- and school-aged children experience influenza each season. Recently, intranasal live attenuated influenza vaccine, trivalent (LAIV) has been approved in Canada. OBJECTIVE: The objective of this study was to determine the cost-effectiveness of LAIV compared with that of the injectable inactivated influenza vaccine, trivalent (TIV) in Canadian children and adolescents from both a payer (eg. Ministry of Health) perspective and a societal perspective. METHODS: A cost-effectiveness model comparing LAIV and TIV in children aged 24-59 months old was supplemented by primary (ie, a survey of 144 Canadian physicians) and secondary (eg, literature) data to model children aged 2-17 years old. Parameter uncertainty was addressed through univariate and probability analyses. RESULTS: Although LAIV increased vaccination costs when compared to TIV, LAIV reduced the number of influenza cases and lowered the number of hospitalizations, emergency room visits, outpatient visits, and parents' days lost from work. The estimated offsets in direct and societal costs saved were CAD$4.20 and CAD$35.34, respectively, per vaccinated child aged 2-17 years old. When costs and outcomes were considered, LAIV when compared to TIV, was the dominant strategy. At a willingness to pay of CAD$50,000 per quality adjusted life year gained, or CAD$100,000 per quality adjusted life year gained, the probabilistic results indicated that the probability of LAIV being cost-effective was almost 1. CONCLUSIONS: LAIV reduces the burden of influenza in children and adolescents. Consistent with previously reported results, vaccinating children with LAIV, rather than TIV, is the dominant strategy from both a societal perspective and a Ministry of Health perspective.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".