Cost-effectiveness of RSVpreF vaccine and nirsevimab for the prevention of respiratory syncytial virus disease in Canadian infants
Bibliographic record
Abstract
Abstract Background Health Canada recently authorized the RSVpreF pregnancy vaccine and nirsevimab to protect infants against respiratory syncytial virus (RSV) disease. Objective Assess the cost-effectiveness of RSVpreF and nirsevimab programs in preventing RSV disease in infants, compared to a palivizumab program. Methods We used a static cohort model of a Canadian birth cohort during their first RSV season to estimate sequential incremental cost-effectiveness ratios (ICERs) in 2023 Canadian dollars per quality-adjusted life year (QALY) for nine strategies implemented over a one-year time period, from the health system and societal perspectives. Sensitivity and scenario analyses were conducted to explore the impact of uncertainties on the results. Results All-infants nirsevimab programs averted more RSV-related outcomes than year-round RSVpreF programs, with the most RSV cases averted in seasonal nirsevimab programs. Assuming list prices for these immunizing agents, all-infants nirsevimab and year-round RSVpreF programs were never cost-effective, with ICERs far exceeding commonly used cost-effectiveness thresholds. Seasonal nirsevimab with catch-up was cost-effective if prioritized for infants at moderate/high-risk (ICER <$28,000 per QALY) or those living in settings with higher RSV burden and healthcare costs (ICER of $5,700 per QALY). Using a $50,000 per QALY threshold, an all-infants nirsevimab program could be optimal if nirsevimab is priced at <$110-190 per dose. A year-round RSVpreF for all pregnant women/pregnant people plus nirsevimab for infants at high-risk was optimal if nirsevimab is priced at >$110-190 and RSVpreF priced at <$60-125. Interpretation Prophylactic interventions can substantially reduce RSV disease in infants, and targeted nirsevimab programs are the most cost-effective option at current product prices.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".