Cost-effectiveness of nirsevimab and maternal RSVpreF for preventing respiratory syncytial virus disease in infants across Canada
Bibliographic record
Abstract
BACKGROUND: Nirsevimab, a long-acting monoclonal antibody, and RSVpreF, a maternal vaccine, are newly approved respiratory syncytial virus (RSV) prophylactics for infants in Canada. Both have the potential to expand prevention efforts, but there is limited evidence regarding their cost-effectiveness and how it varies across the country, despite disparate hospitalisation rates and resource use among different populations. METHODS: We developed a decision tree model to follow twelve monthly birth cohorts through their first year of life, incorporating risk differentiation based on Canadian region, prematurity, and comorbidities. The model tracked medically attended infections, including hospitalisations, intensive care unit admissions, and outpatient visits, comparing costs (in 2024 Canadian dollars) and effectiveness (in quality-adjusted life years (QALYs)) of nine different immunisation strategies compared to no intervention. The analysis was conducted from both healthcare and societal perspectives. We conducted threshold price analyses, varying the price-per-dose of each product to determine the threshold prices at which expanded coverage becomes cost-effective. RESULTS: At base case prices, the optimal strategy varies by region, but in all cases, the optimal strategy is both cost-saving and more effective than no intervention. In southern Canada, it is optimal to immunise only palivizumab-eligible infants (those born very prematurely or with high-risk comorbidities) with nirsevimab, resulting in cost savings of $4.14 and QALY gains of 0.000022 QALY per infant compared to no intervention. In the Northwest Territories, it is best to expand protection with nirsevimab to include all preterm infants (cost savings of $28.68 and QALY gains of 0.00007 per infant). In Nunavik and Nunavut, immunising all infants under 6 months and all infants under twelve months with nirsevimab are the best strategies, respectively (cost savings of $399.61 and QALY gains of 0.000821 per infant in Nunavik, and cost savings of $1067.03 and QALY gains of 0.000884 per infant in Nunavut). Universal, country-wide immunisation with nirsevimab would require a price-per-dose of under $112 to become the most cost-effective prevention strategy. CONCLUSIONS: The optimal strategy for preventing respiratory syncytial virus disease in Canadian infants depends on product price and regional risk level and resource use. Canadian policy should account for these factors.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".