Cost-Effectiveness Analysis of Nirsevimab and RSVpreF Vaccine Prevention Strategies for Respiratory Syncytial Virus Disease in infants: A Canadian Immunisation Research Network (CIRN) Study
Bibliographic record
Abstract
Abstract Background The cost-effectiveness of immunisation strategies with a long-acting monoclonal antibody (nirsevimab) and/or a protein-based maternal vaccine (RSVpreF) for protecting infants from Respiratory Syncytial Virus (RSV)-associated illness has not been previously determined for Canada. We estimated the health benefits and cost-effectiveness of nirsevimab for immunising the entire birth cohort regardless of gestational age or other risk factors. Additionally, we evaluated a combined strategy of year-round vaccination of pregnant women with RSVpreF and immunisation of high-risk infants with nirsevimab during RSV season. Methods We developed a discrete-event simulation model, parameterized with the data on RSV incidence, outpatient care, hospitalisations, and deaths. Intervention scenarios targeting twelve monthly birth cohorts and pregnant women were evaluated over a time horizon of one year. Taking into account the costs associated with RSV-related outcomes, we calculated the net monetary benefit using the quality-adjusted life-year (QALY) gained. Further, we determined the range of price-per-dose (PPD) for nirsevimab and RSVpreF within which the program was cost-effective. Cost-effectiveness analyses were conducted from both healthcare and societal perspectives. Findings Using a willingness-to-pay of CAD$50,000 per QALY gained, we found that immunising the entire birth cohort with nirsevimab would be cost-effective from a societal perspective for a PPD of up to $290, with an annual budget impact of $83,978 for 1,113 infants per 100,000 population. An alternative, combined strategy of vaccinating pregnant women and immunising only high-risk infants would lead to a lower budget impact of $49,473 per 100,000 population with a PPD of $290 and $195 for nirsevimab and RSVpreF, respectively. This combined strategy would reduce infant mortality by 76% to 85%, comparable to 78% reduction achieved through a nirsevimab-only program for immunising the entire birth cohort. PPD for cost-effective programs with nirsevimab was sensitive to the target population among infants. Interpretation Passive immunisation of infants under 6 months of age with nirsevimab and vaccination of pregnant women with RSVpreF could be a cost-effective strategy for protecting infants during their first RSV season. Funding This study was supported by the Canadian Immunisation Research Network (CIRN) and the Canadian Institutes of Health Research (CIHR). Seyed M. Moghadas acknowledges support from the Natural Sciences and Engineering Research Council of Canada (MfPH and Discovery grants). Alison P. Galvani acknowledges support from the The Notsew Orm Sands Foundation. Research in context Evidence before this study Prevention of RSV disease in infants under 1 year of age has relied on palivizumab, a short-acting monoclonal antibody, administered monthly to high-risk infants during the period in which RSV is circulating in annual epidemics. New preventive measures including nirsevimab (a long-acting monoclonal antibody for immunising infants) and RSVpreF (a protein-based vaccine for immunising pregnant women) have been developed to reduce the risk of severe RSV illness in the first six months of life. However, no prior study has evaluated cost-effectiveness of these interventions in Canada with recently available efficacy estimates from randomised controlled clinical trials. Added value of this study Using a discrete-event simulation model, we found that immunising the entire birth cohort with nirsevimab would be cost-effective from a societal perspective for a price per dose of up to $290. Year-round vaccination of pregnant women with RSVpreF, followed by immunising infants at high-risk of severe RSV disease with nirsevimab as a combined strategy required a lower budget impact compared to the nirsevimab-only program for the entire birth cohort during the RSV season, while averting similar RSV-related infant mortality. Implications of all the available evidence Prevention strategies against RSV disease in infants using nirsevimab and RSVpreF vaccine could be cost-effective. A combined strategy of these interventions could reduce the budget impact to the healthcare system.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".