Cost-Effectiveness of Infant and Maternal RSV Immunization Strategies, in British Columbia, Canada
Bibliographic record
Abstract
Abstract Background Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infections in young children and results in significant healthcare burden and costs. To reduce the impact of RSV in this population, the monoclonal antibody palivizumab has historically been used. Recently, new preventive options have become available, including a longer-acting monoclonal antibody (nirsevimab) and a maternal vaccine (RSVpreF). Methods We developed a discrete-event simulation model using epidemiological and cost data from British Columbia, Canada, and published efficacy estimates. The model simulated a cohort of 100,000 newborns and followed them up to 24 months. We conducted the analysis from a healthcare system perspective, evaluating five immunization strategies: (1) the historical palivizumab standard of care for high-risk children; (2) nirsevimab for high- and moderate-risk children; (3) in-season maternal RSVpreF vaccination combined with nirsevimab for high-risk children; (4) in-season maternal RSVpreF plus nirsevimab for high- and moderate-risk children; and (5) nirsevimab for all infants. We conducted a sequential cost-effectiveness analysis, ordering strategies by cost, excluding dominated or extendedly dominated options, and evaluating the remaining strategies stepwise. To support policy interpretation, we also performed a pairwise analysis comparing each strategy directly with the historical standard of care. Results In the sequential analysis, strategy 2 was the most cost-effective option. Strategy 4 provided additional health gains but was not cost-effective incrementally (ICER ≈ $119,000 per QALY vs strategy 2). Strategy 5 offered the greatest overall health benefits but was the least cost-effective option. When compared directly with the historical standard of care, however, strategy 4 was cost-effective (ICER ≈ $18,000 per QALY). Interpretation These findings support policy recommendations to prioritize nirsevimab for high- and moderate-risk infants as the most cost-effective strategy. Maternal RSVpreF vaccination offers added health benefits and is cost-effective compared with the historical standard of care, though not when considered incrementally.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".