Cost-Effectiveness of Nirsevimab for the Prevention of Respiratory Syncytial Virus Infection in Infants
Bibliographic record
Abstract
During CADTH’s search of the economic literature, 3 economic studies were identified that assessed the cost-effectiveness of a long-acting monoclonal antibody, nirsevimab, as an intervention to prevent respiratory syncytial virus in infants in high-income countries, including 1 study set in Nunavik. The 3 studies were conducted for different geographical locations: Canada, the US, and England and Wales. While each study conducted an economic evaluation, their approaches differed: 1 was a cost-consequence analysis and the other 2 were cost-utility analyses. The results from the 3 studies varied considerably, and the nirsevimab programs differed (e.g., in terms of patients eligible for immunization). In general, nirsevimab was generally more effective and associated with lower total costs than comparator programs. The results were sensitive to the modelled region, source of efficacy data, price of nirsevimab, and severity of the respiratory syncytial virus season. The generalizability of the identified studies to Canadian policy-making may be limited given the population compositions and cost parameters included in the models. To understand the potential cost-effectiveness of nirsevimab, a de novo economic evaluation would be required that compared nirsevimab with the existing preventive strategies employed in Canada (which may include monoclonal antibodies for infants) and is conducted in a Canadian setting.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".