Cost-Effectiveness of an RSVpreF Vaccine for Prevention of Respiratory Syncytial Virus Outcomes in Infants
Bibliographic record
Abstract
From CADTH’s search of the economic literature, 4 economic studies were identified that assessed the cost-effectiveness of respiratory syncytial virus (RSV) immunization during pregnancy in high-income countries, including 1 study set in Nunavik, Quebec. Only 1 of these studies specifically considered the product of interest (RSVpreF), and only in a scenario analysis. In the 4 identified studies that evaluated the cost-effectiveness of RSV immunization during pregnancy, the outcomes predicted by the models focused on those related to infants. There is a lack of evidence on outcomes — thus cost-effectiveness — for the persons who are pregnant. The results from the 4 studies varied considerably. RSV immunization during pregnancy ranged from being more effective and associated with lower total costs (dominant) to more than $200,000 per quality-adjusted life-year gained when compared with no intervention. The results depended on the modelled region, efficacy, pricing, and severity of the RSV season. In 2 studies, year-round RSV immunization during pregnancy was not considered cost-effective compared with seasonal RSV prophylaxis with long-acting monoclonal antibodies (mAbs), such as nirsevimab, when the price per dose was the same as that of the long-acting mAb. RSV immunization during pregnancy was estimated to become cost-effective when its acquisition cost per dose was 2 to 5 times lower than that of the long-acting mAb.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".