A Health Economic Evaluation for Implementing an Extended Half-life Monoclonal Antibody for All Infants vs. Standard Care for Respiratory Virus Syncytial Prophylaxis in Canada
Bibliographic record
Abstract
Abstract Respiratory syncytial virus (RSV) is a highly infectious virus, and infants and young children are particularly vulnerable to its progression to severe lower respiratory tract illness (LRTI). Nirsevimab, an extended half-life monoclonal antibody, was recently approved in Canada as a passive immunization intervention for the prevention of RSV LRTI. A static decision tree model was utilized to determine the cost-effectiveness of nirsevimab in Canadian infants compared to current standard of care (palivizumab for infants born preterm, and with specific chronic conditions) and generate an optimal price per dose (PPD) at accepted willingness-to-pay (WTP) thresholds. Various health outcomes (including hospitalization, ICU, and mechanical ventilation) and healthcare costs were calculated over one RSV season, with any necessary follow-up prophylaxis in the second season for three infant categories (palivizumab-eligible, preterm, and term). All health-related parameters and costs were tailored to the Canadian environment. Compared to scenarios where only at-risk segments of the infant population received nirsevimab, the base case (administering nirsevimab to all infants in their first RSV season) was the most cost-effective versus standard care: the PPD was $692 at a $40,000/QALY WTP threshold, using average costing data assumptions across all scenarios. Compared to standard care, the base case scenario could avoid 18,249 RSV-related health outcomes (reduction of 9.96%). Variations in discount rate, distribution of monthly RSV infections, nirsevimab coverage rate for infants born at term, and palivizumab cost had the most significant model impact. Passive immunization of all infants with nirsevimab can significantly reduce RSV-related health and economic burden across Canada.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".