Health economic evaluation of implementing a universal immunization program with nirsevimab compared to standard of care for the prevention of respiratory syncytial virus disease in Canadian infants
Bibliographic record
Abstract
Respiratory syncytial virus (RSV) is a highly contagious pathogen and a leading cause of severe lower respiratory tract illness (LRTI) in infants and young children, irrespective of risk factors. Nirsevimab, an extended half-life monoclonal antibody, was approved in Canada in 2023 as a passive immunizing agent for the prevention of RSV LRTI. This study evaluated the optimal price per dose (PPD) at commonly accepted willingness-to-pay (WTP) thresholds among Canadian infants compared to the current standard of care (i.e. palivizumab for preterm infants and those with specific medical conditions). A static decision tree model was developed to assess the impact of nirsevimab on RSV-related health and economic outcomes among Canadian infants - including outpatient physician and emergency department visits, inpatient hospitalizations including intensive care unit (ICU) admissions and mechanical ventilation, and the associated healthcare costs of these outcomes. The model utilized Canadian epidemiological and cost inputs where possible, adopting a societal perspective. Compared to the standard of care, nirsevimab was expected to prevent 47,609 RSV-related health events, including 2,296 hospitalizations and a reduction of approximately $45 million in direct healthcare costs. At a WTP threshold of $50,000 per quality-adjusted life-year (QALY), the estimated base case PPD was $536, based on average cost assumptions across several costing scenarios. These findings suggest that universal immunization with nirsevimab could significantly reduce the health and economic burden of RSV among Canadian Infants.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".