Comparison of the public health impact of RSV disease prevention options for infants: a static decision model of the US birth cohort
Bibliographic record
Abstract
BACKGROUND: In the U.S.A. three prophylactic interventions are approved for the prevention of respiratory syncytial virus (RSV) lower respiratory tract disease (LRTD) in infants: nirsevimab and clesrovimab (extended half-life monoclonal antibodies) and the maternal RSVpreF vaccine. We compared the impact of these interventions on RSV-LRTD events and costs versus the previous standard-of-practice (SoP; palivizumab-only strategy). RESEARCH DESIGN AND METHODS: Using a static decision-analytic model, we estimated the public health impact of nirsevimab, clesrovimab, and RSVpreF following the latest recommendations on RSV-related outcomes and costs in a US birth cohort during their first RSV season compared to the pre-2023 SoP. RESULTS: The model estimated that nirsevimab would avert 364,204 RSV-LRTDs including 32,404 hospitalizations, saving $1,289 million in direct and indirect costs. Depending on the assumed duration of protection, clesrovimab was estimated to avert 173,276-261,358 RSV-LRTDs of which 23,957-30,483 were hospitalizations, resulting in savings of $912-$1,150 million in total costs. RSVpreF maternal vaccination would avert 76,915 RSV-LRTDs including 9,649 hospitalizations, equating to $345 million in total cost savings. CONCLUSIONS: While all three interventions are estimated to reduce RSV-LRTD burden in infants, all-infant protection with nirsevimab was estimated to avert more events and associated medical costs for all infant subgroups compared to clesrovimab or RSVpreF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".