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 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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".