Expected Impact of Universal Immunization With Nirsevimab Against RSV-Related Outcomes and Costs Among All US Infants in Their First RSV Season: A Static Model
Bibliographic record
Abstract
BACKGROUND: Respiratory syncytial virus (RSV) is associated with substantial morbidity in the United States, especially among infants. Nirsevimab, an investigational long-acting monoclonal antibody, was evaluated as an immunoprophylactic strategy for infants in their first RSV season and for its potential impact on RSV-associated, medically attended lower respiratory tract illness (RSV-MALRTI) and associated costs. METHODS: A static decision-analytic model of the US birth cohort during its first RSV season was developed to estimate nirsevimab's impact on RSV-related health events and costs; model inputs included US-specific costs and epidemiological data. Modelled RSV-related outcomes included primary care and emergency room visits, hospitalizations including intensive care unit admission and mechanical ventilations, and RSV-related mortality. RESULTS: Under current standard of care, RSV caused 529 915 RSV-MALRTIs and 47 281 hospitalizations annually, representing $1.2 billion (2021 US dollars [USD]) in costs. Universal immunization of all infants with nirsevimab is expected to reduce 290 174 RSV-MALRTI, 24 986 hospitalizations, and expenditures of $612 million 2021 USD. CONCLUSIONS: An all-infant immunization strategy with nirsevimab could substantially reduce the health and economic burden for US infants during their first RSV season. While this reduction is driven by term infants, all infants, including palivizumab-eligible and preterm infants, would benefit from this strategy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".