Evaluating the Potential Clinical and Economic Impact of Clesrovimab for Infants in England and Wales
Bibliographic record
Abstract
Abstract Background Respiratory syncytial virus (RSV) infects nearly all children under two years, causing mild lower respiratory tract infections (LRTIs) although serious disease, mortality and long-term recurrent wheezing can also occur. This study evaluates the health and economic impact of various RSV prevention strategies (monoclonal antibodies [mAb] such as clesrovimab, nirsevimab, palivizumab [high risk infants only] or maternal vaccination) in infants in England and Wales. Methods A previously published decision tree model was adapted to estimate RSV LRTIs and associated costs over one year among a cohort of infants entering their first RSV season in England and Wales. Clinical trial data were used for efficacy inputs alongside country-specific epidemiology and costs to assess the impact of year-round and seasonal mAb programs (without and with catch-up [for infants born out of season]) alongside maternal vaccination. Deterministic sensitivity analyses of incremental treatment costs and QALYs were performed. Results Seasonal administration of clesrovimab with and without catch-up led to fewer RSV-associated LRTI cases and lower treatment costs compared to other RSV intervention strategies. Without catch-up, clesrovimab reduced RSV-LRTI cases by 18%-27% versus palivizumab, 3%-4% versus nirsevimab and 2%-13% versus maternal vaccine. With catch-up, RSV-LRTI outcomes reductions by clesrovimab were greater: 60%-70% versus palivizumab, 14%-20% versus nirsevimab, and 43%-54% versus maternal vaccination. It also reduced treatment costs by £37.0 million compared to palivizumab, £4.0 million compared to nirsevimab, and £26.0 million compared to maternal vaccination. Coverage and efficacies were the most influential parameters. Conclusions Clesrovimab is projected to substantially reduce RSV-associated clinical outcomes and costs compared to both standard of care and potential new RSV prevention measures in England and Wales.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".