Cost-effectiveness of strategies using preventive interventions to protect infants in Chile from respiratory syncytial virus
Bibliographic record
Abstract
BACKGROUND: Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract illness (LRTI; RSV-LRTI) among infants in Chile; young infants and infants born prematurely are at greatest risk. RESEARCH DESIGN AND METHODS: A cohort model was developed to evaluate cost-effectiveness of strategies preventing RSV-LRTI in infants. Using the model, we calculated the economically justifiable price (EJP) of maternal RSVpreF vaccination (MV) versus no intervention and then evaluated the cost-effectiveness of MV (cost/dose assumed at EJP) with complementary use of monoclonal antibody nirsevimab for unprotected infants (MV+N) versus nirsevimab alone (NA) to prevent RSV-LRTI. Nirsevimab published price was $260.00; costs/prices reported in 2023 US$. RESULTS: NA yielded 20,247 cases (hospital: 3,773, emergency ward: 16,474) and $57.2 million (M) in total costs (medical: $6.3 M, intervention: $48.7 M, indirect: $2.2 M). MV+N yielded 23,906 cases (hospital: 3,137, emergency ward: 20,769) and $28.7 M in costs (medical: $4.8 M, intervention: $21.7 M [RSVpreF assumed $75.77/dose; nirsevimab procured $260.00/dose], indirect: $2.2 M). With costs lower by $28.4 M and increased quality-adjusted life-years, MV+N would be cost-saving versus NA. CONCLUSIONS: RSVpreF vaccination among pregnant women along with nirsevimab for unprotected infants in Chile would be the most efficient use of resources, yielding substantial cost savings compared to use of nirsevimab alone.
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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 | 0.003 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".