Cost-effectiveness of Palivizumab for Respiratory Syncytial Virus: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Palivizumab prophylaxis is used as passive immunization for respiratory syncytial virus (RSV). However, because of its high cost, the value of this intervention is unclear. OBJECTIVE: To systematically review the cost-effectiveness of palivizumab prophylaxis compared with no prophylaxis in infants <24 months of age. DATA SOURCES: Medline, Embase, and Cochrane Library up to August 2018. STUDY SELECTION: Two reviewers independently screened results to include economic evaluations conducted between 2000 and 2018 from Organization for Economic Cooperation and Development countries. DATA EXTRACTION: Two reviewers independently extracted outcomes. Quality appraisal was completed by using the Joanna Briggs Institute checklist. Costs were adjusted to 2017 US dollars. RESULTS: = 5). Study quality was high; 23 studies met >80% of the Joanna Briggs Institute criteria. Palivizumab prophylaxis ranged from a dominant strategy to having an incremental cost-effectiveness ratio of $2 526 203 per quality-adjusted life-year (QALY) depending on study perspective and targeted population. From the payer perspective, the incremental cost-effectiveness ratio for preterm infants (29-35 weeks' gestational age) was between $5188 and $791 265 per QALY, with 90% of estimates <$50 000 per QALY. Influential parameters were RSV hospitalization reduction rates, palivizumab cost, and discount rate. LIMITATIONS: Model design heterogeneity, model parameters, and study settings were barriers to definitive conclusions on palivizumab's economic value. CONCLUSIONS: Palivizumab as RSV prophylaxis was considered cost-effective in prematurely born infants, infants with lung complications, and infants from remote communities.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".