COST-EFFECTIVENESS OF PALIVIZUMAB FOR RESPIRATORY SYNCYTIAL VIRUS PROPHYLAXIS IN PREMATURE INFANTS WITH A GESTATIONAL AGE OF 32–35 WEEKS IN CANADA
Bibliographic record
Abstract
Objective To evaluate the cost-effectiveness (CE) of palivizumab as respiratory syncytial virus (RSV) prophylaxis in premature infants 32 to 35 weeks gestational age (GA), without chronic lung disease. Methods A decision analytic model was designed to compare direct and indirect medical costs of the patient with future lost productivity and benefits of prophylaxis. Five types of sensitivity analyses were performed to ascertain robustness of the model based on mortality, health utility scales, variable discounting rates, administration costs and vial sharing. Setting Canadian publicly funded health care system (base-case analysis). Primary Outcomes Expected costs and incremental CE ratio expressed as cost per quality-adjusted life-year (QALY) gained using $ Canadian (CAD) 2006. Results Expected costs were higher for palivizumab prophylaxis compared with no prophylaxis. The incremental CE ratio for the base-case scenario was $17,253 per QALY after discounting, which is considered highly cost-effective. The model was not sensitive to variation in the RSV mortality rate. Sub-analyses varying the number of risk factors in a Canadian validated risk-scoring tool were sensitive to the resulting variation in RSV-related hospitalization rates. In instances where one risk factor or less was present, palivizumab was not cost-effective. However, for infants with two or more risk factors, or at least moderate risk, palivizumab had incremental costs per QALYs that indicated moderate to strong evidence for adoption (range: −$2,881 [cost savings] to $77,668 per QALY). Conclusions Palivizumab was cost-effective and our model supports prophylaxis for infants born at 32 to 35 weeks GA, particularly those with two or more risk factors.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".