Comparison of the Cost of Hospitalization for Respiratory Syncytial Virus Disease Versus Palivizumab Prophylaxis in Canadian Inuit Infants
Bibliographic record
Abstract
BACKGROUND: The objectives were to compare actual respiratory syncytial virus (RSV) hospitalization rates and costs in a cohort of Inuit infants to hypothetical palivizumab prophylaxis strategies for infants of all gestational ages in the Eastern Canadian Arctic. METHODS: Incidence and costs of RSV hospitalization were collected for infants admitted to the Baffin Regional Hospital in 2002, before the initiation of palivizumab. There was a comparison of the actual costs to the costs associated with 8 palivizumab strategies stratified by age (<6 months, <1 year) and location (overall, town [Iqaluit], rural communities). It was assumed that each category would receive universal palivizumab prophylaxis resulting in a 78% decrease in RSV admissions. The net costs incurred, number needed to treat (NNT), and incremental costs per hospitalization avoided were calculated for each comparison. RESULTS: There was a great variation in the rates and costs associated with RSV admissions between Iqaluit and the communities. For infants <1 year of age residing in Iqaluit, the mean admission cost was $3915, and palivizumab prophylaxis had an NNT of 20.4 and cost of $162,551 per admission avoided. For rural infants <6 months, the mean cost of admission was $23,030, and palivizumab prophylaxis resulted in an NNT of 3.9 to 2.5 and cost savings of up to $8118 per admission avoided. CONCLUSIONS: Due to the high rates and costs associated with RSV admissions, administration of palivizumab in rural communities in the Canadian Arctic to infants less than 6 months of age could result in net cost savings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".