Cost-effectiveness of wastewater and environmental monitoring of respiratory syncytial virus to guide universal infant immunoprophylaxis in Canada
Bibliographic record
Abstract
AimsTo compare the cost-effectiveness of wastewater and environmental monitoring (WEM) versus clinical surveillance (CS)-guided respiratory syncytial virus (RSV) prophylaxis programs in Canada.Materials and MethodsA cost-utility model was developed comprising two identical decision trees for RSV-WEM and RSV-CS. Within each tree, children could conservatively receive nirsevimab prophylaxis (71% coverage) or not at the start of the RSV season and subsequently experience an RSV-related hospitalization, medically-attended, non-hospitalized RSV-infection, or be uninfected/non-medically attended. All children could experience respiratory morbidity up to age 18 years, with higher rates following RSV-related hospitalization. All prophylaxis and RSV-related costs were identical for RSV-WEM and RSV-CS. No costs were assumed for RSV-CS; whereas a cost of CAD$12.31 per infant (infrastructure: CAD$4.07 plus sampling: CAD$8.24) was assumed if a new RSV-WEM system was initiated, with all infrastructure costs included in year 1. Predicated on data from the 2022-23 Ontario RSV season, RSV-WEM was assumed to provide a 15.1% benefit for earlier initiation of the prophylaxis program versus RSV-CS. Outcomes were modelled over a 18-year time horizon (1.5% discounting).ResultsRSV-WEM dominated (lower costs and higher utilities) RSV-CS and remained unaltered in all scenario analyses. Scenarios included: amortization of RSV-WEM infrastructure costs over 5 years; using existing WEM infrastructure for RSV detection; 25% reduction in extra cases identified by RSV-WEM; 50–90% prophylaxis coverage based on real-world data; and 25% increase in the cost of RSV-WEM.ConclusionsThe integration of RSV-WEM appears a highly cost-effective strategy (vs RSV-CS exclusively) to guide earlier launch of RSV seasonal prophylaxis in Canada.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".