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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".