Cost-effectiveness of wastewater and environmental monitoring of respiratory syncytial virus to guide universal infant immunoprophylaxis in Canada
Bibliographic record
Abstract
To compare the cost-effectiveness of wastewater and environmental monitoring (WEM) versus clinical surveillance (CS)-guided respiratory syncytial virus (RSV) prophylaxis programs in Canada. A 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 an 18-year time horizon (1.5% discounting). RSV-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. The integration of RSV-WEM appears a highly cost-effective strategy (vs RSV-CS exclusively) to guide the earlier launch of RSV seasonal prophylaxis in Canada. What is already known about the topic? Respiratory syncytial virus (RSV) is an important cause of respiratory illness in children, and, in the worst cases, can require hospital care. To maximize the effectiveness of drugs designed to prevent RSV by temporarily boosting the child’s immune system (e.g. palivizumab and nirsevimab), their use needs to be matched to RSV activity in the community. Wastewater and environmental monitoring (WEM) is a technology that can detect the presence of viruses in sewage samples to show when specific diseases are increasing. What does the paper add to existing knowledge? A key question to answer for any investment in a new technology is whether the expected benefits justify the costs associated with setup and use. Cost-utility analysis sets the financial cost of a new technology against any savings it may produce plus the value of any improvements to people’s everyday life. This first assessment of the cost-utility of WEM compares it to clinical surveillance (CS), which is the current standard for assessing RSV activity. The results showed WEM to be a cost-saving approach over CS to guide a Canadian all-infant immunization program with nirsevimab. This means that the costs of implementing the WEM program can be offset by the savings from reducing the need for medical care. What insights does the paper provide for informing healthcare-related decision making? Our cost-utility analysis on the integration of RSV-WEM provides practical information for both provincial and local public health authorities to utilize and supports the implementation of such resources in Canada and other countries. Adoption of RSV-WEM has the potential to precisely guide the initiation of RSV immunization programs which will help reduce avoidable illness and hospitalization, creating cost savings and improving the lives of the protected children and their parents.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".