Cost-effectiveness of screening and treatment for schistosomiasis among refugees coming to Canada
Bibliographic record
Abstract
Background: Depending on their countries of origin, between 12% and 73% of resettled refugees and asylum seekers from endemic countries are infected with schistosomiasis when they arrive in Canada. Many are asymptomatic, but they are at risk for complications that may develop decades later. In Canada, clinicians previously practiced watchful waiting, treating patients if they developed symptoms; but in 2011 new guidelines recommended screening and treatment instead. In the United States, refugees from Africa are presumptively treated for schistosomiasis before they leave their country of origin. The cost-effectiveness of screening or presumptive treatment for schistosomiasis has never been studied. Methods: We constructed a decision-tree model to examine the cost-effectiveness of three management strategies: watchful waiting; screening and treatment; and presumptive treatment. We obtained model data from the literature and other sources, predicting deaths and chronic complications caused by schistosomiasis; as well as costs, and net monetary benefit. Results: Presumptive treatment was cost-saving if the prevalence of schistosomiasis in the target population was greater than 2.4%. In our base case analysis, presumptive treatment was associated with an increase of 0.15 quality-adjusted life years and a cost savings of $383 per person, compared to watchful waiting. It was also more effective and less costly than screening and treatment. Interpretation: Presumptive treatment for schistosomiasis among recently resettled refugees and asylum claimants to Canada is less costly and more effective than watchful waiting or screening and treatment, in groups with prevalence greater than 2.4%. Our results support a revision of the current Canadian guidelines.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".