The cost-effectiveness of schistosomiasis screening and treatment among recently resettled refugees to Canada: an economic evaluation
Bibliographic record
Abstract
BACKGROUND: parasite when they arrive in Canada. We assessed, from a systemic perspective, which of the following management strategies by health care providers is cost-effective: testing for schistosomiasis and treating if the individual is infected, treating presumptively or waiting for symptoms to emerge. METHODS: We constructed a decision-tree model to examine the cost-effectiveness of 3 management strategies: watchful waiting, screening and treatment, and presumptive treatment. We obtained data for the model from the literature and other sources, to predict 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 2.1%. In our baseline analysis, presumptive treatment was associated with an increase of 0.156 quality-adjusted life years and a cost saving of $405 per person, compared with watchful waiting. It was also more effective and less costly than screening and treatment. INTERPRETATION: Among recently resettled refugees and asylum claimants in Canada, from countries where schistosomiasis is endemic, presumptive treatment was predicted to be less costly and more effective than watchful waiting or screening and treatment. Our results support a revision of the current Canadian recommendations.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 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".