Economic Evaluation of Early Psychosis Interventions From A Canadian Perspective
Bibliographic record
Abstract
BACKGROUND: Compared to treatment as usual (TAU), early psychosis intervention programs (EPI) have been shown to reduce mortality, hospitalizations and days of assisted living while improving employment status. AIMS: The study aim was to conduct a cost-benefit analysis (CBA) and a cost-effectiveness analysis (CEA) to compare EPI and TAU in Canada. METHODS: A decision-analytic model was used to estimate the 5-year costs and benefits of treating patients with a first episode of psychosis with EPI or TAU. EPI benefits were derived from randomized controlled trials (RCTs) and Canadian administrative data. The cost of EPI was based on a published survey of 52 EPI centers in Canada while hospitalizations, employment and days of assisted living were valued using Canadian unit costs. The outcomes of the CBA and CEA were expressed in terms of net benefit (NB) and incremental cost per life year gained (LYG), respectively. Scenario analyses were conducted to examine the impact of key assumptions. Costs are reported in 2019 Canadian dollars. RESULTS: Base case results indicated that EPI had a NB of $85,441 (95% CI: $41,140; $126,386) compared to TAU while the incremental cost per LYG was $26,366 (95% CI: EPI dominates TAU (less costs, more life years); $102,269). In all sensitivity analyses the NB of EPI remained positive and the incremental cost per LYG was less than $50,000. CONCLUSIONS: In addition to EPI demonstrated clinical benefits, our results suggest that large-scale implementation of EPI in Canada would be desirable from an economic point of view .
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".