Cost-effectiveness of asenapine in the treatment of bipolar disorder in Canada
Bibliographic record
Abstract
BACKGROUND: Bipolar disorder (BPD) is prevalent and is associated with a significant economic burden. Asenapine, the first tetracyclic antipsychotic approved in Canada for the treatment of BPD, has shown a comparable efficacy profile to other atypical antipsychotics. In addition, it is associated with a favourable metabolic profile and minimal weight gain potential. This study aimed to assess the economic impact of asenapine compared to olanzapine in the treatment of BPD in Canada. METHODS: A decision tree combined with a Markov model was constructed to assess the cost-utility of asenapine compared with olanzapine. The decision tree takes into account the occurrence of extrapyramidal symptoms (EPS), the probability of switching to a different antipsychotic, and the probability of gaining weight. The Markov model takes into account long-term metabolic complications including diabetes, hypertension, coronary heart diseases (CHDs), and stroke. Analyses were conducted from both a Canadian Ministry of Health (MoH) and a societal perspective over a five-year time horizon with yearly cycles. RESULTS: In the treatment of BPD, asenapine is a dominant strategy over olanzapine from both a MoH and a societal perspective. In fact, asenapine is associated with lower costs and more quality-adjusted life years (QALYs). Results of the probabilistic sensitivity analysis indicated that asenapine remains a dominant strategy in 99.2% of the simulations, in both a MoH and a societal perspective, and this result is robust to the many deterministic sensitivity analyses performed. CONCLUSIONS: This economic evaluation demonstrates that asenapine is a cost-effective strategy compared to olanzapine in the treatment of BPD 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 | 0.000 | 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".