A population-based cost-effectiveness analysis of olanzapine and risperidone among ambulatory patients with schizophrenia.
Bibliographic record
Abstract
BACKGROUND: Olanzapine and risperidone are atypical antipsychotics exhibiting different pharmacological properties that are likely to translate into differences in outcomes, tolerability and safety. As well, their acquisition cost differs. These differences may impact their cost-effectiveness. OBJECTIVE: To estimate the incremental cost-effectiveness ratio (ICER) of olanzapine and risperidone in an outpatient population. METHODS: We carried out a cost-effectiveness analysis based on resource utilization data gathered from Quebec's provincial health insurance board databases. Patients previously diagnosed with schizophrenia who received a first prescription of olanzapine or risperidone between 1 January 1997 and 31 August 1999 were followed for 365 days. Absence of hospitalization for mental illness served as the clinical indicator of effectiveness. Direct health care costs for mental illness were considered. Adjusted ICERs were calculated, and their 95% confidence intervals (CI) were assessed using a non-parametric bootstrap. RESULTS: A total of 6,334 patients were included in the analysis. The ICER for olanzapine was (CA$) 86,918 (95%CI, 27,709 to 237,040) per additional effective treatment per year, among patients hospitalized prior to their treatment. Among those who were not hospitalized prior to their treatment, olanzapine was dominated (95%CI, CA$1.7M to dominated). CONCLUSION: Results suggest that, in this population, direct mental health care costs could be minimized by using risperidone instead of olanzapine as the initial treatment.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".