Cost-effectiveness of a free drug program for schizophrenia in Beijing, China
Bibliographic record
Abstract
BACKGROUND: Beijing municipal government launched a Free Drug Program (FDP) in 2013 to reduce the financial burden of schizophrenia patients. OBJECTIVES: To assess the cost-effectiveness of a FDP designed for schizophrenia patients in Beijing, China. METHODS: In all, 2007 schizophrenia patients enrolled in an FDP (FDP group) and 2001 schizophrenia patients who were not enrolled (non-FDP group) were randomly selected for a cross-sectional survey in August 2015. The study sought to develop a cost-effectiveness model to assess the FDP from the societal perspective in Beijing, China. Scenario analyses explored the potential strategies to further improve the cost-effectiveness of the FDP. RESULTS: The FDP group was associated with lower socioeconomic status and more advanced disease than the non-FDP group (unemployment rate: 48.8% vs 37.3%, p < .001; disability rate: 91.2% vs 64.1%, p < .001; overall comorbidity rate: 34.9% vs 28.8%, p < .001). The two groups exhibited similar disease severity and quality of life. However, the FDP group was associated with significantly lower direct medical costs (coefficient -.342, p = .003) and indirect costs (coefficient -.473, p < .001) than the non-FDP group. The base case incremental cost-effectiveness ratio (ICER) per gained quality-adjusted life year (QALY) for the FDP group relative to the non-FDP group was 1.480 times of 2015 China's gross domestic product per capita. Home drug delivery and long-lasting injection treatment could reduce the ICER for the FDP group relative to the non-FDP group by 57.8% and 29.8%, respectively. CONCLUSION: The FDP was attractive to schizophrenia patients with lower socioeconomic status and more advanced disease. The cost-effectiveness of the FDP was acceptable and could be further improved by home drug delivery and long-lasting injection 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".