Maximum potential cost-savings attributable to generic substitution of antipsychotics 2008 to 2013
Bibliographic record
Abstract
Background: Schizophrenia is a costly illness to treat, especially during a time of escalating medicine inflation costs, putting a large economic strain on patients, their families and the community. Treatment, however, can become more affordable through generic substitution. Objective: To determine the maximum potential cost-saving through generic substitution for both originator and more expensive generic items while observing the prescribing patterns of antipsychotics. Method: Antipsychotic medicine usage was analysed retrospectively during the study period 2008 to 2013 using data obtained from a nationally representative Pharmaceutical Benefit Management Company. The study population consisted of 4410 patients with ICD-10 codes (F20-F20.9) who had paid claims for an antipsychotic reimbursed from their prescribed minimum benefits. Active ingredients were identified using the MIMS classification system. Maximum potential cost savings were determined by substituting all originator and more expensive generic antipsychotic items with the cost of the least expensive generic antipsychotic item available. Results: Through generic substitution, a total potential cost-saving of ZAR4 642 685.45 could be possible from 2008 to 2013. Average cost per items increased from ZAR600.53 ± ZAR435.00 (median ZAR 539.82) in 2008 to ZAR1 196.59 ± ZAR 942.16 (median ZAR 940.72) in 2013 and had a significant effect on patients' contribution, which increased with 726.94% from 2005 to 2008. Psychiatrists prescribed the majority of antipsychotics. Although generic items claimed increased by 60.31% during the study period, psychiatrist still favoured non-generic prescribing (40.63%). Conclusions: Potential economic benefits can be generated with generic substitution.
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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 | 0.001 | 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 | 0.001 |
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".