An Exploratory Study Into the Use and Pricing of Schedule Zero Medicines Post Exemption From the Single Exit Price Policy in South Africa
Bibliographic record
Abstract
BACKGROUND: South Africa, promulgated pricing legislation in 2004 for scheduled medicines except for, schedule zero medicines. The aim of this study was to explore the impact of schedule zero medicine exemption from the Single Exit Price policy in the private sector. OBJECTIVES: The objectives of this study were to determine prescription pricing trends and whether any price differential for a basket of schedule zero analgesics existed across three economic areas; the number of brands available; and prices of over the counter and prescription medicines. METHODS: The medical scheme database was obtained for the period January to June 2014 for prescription schedule zero medicine analysis. Outlets within three economic areas in the eThekwini Municipality were identified for data sampling using a mystery shopper approach. Schedule zero analgesic prices and pack-sizes were recorded on the day of the visit, which occurred from 12 March to 11 April 2015. RESULTS: An analysis of 14 106 prescriptions from the medical scheme database showed significant differences (p < 0.05; CI 95%) when the submitted prices were compared to the cash prices. Dispensing fees were also significantly different (p < 0.05; CI 95%). 35 outlets were visited in which 65 schedule zero analgesics were noted. An ANOVA testing for differences between the economic areas indicated significant differences for 4 products. CONCLUSION: A more extensive study should be conducted to verify the pricing of schedule zero pricing on prescriptions to ensure that they are not linked to any perverse incentives.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".