Pricing appraisal of anti-cancer drugs in the South East Asian, Western Pacific and East Mediterranean Region
Bibliographic record
Abstract
BACKGROUND: Globally, cancer is one of the leading causes of mortality. High treatment cost, partly owing to higher prices of anti-cancer drugs, presents a significant burden on patients and healthcare systems. The aim of the present study was to survey and compare retail prices of anti-cancer drugs between high, middle and low income countries in the South-East Asia, Western Pacific and Eastern Mediterranean regions. METHODS: Cross-sectional survey design was used for the present study. Pricing data from ten counties including one from South-East Asia, two from Western Pacific and seven from Eastern Mediterranean regions were used in this study. Purchasing power parity (PPP)-adjusted mean unit prices for 26 anti-cancer drug presentations (similar pharmaceutical form, strength, and pack size) were used to compare prices of anti-cancer drugs across three regions. A structured form was used to extract relevant data. Data were entered and analysed using Microsoft Excel®. RESULTS: Overall, Taiwan had the lowest mean unit prices while Oman had the highest prices. Six (23.1%) and nine (34.6%) drug presentations had a mean unit price below US$100 and between US$100 and US$500 respectively. Eight drug presentations (30.7%) had a mean unit price of more than US$1000 including cabazitaxel with a mean unit price of $17,304.9/vial. There was a direct relationship between income category of the countries and their mean unit price; low-income countries had lower mean unit prices. The average PPP-adjusted unit prices for countries based on their income level were as follows: low middle-income countries (LMICs): US$814.07; high middle income countries (HMICs): US$1150.63; and high income countries (HICs): US$1148.19. CONCLUSIONS: There is a great variation in pricing of anticancer drugs in selected countires and within their respective regions. These findings will allow policy makers to compare prices of anti-cancer agents with neighbouring countries and develop policies to ensure accessibility and affordability of anti-cancer drugs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".