Availability, Affordability, and Variations in Price of GI Cancer Medicines in Nepal
Bibliographic record
Abstract
PURPOSE: Access to cancer medicines is a function of both availability and affordability. In Nepal, where patients are responsible for procuring and purchasing treatments out of pocket, access is limited by both unavailability of medicines and unaffordability of available medicines. This study aimed to examine the availability, affordability, and price variation of GI cancer medicines in Nepal's public cancer hospitals. METHODS: A cross-sectional survey was conducted among four public cancer hospitals across Nepal between October 2022 and January 2023. Availability, affordability, and price variations of 26 therapeutic regimens for gastroesophageal, colorectal, hepatobiliary, and pancreatic cancers were examined. The maximum and minimum monthly retail prices of each individual available medicine and regimen were compared, and interhospital and intrahospital differences in price were calculated. Affordability was assessed by comparing monthly treatment costs with the monthly national per capita gross domestic product (GDP). RESULTS: Fourteen of 26 (53.8%) regimens were available in at least one hospital, whereas nine (34.6%) were available in all four public cancer hospitals. We discovered differences as high as 422% in capecitabine pricing within the same hospital, and differences in irinotecan pricing of 997% across hospitals. With the exception of capecitabine monotherapy, and fluorouracil plus cisplatin, all of the remaining available GI cancer treatments have monthly prices that exceed the monthly per capita GDP of Nepal. CONCLUSION: GI cancer drug access in Nepal is limited by low availability and significant price variation. Intrahospital and interhospital price disparities may influence patients to seek out different prices across institutions to avoid financial toxicity, adding logistical burden. Price regulation, transparency, and local manufacturing are needed to improve equitable access to cancer medicines.
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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) | 0.001 | 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 | 0.000 |
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".