Evidence-based cost estimation of essential medicines for pediatric cancer care in Peru.
Bibliographic record
Abstract
e18533 Background: Inequitable access to essential medicines is a large contributor to the inferior outcomes experienced by children with cancer living in low-and-middle-income countries. In collaboration with the Peruvian Ministry of Health and the Pan American Health Organization, we aimed to predict annual aggregate drug costs as well as primary cost drivers for treating Peruvian children with cancer to inform more accurate and efficient drug procurement. Methods: FORxECAST is a pediatric cancer-specific model that projects required drug quantity and cost for 18 common pediatric cancers, drawing on internationally adopted standard treatment protocols. It is customizable to geographic region, local cancer incidence, stage distribution, and domestic drug prices. To estimate aggregate costs utilizing FORxECAST, Peruvian incidence, stage at diagnosis, and per-unit drug prices were provided by local stakeholders. FORxECAST-embedded standardized data for country-specific age, sex, and BSA sourced by the World Health Organization and the Global Childhood Cancer Microsimulation Model were used. To provide comparative analyses, aggregate costs adapted for Peruvian incidence were calculated utilizing reference drug prices available through the Management Sciences for Health (MSH) International Medical Products Price Guide. Results: FORxECAST projected Peruvian aggregate drug costs to be USD$1,672,795.47 annually, which was 47% less than expected by median MSH prices and represents 4% of the total Peruvian cancer care budget. 19 of 22 medicines were cheaper on a per-unit basis relative to MSH. Dactinomycin was the costliest per-unit agent but contributes to less than 2% of the overall budget due to low aggregate demand. Primary cost drivers were 6-mercaptopurine, intravenous methotrexate, and asparaginase, due to the large quantities needed for treatment of Peru’s most common pediatric cancer: acute lymphoblastic leukemia. However, because of their low per-unit prices, sensitivity analyses showed that the annual drug budget would remain lower than predicted by MSH, even if the per-unit price for these 3 agents increased by 50% (USD$2,431,550.71 vs. USD$3,099,401.31). Conclusions: The cost of procuring accurate quantities of essential pediatric cancer medicines to meet population-level need is a small percentage of the total cancer budget for the Peruvian healthcare system. Given the potential impact that improved drug access could have on pediatric cancer survival, these data can justify health systemic efforts to effectively earmark funds for pediatric cancer care and minimize risks of stockout. Our analyses also demonstrate the importance of incorporating context-specific drug prices when estimating domestic budget impact. Next steps include comparative analyses with neighboring health systems in Latin America to assess regional price variation and inform opportunities for pooled procurement.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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 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".