Evidence-based cost estimation of essential medicines for pediatric cancer care in Peru.
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,006 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».