Shortages and price variability of essential cytotoxic medicines for treating children with cancers
Bibliographic record
Abstract
Introduction Low-income and middle-income countries (LMICs) face the largest burden of mortality from childhood cancers with limited access to curative therapies. Few comparative analyses across all income groups and world regions have examined the availability and acquisition costs of essential medicines for treating cancers in children. Methods A cross-sectional survey involved countries in five income groups—low-income (LIC), lower-middle-income (LMC), upper-middle-income (UMC), two high-income country groups (HIC1, HIC2). Physicians and pharmacists reported institutional use, availability, stock outs and prices (brand and generic products) of 34 essential medicines. Price comparisons used US$, applying foreign exchange rates (XR) and purchasing power parity (PPP) adjustments. Medicine costs for treating acute lymphoblastic leukaemia (ALL), Burkitt lymphoma (BL) and Wilms tumour (WT) were calculated (child 29 kg, body surface area 1 m 2 ). Comparisons were conducted using non-parametric Kruskal-Wallis tests. Results Fifty-eight respondents (50 countries) provided information on medicine use, availability and stock outs, with usable price data from 42 facilities (37 countries). The extent of use of International Society of Paediatric Oncology core and ancillary medicines varied across income groups (p<0.0001 and p=0.0002 respectively). LMC and LIC facilities used fewer medicines than UMC and HIC facilities. UMC and LMC facilities were more likely to report medicines not available or stockouts. Medicine prices varied widely within and between income bands; generic products were not always cheaper than brand equivalents. PPP adjustment showed relatively higher prices in UMC and LMC facilities for some medicines. Medicine costs were highest in HICs for ALL (p=0.0075 XR; p=0.0178 PPP-adjusted analyses) and WT (p =<0.0001 XR; p=0.0007 PPP-adjusted). Medicine costs for BL were not significantly different. Conclusion Problems with the availability of essential medicines, dependable supply chains, confidential medicine prices and wide variability in treatment costs contribute to persistent challenges in the care of children with treatable cancers, especially in LMICs.
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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) | 0.000 | 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".