Strengthening access to cancer medicines for children in East Africa: policy options to enhance medicine procurement, forecasting, and regulations
Bibliographic record
Abstract
Gaps in access to quality essential medicines remain a major impediment to the effective care of children with cancer in low-and middle-income countries (LMICs). The World Health Organization reports that less than 30% of LMICs have consistent availability of childhood cancer medicines, compared to over 95% in high-income countries. Information provided within this policy brief is drawn from a review of the literature and a mixed-methods study published in the Lancet Oncology that analyzed determinants of cancer medicine access for children in Kenya, Tanzania, Uganda, and Rwanda. Three key policy options are presented to guide strategic policy direction and critical health system planning for strengthening access to cancer medicines for children: pooled procurement, evidence-based forecasting, and regional harmonization of regulatory processes. Enhancing regional pooled procurement to address fragmented markets and improve medicine supply, investing in health information systems for improved forecasting and planning of childhood cancer medicine needs, and promoting regulatory harmonization to streamline medicine approval and quality assurance across East Africa are recommended. This policy brief is intended for policymakers, clinicians, and health-system planners involved in the procurement, supply chain management, policy and financing of childhood cancer medicines.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".