Insights from the WHO and National Lists of Essential Medicines: Focus on Pediatric Diabetes Care in Africa
Bibliographic record
Abstract
BACKGROUND: Access to essential medicines in pediatric endocrinology and diabetes is limited in resource-limited countries. The World Health Organization (WHO) maintains two non-binding lists of essential medicines (EMLs) which are often used as a template for developing national EMLs. METHODS: We compared a previously published master list of medicines for pediatric endocrinology and diabetes with the WHO EMLs and national EMLs for countries within the WHO African region. To better understand actual access to medicines by patients, we focused on diabetes and surveyed pediatric endocrinologists from 5 countries and assessed availability and true cost for insulin and glucagon. RESULTS: Most medicines that are essential in pediatric endocrinology and diabetes were included in the national EMLs. However, essential medicines, such as fludrocortisone, were present in less than 30% of the national EMLs despite being recommended by the WHO. Pediatric endocrinologists from the 5 focus countries reported significant variation in terms of availability and public access to insulin, as well as differences between urban and rural areas. Except for Botswana, glucagon was rarely available. There was no significant relationship between Gross National Income and the number of medicines included in the national EMLs. CONCLUSIONS: Governments in resource-limited countries could take further steps to improve EMLs and access to medicines such as improved collaboration between health authorities, the pharmaceutical industry, patient groups, health professionals, and capacity-building programs such as Paediatric Endocrinology Training Centres for Africa.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".