Selection of WHO-recommended essential medicines for non-communicable diseases on National Essential Medicines Lists
Bibliographic record
Abstract
BACKGROUND: Non-communicable diseases (NCDs) are the leading cause of death worldwide. Inadequate and inequitable access to essential NCD medicines is a major concern, particularly in low- and middle-income countries. National Essential Medicines Lists (EMLs) are important policy tools that indicate which medicines are prioritized as essential within a country's health system. This study sought to analyze a wide range of national essential medicines lists (EMLs) for their inclusion of priority non communicable disease (NCD) interventions recommended by the World Health Organization (WHO). METHODS: Three lists of WHO endorsed priority NCD interventions were included. A database with 137 national EMLs and the WHO EML was created from the WHO Repository and these EMLs were compared for listing of priority NCD interventions. RESULTS: Across 137 countries with national EMLs, the median percentage of 20 Best Buys interventions listed was 90% (IQR 80-95) and 31 Package of essential noncommunicable disease interventions (PEN) interventions listed was 94% (IQR 90-97), of 9 HEARTS interventions was 100% (IQR 89-100), and of the 43 unique interventions across the three priority lists was 88% (IQR 84-93). Less than 80% of the 43 interventions were listed by 22 (16%) countries and less than half of the interventions were listed by 2 countries: Angola (35%) and Cambodia (23%). Interventions listed on the fewest number of national EMLs were: influenza vaccine, HPV vaccine, hepatitis B vaccine, cervical cancer chemotherapy, codeine, promethazine, senna, and oxygen. CONCLUSION: Most NCD interventions have been prioritized in national policy in most cases. The majority of priority medicines for NCDs described within key WHO NCD technical packages are listed on nearly all national EMLs across 137 countries of all income levels. Most NCD interventions have been prioritized in national policy in most cases, but in some countries and for select interventions such as the HPV vaccine, prioritization may be reviewed.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".