Assessing variation among the national essential medicines lists of 21 high-income countries: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: Essential medicines lists have been created and used globally in countries that range from low-income to high-income status. The aim of this paper is to compare the essential medicines list of high-income countries with each other, the WHO's Model List of Essential Medicines and the lists of countries of other income statuses. DESIGN: High-income countries were defined by World Bank classification. High-income essential medicines lists were assessed for medicine inclusion and were compared with the subset of high-income countries, the WHO's Model List and 137 national essential medicines lists. Medicine lists were obtained from the Global Essential Medicines database. Countries were subdivided by income status, and the groups' most common medicines were compared. Select medicines and medicine classes were assessed for inclusion among high-income country lists. RESULTS: The 21 high-income countries identified were most like each other when compared with other lists. They were more like upper middle-income countries and least like low-income countries. There was significant variability in the number of medicines on each list. Less than half (48%) of high-income countries included a newer diabetes medicines in their list. Most countries (71%) included naloxone while every country including at least one opioid medicine. More than half of the lists (52%) included a medicine that has been globally withdrawn or banned. CONCLUSION: Essential medicines lists of high-income countries are similar to each other, but significant variations in essential medicine list composition and specifically the number of medications included were noted. Effective medicines were left off several countries' lists, and globally recalled medicines were included on over half the lists. Comparing the essential medicines lists of countries within the same income status category can provide a useful subset of lists for policymakers and essential medicine list creators to use when creating or maintaining their lists.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".