Global status of essential medicine selection: a systematic comparison of national essential medicine lists with recommendations by WHO
Bibliographic record
Abstract
OBJECTIVES: Examining the availability of essential medicines is a necessary step to monitor country-level progress towards universal health coverage. We compared the 2017 essential medicine lists (EML) of 137 countries to the WHO Model List to assess differences by drug class and country setting. METHODS: We extracted all medicines prioritised at country level from most recently available national EMLs and compared each national EML with the 2017 WHO Model List of Essential Medicines (MLEM) as the reference standard. We assess EMLs by WHO region and for different types of medicine subgroups (eg, cancer, anti-infectives, cardiac, psychiatric and anaesthesia medicines) using within second-level anatomical therapeutic class (ATC) drug classes of the ATC Index. RESULTS: We included 406 medicines from WHO's 2017 MLEM to compare to 137 concurrent national EMLs. We found a median of 315 (range from 44 to 983) medicines listed on national EMLs. The global median F1 score was 0.59 (IQR 0.47-0.70, maximum possible score indicating alignment with MLEM is 1). The F1 score was the highest (ie, most similar to MLEM) in the South-East Asia region and the lowest in the European region (ie, most dissimilar to MLEM). The F1 score was highest for stomatological preparations (median: 1.00), gynaecological-anti-infectives and antiseptics (median: 1.00), and medicated dressings (median: 1.00), and lowest for 9 anatomical or pharmacological groups (median: 0.00, eg, treatments for bone diseases, digestive enzymes). CONCLUSIONS: Most countries are expected to improve their national health coverage by 2030 offering access to essential medicines, but our results revealed substantial gaps in selection of medicines at the national level compared with those recommended by WHO. It is crucial that governments consider investing in those effective medicines that are now neglected and continue monitoring progress towards essential medicine access as part of universal health coverage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.005 | 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".