Withdrawn medicines included in the essential medicines lists of 136 countries
Bibliographic record
Abstract
BACKGROUND: Essential medicines lists and related policies are intended to meet the priority health needs of populations and their implementation is associated with more appropriate use of medicines. The World Health Organization (WHO) recommends that countries carefully select the medicines to be included in their national essential medicines lists. Lists that are used to prioritize access to important treatments should not include medicines that have been withdrawn elsewhere because of an unfavourable benefit-to-harm balance; however, countries still list and use medicines that have been withdrawn worldwide. The objective of this study was to determine whether the national essential medicines lists of 137 countries include medicines that have been withdrawn in other countries. METHODS AND FINDINGS: We performed an audit of national essential medicines lists for medicines that had been withdrawn. Medicines withdrawn from worldwide markets between 1953 and 2014 were identified using a systematic review of published literature and regulatory documents. The reviewers used sources including the WHO's database of drugs, PubMed, and the websites of regulatory agencies to obtain information regarding adverse effects associated with the medicines, the year of first withdrawal, markets of withdrawal, and the level of evidence supporting each withdrawal. We recorded the number of countries with a withdrawn medicine included in their national medicines list, the number of withdrawn medicines included in each nation's list, and the number of national essential medicines including each withdrawn medicine. 97 medicines were withdrawn in at least one country but still included in one more national essential medicines list. Of 137 countries with a national essential medicines list, 136 lists included at least one withdrawn medicine, with 54% of the lists containing 5 or fewer withdrawn medicines, and 27% including 10 or more withdrawn medicines. 11 medicines were withdrawn worldwide but still included on at least one national essential medicines list. Countries with longer essential medicines lists had more withdrawn medicines included in their lists. CONCLUSIONS: This study found that withdrawn medicines are included in all but one national essential medicines list, representing a need for more stringent processes for selecting and removing medicines on these lists. Countries may wish to apply special scrutiny to medicines withdrawn in other nations when selecting medicines to include on their lists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.032 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".