Relation between opioid consumption and inclusion of opioids in 137 national essential medicines lists
Bibliographic record
Abstract
INTRODUCTION: Opioids are deemed essential medicines by the World Health Organization (WHO). However, many countries have inadequate access to them. Whether including opioids in national essential medicines lists (EMLs) influences national opioid consumption has not been evaluated. METHODS: We conducted a cross-sectional study to determine whether the listing of opioids in national EMLs was associated with consumption. We quantified the numbers and types of all opioids included in 137 national EMLs, for comparison with opioids in the WHO's Model List of Essential Medicines. Using the International Narcotics Control Board (INCB) consumption statistics for 2015-2017, we assessed the relation between annual mean opioid consumption (mg/person) and the numbers of opioids included in EMLs, controlling for region, population, healthcare expenditure, life expectancy, gross domestic product, human development and corruption. RESULTS: Five opioids were included in the 20th edition of the WHO's Model List of Essential Medicines: codeine, fentanyl, loperamide, methadone and morphine. On average, countries' lists included significantly (p<0.05) more opioids than the WHO's Model List. However, there were wide variations in the numbers (median 6 opioids; IQR: 5-9) and types (n=33) of opioids included in national EMLs. Morphine (95%), fentanyl (83%) and codeine (69%) were the most commonly included opioids. Most national EMLs were out of date (median publication date: 2011, IQR: 2009-2013). After adjusting for country characteristics, there was no relation between mean opioid consumption and the number of opioids in EMLs. CONCLUSIONS: Including opioids in national EMLs was not associated with consumption. National EMLs should be regularly updated to reflect the availability of opioids and the populations' needs for managing pain.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".