Global, regional, and national consumption of controlled opioids: a cross-sectional study of 214 countries and non-metropolitan territories
Bibliographic record
Abstract
INTRODUCTION: The consumption of opioids has increased globally since the 1990s. Previous studies of global opioid consumption have concentrated on morphine alone or a subset of opioids, with a focus on cancer pain and palliative care. In this study, we have determined the global, regional, and national consumption of all controlled opioids, including anaesthetics, analgesics, antidiarrheals, opioid substitution therapies, and cough suppressants. METHODS: We conducted a cross-sectional study using data from the International Narcotics Control Board (INCB). We calculated mean opioid consumption (mg/person) globally, regionally, and nationally for 2015-2017, where consumption refers to the total amount of controlled opioids distributed for medical purposes and excludes recreational use. We ranked countries by total consumption and quantified the types of opioids consumed globally. RESULTS: Between 2015 and 2017, 90% of the world's population consumed only 11% of controlled opioids. An average of 32 mg/person was consumed annually, but this was not equally distributed across the world. Consumption was the highest in Germany (480 mg/person), followed by Iceland (428 mg/person), the United States (398 mg/person) and Canada (333 mg/person). Oxycodone (35%) was the most heavily consumed controlled opioid globally, followed by morphine (15.9%), methadone (15.8%) and tilidine (14%). CONCLUSION: Large disparities persist in most of the world in accessing essential opioid medicines. Consumption patterns should continue to be monitored, and collaborative strategies should be developed to promote access and the appropriate prescribing of opioids in all countries and non-metropolitan territories.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".