Global patterns of opioid use and dependence: harms to populations, interventions, and future action
Why this work is in the frame
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Bibliographic record
Abstract
We summarise the evidence for medicinal uses of opioids, harms related to the extramedical use of, and dependence on, these drugs, and a wide range of interventions used to address these harms. The Global Burden of Diseases, Injuries, and Risk Factors Study estimated that in 2017, 40·5 million people were dependent on opioids (95% uncertainty interval 34·3-47·9 million) and 109 500 people (105 800-113 600) died from opioid overdose. Opioid agonist treatment (OAT) can be highly effective in reducing illicit opioid use and improving multiple health and social outcomes-eg, by reducing overall mortality and key causes of death, including overdose, suicide, HIV, hepatitis C virus, and other injuries. Mathematical modelling suggests that scaling up the use of OAT and retaining people in treatment, including in prison, could avert a median of 7·7% of deaths in Kentucky, 10·7% in Kiev, and 25·9% in Tehran over 20 years (compared with no OAT), with the greater effects in Tehran and Kiev being due to reductions in HIV mortality, given the higher prevalence of HIV among people who inject drugs in those settings. Other interventions have varied evidence for effectiveness and patient acceptability, and typically affect a narrower set of outcomes than OAT does. Other effective interventions focus on preventing harm related to opioids. Despite strong evidence for the effectiveness of a range of interventions to improve the health and wellbeing of people who are dependent on opioids, coverage is low, even in high-income countries. Treatment quality might be less than desirable, and considerable harm might be caused to individuals, society, and the economy by the criminalisation of extramedical opioid use and dependence. Alternative policy frameworks are recommended that adopt an approach based on human rights and public health, do not make drug use a criminal behaviour, and seek to reduce drug-related harm at the population level.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it