Public health monitoring of cannabis use in Europe: prevalence of use, cannabis potency, and treatment rates
Bibliographic record
Abstract
BACKGROUND: Cannabis is one of the most widely used substances worldwide. Heavy use is associated with an increased risk of cannabis use disorders, psychotic disorders, acute cognitive impairment, traffic injuries, respiratory problems, worse pregnancy outcomes, and there are indications for genotoxic and epigenotoxic adverse effects. International regulation of medical and non-medical cannabis use is changing rapidly and substantially, highlighting the importance of robust public health monitoring. This study aimed to describe the trends of key public health indicators in European Union (27 member states + UK, Norway and Turkey) for the period 2010 to 2019, their public health implications, and to identify the steps required to improve current practice in monitoring of cannabis use and harm in Europe. METHODS: Data on four key cannabis indicators (prevalence of use, prevalence of cannabis use disorder [CUD], treatment rates, and potency of cannabis products) in Europe were extracted from the United Nations Office on Drugs and Crime, European Monitoring Centre for Drugs and Drug Addiction and the Global Burden of Disease study. For prevalence of use and CUD, the first and last available estimate in each country were compared. For treatment rates and cannabis potency, linear regression models were conducted. FINDINGS: Between 2010 and 2019, past-month prevalence of cannabis use increased by 27% in European adults (from 3·1 to 3·9%), with most pronounced relative increases observed among 35-64 year-olds. In 13 out of 26 countries, over 20% of all past-month users reported high-risk use patterns. The rate of treatment entry for cannabis problems per 100,000 adults increased from 27·0 (95% CI: 17·2 to 36·8) to 35·1 (95% CI: 23·6 to 46·7) and has mostly plateaued since 2015. Modest increases in potency were found in herbal cannabis (from 6·9% to 10·6% THC) while median THC values tripled in cannabis resin (from 7·6% to 24·1% THC). INTERPRETATION: In the past decade, cannabis use, treatment rates and potency levels have increased in Europe highlighting major concerns about the public health impact of cannabis use. Continued monitoring and efforts to improve data quality and reporting, including indicators of high-risk use and cannabis-attributable harm, will be necessary to evaluate the health impact of international changes in cannabis regulation. FUNDING: This study received no specific funding.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.001 | 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".