Trends and projection in the proportion of (heavy) cannabis use in Germany from 1995 to 2021
Bibliographic record
Abstract
AIMS: To measure the current trends of cannabis use in Germany, measure trends in the proportion of heavy cannabis users and estimate future cannabis use rates. DESIGN: Repeated waves of the Epidemiological Survey on Substance Abuse, a cross-sectional survey conducted between 1995 and 2021 with a two-stage participant selection strategy where respondents completed a survey on substance use delivered through the post, over the telephone or on-line. SETTING: Germany. PARTICIPANTS/CASES: German-speaking participants aged between 18 and 59 years living in Germany who self-reported on their cannabis use in the past 12 months (n = 78 678). With the application of a weighting scheme, the data are nationally representative. MEASUREMENTS: Questions on the frequency of cannabis use in the past 12 months and self-reported changes in frequency of use due to the COVID-19 pandemic. FINDINGS: The prevalence of past 12-month cannabis users increased from 4.4% [95% confidence interval (CI) = 3.7, 5.1] in 1995 to 10.0% (95% CI = 8.9, 11.3) in 2021. Modeling these trends revealed a significant increase that accelerated over the past decade. The proportion of heavy cannabis users [cannabis use (almost) daily or at least 200 times per year] among past-year users has remained steady from 1995 (11.4%, 95% CI = 7.7, 16.5) to 2018 (9.5%, 95% CI = 7.6, 11.9), but significantly increased to 15.7% (95% CI = 13.1, 18.8) in 2021 during the COVID-19 pandemic. Extrapolating from these models, the prevalence of 12-month cannabis users in 2024 is expected to range between 10.4 and 15.0%, while the proportion of heavy cannabis users is unclear. CONCLUSIONS: Trends from 1995 to 2021 suggest that the prevalence of past 12-month cannabis users in Germany will continue to increase, with expected rates between 10.4 and 15.0% for the German-speaking adult population, and that at least one in 10 cannabis users will continue to use cannabis heavily (almost daily or 200 + times in the past year).
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".