Comparison of Water Pipes vs Other Modes of Cannabis Consumption and Subsequent Illicit Drug Use in a Longitudinal Cohort of Young Swiss Men
Bibliographic record
Abstract
Importance: There are concerns that the use of water pipes to consume cannabis is associated with increased risks of engaging in more addictive behaviors. Objective: To examine whether consuming cannabis with a water pipe was associated with later consumption of other illicit drugs compared with not using a water pipe. Design, Setting, and Participants: The Cohort Study on Substance Use Risk Factors (C-SURF) was a population-based study, recruiting 5987 Swiss men aged 18 to 25 years from 3 of 6 Swiss Armed Forces recruitment centers (response rate: 79.2%). The baseline assessment (t0) was done from 2010 to 2012, first follow-up (t1) from 2012 to 2014, and second follow-up (t2) from 2016 to 2018. Our sample included men who participated in both t0 and t2 assessments and used cannabis but no other illicit drugs at t0. Data analysis was performed from July 2020 to January 2021. Exposures: Cannabis use frequency and route of administration from self-administered questionnaires completed at t0 and t2. Main Outcomes and Measures: Outcome measures were initiation of illicit drug use and cannabis use disorder, identified by the Cannabis Use Disorder Identification Test. To examine whether water pipe use at t0 was associated with illicit drug use at t2, multivariable logistic regression analysis was performed. Results: Among 1108 Swiss male cannabis users who did not use other illicit drugs at t0, the mean (SD) age was 20 (1.2) years, 617 (55.7%) were from Switzerland's French-speaking region, and 343 (30%) used water pipes to consume cannabis. Water pipe users at t0 were more likely to use other illicit drugs at t2 compared with water pipe nonusers (adjusted odds ratio [aOR], 1.54; 95% CI, 1.10-2.16). The odds of using middle-stage drugs (including stimulants, hallucinogens, and inhaled drugs) at t2 were increased for water pipe users (aOR, 1.61; 95% CI, 1.13-2.29). Water pipe use at t0 was not associated with cannabis use disorder at t2 after adjusting for cannabis use frequency. Conclusions and Relevance: This cohort study's results suggest that, among Swiss young men, water pipe use is associated with other illicit drug use later in life, particularly middle-stage illicit drugs. Preventive programs must focus on the potential of later harm to cannabis users who use water pipes but have not yet started taking other illicit drugs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".