Vaping cannabis (marijuana) has the potential to reduce tobacco smoking in cannabis users
Bibliographic record
Abstract
Budney et al. 1 provide an informative and balanced overview of issues regarding the emerging use of ‘vaping’ as a route of administration (ROA) for cannabis. Electronic cigarettes (e-cigs) have caused considerable debate, and while similar issues apply to vaporizers for delivering cannabis, the implications of their growing availability and impact on cannabis use have been largely overlooked. In addition to issues highlighted by Budney et al., we argue that one of the greatest harms associated with cannabis is its strong relationship with tobacco, and vaping cannabis has the potential to reduce both cannabis-related pulmonary harms and tobacco addiction. In Europe, co-administration of cannabis and tobacco (e.g. in ‘joints’ where tobacco and cannabis are combined) is by far the most common ROA 2. Tobacco use is over-represented in cannabis smokers, with up to 90% reporting life-time exposure 3. In adolescents, cannabis use is predictive of later tobacco smoking, labelled the ‘reverse gateway’ 4; for example, cannabis users may only be exposed to tobacco through smoking ‘joints’, leading to sustained tobacco use/dependence. Additionally, cigarette smoking mediates the relationship between cannabis use and cannabis dependence 5. Increased vaping could lead to the dissociation of cannabis and tobacco: preliminary research suggests that using a cannabis–tobacco mixture in a vaporizer is extremely rare, with only two of 96 people interviewed using it in this way 6. However, there is an urgent need to collect epidemiological data and to conduct randomized controlled trials on vaporizers as an intervention in cannabis users 7-9. In our opinion, it will be crucial for these studies to investigate any effects of cannabis vaping on subsequent use of tobacco and also nicotine (e.g. e-cig) use. Whether vaping will become widely adopted by cannabis users is unclear. Data from 30 000+ cannabis users collected as part of the annual Global Drug Survey (GDS) and curated as part of the online harm reduction code 10 sheds some light on this. Vaping was only commonly used by 8% of the sample, but was rated as the most important harm reduction strategy and had a positive/neutral effect upon pleasure. Although this is a self-nominating sample, its size and cross-cultural representativeness offers some insight into the changes occurring in cannabis ROAs. Furthermore, GDS data from 2014 suggested countries with the lowest level of tobacco-based ROAs, such as the United States and Canada, also had the highest level of vaporizer use. Additionally, this data suggests non-tobacco ROAs predicted motivation to use less tobacco 2. Therefore, tentatively, there could be reason to be optimistic about the potential of vaporizers. If vaporizers can reduce cannabis and tobacco co-administration, the outcome could be a reduction of tobacco use/dependence among cannabis users and a resultant reduction in harms associated with cannabis. Indeed, if vaping cannabis becomes commonplace in the future, the next generation of cannabis users might never be exposed to nicotine or tobacco in the first place. CH and TPF are funded by the Medical Research Council. AW is founder and managing director of GDS.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.004 |
| 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".