Illicit Cannabis Use to Self-Treat Chronic Health Conditions in the United Kingdom: Cross-Sectional Study
Bibliographic record
Abstract
Background: In 2019, it was estimated that approximately 1.4 million adults in the United Kingdom purchased illicit cannabis to self-treat chronic physical and mental health conditions. This analysis was conducted following the rescheduling of cannabis-based medicinal products (CBMPs) in the United Kingdom but before the first specialist clinics had started treating patients. Objective: The aim of this study was to assess the prevalence of illicit cannabis consumption to treat a medically diagnosed condition following the introduction of specialist clinics that could prescribe legal CBMPs in the United Kingdom. Methods: Adults older than 18 years in the United Kingdom were invited to participate in a cross-sectional survey through YouGov between September 22 and 29, 2022. A series of questions were asked about respondents' medical diagnoses, illicit cannabis use, the cost of purchasing illicit cannabis per month, and basic demographics. The responding sample was weighted to generate a sample representative of the adult population of the United Kingdom. Modeling of population size was conducted based on an adult (18 years or older) population of 53,369,083 according to 2021 national census data. Results: There were 10,965 respondents to the questionnaire, to which weighting was applied. A total of 5700 (51.98%) respondents indicated that they were affected by a chronic health condition. The most reported condition was anxiety (n=1588, 14.48%). Of those enduring health conditions, 364 (6.38%) purchased illicit cannabis to self-treat health conditions. Based on survey responses, it was modeled that 1,770,627 (95% CI 1,073,791-2,467,001) individuals consume illicit cannabis for health conditions across the United Kingdom. In the multivariable logistic regression, the following were associated with an increased likelihood of reporting illicit cannabis use for health reasons-chronic pain, fibromyalgia, posttraumatic stress disorder, multiple sclerosis, other mental health disorders, male sex, younger age, living in London, being unemployed or not working for other reasons, and working part-time (P<.05). Conclusions: This study highlights the scale of illicit cannabis use for health reasons in the United Kingdom and the potential barriers to accessing legally prescribed CBMPs. This is an important step in developing harm reduction policies to transition these individuals, where appropriate, to CBMPs. Such policies are particularly important considering the potential risks from harmful contaminants of illicit cannabis and self-treating a medical condition without clinical oversight. Moreover, it emphasizes the need for further funding of randomized controlled trials and the use of novel methodologies to determine the efficacy of CBMPs and their use in common chronic conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".