Illicit Cannabis Use to Self-Treat Chronic Health Conditions in the United Kingdom: Cross-Sectional Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».