Cannabis harm reduction in settings where cannabis is illegal: an international Delphi consensus study
Notice bibliographique
Résumé
Cannabis use carries several mental and physical health risks, including greater prevalence and more severe symptomatology of psychosis, depression, and anxiety, respiratory problems, and physical harms from driving while intoxicated [1, 2]. While abstinence is generally the safest option for reducing cannabis-related health risks [3], 4.1% of the global population and 7.6% of the UK population report using cannabis [4] and cessation of cannabis use can be difficult to achieve. These statistics highlight the need for cannabis harm reduction - a pragmatic approach focused on strategies to minimise drug-related harms without necessarily promoting abstinence. Harm reduction is a well-developed concept for most drugs but is perhaps most widely understood and applied for opioid use. Public health initiatives like needle and syringe exchange services, safe injection sites, and take-home naloxone programmes have been implemented in several countries, including the UK, and have been shown to reduce opioid-related mortality and morbidity [5-7]. In the nicotine field, vaping and other heat-not-burn tobacco products are increasingly considered safer alternatives for people who smoke cigarettes [8, 9]. Similarly, some of the most prevalent and serious cannabis-related harms, including cannabis use disorder, psychosis, and respiratory problems, may be prevented through safer use practices [3, 10-14]. However, despite the prevalence of and harms associated with cannabis use, “cannabis harm reduction” is conceptually less well-defined. Examples from the Lower-risk Cannabis Use Guidelines (LRCUG), which are modifiable use behaviour-related risk factors identified by international cannabis expert teams, include choosing products with low levels of Δ9- tetrahydrocannabinol (THC), the main psychoactive compound in cannabis, and choosing consumption methods like vaporisers, e-cigarette devices, and edibles over smoking [3, 15, 16]. Guidelines such as the LRCUG provide tangible and evidence-based information to people who use cannabis (PWUC) about how they can reduce their risks from using cannabis, without necessarily having to stop completely. However, while the LRCUG and their recommendations are not limited to settings where cannabis is legal for use and involve quality-regulated supply, there is also a need for harm reduction advice specifically targeted at people who use cannabis in illegal and unregulated markets, where reliable information about products is typically unavailable and additional sources of harm might exist. For instance, in one study, cannabis vape liquids seized from English schools were found to rarely contain THC and instead contained more dangerous substances such as synthetic cannabinoids [17]. This illustrates that vaping e-liquids may pose a higher risk than smoking cannabis flower in settings where cannabis is illegal. In this instance, relevant actionable harm reduction advice might be to use cannabis flower with a medical-grade dry-herb vaporiser, or to test THC vape liquid at a drug checking service (where available) before using. Similarly, it can be difficult for PWUC to know the THC content in cannabis purchased from an illicit market, or even access low-THC products in an illicit market saturated with high-potency cannabis [9], which means that using low-potency products is distinctly difficult to achieve in such settings. This study will use a Delphi method to develop consensus-based cannabis harm reduction guidelines aimed for people consuming cannabis in settings where supply is illegal. The identification of key harm reduction behaviours is necessary to inform the development of targeted interventions that decrease harmful use practices in such settings, ultimately reducing the health burden of cannabis use. 1. Hoch, E., et al., Cannabis, cannabinoids and health: a review of evidence on risks and medical benefits. Eur Arch Psychiatry Clin Neurosci, 2024. 2. Solmi, M., et al., Balancing risks and benefits of cannabis use: umbrella review of meta-analyses of randomised controlled trials and observational studies. Bmj, 2023. 382: p. e072348. 3. Fischer, B., et al., Lower-Risk Cannabis Use Guidelines (LRCUG) for reducing health harms from non-medical cannabis use: A comprehensive evidence and recommendations update. Int J Drug Policy, 2022. 99: p. 103381. 4. United Nations Office on Drugs and Crime, World Drug Report 2024. 2024. 5. Hurley, S.F., D.J. Jolley, and J.M. Kaldor, Effectiveness of needle-exchange programmes for prevention of HIV infection. Lancet, 1997. 349(9068): p. 1797-800. 6. Levengood, T.W., et al., Supervised Injection Facilities as Harm Reduction: A Systematic Review. Am J Prev Med, 2021. 61(5): p. 738-749. 7. McDonald, R. and J. Strang, Are take-home naloxone programmes effective? Systematic review utilizing application of the Bradford Hill criteria. Addiction, 2016. 111(7): p. 1177-87. 8. Simonavicius, E., et al., Heat-not-burn tobacco products: a systematic literature review. Tob Control, 2019. 28(5): p. 582-594. 9. Erku, D., et al., Nicotine vaping products as a harm reduction tool among smokers: Review of evidence and implications for pharmacy practice. Res Social Adm Pharm, 2020. 16(9): p. 1272-1278. 10. Borodovsky, J.T., et al., Characterizing cannabis use reduction and change in functioning during treatment: Initial steps on the path to new clinical endpoints. Psychol Addict Behav, 2022. 36(5): p. 515-525. 11. Di Forti, M., et al., Daily use, especially of high-potency cannabis, drives the earlier onset of psychosis in cannabis users. Schizophr Bull, 2014. 40(6): p. 1509-17. 12. Petrilli, K., et al., Association of cannabis potency with mental ill health and addiction: a systematic review. Lancet Psychiatry, 2022. 9(9): p. 736-750. 13. Sherman, B.J., et al., Evaluating cannabis use risk reduction as an alternative clinical outcome for cannabis use disorder. Psychol Addict Behav, 2022. 36(5): p. 505-514. 14. Stone, B.M. and B.J. Sherman, Is it time for a cannabis harm reduction approach? Commentary on Sherman et al. (2022) and Borodovsky et al. (2022). Psychol Addict Behav, 2023. 37(5): p. 709-712. 15. Fischer, B., et al., Lower-Risk Cannabis Use Guidelines: A Comprehensive Update of Evidence and Recommendations. Am J Public Health, 2017. 107(8): p. e1-e12. 16. Fischer, B., et al., Lower Risk Cannabis use Guidelines for Canada (LRCUG): a narrative review of evidence and recommendations. Can J Public Health, 2011. 102(5): p. 324-7. 17. Cozier, G., et al., Synthetic cannabinoids in e-cigarettes seized from English schools. Addiction, 2025.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,010 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».