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Enregistrement W2297991951 · doi:10.1111/add.13272

Commentary on Imtiaz <i>et al</i>. (2016): The burden of disease attributable to cannabis—implications for policy

2016· letter· en· W2297991951 sur OpenAlexaboutno aff
Wayne Hall

Notice bibliographique

RevueAddiction · 2016
Typeletter
Langueen
DomaineMedicine
ThématiqueCannabis and Cannabinoid Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCannabisMedicineEnvironmental healthAttributable riskDiseaseConfoundingEffects of cannabisDisease burdenPsychiatryBurden of diseasePopulationPathology

Résumé

récupéré en direct d'OpenAlex

The contribution that current patterns of cannabis use make to the burden of disease is very useful information, but it cannot decide policy. Sameer Imtiaz and colleagues have used the methods developed as part of the 2010 Global Burden of Disease Comparative Risk Assessment to estimate the contribution that cannabis use made to the burden of disease (BoD) in 2012 in Canada, a developed country where the prevalence of cannabis use is among the highest in the world 1. They included among the cannabis-related causes of disease burden: cannabis use disorders (CUD), road traffic injuries and deaths, schizophrenia and lung cancer. CUD are a consequence of cannabis use by definition; recent meta-analyses suggest that cannabis use modestly increases the risk of road traffic injuries and deaths, and there is reasonable evidence that regular cannabis is a contributory cause of the onset of psychoses 2, although some argue that we cannot exclude the possibility of residual confounding 3. The inclusion of lung cancer is more contentious, given that a recent meta-analysis concluded that there was not sufficient evidence to assess whether cannabis smoking increased the risk of lung cancer 4. The authors included uncertainty bounds around their estimates to reflect the fact that many of these estimated risks are based on a small number of epidemiological studies which have weakly quantified cannabis use. Their comparison of the BoD attributable to alcohol, tobacco and cannabis shows that cannabis contributes a very small fraction of the contribution of alcohol and tobacco. This reflects the combined effects of the very large difference in the prevalence of the regular use of these three drugs and the fact that there is a much larger research literature linking alcohol and tobacco to many more causes of death and disability than cannabis. A comparison of BoD attributed to cannabis and opioids shows that cannabis use is much more prevalent than opioid use, but the opioid drugs make a much larger contribution to BoD because they cause many more deaths in young adults by overdose, suicide, accidents and blood-borne virus (BBV) infections. Only 20 years ago, statements about the comparative harms of cannabis, alcohol, tobacco and opioids were highly controversial. Some authors (e.g. 5) insisted that such comparisons could not and should not be made. A paper that Robin Room, Susan Bondy and I prepared for a 1997 World Health Organization (WHO) report on the health effects of cannabis 6 caused enormous public controversy and was disowned by WHO (see 7). The successful development of the comparative risk assessment approach used in the GBD estimates has thankfully put paid to these objections to comparative assessments of contributions that different types of drugs make to BoD. Imtiaz et al. acknowledge an important qualification that applies to all such comparisons; namely, that they reflect the BoD attributable to current patterns of cannabis use. Those who defend current cannabis policies can argue reasonably that these policies can claim some credit for this fact. Many adults in developed countries have used cannabis at some time in their lives, but only a very small proportion of these (approximately 10%) have used regularly, and a still smaller proportion have used daily over decades. The latter is a miniscule proportion by comparison with the proportions of tobacco smokers who smoke daily and alcohol consumers who drink in a risky manner. It is clear that CUD can affect a substantial number of young people in developed societies. This fact demands the policy response suggested by the authors; namely, screening and early intervention in medical settings where problem cannabis users are likely to be seen. Is the contribution that cannabis use makes to BoD enough to justify a change from current prohibitionist policies to more liberal policies that may include legalization for recreational use, as has occurred in four US states? This is properly a matter for democratic deliberation that considers the social and economic costs and benefits of adopting different policies towards cannabis. Data on the effects of cannabis on BoD, as used currently, will play a useful role in such deliberations, but it cannot be decisive 8. None. I thank Sarah Yeates for her assistance in preparing this commentary.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,053
Score d'incertitude au seuil0,695

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,021
Tête enseignante GPT0,330
Écart entre enseignants0,309 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2016
Routes d'admission1
Résumé présentoui

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