On the relationship between epidemiology and policy
Notice bibliographique
Résumé
We thank Professor Wayne Hall for his insightful comments 1 on our recent study characterizing cannabis-attributable harms in Canada 2. Although agreeing with his general notion that epidemiological results should not, and cannot, determine general policy questions such as legalization, there are three important linkages which should be taken into consideration. First, the level of policy regulation should be proportionate to the degree of potential harm 3. In other words, substances with greater harm potential should be regulated more than substances with lesser harm potential. Obviously, less regulation of psychoactive substances may lead to their greater availability, and therefore greater harm (as evidenced for alcohol 4 and prescription opioids 5; for Canada see 6), but there are differences in the pharmacological and toxicological properties of the substances 7, 8. An important epidemiological indicator in this regard would be harm per user or harm per heavy user 9, which would also suggest cannabis as having lesser harm potential than alcohol, tobacco or prescription opioids in Canada. Based on the data presented in our study 2, one death per 10 000 users would be expected for cannabis, whereas the corresponding estimates for alcohol, tobacco and prescription opioids would be considerably higher (four, 100 and three deaths per 10 000 users, respectively). Secondly, the type of harm and the kind of risk relations can point to specific policies. Given that the major fatal risks of cannabis use are injuries, and in particular road traffic injuries 10, specific policies (e.g. per se laws) are indicated, independent of the overall legal status of cannabis (i.e. per se laws can be applied to substances that are under prohibition, decriminalized or legalized 11). Another implication is that most of the harm from cannabis relates to heavy users, which implies the need for specifically targeted policies 12 compared to substances where the prevention paradox applies 13. Finally, comparisons of epidemiological outcomes through comparative risk assessments should always take the knowledge base into consideration. For instance, there is far more accumulated evidence on alcohol and chronic disease risks 14, 15 than similar evidence on cannabis 16. This is due, in part, to the greater availability of alcohol, and thus data could be integrated more easily into large medical cohort studies. This situation will improve with a more evidence-based approach to drug policies in the future. Therefore, policies can, by no means, be derived exclusively from epidemiological results per se, but should be informed by such results. None.
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 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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».