Considering prescription opioid‐related harms in the population: Response to commentaries
Notice bibliographique
Résumé
The commentaries to our recent paper 1 make some important and insightful points to which we briefly respond. Rintoul et al. 2 correctly point out that little is known from a population health—or social determinants—perspective about medical prescription opioid (PO) use and PO-related harms. Specifically, there are few data on how basic social variables such as socio-demographics, education, socio-economic status, etc. influence the above phenomena. Such gradients are fairly well explored for other legal and illegal drug use 3, 4; a recent examination of non-medical PO use among Ontario adults and students found a broad distribution across socio-economic classes 5. The empirical situation is quite different with regard to another population level-effect (as raised by Samet & Tsui 6); namely, the correlation between levels of PO dispensing and corresponding levels of harms in a population. Several studies from different North American jurisdictions have documented that the levels of non-medical PO use, as well as PO-related morbidity and mortality outcomes, covary notably with PO dispensing levels 7, 8; this may imply that meaningfully lowered PO dispensing levels result in lower levels of harm outcomes; a key question is, however, on how to achieve such reductions without undermining pain care access and quality. An important issue, here—also in light of the limited evidence on the effectiveness of PO therapy for chronic pain—is a focus upon high-dose PO prescribing, accounting for large shares of overall POs consumed yet also documented as a key determinant of PO harms (e.g. mortality) 9, 10. Clinically sensible reductions, especially in high PO dosing, may bring ways to substantively reduce overall PO consumption, and harms, in populations, and relevant mechanisms have been proposed (e.g. registration for high doses) 11. Another key area, especially in PO-rich North America, is the widespread and generous use of POs for problems where alternative interventions may be equally beneficial yet coming with fewer risks. Griffiths et al. 12 point out correctly that both in terms of PO use environments and problems, the European Union (EU) ought to be considered a heterogeneous entity, featuring substantive interjurisdictional differences 13. This compares to North America, where extensive differences exist in PO use or PO-related problems between provinces (Canada) or states (United States 14, 15). A key question for the EU region is whether PO-related problems there are (i) delayed, (ii) insufficiently noticed or (iii) simply not occurring at levels they are in North America, and what potential determinants for these realities are, as has been observed for other drug issues (e.g. cocaine/crack 16). This question is of particular interest, given that numerous EU jurisdictions have also recently seen substantive increases in overall PO dispensing levels. Finally, the question of effective policy measures is raised. There is still only limited evidence regarding the impacts of prescription monitoring programmes (PMPs), especially with regard to reducing PO-related harms 17, 18; in addition, clinical PO use guidelines are increasingly present, yet little is known about their impact on PO-related harms 19. These areas urgently require attention from rigorous research. None. B.F. and J.R. acknowledge several Canadian Institutes of Health Research (CIHR) grants (specifically grant numbers 1234 and 5678) which supported the present work. B.F. also acknowledges salary and research support from a CIHR/PHAC Applied Public Health Research Chair.
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 | 0,000 | 0,000 |
| 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 | 0,000 | 0,001 |
| 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 ».