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Enregistrement W2166510921 · doi:10.1111/add.12343

Prescription opioid deaths: we need to treat sick populations, not just sick individuals

2014· article· en· W2166510921 sur OpenAlexaboutno aff
Angela Rintoul, Malcolm Dobbin

Notice bibliographique

RevueAddiction · 2014
Typearticle
Langueen
DomaineMedicine
ThématiqueOpioid Use Disorder Treatment
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineLife expectancyPrescription drugPopulationMedical prescriptionHealth careDisadvantagedEnvironmental healthEconomic growthNursing

Résumé

récupéré en direct d'OpenAlex

In this issue, Fischer et al. describe why North America currently leads the prescription opioid (PO) drug epidemic 1. Despite substantially higher per-capita expenditure on health care and pharmaceuticals than other high-income countries (HICs), the United States has relatively poor population health outcomes. In 2007, the United States ranked 37th in the world for life expectancy at birth, with large racial and geographic disparities. The United States differs from many HICs, as it lacks universal health insurance: 19% of the population do not have health insurance, and two-thirds of those insured rely on private insurance. The risk of PO overdose deaths is associated with lower socio-economic status 2, 3. Disturbingly, US life expectancy for the most disadvantaged has fallen by 4 years since 1990 4. Increasing PO drug overdoses since 1990 disproportionately affecting non-Hispanic whites may have contributed to this decline. Added to this, clinical studies fail to demonstrate the long-term efficacy and safety of POs for chronic non-cancer pain. However, HIC consumption continues to increase, despite more than a decade of documentation of serious harm and countermeasures such as prescription monitoring programmes (PMPs) and guidelines for prescribing. Voluntary use of PMPs 5 and compliance with guidelines is poor 6, and there is little evidence to support that patients at risk of aberrant behaviour can be identified reliably. Prescribers can be disadvantaged by dysfunctional funding arrangements, with pressure to contain costs, particularly where funded by for-profit insurance. This contributes to short consultation times and many non-reimbursed tasks involved in the comprehensive management of complex problems in patients suffering multiple comorbidities. As consultation times shorten, there is pressure to treat quickly: in Australia, 85% of general practitioner encounters results in a prescription, and oxycodone is the seventh most frequently prescribed medication 7. It is now well established that inappropriate and intense marketing of OxyContin in the United States contributed to increased consumption 8. Professional guidelines claimed inaccurately that the risk of addiction or other serious adverse effects was low 9. In Canada, industry involvement in provision of training materials for medical students may also have played a role 10. Developers of a transdermal oxycodone patch in Australia have made unlikely claims that their product will have lower abuse liability and prevent tolerance 11—another manifestation of an ‘industrial epidemic’ resulting from the commercialization of potentially dangerous products 12. A biomedical focus on downstream interventions with individual patients obscures structural factors, such as an increasingly ‘opioid-rich’ environment in many HICs. This may be a consequence of the ‘individualistic fallacy’, where undue attention has been paid to individual characteristics and the wider influence of population level determinants of this epidemic have been given less attention 13. Epidemiological studies are now describing a temporal and spatial association between increased opioid consumption and adverse outcomes, including abuse, medical emergencies and death 14. Widespread prescribing provides more opportunities for diversion, non-medical use and unintentional poisoning, including among children 15. This results in stigmatization of patients considered ‘risky’ and undertreats those in need 16. A public health response requires treating sick populations, not just sick individuals 17; this means shifting expectations and practices around the total opioid supply. In the face of this unremitting epidemic of death and morbidity, it is now appropriate to consider shifting mean consumption by carefully decreasing the total supply of opioids to less dangerous levels. Collaboration across domains, including worker insurance organizations, health-care providers and other agencies, appears to have stalled the escalating numbers of deaths and other harm associated with PO misuse in Washington State 18. A planned and systematic approach can result in considerable changes to identify risks and enhance safe supply, starting with those patients receiving high daily PO doses associated with a heightened risk of poisoning death. Patients prescribed high daily PO doses consume a staggeringly high proportion of all opioids: in two separate insurance populations, the top 5% of users accounted for 70 and 48% of total use 19. Consumers need to be supported carefully in this process; those experiencing addiction cannot be abandoned. The inability to curb dangerous promotion, supply and consumption of PO demonstrates a regulatory failure to protect public health. Other countries must learn quickly from the North American experience to forestall the emergence of similar tragedies. Malcolm Dobbin has received honoraria from Pfizer for lectures, which were donated to charity.

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 candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,508
Score d'incertitude au seuil1,000

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 recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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.

Tête enseignante Opus0,032
Tête enseignante GPT0,292
Écart entre enseignants0,259 · 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.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations3
Publié2014
Routes d'admission1
Résumé présentoui

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