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Enregistrement W1727275461 · doi:10.1111/add.12308

Variations in prescription opioids and related harms: a key to understanding and effective policy

2014· article· en· W1727275461 sur OpenAlexaboutno aff
Jeffrey H. Samet, Judith I. Tsui

Notice bibliographique

RevueAddiction · 2014
Typearticle
Langueen
DomaineMedicine
ThématiqueOpioid Use Disorder Treatment
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychological interventionMedical prescriptionDilemmaHealth careMedicineProfit (economics)Public economicsPublic relationsBusinessPsychologyPsychiatryPolitical scienceNursingEconomicsLaw

Résumé

récupéré en direct d'OpenAlex

Why does North America have the dubious distinction of excelling in the non-medical use of prescription opioids (POs) and incurring their related harms? Fischer et al. present an analysis comparing North America to the rest of the world and identify factors that resonate with common sense 1. The three factors identified (i.e. consumption of more POs, less regulatory access restrictions and patient expectations for effective treatment within a ‘for-profit’ oriented health-care system) lead to an unfortunate reality: exposure to opioids seems omnipresent in North America. These authors have utilized an established health services research tool, namely identifying variation in practice patterns, to identify big issues that probably directly and indirectly impact the indisputable finding of opioid prescribing problems in North America. However, the anticipated chorus of calls to ‘shut down the candy store’ risk oversimplifying the solution to the dilemma: how do we adequately treat pain and simultaneously avoid such pervasive exposure to opioids that, invariably, adverse consequences ensue? While Fischer and colleagues present a substantive and thought-provoking descriptive review of differences in health systems, policy, regulations and culture between North America and other high-income countries which may explain the disparity in non-medical use of POs and subsequent harms, there is still a need for detail about the evidence linking those factors to harms. What are the relative contributions of the factors that they outline? What does variation in practices to mitigate such harms reveal about potential interventions to address these problems? This information is key in order to devise effective strategies to combat prescription opioid abuse. One approach that merits serious consideration is to pursue further the path that these authors initiated, by applying the methodology of studying small area variations in the non-medical use of POs and their related harms 2. A logical next step is to circle back to North America to examine what is happening in different regions within the United States and Canada. It is very likely that what happens in Tampa, Florida does not occur in Sacramento, California. Indeed, prior research has demonstrated state and regional differences in opioid prescribing in the United States 3-5. However, we still lack detailed evidence regarding whether these variations translate into harms related to non-medical use of prescription opioids, although some preliminary research may suggest that this is the case 6. Research on other medical conditions has uncovered geographic variation in the United States which many have suggested reflects patterns of overuse that do not translate into tangible benefits (and may even be linked to harm) 7, 8. Others have pointed out that variation may relate more to ‘discretionary decision-making’ (i.e. medical decisions for which there is little evidence to provide guidance) 9. Certainly the lack of high-quality evidence as to whether opioids are effective and safe for chronic pain 10, 11 creates a scenario ripe for practice heterogeneity. In the absence of firm guidelines, providers may be pushed and pulled by the various forces described in this review. The challenge that must be taken up is to measure these relationships in a quantifiable way. Therein lies an opportunity for further understanding of a perplexing and tragic phenomenon, the implication of medical practice contributing to the most common cause of death of young people, overdose and poisoning, exceeding that of motor vehicle crashes in the United States since 2008 12, 13. What variations might be uncovered? What findings might lead to practical policy changes? How can such new knowledge alter public opinion and health-care providers practice patterns? We will offer a few possibilities. One could compare states with and without more strict regulatory policies for opioid prescribing or that have enacted Physician Monitoring Programs 14, 15. Such an analytical approach has been conducted effectively in past research related to driving and legal limits for alcohol use 16. These analyses might yield insight about the impact of such interventions on non-medical use of prescription opioids and related harms. Further examination is possible of small area (e.g. zipcode) variation in the prescription of opioids to determine if it indeed tracks directly to prescription opioid-related harms. If such a finding were uncovered, it would be potent data to share with prescribers as, in reality, physicians do not want to be unaware co-conspirators in this epidemic. ‘Primum non nocere—first do no harm’, as stated in the Hippocratic oath, is in fact taken seriously by most physicians. A third example could compare regions that require continuing medical education on safe opioid prescribing in order to be re-credentialed by the jurisdiction's medical board to those that have no comparable stipulation. Finally, mapping health-care teams that follow recommended safe opioid prescribing practices 17 and comparing such geographic regions to those that do not use such consensus guidelines might reveal differences in key outcomes. These are four examples of many potential approaches to examine practice variation as a means to provide further insight about how best to address a recognized problem that has indisputably and disproportionately adversely impacted North America. 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 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: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,486
Score d'incertitude au seuil0,306

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)0,0000,000

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 Opus0,011
Tête enseignante GPT0,265
Écart entre enseignants0,254 · 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'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

Citations1
Publié2014
Routes d'admission1
Résumé présentoui

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