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Enregistrement W3016193705 · doi:10.1101/2020.04.07.20049015

Time trends and prescribing patterns of opioid drugs in UK primary care patients with non-cancer pain: a retrospective cohort study

2020· preprint· en· W3016193705 sur OpenAlexaboutno aff
Meghna Jani, Belay Birlie Yimer, Thérèse Sheppard, Mark Lunt, William G Dixon

Notice bibliographique

RevuemedRxiv · 2020
Typepreprint
Langueen
DomaineMedicine
ThématiqueOpioid Use Disorder Treatment
Établissements canadiensnon disponible
Organismes subventionnairesVersus ArthritisCentre for Epidemiology Versus Arthritis, University of ManchesterNational Institute for Health and Care Research
Mots-clésOxycodoneMedicineOpioidMedical prescriptionRetrospective cohort studyBuprenorphineCodeineMorphineCohortEmergency medicineInternal medicinePharmacology

Résumé

récupéré en direct d'OpenAlex

ABSTRACT Background The U.S. opioid epidemic has led to similar concerns about prescribed opioids in the U.K. In new users, escalation to more potent and high-dose opioids may contribute to long-term use as well as opioid-related morbidity/mortality. The scale of such escalation is unclear for non-cancer pain. Additionally, physician prescribing behaviour has been described as a key driver of rising opioid prescriptions and long-term opioid use. No studies have investigated the extent to which regions, practices, prescribers, vary in opioid prescribing, whilst accounting for case-mix. Methods Using a retrospective cohort study we used U.K. primary-care electronic health records from Clinical Practice Research Datalink to: (i)describe prescribing trends between 2006-17 (ii)evaluate the transition of opioid dose and potency in the first 2-years from initial prescription (iii)quantify and identify risk factors for long- term opioid use (iv)quantify the variation of long-term use attributed to region, practice and prescriber, accounting for case-mix and chance variation. Adult patients with a new prescription of an opioid without cancer were included. Findings 1,968,742 new-users of opioids were identified. Rates of codeine use were highest, increasing five-fold from 2006-2017, reaching up to 2,456 prescriptions/10,000 people/year. Morphine, buprenorphine and oxycodone prescribing rates continued to rise steadily throughout the study period. Of those who started on high (100-200 Morphine Milligram Equivalents [MME]/day) or very high dose opioids (>200 MME/day), 4.9% and 10.3% remained in the same or higher MME/day category throughout 2-years, respectively. Following opioid initiation, 15% became long-term opioid users. In the fully adjusted model, MME at initiation, older- age, social deprivation, fibromyalgia, rheumatological conditions, substance abuse, suicide/self-harm and gabapentinoid use were associated with the highest odds of long-term use. After adjustment for case-mix, the North-West, Yorkshire, South- West; 103 practices (25.6%) and 540 prescribers (3.5%) were associated with a significantly higher risk of long-term use. Interpretation Patients commenced on high MMEs were more likely to stay in the same state for a subsequent 2-years and were at increased risk of long-term use. In the first UK study evaluating long-term opioid prescribing with adjustment for patient-level characteristics, variation in regions and especially practices and prescribers were observed. Our findings support greater calls for action for reduction in practice and prescriber variation by promoting safe practice in opioid prescribing. Funding Versus Arthritis and National Institute for Health Research Research in Context Evidence before this study Drug dependence and deaths due to opioids have led to an opioid-overdose crisis in several countries globally including the US and Canada, and subsequent concerns about overprescribing in the UK. Physician prescribing behaviour has implicated as a key driver of rising opioid prescriptions and long-term opioid use however this needs to be assessed in the context of region, GP practice and individual patients. We searched Pubmed and Google Scholar between January 2005 and November 2019, with the terms “opioid” AND/OR “opiate”, “chronic pain” AND/OR “non-cancer pain”, and UK AND/OR England AND/OR “Great Britain” AND/OR “NHS”. We also reviewed relevant reports from Public Health England and other national bodies. The more recent trends for opioid prescribing have included all prescriptions including those for cancer pain, and those that include primary care UK prescription data for non-cancer indications are several years out of date. No studies evaluated how opioid dose and potency changes over time in individual patients after starting an opioid for the first time to assess escalation or tapering. National variation in opioid prescribing reported thus far has not accounted for patient case-mix. No studies have assessed the effect of the prescriber on opioid prescribing adjusting for regional, practice level variation and for individual characteristics. Added value of this study There has been a substantial overall increase in opioid-prescribing for non-cancer pain with clear drug-specific trends between 2006-17. To our knowledge, this is the first UK study that has evaluated the sequential transition on how dose/potency vary when a patient is first prescribed an opioid in primary care. Furthermore we report for the first time the effect of individual risk factors, UK regions, GP practice and prescriber (whilst considering these elements together) on long-term opioid use. Implications of all the available evidence Our study highlights the key subpopulations in a UK primary care setting at risk of developing long-term opioid use and the need for closer monitoring of at risk patients. Marked variation between region, practice and prescribers still exists after adjusting for case-mix warranting evidence-based harmonised opioid prescribing guidelines with clearer MME/day thresholds. On a practice level, guidance on regular review and dose reduction, as well as using prescriber and practice variations as a proxy for quality of care through audit and feedback, to highlight unwarranted variation to prescribers, could help drive safer prescribing.

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 candidatesMéta-épidémiologie (sens strict)
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 enseignants0,030
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)0,0010,000
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 Opus0,008
Tête enseignante GPT0,244
Écart entre enseignants0,236 · 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

Citations22
Publié2020
Routes d'admission1
Résumé présentoui

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