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Record 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 on OpenAlexaboutno aff
Meghna Jani, Belay Birlie Yimer, Thérèse Sheppard, Mark Lunt, William G Dixon

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
FundersVersus ArthritisCentre for Epidemiology Versus Arthritis, University of ManchesterNational Institute for Health and Care Research
KeywordsOxycodoneMedicineOpioidMedical prescriptionRetrospective cohort studyBuprenorphineCodeineMorphineCohortEmergency medicineInternal medicinePharmacology

Abstract

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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.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.244
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations22
Published2020
Admission routes1
Has abstractyes

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