OP0087 NATIONAL VARIATION AND FACTORS ASSOCIATED WITH THE TRANSITION FROM FIRST USE TO LONG-TERM OPIOID USE FOR NON-CANCER PAIN

Abstract

Background:Prescribing behaviour of physicians has been described as a key driver of rising opioid prescriptions and long-term opioid use. However, the effect of prescribers requires interpretation within context. No studies have investigated the extent to which regions, practices and prescribers, vary in opioid prescribing accounting for case-mix by considering this hierarchy together.Objectives:(i) Quantify and identify risk factors for the transition from new-user to long-term opioid user (ii) Quantify variation of long-term use attributed to region, practice and prescriber, accounting for patient mix and chance variation.Methods:We conducted a retrospective observational UK study between 2006-2017 using Clinical Practice Research Datalink. Opioids new users, ≥18 years, without cancer were identified. Long-term opioid use was defined as ≥3 opioid prescriptions issued within a 90-day period from index date, or ≥1 opioid prescription lasting at least 90 days in the first year. A multi-level random-effects logistic regression model was used to examine the association of patient characteristics with the odds of becoming a long-term opioid user. To examine variation in opioid use amongst prescribers, GP practices and region after adjusting for patient case-mix, we used a nested random-effect structure. A ‘high-risk’ region, prescriber or practice was defined as those where the entire adjusted 95% CI lay above the population average.Results:1,968,742 new opioid users were included; 14.6% patients transitioned to long-term use. In the fully adjusted model, factors associated with higher odds of long-term opioid use included high morphine milligram equivalents (MME)/day at first prescription, older age, deprivation, fibromyalgia, rheumatological conditions, major surgery (Table). After adjustment for case-mix, the North-West, Yorkshire and South-West were found to be high-risk regions for long-term use. 103 practices (25.6%) and 540 prescribers (3.5%) were associated with a significantly higher risk of long-term use. The odds of becoming a long-term user for a patient belonging to these prescribers reached up to >3.5 times than the population average.Conclusion:Prescribing factors, age, deprivation and conditions including fibromyalgia and rheumatological conditions were associated with higher odds of long-term opioid 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 to reduce practice and prescriber variation by promoting safe practice in opioid prescribing.Table.Factors associated with long-term opioid use using a multi-level model accounting for clustering of individuals within prescriber, practice and regionIndividual factorsAdjusted Odds Ratio (95% CI) *Prescribing factorsIndex daily MME >2007.59 (6.29, 9.16)Index daily MME 100-2001.12 (1.03, 1.21)Index daily MME 50-1001.58 (1.49, 1.68)Index daily MME <50RefGabapentinoid use2.51 (2.43, 2.60)Psychotropic use1.28 (1.17, 1.40)Age>754.35 (4.26, 4.45)65-753.57 (3.50, 3.65)55-653.03 (2.96, 3.09)35-551.91 (1.88, 1.95)Age <35RefDeprivation (Townsend score)Quintile 5 (Most deprived)1.54 (1.51, 1.57)Quintile 41.34 (1.31, 1.36)Quintile 31.20 (1.18, 1.22)Quintile 21.09 (1.07, 1.11)Quintile 1 (Least deprived)RefPre-existing conditions/ prior proceduresFibromyalgia1.81 (1.49, 2.20)Substance use disorder1.76 (1.70, 1.83)Suicide and self-harm1.56 (1.51, 1.61)Rheumatological conditions Ψ1.54 (1.49, 1.59)Alcohol abuse1.50 (1.45, 1.55)Depression1.28 (1.26, 1.30)Major Surgery1.09 (1.06, 1.13)Abbreviations: MME, Morphine Milligram Equivalent; *p<0.05. Index daily MME/day is the MME/day at first prescription (MME= daily dose in milligrams X opioid conversion ratio). Ψ Defined by Charlson score including rheumatoid arthritis, SLE, myositis.Disclosure of Interests:Meghna Jani Speakers bureau: Grifols, Belay Birlie Yimer: None declared, Therese Sheppard: None declared, Mark Lunt: None declared, William Dixon Consultant of: Bayer and Google