Prevalence of opioid dependence in Scotland 2015-2020: A multi-parameter estimation of prevalence (MPEP) study.

BACKGROUND AND AIMS: Drug-related deaths in Scotland more than doubled between 2011 and 2020. To inform policymakers and understand drivers of this increase, we estimated the number of people with opioid dependence aged 15-64 from 2014/15 to 2019/20. DESIGN: We fitted a Bayesian multi-parameter estimation of prevalence (MPEP) model, using adverse event rates to estimate prevalence of opioid dependence jointly from Opioid Agonist Therapy (OAT), opioid-related mortality and hospital admissions data. Estimates are stratified by age group, sex and year. SETTING: Scotland, 2014/15 to 2019/20. PARTICIPANTS: People with opioid dependence and potential to benefit from OAT, whether ever treated or not. Using data from the Scottish Public Health Drug Linkage Programme, we identified a baseline cohort of individuals who had received OAT within the last 5 years, and all opioid-related deaths and hospital admissions (whether among or outside of this cohort). MEASUREMENTS: Rates of each adverse event type and (unobserved) prevalence were jointly modelled. FINDINGS: The estimated number and prevalence of people with opioid dependence in Scotland in 2019/20 was 47 100 (95% Credible Interval [CrI] 45 700 to 48 600) and 1.32% (95% CrI 1.28% to 1.37%). Of these, 61% received OAT during 2019/20. Prevalence in Greater Glasgow and Clyde was estimated as 1.77% (95% CrI 1.69% to 1.85%). There was weak evidence that overall prevalence fell slightly from 2014/15 (change -0.07%, 95% CrI -0.14% to 0.00%). The population of people with opioid dependence is ageing, with the estimated number of people aged 15-34 reducing by 5100 (95% CrI 3800 to 6400) and number aged 50-64 increasing by 2800 (95% CrI 2100 to 3500) between 2014/15 and 2019/20. CONCLUSIONS: The prevalence of opioid dependence in Scotland remained high but was relatively stable, with only weak evidence of a small reduction, between 2014/15 and 2019/20. Increased numbers of opioid-related deaths can be attributed to increased risk among people with opioid dependence, rather than increasing prevalence.

Mortality among individuals prescribed opioid-agonist therapy in Scotland, UK, 2011-20: a national retrospective cohort study.

BACKGROUND: Drug-related death (DRD) rate in Scotland, UK, has increased rapidly to one of the highest in the world. Our aim was to examine the extent to which opioid-agonist therapy (OAT) in Scotland is protective against drug-related mortality and how this effect has varied over time. METHODS: We included individuals in Scotland with opioid use disorder who received at least one OAT prescription between Jan 1, 2011, and Dec 31, 2020. We calculated drug-related mortality rates and used Quasi-Poisson regression models to estimate trends over time and by OAT exposure, adjusting for potential confounding. FINDINGS: In a cohort of 46 453 individuals prescribed OAT with a total of 304 000 person-years of follow-up, DRD rates more than trebled from 6·36 per 1000 person-years (95% CI 5·73-7·01) in 2011-12 to 21·45 (20·31-22·63) in 2019-20. DRD rates were almost three and a half times higher (hazard ratio 3·37; 95% CI 1·74-6·53) for those off OAT compared with those on OAT after adjustment for confounders. However, confounder adjusted DRD risk increased over time for both people off and on OAT. INTERPRETATION: Drug-related mortality rates among people with opioid use disorders in Scotland increased between 2011 and 2020. OAT remains protective but is insufficient on its own to slow the increase in DRD risk for people who are opioid dependent in Scotland. FUNDING: Scottish Government Drug Deaths Taskforce, Public Health Scotland, and National Institute for Health and Care Research.

A tutorial comparing different covariate balancing methods with an application evaluating the causal effects of substance use treatment programs for adolescents.

Randomized controlled trials are the gold standard for measuring causal effects. However, they are often not always feasible, and causal treatment effects must be estimated from observational data. Observational studies do not allow robust conclusions about causal relationships unless statistical techniques account for the imbalance of pretreatment confounders across groups and key assumptions hold. Propensity score and balance weighting (PSBW) are useful techniques that aim to reduce the observed imbalances between treatment groups by weighting the groups to look alike on the observed confounders. Notably, there are many methods available to estimate PSBW. However, it is unclear a priori which will achieve the best trade-off between covariate balance and effective sample size for a given application. Moreover, it is critical to assess the validity of key assumptions required for robust estimation of the needed treatment effects, including the overlap and no unmeasured confounding assumptions. We present a step-by-step guide to the use of PSBW for estimation of causal treatment effects that includes steps on how to evaluate overlap before the analysis, obtain estimates of PSBW using multiple methods and select the optimal one, check for covariate balance on multiple metrics, and assess sensitivity of findings (both the estimated treatment effect and statistical significance) to unobserved confounding. We illustrate the key steps using a case study examining the relative effectiveness of substance use treatment programs and provide a user-friendly Shiny application that can implement the proposed steps for any application with binary treatments.