This project will use linked administrative data to understand risk for mortality and other adverse outcomes during and after opioid agonist treatment (OAT). It will use standard biostatistical approaches and sophisticated machine learning techniques.
School of Public Health and Community Medicine, UNSW
Monash Addiction Research Centre, Monash University
Adelaide Medical School, University of Adelaide
North America is in the midst of an opioid use epidemic, and opioid use is also increasing dramatically in Australia. OAT is an effective treatment for opioid dependence, but there are important questions regarding risk of adverse clinical outcomes, including death, that are yet to be answered. Who is most at risk of adverse outcomes? What patient, provider and treatment setting actors may influence this risk? Answers to these and other questions are critical to inform the massive scale-up of OAT internationally that will be required to respond to the opioid epidemic.
Aim 1: Determine the magnitude of risk for specific adverse clinical outcomes (e.g. mortality, hospitalization and ED presentation, and unplanned treatment cessation) during and after OAT with methadone and buprenorphine
Aim 2: Identify patient, treatment setting, and provider risk factors associated with adverse clinical outcomes during and after OAT with methadone and buprenorphine
Aim 3: Develop a risk prediction model to identify patients at greatest risk of adverse clinical outcomes during and after OAT
The study will use a population cohort of OAT patients (n≈45,000) treated between 2001 (when buprenorphine became available for OAT in New South Wales) and 2016. These data will be probabilistically linked63 to State-wide hospitalization, emergency department, incarceration and mortality data. Linkage will be undertaken by dedicated data linkage institutions and data custodians using best practice protocols that protect individual privacy and confidentiality, with extensive clerical review to maximize linkage sensitivity and specificity. We will examine incidence (Aim 1) and risk (Aim 2) for specific adverse clinical outcomes during OAT, with a special focus on the period of OAT induction, as well as the remainder of time in OAT and the 4 weeks immediately following cessation of OAT. Adverse clinical outcomes to be examined will include all-cause and cause-specific (drug, self-harm/suicide, and injury-related) emergency department visits, hospitalisation and mortality and unplanned treatment cessation. Then, we will develop a risk prediction model to identify patients at greatest risk of adverse outcomes during OAT (Aim 3). While Aims 1 and 2 are focused on understanding the magnitude of risk for an outcome associated with a specific factor, Aim 3 is focused on maximizing the predictive ability of the model to enable the real-time identification of individual patients at risk of adverse clinical outcomes during OAT.
All data for this project has been linked and we are currently in the process of undertaking the various analyses outlined in the objectives. The results from the project so far are summarised below.
1. Jones NR, Nielsen S, Farrell M, et al. Retention of opioid agonist treatment prescribers across New South Wales, Australia, 2001–2018: Implications for treatment systems and potential impact on client outcomes. Drug and Alcohol Dependence 2021; 219: 108464.
2. Lewer D, Jones NR, Hickman M, et al. Risk of discharge against medical advice among hospital inpatients with a history of opioid agonist therapy in New South Wales, Australia: A cohort study and nested crossover-cohort analysis. Drug Alcohol Depend 2020; 217: 108343.
3. Jones NR, Hickman M, Larney S, et al. Hospitalisations for non-fatal overdose among people with a history of opioid dependence in New South Wales, Australia, 2001-2018: Findings from the OATS retrospective cohort study. Drug Alcohol Depend 2020: 108354.
4. Lewer D, Jones NR, Hickman M, Nielsen S, Degenhardt L. Life expectancy of people who are dependent on opioids: A cohort study in New South Wales, Australia. J Psychiatr Res 2020; 130: 435-40.
5. Larney S, Hickman M, Fiellin DA, et al. Using routinely collected data to understand and predict adverse outcomes in opioid agonist treatment: Protocol for the Opioid Agonist Treatment Safety (OATS) Study. BMJ open 2018; 8(8): e025204.
6. Jones NR, Shanahan M, Dobbins T, et al. Reductions in emergency department presentations associated with opioid agonist treatment vary by geographic location: A retrospective study in New South Wales, Australia. Drug Alcohol Rev 2019; 38(6): 690-8.
7. Bharat C, Larney S, Barbieri S, et al. The effect of person, treatment and prescriber characteristics on retention in opioid agonist treatment: a 15-year retrospective cohort study. Addiction 2021.
8. Chaillon A, Bharat C, Stone J, et al. Modeling the population-level impact of opioid agonist treatment on mortality among people accessing treatment between 2001 and 2020 in New South Wales, Australia. Addiction 2021.
9. Colledge-Frisby S, Jones N, Larney S, et al. The impact of opioid agonist treatment on hospitalisations for injecting-related diseases among an opioid dependent population: A retrospective data linkage study. Drug and Alcohol Dependence 2022; 236: 109494.
10. Brothers TD, Lewer D, Jones N, et al. Opioid agonist treatment and risk of death or rehospitalization following injection drug use-associated bacterial and fungal infections: A cohort study in New South Wales, Australia. PLoS Med 2022; 19(7): e1004049.
11. Jones NR, Hickman M, Nielsen S, et al. The impact of opioid agonist treatment on fatal and non-fatal drug overdose among people with a history of opioid dependence in NSW, Australia, 2001-2018: Findings from the OATS retrospective linkage study. Drug Alcohol Depend 2022; 236: 109464.
12. Larney S, Jones N, Fiellin DA, et al. Data resource profile: The Opioid Agonist Treatment and Safety (OATS) Study, New South Wales, Australia. Int J Epidemiol 2020.