Using routinely collected data to understand and predict adverse outcomes in opioid agonist treatment
North America is amid an opioid use epidemic. Opioid agonist treatment (OAT) effectively reduces extra-medical opioid use and related harms. As with all pharmacological treatments, there are risks associated with OAT, including fatal overdose. There is a need to better understand risk for adverse outcomes during and after OAT, and for innovative approaches to identifying people at greatest risk of adverse outcomes. The Opioid Agonist Treatment and Safety study aims to address these questions so as to inform the expansion of OAT in the USA.
Methods and analysis
This is a retrospective cohort study using linked, routinely collected health data for all people seeking OAT in New South Wales, Australia, between 2001 and 2017. Linked data include hospitalisation, emergency department presentation, mental health diagnoses, incarceration and mortality. We will use standard regression techniques to model the magnitude and risk factors for adverse outcomes (eg, mortality, unplanned hospitalisation and emergency department presentation, and unplanned treatment cessation) during and after OAT, and machine learning approaches to develop a risk-prediction model.
Strengths and limitations of this study
- The use of a population cohort of people with opioid use disorder, and people moving in and out of opioid agonist treatment with methadone and/or buprenorphine over an extended period (2001–2017).
- Linkage of disparate datasets addressing physical health, mental health and substance use, criminal justice and mortality.
- Cross-national funding and collaboration to inform responses to an epidemic.
- A key limitation is a lack of primary care data to better quantify physical comorbidity.