Canadian Addiction Treatment Centre (CATC) opioid agonist treatment cohort in Ontario, Canada.

Abstract

The Canadian Addiction Treatment Centre (CATC) cohort was established during a period of increased provision of opioid agonist treatment (OAT), to study patient outcomes and trends related to the treatment of opioid use disorder (OUD) in Canada. The CATC cohort's strengths lie in its unique physician network, shared care model and event-level data, making it valuable for validation and integration studies. The CATC cohort is a valuable resource for examining OAT outcomes, providing insights into substance use trends and the impact of service-level factors. The CATC cohort comprises 32 246 people who received OAT prescriptions between April 2014 and February 2021, with ongoing tri-annual updates planned until 2027. The cohort includes data from all CATC clinics' electronic medical records and includes demographic information and OAT clinical indicators. This cohort profile describes the demographic and clinical characteristics of patients being treated in a large OAT physician network. As well, we report the longitudinal OAT retention by treatment type during a time of increasing exposure to a contaminated dangerous drug supply. Notable findings also include retention differences between methadone (32% of patients at 1 year) and buprenorphine (20% at 1 year). Previously published research from this cohort indicated that patient-level factors associated with retention include geographic location, concurrent substance use and prior treatment attempts. Service-level factors such as telemedicine delivery and frequency of urine drug screenings also influence retention. Additionally, the cohort identified rising OAT participation and a substantial increase in fentanyl use during the COVID-19 pandemic. Future research objectives are the longitudinal evaluation of retention and flexible modelling techniques that account for the changes as patients are treated with OAT. Furthermore, future research aims are the use of conditional models, and linkage with provincial-level administrative datasets.