Data-supported decision making: Optimising substance dependence treatment using linked data

February 2020

Miss Chrianna Bharat
Scientia Professor Louisa Degenhardt
Dr Sarah Larney

Other Collaborators: 

Associate Professor Timothy Dobbins, School of Public Health and Community Medicine, UNSW

Project description: 

The prevention and treatment of substance dependence is a global health priority. While considerable research attention is given to developing novel approaches for preventing and treating substance dependence, there also exists the potential to strengthen the delivery of those services which are currently available. Identifying population subgroups with distinct characteristics that are predictive of their subsequent outcomes is one means through which this might be possible. This project will use statistical techniques to identify population subgroups to support the design and implementation of tailored prevention and treatment services, to optimise clinical outcomes and reduce health care costs related to substance dependence. 

The overarching aim of this project is to develop and validate models to estimate risk of substance dependence related outcomes. This will be carried out in different populations for various substance types using individual-level data from multiple linked data projects. Combined, these models will traverse the fields of prevention, identification and treatment provision. Outcomes which will be investigated include:

  1. Alcohol dependence among young adolescents
  2. Retention among people in opioid agonist treatment
  3. Adverse events among people prescribed opioids


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