Data-Supported Decision Making: optimising substance dependence treatment using linked data

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Substance use disorders (SUDs) are a major contributor to global disease burden. The UN has identified strengthening SUD prevention and treatment as a public health priority. Predictive modelling using linked administrative data has already been used to develop risk algorithms optimising patient outcomes in other health disciplines. Using multiple data linkages, this PhD will predict:
1. Treatment outcomes (offending, remission) among opioid dependent patients (N=~45,000) to enhance treatment delivery;
2. Clinical outcomes (hospitalisations, overdoses) among problematic alcohol users presenting to healthcare services (N=~130,000) to facilitate point-of-care decision-making;
3. Overdose and misuse among patients prescribed opioids (N=~2,000,000) to inform prescribing practices.
  • Masters or Honours in Bio/statistics, epidemiology or a related field.
  • Strong skills in statistical modelling, with experience in machine learning or predictive modelling highly regarded.
  • Track record of publication of peer-reviewed scientific articles.
  • Excellent written and verbal communication skills.
Supervisory team

National Drug and Alcohol Research Centre

National Drug and Alcohol Research Centre

National Drug and Alcohol Research Centre
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