Predicting the Next Overdose (022)


Study Information

Predictive analytics is a powerful tool to determine the risk of fatal and nonfatal overdose and re-arrest and re-offending of criminal justice-involved individuals living with opioid use disorder.

The University of Chicago is developing open-source software that can be used by researchers and practitioners to predict overdose and re-offending risk of their population. This project will use large administrative datasets and machine-learning technology to develop a framework for transparent predictive models and simulations to help identify people at highest risk for overdose and/or re-offending and how populations will benefit from interventions, and explore the likely policy impact of observed relationships among emerging trends to improve outcomes.

Study Team

PI: Harold Pollack, PhD

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Study Aims

• Create a linked administrative dataset of first response, criminal justice, and treatment system data, with identified features associated with overdose and re-offending risk

• Implement machine-learning analysis for identified samples of individuals and locations

• Create open-source software to share with researchers and practitioners throughout JCOIN

Research Type

Modeling Project