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A Risk-Structured Model of the Influence of Mental Health on Opioid Addiction
Katelyn R Newton1, London M Luttrell2, Julie C Blackwood3
1Department of Mathematics, University of Portland, 5000 N Willamette Blvd, Portland, 97203, Oregon, USA. teamnewtonk@gmail.com.
Bulletin of Mathematical Biology
|April 9, 2025
Summary
Mathematical modeling helps understand the opioid crisis. This study introduces a new model accounting for varied addiction risks, especially in those with mental health disorders, to better inform prevention and treatment strategies.
Area of Science:
- Epidemiology
- Mathematical Biology
- Public Health
Background:
- Opioid overdose deaths are a major public health crisis in the US.
- Opioid Use Disorder (OUD) incurs significant economic costs.
- Existing models often overlook individual differences in addiction risk.
Purpose of the Study:
- To adapt the Susceptible-Infected-Recovered (SIR) model for the opioid epidemic.
- To incorporate risk heterogeneity, particularly for individuals with mental health disorders.
- To differentiate addiction pathways (prescription vs. social).
Main Methods:
- Developed a compartmental model extending the SIR paradigm.
- Incorporated risk stratification to account for heterogeneous addiction susceptibility.
- Calibrated the model using existing data to estimate parameters.
Main Results:
- Quantified the impact of risk heterogeneity on OUD dynamics.
- Highlighted the disproportionate burden of OUD among individuals with mental health disorders.
- Provided insights into the addiction process by differentiating pathways.
Conclusions:
- Risk heterogeneity is crucial for understanding and modeling the opioid epidemic.
- The model offers a more nuanced approach to OUD compared to uniform risk models.
- Findings can inform targeted prevention and treatment strategies for OUD.
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