An individual-based dynamic model to assess interventions to mitigate opioid overdose risk

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• Illicit opioid overdose deaths are a critical public health crisis in North America.
• Mathematical modeling offers a powerful approach to understanding overdose epidemiology and the impact of interventions.
• This study focuses on the population of individuals engaging in unregulated opioid use in Toronto.

Purpose of the Study:

• To quantify and predict the impact of key harm reduction strategies on fatal and nonfatal overdoses.
• To evaluate different levels of scale-up for interventions.
• To inform public health policy regarding opioid overdose prevention in Toronto.

Main Methods:

• An individual-based model was developed, incorporating demographic and behavioral variations.
• Key overdose risk factors were identified and integrated into a dynamic framework.
• The model was calibrated using Toronto's 2019 overdose data, simulating interventions like OAT, SCS, THN, drug-checking, and fentanyl reduction.

Main Results:

• Model simulations accurately reflected Toronto's 2019 overdose data (3690.6 nonfatal, 295.4 fatal).
• Full scale-up of interventions could avert significant deaths: 290 by THN, 248 by fentanyl reduction, 173 by OAT, 124 by SCS, and 100 by drug-checking.
• Only drug-checking and fentanyl reduction decreased nonfatal overdoses.

Conclusions:

• A multi-faceted harm reduction approach is essential.
• Scaling up take-home naloxone (THN) and reducing fentanyl in the drug supply demonstrated the greatest potential for reducing fatal opioid overdoses in Toronto.
• Model simulations provide valuable insights for assessing and guiding public health policy on harm reduction strategies.