Reducing Substance Use-Related Harms: A Simulation-Optimization Framework for the Design and Evaluation of Harm...

Area of Science:

• Public Health
• Health Informatics
• Epidemiology

Background:

• Rising overdose deaths and infectious diseases among persons who inject drugs (PWID) necessitate innovative harm reduction (HR) strategies.
• Opioid-focused harm reduction vending machines (HRVMs) offer a scalable solution for distributing essential supplies to mitigate health risks.

Purpose of the Study:

• To develop and validate a simulation-optimization framework for optimizing HRVM services.
• To determine optimal product allocation within HRVMs to minimize opioid-related harms, including overdose and infectious diseases.

Main Methods:

• An agent-based simulation-optimization framework was developed and validated.
• The model incorporated demand dynamics, restocking, regional characteristics, and evaluated health outcomes using disability-adjusted life-years (DALYs).
• Scenario-based analyses examined variations in HRVM configurations, fentanyl prevalence, and utilization of harm reduction supplies.

Main Results:

• The optimal baseline allocation for HRVMs was estimated at 48.5% fentanyl test strips (FTS), 26.2% naloxone, and 25.3% safer injection kits.
• Sensitivity analyses revealed that optimal allocations vary based on fentanyl prevalence and FTS utilization, with higher FTS use favoring FTS allocation.
• Adding addiction treatment referral services reduced DALYs and costs, while safer injection kits consistently reduced blood-borne infections.

Conclusions:

• The simulation-optimization framework aids in HRVM service planning and evaluation, emphasizing strategic inventory management.
• HRVMs represent scalable, cost-effective HR interventions with potential for significant public health impact.
• Integrating HRVMs with addiction care services is crucial for enhancing health outcomes and reducing societal costs associated with substance use.