Curbing the Opioid Crisis: Optimal Dynamic Policies for Preventive and Mitigating Interventions

Problem statement : This paper addresses the challenge of effectively responding to the opioid epidemic stemming from prescription pills through a public health lens. It centers on the strategic distribution of resources across diverse interventions aimed at preventing and mitigating the consequences of opioid use disorder (OUD) and overdose occurrences. Methodology : This paper proposes a decision aid tool built on the expected utility theory that leverages a Susceptible-Infected-Removed compartmental model to simulate the dynamics of the epidemic in a population. This model then feeds into a Markov Decision Process (MDP) model to generate optimal policies upon the current state of the epidemic. The optimal policies allocate the intervention budget to primary preventive and mitigating interventions in each decision period by minimizing the cost of fatal overdoses relative to the population’s number of individuals with OUD, considering the impact magnitude of each intervention, based on the current state of the epidemic. A 10-year simulation of the epidemic’s progression is conducted to assess the dynamic efficacy of the proposed decision tool. Results : The findings reveal an average reduction of 29% in total costs compared to the scenario without interventions and a decrease of 12% in total costs on average compared to the scenario with a 50-50 allocation. The extensive sensitivity analysis of key parameters validates the decision aid tool. We observe that it is optimal to allocate a significant portion of the budget to prevention when the rate of opioid pill acquisition rises. Even with a heightened rate of fatal overdoses, it remains optimal to mostly invest in preventive interventions, as long as fatal overdose rates are lower than opioid access rates. Practical implications : This study provides practitioners with a tool to effectively address the opioid epidemic and enhance public health by deciding how to allocate their budget to various levels of intervention.