Modeling the prescription opioid epidemic

TL;DR

This paper develops a mathematical model of the prescription opioid epidemic, highlighting the importance of strict prescription control and treatment strategies to prevent endemic addiction.

Contribution

It introduces a novel mathematical model capturing prescription, illicit, and treatment dynamics of opioid addiction, validated with epidemic data.

Findings

No addiction-free equilibrium without strict prescription control

Low endemic addiction levels achievable with prevention and treatment

Relapse probability impacts long-term addiction prevalence

Abstract

Opioid addiction has become a global epidemic and a national health crisis in recent years, with the number of opioid overdose fatalities steadily increasing since the 1990s. In contrast to the dynamics of a typical illicit drug or disease epidemic, opioid addiction has its roots in legal, prescription medication - a fact which greatly increases the exposed population and provides additional drug accessibility for addicts. In this paper, we present a mathematical model for prescription drug addiction and treatment with parameters and validation based on data from the opioid epidemic. Key dynamics considered include addiction through prescription, addiction from illicit sources, and treatment. Through mathematical analysis, we show that no addiction-free equilibrium can exist without stringent control over how opioids are administered and prescribed, effectively transforming the dynamics of the opioid epidemic into those found in a purely illicit drug model. Numerical sensitivity analysis suggests that relatively low states of endemic addiction can be obtained by primarily focusing on medical prevention followed by aggressive treatment of remaining cases - even when the probability of relapse from treatment remains high. Further empirical study focused on understanding the rate of illicit drug dependence versus overdose risk, along with the current and changing rates of opioid prescription and treatment, would shed significant light on optimal control efforts and feasible outcomes for this epidemic and drug epidemics in general.