Modeling the Prescription Opioid Epidemic

• Published in: Bulletin of mathematical biology 2019-07, Vol.81 (7), p.2258-2289
• Main Authors: Battista, Nicholas A., Pearcy, Leigh B., Strickland, W. Christopher
• Format: Article
• Language: English
• Subjects: Addictions; Addicts; Analgesics, Opioid - administration & dosage; Analgesics, Opioid - adverse effects; Basic Reproduction Number - statistics & numerical data; Cell Biology; Computer Simulation; Dependence; Disease control; Drug abuse; Drug addiction; Drug dependence; Drug overdose; Empirical analysis; Epidemics; Humans; Life Sciences; Mathematical and Computational Biology; Mathematical Concepts; Mathematical models; Mathematics; Mathematics and Statistics; Models, Biological; Narcotics; Opioid Epidemic - mortality; Opioid Epidemic - prevention & control; Opioid-Related Disorders - epidemiology; Opioid-Related Disorders - prevention & control; Opioid-Related Disorders - therapy; Opioids; Optimal control; Overdose; Prescription drugs; Sensitivity analysis; Substance abuse treatment; United States - epidemiology
• ISSN: 0092-8240; 1522-9602
• DOI: 10.1007/s11538-019-00605-0

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, in which case we estimate that the epidemic would cease to be self-sustaining. 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.