Gaining Policy Insight with a System Dynamics Model of Pain Medicine Prescribing, Diversion and Abuse

Abstract

Misuse of pain medicine is a leading cause of accidental deaths in the USA-over 15 000 deaths annually. A system dynamics model was developed and used to study the impact of three types of policy interventions to reduce adverse outcomes. Besides medical usage, the model also includes the diversion of pain medicines and their nonmedical use. Model behavior compared favourably with historical data at multiple points in the system. A prescription drug monitoring program intervention reduced nonmedical overdoses through supply restriction, and an intervention to reduce popularity of nonmedical usage reduced nonmedical overdoses by curtailing demand. A tamper-resistant drug formulation intervention resulted in decreased risk to individuals but increased overdose deaths. Key policy insights are that supply and demand must be carefully considered; that the feedback loops, which created the problem, can help to identify solutions; and that outcome metrics must be carefully chosen. Copyright © 2015 John Wiley & Sons, Ltd. Supporting information may be found in the online version of this article.Keywords simulation; public health; feedback insights; outcome metrics This present research was motivated by the dramatic and well-publicized increase in accidental deaths attributed to the abuse of pain medications, which represents a substantial US public health problem (Volkow and McLellan, 2011). Despite the increase in adverse outcomes-many more people are dying from pharmaceutical opioids than from heroin, cocaine and methamphetamines combined-nonmedical use of pain medicine remains largely unabated by current policies and regulations (Paulozzi et al., 2011). Resistance to policy interventions likely stems from the complexity of the system and the interaction of many factors related to pain treatment, drug diversion and nonmedical usage (Webster et al., 2011). Such complex social systems are well known for resisting policy interventions, often exhibiting unintended consequences or unanticipated sources of policy

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