Dynamic model of nonmedical opioid use trajectories and potential policy interventions

Abstract

Background: Nonmedical use of pharmaceutical opioid analgesics (POA) increased dramatically over the past two decades and remains a major health problem in the United States, contributing to over 16 000 accidental poisoning deaths in 2010. Objectives: To create a systems-oriented theory/model to explain the historical behaviors of interest, including the various populations of nonmedical opioid users and accidental overdose mortality within those populations. To use the model to explore policy interventions including tamper-resistant drug formulations and strategies for reducing diversion of opioid medicines. Methods: A system dynamics model was constructed to represent the population of people who initiate nonmedical POA usage. The model incorporates use trajectories including development of use disorders, transitions from reliance on informal sharing to paying for drugs, transition from oral administration to tampering to facilitate non-oral routes of administration, and transition to heroin use by some users, as well as movement into and out of the population through quitting and mortality. Empirical support was drawn from national surveys (NSDUH, TEDS, MTF, and ARCOS) and published studies. Results: The model was able to replicate the patterns seen in the historical data for each user population, and the associated overdose deaths. Policy analysis showed that both tamper-resistant formulations and interventions to reduce informal sharing could significantly reduce nonmedical user populations and overdose deaths in the long term, but the modeled effect sizes require additional empirical support. Conclusion: Creating a theory/model that can explain system behaviors at a systems level scale is feasible and facilitates thorough evaluation of policy interventions.

• Drug take-backs
• drug sharing
• health policy
• pharmaceutical opioids
• system dynamics
• tamper resistance

Acknowledgements

We gratefully acknowledge support from our key collaborators, Dennis McCarty, PhD, and Neal Wallace, PhD. We also very much appreciate the guidance and oversight provided by other members of our expert advisory panel, Lynn Webster, MD, and Aaron Gilson, PhD. We also acknowledge graduate students Amanuel Zimam, Christan Echt, and Teresa Schmidt for their help in locating data resources and relevant literature.

Funding

Support was provided by NIH/NIDA grant 5R21DA031361-02.

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this paper.

Supplementary material available online

Model diagram and list of equations and parameters.