Balanced Opioid Prescribing via a Clinical Trade-Off: Pain Relief vs. Adverse Effects of Discomfort, Dependence, and Tolerance/Hypersensitivity

Abstract

In the backdrop of the opioid epidemic, opioid prescribing has distinct medical and social challenges. Overprescribing contributes to the ongoing opioid epidemic, whereas underprescribing yields inadequate pain relief. Moreover, opioids have serious adverse effects including tolerance and increased sensitivity to pain, paradoxically inducing more pain. Prescribing trade-offs are recognized but not modeled in the literature. We study the prescribing decisions for chronic, acute, and persistent pain types to minimize the cumulative pain that incorporates opioid adverse effects (discomfort and dependence) and the risk of tolerance or hypersensitivity (THS) developed with opioid use. After finding closed-form solutions for each pain type, we analytically investigate the sensitivity of acute pain prescriptions and examine policies on incorporation of THS, patient handover, and adaptive treatments. Our analyses show that the role of adverse effects in prescribing decisions is as critical as that of the pain level. Interestingly, we find that the optimal prescription duration is not necessarily increasing with the recovery time. We show that not incorporating THS or information curtailment at patient handovers leads to overprescribing that can be mitigated by adaptive treatments. Last, using real-life pain and opioid use data from two sources, we estimate THS parameters and discuss the proximity of our model to clinical practice. This paper has a pain management framework that leads to tractable models. These models can potentially support balanced opioid prescribing after their validation in a clinical setting. Then, they can be helpful to policy makers in assessment of prescription policies and of the controversy around over- and underprescribing. History: This paper has been accepted for the Decision Analysis Special Issue on Emerging Topics in Health Decision Analysis. Funding: This work was supported by the National Institutes of Health [Grants K08 EB022631, R24 DA055306, R33 DA046085, and U24 NS115708]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2021.0447 .