Alicia Sasser Modestino

arXiv2022

Optimizing Return and Secure Disposal of Prescription Opioids to Reduce the Diversion to Secondary Users and Black Market

Opioid Use Disorder (OUD) has reached an epidemic level in the US. Diversion of unused prescription opioids to secondary users and black market significantly contributes to the abuse and misuse of these highly addictive drugs, leading to the increased risk of OUD and accidental opioid overdose within communities. Hence, it is critical to design effective strategies to reduce the non-medical use of opioids that can occur via diversion at the patient level. In this paper, we aim to address this critical public health problem by designing strategies for the return and safe disposal of unused prescription opioids. We propose a data-driven optimization framework to determine the optimal incentive disbursement plans and locations of easily accessible opioid disposal kiosks to motivate prescription opioid users of diverse profiles in returning their unused opioids. We develop a Mixed-Integer Non-Linear Programming (MINLP) model to solve the decision problem, followed by a reformulation scheme using Benders Decomposition that results in a computationally efficient solution. We present a case study to show the benefits and usability of the model using a dataset created from Massachusetts All Payer Claims Data (MA APCD). Our proposed model allows the policymakers to estimate and include a penalty cost considering the economic and healthcare burden associated with prescription opioid diversion. Our numerical experiments demonstrate the ability of model and usefulness in determining optimal locations of opioid disposal kiosks and incentive disbursement plans for maximizing the disposal of unused opioids. The proposed optimization framework offers various trade-off strategies that can help government agencies design pragmatic policies for reducing the diversion of unused prescription opioids.

math.OC↗

arXiv2020

A Big Data Analytics Framework to Predict the Risk of Opioid Use Disorder

Overdose related to prescription opioids have reached an epidemic level in the US, creating an unprecedented national crisis. This has been exacerbated partly due to the lack of tools for physicians to help predict the risk of whether a patient will develop opioid use disorder. Little is known about how machine learning can be applied to a big-data platform to ensure an informed, sustained and judicious prescribing of opioids, in particular for commercially insured population. This study explores Massachusetts All Payer Claims Data, a de-identified healthcare dataset, and proposes a machine learning framework to examine how naïve users develop opioid use disorder. We perform several feature selections techniques to identify influential demographic and clinical features associated with opioid use disorder from a class imbalanced analytic sample. We then compare the predictive power of four well-known machine learning algorithms: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting to predict the risk of opioid use disorder. The study results show that the Random Forest model outperforms the other three algorithms while determining the features, some of which are consistent with prior clinical findings. Moreover, alongside the higher predictive accuracy, the proposed framework is capable of extracting some risk factors that will add significant knowledge to what is already known in the extant literature. We anticipate that this study will help healthcare practitioners improve the current prescribing practice of opioids and contribute to curb the increasing rate of opioid addiction and overdose.

stat.AP↗