EURO 2024 Copenhagen
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1245. Evaluating diversion and treatment policies for opioid use disorder

Invited abstract in session TB-28: Advancements of OR-analytics in statistics, machine learning and data science 5, stream Advancements of OR-analytics in statistics, machine learning and data science.

Tuesday, 10:30-12:00
Room: 065 (building: 208)

Authors (first author is the speaker)

1. Laura Albert
Industrial & Systems Engineering, University of Wisconsin-Madison

Abstract

The United States (US) opioid crisis has led to over 840,000 fatalities since the 1990s. It has strained hospitals, treatment facilities, and law enforcement agencies due to the enormous resources and procedures needed to respond to the crisis. As a result, many individuals who use opioids never receive or finish the treatment they need and instead have many interactions with hospitals or the criminal justice system. This paper introduces a discrete event simulation model that evaluates three opioid use disorder treatment policies: arrest diversion, re-entry case management, and overdose diversion. Publicly available data from 2011 to 2019 in Dane County, Wisconsin (in the US), is used to forecast opioid-related outcomes through 2032. Through analyzing a variety of policy-mix implementations, this study offers a versatile framework for evaluating policies at various implementation levels. The results demonstrate that treatment policies that create new pathways and programming by utilizing treatment services and successfully divert at least 20% of eligible individuals can lead to more opioid-resilient communities and result in substantial savings.

Keywords

Status: accepted

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