The U.S. is currently facing an opioid crisis. Naltrexone is a common treatment for drug addiction; it reduces the desire to take opiates. However, addicts often stop treatment or continue to use opioids while in treatment. This results in increased fatalities and associated costs. A Markov-chain model is presented to analyze the progression of opioid addiction to assist the medical community in developing appropriate treatments. The model includes patients who continue opiate use while on naltrexone (blocked patients) and those who use opiates after missing naltrexone doses (unblocked patients). The other types of patients are abstinent (the best-case scenario) and dropout (the worst-case scenario). The Markov-chain model is built on probability estimates of transitions from one stage to another; the model predicts the proportion of patients in the different stages for a given rate of intervention on dropouts. Many factors, including psychological, environmental, sociodemographic, and access-to-healthcare, impact transition probabilities and thereby the observational data used for constructing the Markov-chain model. Markov chains have been used successfully in predicting the progression of HIV (Human Immunodeficiency Virus) and other diseases. Modeling statistically provides an offline method, based on existing data, to develop successful strategies for addressing this public-health crisis.
A. Gosavi et al., "A Markov Chain Approach for Forecasting Progression of Opioid Addiction," Proceedings of the 2020 IISE Annual Conference (2020, Virtual), pp. 399 - 404, Institute of Industrial and Systems Engineers (IISE), Nov 2020.
2020 IISE Annual Conference (2020: Nov. 1-3, Virtual)
Engineering Management and Systems Engineering
Keywords and Phrases
Addiction; Healthcare; Interventions; Markov chains; Opioid
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2020 Institute of Industrial and Systems Engineers (IISE), All rights reserved.
03 Nov 2020