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Leveraging Statistical Methods for an Analysis of Demographic Factors of Opioid Overdose Deaths

Authors:
Amna Alalawi
Daniel Fooks
Les Sztandera
Sean Zakrzewski

Keywords: data analytics; big data; opioids; drug overdose

Abstract:
Deaths from drug overdose including opioid overdose have been increasing at an alarming rate, and authorities still find tackling this problem an acute challenge. This paper applies Artificial Intelligence and statistical techniques to big data to identify the demographic and socio-economic factors that have led to the increasing number of drug overdose deaths in Allegheny County, Pennsylvania, United States. Using Artificial Intelligence software, we analyzed a dataset of over 3,500 patients alongside demographic and socio-economic variables to gain detailed insights into the issue, insights that we can generalize to craft solutions to this problem in both domestic and global communities. Our findings revealed patterns ranging from possible psychological and behavioral factors and drug use on weekends, as well as a direct market supply effect on the number of deaths. These findings imply the need for authorities to offer educational workshops to individuals and their families about the dangers of the current drug epidemic and to design an effective policy for the oversight of drug market supply that includes taking firm action against violators.

Pages: 49 to 54

Copyright: Copyright (c) IARIA, 2019

Publication date: September 22, 2019

Published in: conference

ISSN: 2308-4464

ISBN: 978-1-61208-741-2

Location: Porto, Portugal

Dates: from September 22, 2019 to September 26, 2019