Insights from Statistical Analysis of Opioid Data

TL;DR

This paper analyzes publicly available opioid data to identify factors contributing to the epidemic, focusing on prescribing patterns and neighborhood demographics, providing insights for public health strategies.

Contribution

It is among the first to academically analyze opioid epidemic data, examining prescribing behaviors and neighborhood factors to inform mitigation efforts.

Findings

Prescribing patterns influence opioid overdose rates

Income, age, and education levels correlate with opioid incidences

Data analysis offers actionable insights for public health policies

Abstract

Opioid overdose has emerged as a full blown epidemic in the United States. In the last few years, there has been an alarming increase in Opioid related deaths, resulting in the loss of 63,600 lives in 2016 alone. The epidemic which is killing more than 100 people each day, was declared as a public health emergency by the US government, in October 2017. Although a few health related companies and commercial firms have examined this important issue from various available data sources, to the best of our knowledge, the academic community has not been engaged in research in this important topic. It can be safely noted that the study of the epidemic, from the data analytics perspective, is in its infancy. Given that a significant amount of Opioid related data is available in public domain, it provides the academic community an opportunity to analyze such data to provide recommendations to the public health authorities to mitigate the impact of the epidemic. In that vein, we collected some publicly available data to analyze the important contributing factors of the epidemic. In particular, we examine the role of the individuals prescribing Opioid drugs on the spread of the epidemic. In addition, we examine the impact of income level, age and educational level of various neighborhoods in a large US city, on Opioid related incidences, to find any correlation between them.