Understanding the factors driving the opioid epidemic using machine learning

TL;DR

This study uses machine learning techniques to analyze neighborhood factors influencing the opioid epidemic in Delaware, revealing dynamic correlations with environmental, crime, education, and socio-economic variables over time.

Contribution

It applies machine learning and SHAP explanations to identify non-linear relationships and temporal changes in neighborhood factors related to opioid risk, advancing beyond traditional statistical methods.

Findings

Environmental, education, and crime factors are highly correlated with opioid risk.

The correlation of environment, crime, and health variables increased as the epidemic shifted to illegal opioids.

Education factors have consistently shown high correlation, increasing slightly over time.

Abstract

In recent years, the US has experienced an opioid epidemic with an unprecedented number of drugs overdose deaths. Research finds such overdose deaths are linked to neighborhood-level traits, thus providing opportunity to identify effective interventions. Typically, techniques such as Ordinary Least Squares (OLS) or Maximum Likelihood Estimation (MLE) are used to document neighborhood-level factors significant in explaining such adverse outcomes. These techniques are, however, less equipped to ascertain non-linear relationships between confounding factors. Hence, in this study we apply machine learning based techniques to identify opioid risks of neighborhoods in Delaware and explore the correlation of these factors using Shapley Additive explanations (SHAP). We discovered that the factors related to neighborhoods environment, followed by education and then crime, were highly correlated with higher opioid risk. We also explored the change in these correlations over the years to understand the changing dynamics of the epidemic. Furthermore, we discovered that, as the epidemic has shifted from legal (i.e., prescription opioids) to illegal (e.g.,heroin and fentanyl) drugs in recent years, the correlation of environment, crime and health related variables with the opioid risk has increased significantly while the correlation of economic and socio-demographic variables has decreased. The correlation of education related factors has been higher from the start and has increased slightly in recent years suggesting a need for increased awareness about the opioid epidemic.