Understanding the factors driving the opioid epidemic using machine learning. (arXiv:2108.07301v1 [cs.LG])

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.

Source: https://arxiv.org/abs/2108.07301

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