Social vulnerability predictors of drug poisoning mortality: A machine learning analysis in the United States

Background and Objectives: Drug poisoning is a leading cause of unintentional deaths in the United States. Despite the growing literature, there are a few recent analyses of a wide range of community-level social vulnerability features contributing to drug poisoning mortality. Current studies on this topic face three limitations: often studying a limited subset of vulnerability features, focusing on small sample sizes, or solely including local data. To address this gap, we conducted a national-level analysis to study the impacts of several social vulnerability features in predicting drug mortality rates in the United States. Methods: We used machine learning to investigate the role of 16 social vulnerability features in predicting drug mortality rates for US counties in 2014, 2016, and 2018—the most recent available data. We estimated each vulnerability feature's gain relative contribution in predicting drug poisoning mortality. Results: Among all social vulnerability features, the percentage of noninstitutionalized persons with a disability is the most influential predictor, with a gain relative contribution of 18.6%, followed by population density and the percentage of minority residents (13.3% and 13%, respectively). Percentages of households with no available vehicles, mobile homes, and persons without a high school diploma are the following features with gain relative contributions of 6.3%, 5.8%, and 5.1%, respectively. Conclusion and Scientific Significance: We identified social vulnerability features that are most predictive of drug poisoning mortality. Public health interventions and policies targeting vulnerable communities may increase the resilience of these communities and mitigate the overdose death and drug misuse crisis.