Opioid mortality in the US: quantifying the direct and indirect impact of sociodemographic and socioeconomic factors

This paper employs a spatial Durbin panel data model, an extension of the cross-sectional spatial Durbin model to a panel data framework, to quantify the impact of a set of sociodemographic and socioeconomic factors that influence opioid-related mortality in the US. The empirical model uses a pool of 49 US states over six years from 2014 to 2019, and a nearest-neighbor matrix that represents the topological structure between the states. Calculation of direct (own-state) and indirect (cross-state spillover) effects estimates is based on Bayesian estimation and inference reflecting a proper interpretation of the marginal effects for the model that involves spatial lags of the dependent and independent variables. The study provides evidence that opioid mortality depends not only on the characteristics of the state itself (direct effects), but also on those of nearby states (indirect effects). Direct effects are important, but externalities (spatial spillovers) are more important. The sociodemographic structure (age and race) of a state is important whereas economic distress of a state is less so, as indicated by the total impact estimates. The methodology and the research findings provide a useful template for future empirical work using other geographic locations or shifting interest to other epidemics.