Opioid Mortality in the US: Quantifying the Impact of Key Determinants Using a Spatial Panel Data Approach

This paper employs a spatial Durbin panel data model, an extension of the cross-sectional spatial Durbin model to a panel data framework, to estimate the impact of a set of demographic and economic factors on state-level opioid-related mortalities in the US. The empirical model uses a pool of 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 spillovers) effects estimates -- based on Bayesian estimation and inference -- reflects a proper interpretation of the marginal effects for our nonlinear model that involves lags of the dependent variable vector. The study provides evidence for the existence of spatial effects working through the dependent variable vector and points to the importance of larger indirect effects of Asian and Hispanic/Latino minorities on the one side and the population age groups 35-44 years and 65 years and older on the other. This finding echoes the first law of geography that "everything is related to everything else, but near things are more related than distant things" (Tobler 1970). Space -- largely neglected in previous research -- matters for gaining a valid and better understanding of why and how neighboring states contribute to opioid-related mortality in the states.