Examining Opioid Overdose Deaths across Communities Defined by Racial Composition: a Multiscale Geographically Weighted Regression Approach

To provide data that can guide community-targeted practices, policies, and interventions in urban metropolitan areas, we used geospatial analysis to examine the community-level opioid overdose death determinants and their spatial variation across a study area. We obtained spatial datasets containing multiple, high-quality measures of socioeconomic conditions, public health status, and demographics for analysis and visualization in geographic information systems. We employed a multiscale modeling approach (multiscale geographically weighted regression; MGWR) to provide a comprehensive and robust analysis of opioid overdose death determinants, explain how geospatial patterns vary across scales across Milwaukee County in 2019, and examine the differential influence of factors locally, regionally, and globally. We subsequently examined how associations varied with the racial/ethnic composition of communities by dividing Milwaukee County into White-majority, Black-majority, and Hispanic-majority regions according to census data and conducting separate, independent modeling processes. Overall, the multiscale model explained 83% of opioid overdose death variability across neighborhoods in Milwaukee County using 12 selected variables. Statistical analysis and geovisualization of patterns, trends, and clusters using MGWR unveiled dramatic racialized health disparities in Milwaukee, showing how factors that influenced opioid overdose deaths varied across diverse communities in Milwaukee. The observed geographic variation in relationships included the impact of naloxone availability and incarceration rates on overdose deaths with pronounced differences between White communities and communities of color. Understanding, community-level factors that contribute to overdose risk should guide targeted community-level solutions. Overall, our findings demonstrate the value of precision epidemiology using MGWR analysis for defining and guiding responses to public health challenges. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11524-021-00554-x.