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Racialized economic segregation, a key metric that simultaneously accountsfor spatial, social and income polarization, has been linked to adverse healthoutcomes, including morbidity and mortality; however, statistical methods formeasuring the association between racialized economic segregation and healthoutcomes are not well-developed and are usually studied at the individuallevel. In this paper we propose a two-stage Bayesian statistical framework thatprovides a broad, flexible approach to studying the spatially varyingassociation between premature mortality and racialized economic segregation,while accounting for neighborhood-level latent health factors across UScounties. We apply our method by using data from three sources: (1) the CDCWONDER, (2) the County Health Rankings, and (3) the Public Health DisparitiesGeocoding Project. Findings from our study show that the posterior estimates oflatent health factors clearly demonstrate geographical patterning across UScounties. Additionally, our results highlight the importance of accounting forthe presence of spatial autocorrelation in racialized economic segregationmeasures, in health equity focused settings.

A Two-stage Bayesian Model for Assessing the Geography of Racialized Economic Segregation and Premature Mortality Across US Counties