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

翻译：种族化经济隔离（Racialized economic segregation）作为同时反映空间、社会与收入极化特征的关键指标，已被证实与发病率和死亡率等不良健康结局相关。然而，测量种族化经济隔离与健康结局关联的统计方法尚未成熟，且现有研究多聚焦个体层面。本研究提出一种两阶段贝叶斯统计框架，为探究美国各县早逝风险与种族化经济隔离之间的空间异质性关联提供广泛灵活的途径，同时纳入社区层面的潜在健康因子。我们整合三源数据（1）CDC WONDER数据库、（2）县级健康排名（County Health Rankings）及（3）公共卫生差异地理编码项目（Public Health Disparities Geocoding Project）验证该方法。研究发现：潜在健康因子的后验估计值在美国各县呈现显著的地理分布格局。此外，结果表明在健康公平聚焦的研究场景中，必须考虑种族化经济隔离测度中空间自相关性的存在。