We introduce a spatial model for analyzing patient-specific and neighborhood risks of stillbirth and preterm birth in Philadelphia. Using electronic health records and census tract data, we find that both patient-level characteristics (e.g. self-identified race/ethnicity) and neighborhood-level characteristics (e.g. violent crime) are associated with patients' odds of stillbirth or preterm birth. Census tracts with higher rates of women in poverty or on public assistance have greater neighborhood risk for these outcomes, whereas census tracts with higher rates of college-educated women or women in the labor force have lower risk. Our findings could be useful for targeted individual and neighborhood interventions.

翻译：我们提出了一种空间模型，用于分析费城死产和早产的患者个体风险与社区风险。利用电子健康记录和人口普查区数据，我们发现患者层面的特征（例如自我认定的种族/族裔）和社区层面的特征（例如暴力犯罪）均与患者发生死产或早产的几率相关。在贫困女性或依赖公共援助女性比例较高的人口普查区，这些不良结局的社区风险更高；而在受过大学教育女性或劳动参与女性比例较高的人口普查区，风险则较低。我们的研究结果可为针对个体和社区的精准干预提供参考。