We introduce a Bayesian conditional autoregressive model for analyzing patient-specific and neighborhood risks of stillbirth and preterm birth within a city. Our fully Bayesian approach automatically learns the amount of spatial heterogeneity and spatial dependence between neighborhoods. Our model provides meaningful inferences and uncertainty quantification for both covariate effects and neighborhood risk probabilities through their posterior distributions. We apply our methodology to data from the city of Philadelphia. Using electronic health records (45,919 deliveries at hospitals within the University of Pennsylvania Health System) and United States Census Bureau data from 363 census tracts in Philadelphia, we find that both patient-level characteristics (e.g. self-identified race/ethnicity) and neighborhood-level characteristics (e.g. violent crime) are highly associated with patients' odds of stillbirth or preterm birth. Our neighborhood risk analysis further reveals that census tracts in West Philadelphia and North Philadelphia are at highest risk of these outcomes. Specifically, neighborhoods with higher rates of women in poverty or on public assistance have greater neighborhood risk for these outcomes, while neighborhoods 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：我们提出一种贝叶斯条件自回归模型，用于分析城市内死产和早产的患者个体风险与社区风险。该全贝叶斯方法能够自动学习社区间的空间异质性与空间依赖性程度。模型通过后验分布为协变量效应和社区风险概率提供有意义的推断与不确定性量化。我们将此方法应用于美国费城市的数据，利用电子健康记录（宾夕法尼亚大学卫生系统内医院分娩的45,919例数据）及美国人口普查局363个普查区数据，发现患者层面特征（如自我认定的种族/民族）和社区层面特征（如暴力犯罪）均与患者死产或早产几率高度相关。社区风险分析进一步揭示，西费城和北费城的普查区面临这些结局的最高风险。具体而言，贫困女性或接受公共援助女性比例较高的社区，其不良妊娠结局的社区风险更大；而接受大学教育女性或劳动参与女性比例较高的社区风险更低。本研究成果可为目标性个体干预和社区干预提供参考依据。