End-stage renal disease has many adverse complications associated with it leading to 20-50% higher mortality rates in people than those without the disease. This makes it one of the leading causes of death in the United States. This article analyzes the incidence of end-stage renal disease in 2019 in Florida using a multilevel Conditional Autoregressive model under a Bayesian framework at both the Zip Code Tabulation Area and facility levels. The effects of some social factors and indicators of health on the standardized hospitalization ratio of dialysis facilities are quantified. Additionally, as kidney research studies are posed with a great burden due to missing data, we introduce a novel method to impute missing spatial data using spatial state space modeling. The outcomes of this study offer potentially valuable insights for policymakers aiming to develop strategies that enhance healthcare and service quality for disadvantaged populations.

翻译：终末期肾病伴有多种不良并发症，导致患者死亡率较非患者高出20%-50%，使其成为美国主要死因之一。本文采用贝叶斯框架下的多元条件自回归模型，在邮政编码区域和医疗机构两个层面，分析了2019年佛罗里达州终末期肾病的发病率。研究量化了部分社会因素及健康指标对透析机构标准化住院比率的影响。此外，鉴于肾脏研究因数据缺失面临巨大挑战，我们提出了一种利用空间状态空间模型插补缺失空间数据的新方法。本研究结果可为政策制定者制定旨在改善弱势群体医疗服务质量与获取机会的战略提供潜在重要见解。