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End-stage renal disease has many adverse complications associated with itleading to 20-50% higher mortality rates in people than those without thedisease. 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 inFlorida using a multilevel Conditional Autoregressive model under a Bayesianframework at both the Zip Code Tabulation Area and facility levels. The effectsof some social factors and indicators of health on the standardizedhospitalization ratio of dialysis facilities are quantified. Additionally, askidney research studies are posed with a great burden due to missing data, weintroduce a novel method to impute missing spatial data using spatial statespace modeling. The outcomes of this study offer potentially valuable insightsfor policymakers aiming to develop strategies that enhance healthcare andservice quality for disadvantaged populations.

Missing Data Imputation and Multilevel Conditional Autoregressive Modeling of Spatial End-Stage Renal Disease Incidence