Defense Date


Document Type


Degree Name

Doctor of Philosophy


Systems Modeling and Analysis

First Advisor

QiQi Lu


Statistically analyzing socio-epidemiological data help in identifying individuals and communities that experience significant disparities within the healthcare system. This dissertation constitutes of analyzing organ transplant data and end stage renal disease data that experience systemic disparities as two studies using different statistical approaches. Intervention time series analysis models are used to examine and quantify the deficit in organ donor transplant counts in the United States during the COVID-19 pandemic across various socio-cultural factors known to cause disparities in the organ transplant system. Additionally, the effect of the kidney allocation policy introduced in response to the pandemic is modeled as the secondary intervention effect. Forecasts generated were compared with various alternative methods to assess their accuracy. A multilevel conditionally autoregressive model is employed to investigate the spatial effect in end-stage renal disease incidence data under a Bayesian framework. This modeling approach explicitly considers the hierarchical structure in the data, with hospitals nested within ZIP codes. It can effectively account for spatial autocorrelation and heterogeneity. The effects of some social factors and indicators of health on the standardized hospitalization ratio of dialysis facilities are quantified. In addition, we introduce a novel approach to impute missing spatial data using spatial state space models. This proposed method is unique and effective in handling missing data in spatial epidemiological studies.


© Supraja Malladi

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Available for download on Sunday, July 16, 2028