Epidemiological and Economic Evaluation of Disease Burden in the United States: Data, Models, and Applications

Abstract

This dissertation is comprised of three studies that evaluate disease burden in the United States, for specific chronic and infectious diseases. These studies use mathematical modelling to synthesize empirical evidence and estimate both epidemiological and economic outcomes. In the first paper, I investigate trends in the prevalence and incidence of diabetes and diabetes diagnosis among adults ages 20 years and older in the United States, over the period 2000-2016. Using an age-stratified Markov model of undiagnosed and diagnosed diabetes, I examine trends in true incidence of diabetes and the diagnosis rates. The model is estimated using repeated cross-sectional survey data on the prevalence of undiagnosed and diagnosed diabetes from the National Health and Examination Survey (NHANES) 1988-1994 and 1999-2016. In the second paper, I develop a novel model to assess the 10-year risk of fatal-plus-non-fatal cardiovascular disease (CVD) for patients with type 2 diabetes mellitus in the United States. This model is constructed as a sex-and-cohort stratified Cox proportional-hazards model using pooled data on fatal-plus-non-fatal CVD outcomes from 5 prospective cohorts. The resulting risk prediction equation provides more accurate predictions of total CVD risk compared to current risk scores and can to be used in future comparative effectiveness and cost-effectiveness analyses to simulate outcomes of primary intervention policies targeted toward diabetic populations. In the third paper, I investigate disparities in health and economic outcomes associated with N. gonorrhoeae infection in the US in 2015. With probability tree models, I quantify the lifetime quality-adjusted life-years and costs associated with gonorrhea and its sequelae in the US and examine disparities in burden across race/ethnicity. These three studies report findings of substantive importance within each disease area. They also illustrate the utility of mathematical models for synthesizing data, estimating outcomes that would be difficult or impossible to measure empirically, and answering questions directly relevant to the goals of planning and prioritizing prevention policies.