Data-driven methods in modeling healthcare decisions : insights and applications in cardiovascular disease prevention and control
Keywords: Management Science and Engineering.

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the United States (US). In addition, CVD remains a major cause of health disparities and rising health care costs. Because cardiovascular outcomes depend highly on multiple patient characteristics that evolve stochastically over time, this dissertation focuses on developing novel mathematical models to capture heterogeneous population characteristics, and to support actionable policy recommendations and informed decision-making in CVD prevention and control. Efficient prevention and control of CVD includes two types of interventions: primary prevention to prevent the onset of disease, such as lifestyle interventions, and secondary prevention with drug treatment to reduce the progression of disease. In Chapter 2, given recent data on the relationship between sodium intake, hypertension, and associated cardiovascular diseases, we examine the impact of population-wide expansion of the National Salt Reduction Initiative (NSRI), in which food producers agree to lower sodium to levels deemed feasible for different foods. We developed and validated a stochastic microsimulation model of hypertension, myocardial infarction (MI) and stroke in the US population. The model follows individual dietary intakes and CVD risk factors of the population stratified by demographic characteristics. We find that expanding the NSRI nationwide is expected to significantly reduce hypertension and hypertension-related CVD morbidity and mortality among the majority of the population, even in the context of compensatory consumer behaviors. But older women in particular may be at risk for excessively low sodium intake. This suggests that careful consideration should be made of how to target such large-scale population-wide policy interventions to minimize adverse effects among the most vulnerable. Large socioeconomic disparities exist in US dietary habits and CVD morbidity and mortality. While high fruits and vegetable (F& V) consumption is thought to reduce the risk of chronic diseases, dietary patterns and intakes of F& Vs are particularly lower among low-socioeconomic groups. Providing financial incentives for F& V purchases among low-income households has been demonstrated to increase F& V consumption. In Chapter 3, we extend the microsimulation model developed in Chapter 2 to evaluate lifetime costs and health outcomes of subsidizing F& V purchases among Supplemental Nutrition Assistance Program (SNAP) participants in the US, and to assess its impact on CVD disparities. In this model, type II diabetes and obesity were included in addition to MI and stroke to capture the complex interrelationship between changes in F& V prices and its effects on health outcomes. We find that nationwide expansion of the F& V subsidy among SNAP participants would be expected to significantly lower incidence of long-term chronic diseases in the US and would be cost-saving under a wide range of scenarios. The benefits would be accumulated the most among demographic groups for whom healthcare interventions alone have not been sufficient to reduce large disparities in CVD incidence that have been attributed in part to poor nutrition. Moreover, these benefits would likely persist even if the incentive is imperfectly implemented. As a secondary CVD prevention, hypertension management has traditionally been guided by a treat-to-target strategy focusing on achieving specific levels of blood pressure. Because treatment benefits depend on multiple patient characteristics, it has been recommended to personalize blood pressure treatment decisions. In Chapter 4, we develop a Markov decision process (MDP) model for determining the optimal blood pressure treatment for an individual, incorporating a complex variety of individual-level covariates, treatment effect modifiers, and risks and benefits of treatment alternatives. We find that the MDP-based treatment approach would improve overall population health compared to current blood pressure treatment guidelines, and it would be cost-saving. In order to improve usability and interpretability of the MDP model developed in Chapter 4, we use data analytic techniques to approximate the optimal blood pressure treatment policies derived from the MDP model in Chapter 5. We create a more easily interpretable treatment planning framework that offer comparable results to the optimal decisions rules determined by the MDP. In this dissertation, we present the use of data-driven methods in modeling health policy and healthcare decisions, focusing on CVD prevention and control. The methods we used can be adapted to other diseases and settings to promote informed-decision making.