Health-shock can be defined as a health event that causes severe hardship to the household because of the financial burden for healthcare payments and the income loss due to inability to work. It is one of the most prevalent shocks faced by the people of underdeveloped and developing countries. In Pakistan especially, policy makers and healthcare sector face an uphill battle in dealing with health-shock due to the lack of a publicly available dataset and an effective data analytics approach. In order to address this problem, this thesis presents a data analytics and visualization framework for health-shock prediction based on a large-scale health informatics dataset. The framework is developed using cloud computing services based on Amazon web services integrated with Geographical Information Systems (GIS) to facilitate the capture, storage, indexing and visualization of big data for different stakeholders using smart devices. The data was collected through offline questionnaires and an online mobile based system through Begum Memhooda Welfare Trust (BMWT). All data was coded in the online system for the purpose of analysis and visualization. In order to develop a predictive model for health-shock, a user study was conducted to collect a multidimensional dataset from 1000 households in rural and remotely accessible regions of Pakistan, focusing on their health, access to health care facilities and social welfare, as well as economic and environmental factors. The collected data was used to generate a predictive model using a fuzzy rule summarization technique, which can provide stakeholders with interpretable linguistic rules to explain the causal factors affecting health-shock. The evaluation of the proposed system in terms of the interpretability and accuracy of the generated data models for classifying health-shock shows promising results. The prediction accuracy of the fuzzy model based on a k-fold crossvalidation of the data samples shows above 89% performance in predicting health-shock based on the given factors. Such a framework will not only help the government and policy makers to manage and mitigate health-shock effectively and timely, but will also provide a low-cost, flexible, scalable, and secure architecture for data analytics and visualization. Future work includes extending this study to form Pakistan’s first publicly available health informatics tool to help government and healthcare professionals to form policies and healthcare reforms. This study has implications at a national and international level to facilitate large-scale health data analytics through cloud computing in order to minimize the resource commitments needed to predict and manage health-shock.

Mortgage loan debt is prevalent among middle-aged and older Americans. With higher average outstanding balances, many people are unlikely to pay off their mortgage debt by retirement. Meanwhile, as people age, health shocks are more likely to occur. Medical expenses may compete with mortgage payments and relate to financial insecurity in later years. In order to alleviate financial strain during times of financial hardship, senior homeowners may find reverse mortgage the solution they are looking for. Targeting American adults age 50 and older, this dissertation investigates mortgage loan debt and financial security using panel data from the Health and Retirement Study. Chapter 1 provides an overview of this dissertation and three studies. Chapter 2 investigates whether retirement preparedness plays a role in mortgage status at retirement, shown here as whether a person has mortgage debt and how much the remaining balance is (Waves 2004-2014). Chapter 3 examines health impact on likelihood of paying off mortgage loans under different health conditions, with estimates on expected time to mortgage payoff (Waves 2004-2014). Chapter 4 focuses on reverse mortgages and their impact on senior borrowers’ financial satisfaction and liquidity constraint (Waves 2010-2016). Chapter 5 summarizes major findings in three studies and highlights the contribution of this dissertation toward middle-aged and older Americans’ financial security. Limitations of three studies are discussed in Chapter 6. Three studies provide evidence on 1) the importance of preparedness on reduced mortgage burden; 2) adverse impact of health shock on likelihood of mortgage payoff; and 3) using reverse mortgages to reduce financial strain and increase financial satisfaction. Implications are addressed in each study.