After numerous renewals and reignitions since the initiation of the "War on Cancer" more than five decades ago, the recent reignition of "Moonshot to Cure Cancer" points to the systemic persistence of cancer as a major cause of loss of life and livelihood. Literature points to the diminishing returns of cancer research through time, as well as heterogeneities in cancer research centers' innovation strategies. This dissertation focuses on the strategic decision by cancer research centers to invest their resources in conducting early phases of clinical trials on new candidate drugs/treatments (resembling exploration) or late phases of clinical trials that push established candidates towards acquiring FDA approvals (resembling exploitation). The extensive clinical trials data suggests that cancer research centers are not only different in their emphasis on exploratory trials, but also in how their emphasis is changing over time. This research studies the dynamics of this heterogeneity in cancer research centers' innovation strategies, how experiential learning and capability development interact to cause dynamics of divergence among learning agents, and how the heterogeneity among cancer research centers' innovation strategies is affected by the dynamics of learning from experience and capability development. The findings of this dissertation shows that endogenous heterogeneities can arise from the process of learning from experience and accumulation of capabilities. It is also shown that depending on the sensitivity of the outcome of decisions to the accumulated capabilities, such endogenous heterogeneities can be value-creating and thus, justified. Empirical analysis of cancer clinical trials data shows that cancer research centers learn from success and failure of their previous trials to adopt more/less explorative tendencies. It also demonstrates that cancer research centers with a history of preferring exploratory or FDA trials have the tendency to increase their preference and become more specialized in one specific type (endogenous specialization). These behavioral aspects of the cancer research centers' innovation strategies provide some of the tools necessary to model the behavior of the cancer research efforts from a holistic viewpoint. / Doctor of Philosophy / The "Moonshot to Cure Cancer" was renewed most recently in September 2022. However, renewal and reignition of this national collective effort is nothing new; this effort started as "War on Cancer" in 1971 and has been reignited numerous times. After more than 50 years of our collective battle to cure cancer, it claims almost 600,000 lives annually and remains as the second leading cause of death in the US. There are a wide variety of cancer research centers from all around the world contributing to this collective effort and they make considerably different decisions regarding their investment in research. There is evidence suggesting that some of the research centers' investment decisions are not optimal and can be improved. It has been shown that systems such as patent regulations can be revised to encourage such improved decisions among cancer research centers. This dissertation focuses on the process of clinical trials for new drugs/treatments for cancer. New drugs/treatments have to pass different phases of trials to ensure that they are safe and effective before they can acquire FDA approvals. Cancer research centers decide whether to invest in early phases of clinical trials for new drug/treatment candidates or invest in late phases of trials for candidates that have already passed the early phases. The clinical trials data show that there has been a sharp rise in number of early phases of trials on new drugs/treatments; however, the same rise cannot be seen in the late phases of trials resulting in approvals. It can also be seen that different research centers put different levels of emphasis on initiating early phases of trials for new drugs/treatments (exploration). In this dissertation, the hypothesis is that this ongoing dilemma that cancer research centers face to invest on how much emphasis to put on exploration in their clinical trials is affected by learning from experience. To test this hypothesis, a mathematical model is used to show differences in decisions can be causes solely by learning from experience, when the decision maker is learning "what to do" from success/failure of previous efforts and learning "how to do it" from practicing and accumulating the required skills. Then, the hypothesis is formally tested using the clinical trials data. The results show that cancer research centers learn from the success and failure of their previous exploratory trials when deciding on their emphasis on exploration. Also, they accumulate skills, resources, and capabilities relevant to the type of research the conduct more often and specialize in either of late- or early-phases of trials. The findings of this dissertation show that learning from experience can cause in differences in decisions. It also finds evidence that cancer research centers learn to place different levels of emphasis on exploration in their clinical trials. These findings can later be used in models of the cancer research ecosystem to study how funding structures and policies can be changed to improve the outcomes of our collective effort to cure cancer.

Essay #1 - Parental Social Influence in Childhood Obesity Interventions: a Systematic Review The objective of this study is to understand the pathways through which social influence at the family level moderates childhood obesity interventions. We conducted a systematic review of obesity interventions in which parents' behaviors are targeted to change children's obesity outcomes, due to the potential social and environmental influence of parents on the nutrition and physical activity behaviors of children. Results for existing mechanisms that moderate parents' influence on children's behavior are discussed and a causal pathway diagram is developed to map out social influence mechanisms that affect childhood obesity. We provide health professionals and researchers with recommendations to leverage family-based social influence mechanisms for increasing the efficacy of the obesity intervention programs. Essay #2 - Dynamics of Obesity Interventions inside Organizations: a Case Study of Food Carry-Outs in Baltimore A large number of obesity prevention interventions, from upstream (policy and environmental) to downstream (individual level), have been put forward to curb the obesity trend; however, not all those interventions have been successful. Overall effectiveness of obesity prevention interventions relies not only on the average efficacy of a generic intervention, but also on the successful Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) of that intervention. In this study, we aim to understand how effectiveness of organizational level obesity prevention interventions depends on dynamics of AIM. We focus on an obesity prevention intervention, implemented in food carry-outs in low-income urban areas of Baltimore city, which aims to improve dietary behavior for adults through better food access to healthier foods and point-of-purchase prompts. Building on data from interviews and the literature we develop a dynamic model of the key processes of AIM. We first develop a contextualized map of causal relationships integral to the dynamics of AIM, and then quantify those mechanisms using a system dynamics simulation model. With simulation analysis, we show how as a result of several reinforcing loops that span stakeholder motivation, communications, and implementation quality and costs, small changes in the process of AIM can make a big difference in impact. We present how the dynamics surrounding communication, motivation, and depreciation of interventions can create tipping dynamics in AIM. Specifically, small changes in allocation of resources to an intervention could have a disproportionate long-term impact if those additional resources can turn stakeholders into allies of the intervention, reducing the depreciation rates and enhancing sustainability. We provide researchers with a set of recommendations to increase the sustainability of the interventions. Essay #3 - Dynamics of Implementation and Maintenance of Organizational Health Interventions: Case Studies of Obesity Interventions In this study, we present case studies to explore the dynamics of implementation and maintenance of obesity interventions. We analyze how specific obesity prevention interventions are built and eroded, how the building and erosion mechanisms are interconnected, and why we can see significantly different erosion rates across otherwise similar organizations. We use multiple comparative case studies to provide empirical information on the mechanisms of interest, and use qualitative systems modeling to integrate our evolving understanding into an internally consistent and transparent theory of the phenomenon. Our preliminary results identify reinforcing feedback mechanisms, including design of organizational processes, motivation of stakeholders, and communication among stakeholders, which influence implementation and maintenance of intervention components. Over time, these feedback mechanisms may drive a wedge between otherwise similar organizations, leading to distinct configurations of implementation and maintenance processes. / Ph. D.

High infant mortality (IM) rates in the U.S. have been a major public health concern for decades. Many studies have focused on understanding causes, risk factors, and interventions that can reduce IM. However, death of an infant is the result of the interplay between many risk factors, which in some cases can be traced to the infancy of their parents. Consequently, these complex interactions challenge the effectiveness of many interventions. The long-term goal of this study is to advance the common understanding of effective interventions for improving health outcomes and, in particular, infant mortality. To achieve this goal, I implemented systems and data science methods in three essays to contribute to the understanding of IM causes and risk factors. In the first study, the goal was to identify patterns in the leading causes of infant mortality across states that successfully reduced their IM rates. I explore the trends at the state-level between 2000 and 2015 to identify patterns in the leading causes of IM. This study shows that the main drivers of IM rate reduction is the preterm-related mortality rate. The second study builds on these findings and investigates the risk factors of preterm birth (PTB) in the largest obstetric population that has ever been studied in this field. By applying the latest statistical and machine learning techniques, I study the PTB risk factors that are both generalizable and identifiable during the early stages of pregnancy. A major finding of this study is that socioeconomic factors such as parent education are more important than generally known factors such as race in the prediction of PTB. This finding is significant evidence for theories like Lifecourse, which postulate that the main determinants of a health trajectory are the social scaffolding that addresses the upstream roots of health. These results point to the need for more comprehensive approaches that change the focus from medical interventions during pregnancy to the time where mothers become vulnerable to the risk factors of PTB. Therefore, in the third study, I take an aggregate approach to study the dynamics of population health that results in undesirable outcomes in major indicators like infant mortality. Based on these new explanations, I offer a systematic approach that can help in addressing adverse birth outcomes—including high infant mortality and preterm birth rates—which is the central contribution of this dissertation. In conclusion, this dissertation contributes to a better understanding of the complexities in infant mortality and health-related policies. This work contributes to the body of literature both in terms of the application of statistical and machine learning techniques, as well as in advancing health-related theories. / Doctor of Philosophy / The U.S. infant mortality rate (IMR) is 71% higher than the average rate for comparable countries in the Organization for Economic Co-operation and Development (OECD). High infant mortality and preterm birth rates (PBR) are major public health concerns in the U.S. A wide range of studies have focused on understanding the causes and risk factors of infant mortality and interventions that can reduce it. However, infant mortality is a complex phenomenon that challenges the effectiveness of the interventions, and the IMR and PBR in the U.S. are still higher than any other advanced OECD nation. I believe that systems and data science methods can help in enhancing our understanding of infant mortality causes, risk factors, and effective interventions. There are more than 130 diagnoses—causes—for infant mortality. Therefore, for 50 states tracking the causes of infant mortality trends over a long time period is very challenging. In the first essay, I focus on the medical aspects of infant mortality to find the causes that helped the reduction of the infant mortality rates in certain states from 2000 to 2015. In addition, I investigate the relationship between different risk factors with infant mortality in a regression model to investigate and find significant correlations. This study provides critical recommendations to policymakers in states with high infant mortality rates and guides them on leveraging appropriate interventions. Preterm birth (PTB) is the most significant contributor to the IMR. The first study showed that a reduction in infant mortality happened in states that reduced their preterm birth. There exists a considerable body of literature on identifying the PTB risk factors in order to find possible explanations for consistently high rates of PTB and IMR in the U.S. However, they have fallen short in two key areas: generalizability and being able to detect PTB in early pregnancy. In the second essay, I investigate a wide range of risk factors in the largest obstetric population that has ever been studied in PTB research. The predictors in this study consist of a wide range of variables from environmental (e.g., air pollution) to medical (e.g., history of hypertension) factors. Our objective is to increase the understanding of factors that are both generalizable and identifiable during the early stage of pregnancy. I implemented state-of-the-art statistical and machine learning techniques and improved the performance measures compared to the previous studies. The results of this study reveal the importance of socioeconomic factors such as, parent education, which can be as important as biomedical indicators like the mother's body mass index in predicting preterm delivery. The second study showed an important relationship between socioeconomic factors such as, education and major health outcomes such as preterm birth. Short-term interventions that focus on improving the socioeconomic status of a mother during pregnancy have limited to no effect on birth outcomes. Therefore, we need to implement more comprehensive approaches and change the focus from medical interventions during pregnancy to the time where mothers become vulnerable to the risk factors of PTB. Hence, we use a systematic approach in the third study to explore the dynamics of health over time. This is a novel study, which enhances our understanding of the complex interactions between health and socioeconomic factors over time. I explore why some communities experience the downward spiral of health deterioration, how resources are generated and allocated, how the generation and allocation mechanisms are interconnected, and why we can see significantly different health outcomes across otherwise similar states. I use Ohio as the case study, because it suffers from poor health outcomes despite having one of the best healthcare systems in the nation. The results identify the trap of health expenditure and how an external financial shock can exacerbate health and socioeconomic factors in such a community. I demonstrate how overspending or underspending in healthcare can affect health outcomes in a society in the long-term. Overall, this dissertation contributes to a better understanding of the complexities associated with major health issues of the U.S. I provide health professionals with theoretical and empirical foundations of risk assessment for reducing infant mortality and preterm birth. In addition, this study provides a systematic perspective on the issue of health deterioration that many communities in the US are experiencing, and hope that this perspective improves policymakers' decision-making.