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<p>Indiana has one of the worst health rankings in the nation at 41. The Centers for Disease Control and Prevention and the Robert Wood Johnson Foundation, among others have recognized that comprehensive, multi-disciplinary approaches are necessary to improve health in communities. No single organization, large or small, has the capacity to solve chronic disease, and thus coalitions have been touted as a solution to engage communities to better health. Evaluation of coalitions are critical to provide evidence of efficacy and identify factors required to build successful and sustainable health coalitions. A novel partnership between the Indiana State Department of Health (ISDH) and the Indiana Clinical and Translational Sciences Institute (CTSI) called Connections IN Health (CINH), integrates three Indiana chronic disease coalitions to improve the health of Indiana residents. A unique mission of this partnership is to integrate work from the three different disease areas of the coalitions (obesity, cardiovascular disease, and asthma) to enhance community engagement in Indiana counties. Coalition leads for each disease area were hired, as well as a manager to oversee integration of the coalitions. The coalitions are being re-built by increasing and diversifying membership, working together with funders to engage Indiana communities, and providing strong and formalized leadership to coalition members. Assessment of CINH is crucial to provide evidence that this approach of integrated coalition leadership is an archetype for successful health department/clinical translational science award (CTSA) collaboration for other CTSAs, and could be a reproducible approach to improve the translation of research from bench-to-bedside. Traditional evaluations of coalitions focus on singular process and formative assessments, which fail to capture the dynamic and inherently relational aspects of coalition functioning. Thus, I evaluated CINH coalitions using a mixed-methods, multi-level evaluation framework that includes coalition functioning and effectiveness surveys and social network analysis. Using linear and logistic regression models, I found that after CINH was implemented, perceptions of coalition functioning and effectiveness significantly increased among coalition members one- and two-years after the partnership was implemented. I found from a comprehensive social network analysis that CINH was successful in growing and diversifying its coalition networks, that partnership networks became more centralized, and that the networks demonstrated traits of effectiveness based on other coalition network effectiveness studies. We suggest that coalition evaluation researchers move towards a unified evaluation approach that includes perception surveys, social network analysis, external community development, and health outcomes. In addition, an integral part of my work was to share findings back to the coalitions to enhance evaluation and help coalitions achieve their goals. In this thesis, I discuss: evidence that community coalitions can improve health, current evaluation methods for health coalitions, the Connections IN Health partnership, and implementation of a mixed-methods, multi-level evaluation framework. Finally, I present findings from my longitudinal network analysis of the CINH statewide chronic disease coalitions. </p>

<p dir="ltr">Families experiencing homelessness and housing instability (FEH/HI) face myriad challenges, placing their children at risk for adverse outcomes. Research typically adopts a deficit-based approach to meet immediate needs, but this may limit understanding of the children’s experiences. Recognizing children’s strengths is important for improving their health, development, and support. Resilience and family resilience are concepts that draw on a strengths-based approach. However, there is a need for more research to identify the strengths, such as resilience, among families and their children who are not stably housed. The present research seeks to address the gaps in the literature by examining the resilience perceptions and experiences of adolescents in FEH/HI, as much of the existing research focuses on the adult perspectives, within a family resilience framework through multiple studies. Study 1 (Chapter 2) systematically reviewed existing research on resilience and family resilience within FEH/HI. An analysis of 27 studies identified resilience-related factors across individual, interpersonal, and community domains. Study 2 (Chapter 3) integrated a strengths- and deficit-based approach to explore the challenges and strengths of children in FEH/HI from the perspectives of parents/caregivers and service providers. Semi-structured interviews with 17 parents/caregivers and 15 service providers identified challenges and strengths at the individual, interpersonal, and system levels. Study 3 (Chapter 4) investigated how adolescents within FEH/HI experience and make meaning of family resilience in their day-to-day lives using photo-elicitation (PE) and Froma Walsh’s family resilience framework. Four adolescents participated and took photographs depicting family resilience within their families. Together, findings from these studies provide insights into the strengths and resilience within FEH/HI. Moreover, they emphasize the need for strengths-based approaches in research and practice to support the health, development, and wellbeing of children and adolescents in FEH/HI.</p>

<p>Despite the proven clinical benefits of use of adjuvant hormone therapy with tamoxifen or aromatase inhibitors for breast cancer, adherence to and persistence with adjuvant hormone therapy are suboptimal. It is critical to understand the clinical and economic impacts of low adherence to and low persistence with adjuvant hormone therapy in breast cancer. The overall objective was to assess associations between adherence to and persistence with adjuvant hormone therapy and mortality, healthcare utilization, and healthcare costs among older women with breast cancer. A retrospective longitudinal analysis of the Surveillance, Epidemiology, and End Results (SEER) registry linked with Medicare claims was conducted. This study included 25,796 older women diagnosed with hormone receptor-positive stage I-III breast cancer from 2009 through 2017. Adherence was defined as having proportion of days covered (PDC) of 0.80 or more. Persistence was defined as having no hormone therapy discontinuation, i.e., a break of at least 180 continuous days. Length of persistence was calculated as time from therapy initiation to discontinuation. All analyses were conducted using SAS 9.4 and RStudio for Linux environment. An <em>a priori</em> alpha level of 0.05 was used to determine significance for all the analyses. Time-dependent Cox models were used to assess associations between adherence to and persistence with adjuvant hormone therapy and mortality. Hurdle generalized linear mixed models were used to assess associations between adherence and persistence with annual number of hospitalizations, hospital days, hospital outpatient visits, inpatient costs, and outpatient costs across five years to account for excess zeroes.  Generalized linear mixed models were used for other types of healthcare utilization and costs. Annual adherence rates were 78.1 percent, 75.2 percent, 72.4 percent, 70.0 percent, and 61.5 percent from year-one to year-five after hormone therapy initiation. Persistence rates were 87.5 percent, 81.7 percent, 77.1 percent, 72.9 percent, and 68.9 percent through cumulative intervals of one year up to five years after hormone therapy initiation. Adherence was associated with lower risk of all-cause mortality, but was not significantly associated with breast cancer-specific mortality. Both being persistent and longer persistence were associated with lower risk of all-cause mortality and lower risk of breast cancer-specific mortality. Being adherent was associated with fewer hospitalizations, fewer hospital days, fewer emergency room visits, and fewer hospital outpatient visits, but was not associated with physician office visits. Being persistent was associated with fewer hospital days, fewer emergency room visits, and fewer hospital outpatient visits, but was associated with more physician office visits. Longer persistence was associated with fewer hospital days, fewer emergency room visits, and fewer hospital outpatient visits, but was not significantly associated with physician office visits. Adherent women had lower inpatient costs, lower outpatient costs, lower medical costs, and lower total healthcare costs despite higher prescription drug costs. Both being persistent and longer persistence were associated with lower inpatient costs, lower outpatient costs, lower medical costs, and lower total healthcare costs despite higher prescription drug costs. </p>

<p>Predictive modeling in healthcare involves the development of data-driven and computational models which can predict what will happen, be it for a single individual or for an entire system. The adoption of predictive models can guide various stakeholders’ decision-making in the healthcare sector, and consequently improve individual outcomes and the cost-effectiveness of care. With the rapid development in healthcare of big data and the Internet of Things technologies, research in healthcare decision-making has grown in both importance and complexity. One of the complexities facing those who would build predictive models is heterogeneity of patient populations, clinical practices, and intervention outcomes, as well as from diverse health systems. There are many sub-domains in healthcare for which predictive modeling is useful such as disease risk modeling, clinical intelligence, pharmacovigilance, precision medicine, hospitalization process optimization, digital health, and preventive care. In my dissertation, I focus on predictive modeling for applications that fit into three broad and important domains of healthcare, namely clinical practice, public health, and healthcare system. In this dissertation, I present three papers that present a collection of predictive modeling studies to address the challenge of modeling heterogeneity in health care. The first paper presents a decision-tree model to address clinicians’ need to decide among various liver cirrhosis diagnosis strategies. The second paper presents a micro-simulation model to assess the impact on cardiovascular disease (CVD) to help decision makers at government agencies develop cost-effective food policies to prevent cardiovascular diseases, a public-health domain application. The third paper compares a set of data-driven prediction models, the best performing of which is paired together with interpretable machine learning to facilitate the coordination of optimization for hospital-discharged patients choosing skilled nursing facilities. This collection of studies addresses important modeling challenges in specific healthcare domains, and also broadly contribute to research in medical decision-making, public health policy and healthcare systems.</p>

<p>  </p> <p>Food safety researchers and extension workers are focused on educating the different actors of the supply chain, from farm to fork. To accomplish this, researchers identify areas of improvement and investigate the factors that cause or explain food safety behaviors. This thesis is divided into a systematic literature review with a meta-analysis and qualitative synthesis (Ch. 2), then two case studies that use predictive models to find top predictors of food safety behaviors (Ch.3 and 4). The systematic review (Ch.2) investigates online food safety educational programs and their effectiveness, barriers, and recommendations on different subpopulations of students, consumers, and food workers. The findings showed a limited effect on attitudes in the different subpopulations. Several areas for future research and recommendations for educators were identified. The first case study (Ch.3) developed predictive models of different food safety behaviors at ten time points throughout the COVID-19 pandemic. Findings suggest an effect between changes in COVID-19 case numbers and how well attitudes related to COVID-19 can make predictions. Additionally, findings suggest the importance of attitudes when predicting food safety behaviors. Lastly, results identified that the belief that handwashing protects against foodborne illness was more important than the belief that handwashing protects against COVID-19 when predicting handwashing at most time points. These findings can identify insights into consumer behaviors during the pandemic and several possible areas for future research. The second case study (Ch. 4) developed predictive models of consumer flour handling practices and consumer awareness of flour-related recalls and how they are affected by the total number of flour-related recalls for a state where the consumer lives. Findings identified the importance of risk perceptions in predicting consumer flour handling practices. Results also showed that younger consumers were predicted to be more likely to be aware of flour recalls than consumers of older ages. Lastly, results show that the total number of flour-related recalls for a state where the consumer lives do not affect predictions. Findings identify potential challenges to recall communication and areas for future studies.</p>