Epidemiology & Biostatistics Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/7129
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Item COVID-19 Vaccine Hesitancy and Uptake in the United States Considered Through the Lens of Health Behavior Theory(2024) Kauffman, Lauren Emily; Nguyen, Quynh; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Given the low COVID-19 vaccine uptake rates in many areas of the United States despite their demonstrated safety and effectiveness, COVID-19 vaccine hesitancy and vaccination barriers continue to be critical areas of research in epidemiology and behavioral health science. This series of studies focuses on COVID-19 vaccine hesitancy and vaccination barriers, as they relate to vaccination intention and vaccine uptake, considered in the context of established health behavior theories. The first study is a systematic review of existing research on COVID-19 vaccine hesitancy using one or more health behavior theories as key components of the design or analysis. This study examined the types of theories that are most often used, how they are used, and where research gaps exist. The remaining two studies use data from the U.S. COVID-19 Trends and Impact Survey, a national cross-sectional survey. The second study investigates the association between recent feelings of anxiety or depression and vaccination intention, as well as between these feelings and identifying with specific vaccine hesitancy reasons. The third study examines vaccine hesitancy and barriers among those with chronic illness or disease, a particularly vulnerable population. Factor analysis was conducted using constructs from the Theory of Planned Behavior as a framework, and the results were used in a regression model to investigate the association between these underlying factors and vaccination intention. This research demonstrated the usefulness of the Theory of Planned Behavior, the Health Belief Model, and the 3 Cs Model in existing and future COVID-19 vaccine hesitancy research, as well as identified Protection Motivation Theory as a promising area for future research. Additionally, psychological states were demonstrated to be significantly associated with vaccine hesitancy, adjusting for demographic, socioeconomic, and time factors. Lastly, the Theory of Planned Behavior was found to be applicable to those unvaccinated and with chronic illness, as the construct factor scores developed were significantly associated with vaccine hesitancy (adjusting for the presence of specific chronic conditions and demographic, socioeconomic, and time factors). These associations were also consistently demonstrated in subgroup analyses of participants with specific chronic conditions.Item Biomarker Categorization in Transcriptome Meta-analysis by Statistical significance, Biological Significance and Concordance(2020) Ye, Zhenyao; Ma, Tianzhou; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With the advancement of high-throughput technology, transcriptomic studies have been accumulated in the public domain. Meta-analysis combines multiple studies on a related hypothesis and improves the statistical power and reproducibility of single studies. However, a majority of existing meta-analysis methods only consider the statistical significance. We propose a novel method to categorize biomarkers by simultaneously considering statistical significance, biological significance (large effect size), and concordance patterns across studies, accounting for the complex study heterogeneity that exists in most meta-analysis problems. We conducted simulation studies and applied our method to Gynecologic and breast cancer RNA-seq data from The Cancer Genome Atlas to show its strength as compared to adaptively-weighted Fisher’s method. We found several major biomarker categories according to their cross-study patterns, and these categories are enriched in very different sets of pathways, offering different biological functions for future precision medicine.