Epidemiology & Biostatistics
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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.Item COMPLIANCE WITH AGE AT INITIATION OF HUMAN PAPILLOMAVIRUS VACCINE SERIES BY SOCIOECONOMIC STATUS, RACE/ETHNICITY, AND HEALTH INSURANCE COVERAGE AMONG 13-17 YEAR-OLD FEMALES WHO RECEIVED AT LEAST ONE HPV VACCINE SHOT: UNITED STATES, 2011(2014) Rattanawatkul, Kanokphan; Carter-Pokras, Olivia; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Human Papillomavirus (HPV) vaccine has been shown to prevent cervical cancer. Numerous studies have examined factors associated with HPV vaccine series initiation, but little is known about factors associated with age of initiation of HPV vaccine. Using cross-sectional data from the 2011 National Immunization Survey-Teen, this study examined the relationship between Advisory Committee on Immunization Practices' recommended age at initiation of the HPV vaccine series and socioeconomic status, race/ethnicity, and health insurance among 13-17 year-old females who received at least one HPV vaccine shot (n=5,965). On-time initiation of HPV vaccine series was significantly associated with having public health insurance (AOR: 1.825, 95% CI: 1.266, 2.631). Females with college-graduated mothers (AOR: 0.669, 95% CI: 0.487, 0.918) or household income greater than $75,000 (AOR: 0.746, 95% CI: 0.568, 0.98) were less likely to initiate on-time. Research is needed to further investigate the reasons for late initiation among these subgroups.Item Risk prediction models for hip fracture: parametric versus Cox regression(2013) Loo, Geok Yan; Ting Lee, Mei-ling; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Hip fracture is a public health burden due to high morbidity, mortality and cost. Risk prediction models can aid clinical decision-making by identifying individuals at risk. Objective: To build risk prediction model for incident hip fracture using Weibull regression and compare this with Cox regression model. Method: The Study of Osteoporosis prospectively collected risk factors were used to build a risk prediction model for first hip fracture using Threshold regression with Wiener process. Similar predictors were fitted using Cox regression for comparison. Results: There were 632 first hip fractures. Age, bone density, maternal and personal prior fractures were significant risk factors for hip fracture. Weibull had better goodness of fit, higher D-statistic and R-squared values than the exponential. Models did not differ in c-index and ten-fold cross validation showed similar areas under the ROC curves. Conclusion: Parametric and Cox models were comparable. External validation of the prediction model is required.Item Selection of Fixed and Random Effects in Linear Mixed Effects Models With Applications to the Trial of Adolescent Girls(2013) Grant, Edward Michael; Wu, Tong Tong; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Linear mixed effect (LME) models have become popular in modeling data in a wide variety of fields, particularly in public health. These models are beneficial because they are able to account for both the means as well as the covariance structure of clustered or longitudinal data. However, as studies are able to collect an increasing amount of data for large numbers of predictors, a major challenge has been the selection of only important variables to create a more interpretable, parsimonious model. Previous methods for LME models have been inefficient in variable selection, but three new methods attempt to select and estimate both important fixed and important random effects simultaneously. The models are compared through analysis of simulated longitudinal data. Additionally, as an example of the important applications to public health, the methods are applied to the Trial of Activity in Adolescent Girls (TAAG) study, to determine important predictors for Moderate to Vigorous Physical Activity (MVPA).Item DOUBLY PENALIZED LOGISTIC REGRESSION FOR GENOMEWIDE ASSOCIATION STUDIES WITH LINEARLY STRUCTURED GENETIC NETWORKS(2012) Li, Xia; Wu, Tongtong; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This research aims to integrate linear structures of genetic networks into genomewide analysis studies (GWAS). Lasso penalized logistic regression is ideally suited for continuous model selection in case-control disease gene mapping, especially when the number of predictor variables far exceeds the number of observations. But it fails to consider the structure of genetic networks. Imposing an additional weighted fused lasso can further remove irrelevant predictors. Nesterov's method is employed to handle the high dimensionality and complexity of genetic data. It also resolves the non-differentiability problem of the lasso and fused lasso penalties. In simulation studies, this proposed method shows advantages in some cases compared with lasso and fused lasso. We apply this method to the coeliac data on chromosome 8.Item SEMIPARAMETRIC AND NONPARAMETRIC ANALYSIS FOR LONGITUDINAL DATA ON THE RELATIONSHIP BETWEEN CHILDHOOD EXTERNALIZING BEHAVIOR AND BODY MASS INDEX(2011) Wang, Kejia; He, Xin; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis is an extension of the longitudinal data analysis of the association between externalizing behavior in early childhood and body mass index (BMI) from age 2 to 12 years conducted in Anderson et al. (2010). Externalizing behaviors problems are characterized by aggressive, oppositional, disruptive, or inattentive behaviors beyond those that would be expected given a child's age and development. The aim of the thesis is to estimate the children's BMI trajectory and to evaluate to what extent the externalizing behavior is related to BMI using semiparametric and nonparametric time-varying coefficient models. Some valuable insights into how the externalizing behavior and BMI are associated will be provided.