The Skip To Breakfast project: Development, implementation, and feasibility evaluation of an intervention to increase healthful breakfast consumption among fifth grade students and their families

Children and adults frequently skip breakfast and rates are currently increasing. In addition, the food choices made for breakfast are not always healthy ones. Breakfast skipping, in conjunction with unhealthy breakfast choices, leads to impaired cognitive functioning, poor nutrient intake, and overweight. In response to these public health issues, Skip To Breakfast, a behaviorally based school and family program, was created to increase consistent and healthful breakfast consumption among ethnically diverse fifth grade students and their families, using Intervention Mapping™. Four classroom lessons and four parent newsletters were used to deliver the intervention. For this project, a healthy, "3 Star Breakfast" was promoted, and included a serving each of dairy product, whole grain, and fruit, each with an emphasis on being low in fat and sugar. The goal of this project was to evaluate the feasibility and acceptability of the intervention. A pilot-test of the intervention was conducted in one classroom, in a school in Houston, during the Fall 2007 semester. A qualitative evaluation of the intervention was conducted, which included focus groups with students, phone interviews of parents, process evaluation data from the classroom teacher, and direct observation. Sixteen students and six parents participated in the study. Data were recorded and themes were identified. Initial results showed there is a need for such programs. Based on the initial feedback, edits were made to the intervention and program. Results showed high acceptability among the teacher, students, and parents. It became apparent that students were not reliably getting the parent newsletters to their parents to read, so a change to the protocol was made, in which students will receive incentives for having parents read newsletters and return signed forms, to increase parent participation. Other changes included small modifications to the curriculum, such as, clarifying instructions, changing in-class assignments to homework assignments, and including background reading materials for the teacher. The main trial is planned to be carried out in Spring 2008, in two elementary schools, utilizing four, fifth grade classes from each, with one school acting as the control and one as the intervention school. Results from this study can be used as an adjunct to the Coordinated Approach To Child Health (CATCH) program. ^

THE EFFECT OF PERSON-ENVIRONMENT INTERACTION ON ANXIETY, DEPRESSION, AND SUBJECTIVE DISTRESS OF OPTOMETRY STUDENTS

The purpose of this study was to investigate whether an incongruence between personality characteristics of individuals and concomitant charcteristics of health professional training environments on salient dimensions contributes to aspects of mental health. The dimensions examined were practical-theoretical orientation and the degree of structure-unstructure. They were selected for study as they are particularly important attributes of students and of learning environments. It was proposed that when the demand of the environment is disparate from the proclivities of the individual, strain arises. This strain was hypothesized to contribute to anxiety, depression, and subjective distress.^ Select subscales on the Omnibus Personality Inventory (OPI) were the operationalized measures for the personality component of the dimensions studied. An environmental index was developed to assess students' perceptions of the learning environment on these same dimensions. The Beck Depression Inventory, State-Trait Anxiety Inventory and General Well-Being schedule measured the outcome variables.^ A congruence model was employed to determine person-environment (P-E) interaction. Scores on the scales of the OPI and the environmental index were divided into high, medium, and low based on the range of scores. Congruence was defined as a match between the level of personality need and the complementary level of the perception of the environment. Alternatively, incongruence was defined as a mismatch between the person and the environment. The consistent category was compared to the inconsistent categories by an analysis of variance procedure. Furthermore, analyses of covariance were conducted with perceived supportiveness of the learning environment and life events external to the learning environment as the covariates. These factors were considered critical influences affecting the outcome measures.^ One hundred and eighty-five students (49% of the population) at the College of Optometry at the University of Houston participated in the study. Students in all four years of the program were equally represented in the study. However, the sample differed from the total population on representation by sex, marital status, and undergraduate major.^ The results of the study did not support the hypotheses. Further, after having adjusted for perceived supportiveness and life events external to the learning environment, there were no statistically significant differences between the congruent category and incongruent categories. Means indicated than the study sample experienced significantly lower depression and subjective distress than the normative samples.^ Results are interpreted in light of their utility for future study design in the investigation of the effects of P-E interaction. Emphasized is the question of the feasibility of testing a P-E interaction model with extant groups. Recommendations for subsequent research are proposed in light of the exploratory nature of the methodology. ^

An examination of the rate of high school completion experienced by handicapped and nonhandicapped students in the state of Texas

This research examines the graduation rate experienced by students receiving public education services in the state of Texas. Special attention is paid to that subgroup of Texas students who meet Texas Education Agency criteria for handicapped status. The study is guided by two research questions: What are the high school completion rates experienced by handicapped and nonhandicapped students attending Texas public schools? and What are the predictors of graduation for handicapped and nonhandicapped students?^ In addition, the following hypotheses are explored. Hypothesis 1: Handicapped students attending a Texas public school will experience a lower rate of high school completion than their nonhandicapped counterparts. Hypothesis 2: Handicapped and nonhandicapped students attending school in a Texas public school with a budget above the median budget for Texas public schools will experience a higher rate of high school completion than similar students in Texas public schools with a budget below the median budget. Hypothesis 3: Handicapped and nonhandicapped students attending school in large Texas urban areas will experience a lower rate of high school completion than similar students in Texas public schools in rural areas. Hypothesis 4: Handicapped and nonhandicapped students attending a Texas public school in a county which rates above the state median for food stamps and AFDC recipients will experience a lower rate of high school completion than students living in counties below the median.^ The study will employ extant data from the records of the Texas Education Agency for the 1988-1989 and the 1989-1990 school years, from the Texas Department of Health for the years of 1989 and 1990, and from the 1980 Census.^ The study reveals that nonhandicapped students are graduating with a two year average rate of.906, while handicapped students following an Individualized Educational Program (IEP) achieve a two year average rate of.532, and handicapped students following the regular academic program present a two year average graduation rate of only.371. The presence of other handicapped students, and the school district's average expense per student are found to contribute significantly to the completion rates of handicapped students. Size groupings are used to elucidate the various impacts of these variables on different school districts and different student groups.^ Conclusions and implications are offered regarding the need to reach national consensus on the definition and computation of high school completion for both handicapped and nonhandicapped students, and the need for improved statewide tracking of handicapped completion rates. ^

RELATION OF TYPE OF REPORTED ILLNESSES TO PATTERN OF USE OF THE STUDENT HEALTH CENTER BY THE INTERNATIONAL STUDENTS AT TEXAS SOUTHERN UNIVERSITY, HOUSTON, TEXAS

The state of knowledge on the relation of stress factors, health problems and health service utilization among university students is limited. Special problems of stress exist for the international students due to their having to adjust to a new environment. It is this latter problem area that provides the focus for this study. Recognizing there are special stress factors affecting the international students, it is first necessary to see if the problems of cultural adaptation affect them to any greater degree than American students attending the same university.^ To make the comparison, the study identified a number of health problems of both American and international students and related their frequency to the use of the Student Health Center. The expectation was that there would be an association between the number of health problems and the number of life change events experienced by these students and between the number of health problems and stresses from social factors. It was also expected that the number of health problems would decline with the amount of social support.^ The population chosen were students newly enrolled in Texas Southern University, Houston, Texas in the Fall Semester of 1979. Two groups were selected at random: 126 international and 126 American students. The survey instrument was a self-administered questionnaire. The response rate was 90% (114) for the international and 94% (118) for the American students.^ Data analyses consisted of both descriptive and inferential statistics. Chi-squares and correlation coefficients were the statistics used in comparing the international students and the American students.^ There was a weak association between the number of health problems and the number of life change events, as reported by both the international and the American students. The study failed to show any statistically significant association between the number of stress from social factors and the number of health problems. It also failed to show an association between the number of health problems and the amount of social support. These findings applied to both the international and the American students.^ One unexpected finding was that certain health problems were reported by more American than international students. There were: cough, diarrhea, and trouble in sleeping. Another finding was that those students with health insurance had a higher level of utilization of the Health Center than those without health insurance. More international than American students utilized the Student Health Center.^ In comparing the women students, there was no statistical significant difference in their reported fertility related health problems.^ The investigator recommends that in follow-up studies, instead of grouping all international students together, that they be divided by major nationalities represented in the student body; that is, Iranians, Nigerians and others. ^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.