OLDER WOMEN LIVING WITH HIV/AIDS

The purpose of this study is to fill a gap in the literature by investigating how an ignored population of women, women over age 50, copes with HIV/ AIDS. Older women are referred to as "invisible victims" with regard to HIV/AIDS. Previous research on coping with HIV/ AIDS focuses mostly on men. Of the research that does focus on women, older women are often overlooked. Although older women are a minority compared to other HIV-infected populations in the US, they are just as deserving of recognition and care as any other population. Data was collected through open-ended, in-depth interviews with four women individually. Recruitment of the sample is from several health institutions serving HIV/AIDS populations. The major topics discussed in the interviews include: demographics, what it is like to live with HIV or AIDS, and way of coping with HIV/ AIDS, including social support, religion, and health behaviors. The data analysis process is a qualitative one, with exploration of major themes and presentation of rich descriptions to illustrate those themes. Results from the data show that in terms of coping, all four participants found it most difficult to cope with a different aspect of living with HIV. Regardless of this finding, participants still employed similar coping strategies. As hypothesized, social support and religious/ spiritual support are important aspects in coping with HIV for all participants. The use of education as a coping mechanism was not an anticipated result. Yet, education was a constant theme, whether it was educating oneself about the disease to better understand it or educating others as to prevent them from contracting HIV. A variety of different positive coping strategies were employed by the participants in coping with their HIV, including altering negative health habits and staying optimistic. Negative coping strategies were also employed, but these seemed to be discussed less throughout the interviews. Overall, the results of this study demonstrate the resilience of these women in terms of finding ways of living with HIV instead of dying from HIV.

Second, do less harm: The state of states' medical error reporting programs

Statement of the problem and public health significance. Hospitals were designed to be a safe haven and respite from disease and illness. However, a large body of evidence points to preventable errors in hospitals as the eighth leading cause of death among Americans. Twelve percent of Americans, or over 33.8 million people, are hospitalized each year. This population represents a significant portion of at risk citizens exposed to hospital medical errors. Since the number of annual deaths due to hospital medical errors is estimated to exceed 44,000, the magnitude of this tragedy makes it a significant public health problem. ^ Specific aims. The specific aims of this study were threefold. First, this study aimed to analyze the state of the states' mandatory hospital medical error reporting six years after the release of the influential IOM report, "To Err is Human." The second aim was to identify barriers to reporting of medical errors by hospital personnel. The third aim was to identify hospital safety measures implemented to reduce medical errors and enhance patient safety. ^ Methods. A descriptive, longitudinal, retrospective design was used to address the first stated objective. The study data came from the twenty-one states with mandatory hospital reporting programs which report aggregate hospital error data that is accessible to the public by way of states' websites. The data analysis included calculations of expected number of medical errors for each state according to IOM rates. Where possible, a comparison was made between state reported data and the calculated IOM expected number of errors. A literature review was performed to achieve the second study aim, identifying barriers to reporting medical errors. The final aim was accomplished by telephone interviews of principal patient safety/quality officers from five Texas hospitals with more than 700 beds. ^ Results. The state medical error data suggests vast underreporting of hospital medical errors to the states. The telephone interviews suggest that hospitals are working at reducing medical errors and creating safer environments for patients. The literature review suggests the underreporting of medical errors at the state level stems from underreporting of errors at the delivery level. ^

An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^

Estimating parameters in Markov models for longitudinal studies with missing data or surrogate outcomes

The discrete-time Markov chain is commonly used in describing changes of health states for chronic diseases in a longitudinal study. Statistical inferences on comparing treatment effects or on finding determinants of disease progression usually require estimation of transition probabilities. In many situations when the outcome data have some missing observations or the variable of interest (called a latent variable) can not be measured directly, the estimation of transition probabilities becomes more complicated. In the latter case, a surrogate variable that is easier to access and can gauge the characteristics of the latent one is usually used for data analysis. ^ This dissertation research proposes methods to analyze longitudinal data (1) that have categorical outcome with missing observations or (2) that use complete or incomplete surrogate observations to analyze the categorical latent outcome. For (1), different missing mechanisms were considered for empirical studies using methods that include EM algorithm, Monte Carlo EM and a procedure that is not a data augmentation method. For (2), the hidden Markov model with the forward-backward procedure was applied for parameter estimation. This method was also extended to cover the computation of standard errors. The proposed methods were demonstrated by the Schizophrenia example. The relevance of public health, the strength and limitations, and possible future research were also discussed. ^

Sibling relationships in the family of a child with a burn injury

Purpose. Understanding siblings' experiences after a major childhood burn injury was the purpose of this mixed method, qualitative dominant study. The following research questions guided this project: How do siblings describe the impact of a major childhood burn injury experience? How do sibling relationship factors of warmth/closeness, relative status/power, conflict, and rivalry further clarify their relationship and their experience after a major burn injury? ^ Methods. A mixed method, qualitative dominant, design was implemented to understand the sibling experiences in a family with a child suffering from a major burn injury. Informants were selected from patients with childhood burn injuries attending the reconstructive clinic at a Gulf coast children's specialty hospital. The qualitative portion used the life story method, a narrative process, to portray the long-term impact on sibling relationships. A "case" represents a family unit and could be composed of one or multiple family members. Participants from 22 cases (N = 40 participants) were interviewed. Interviews were conducted in person and via telephone. The quantitative portion, or the embedded part of this mixed method design, used the Sibling Relationship Questionnaire Revised (SRQ-R) to conduct an additional structured interview and acquire scoring data. It was postulated that the SRQ-R would provide another perspective on the sibling experience and expand the qualitative data analysis. Thematic analysis was implemented on the qualitative interview data including the qualitative data from the interviews structured on the SRQ-R. Additionally, scores on the SRQ-R were tabulated to further describe the cases. ^ Results. The overall thematic pattern for the sibling relationship in families having a child with a major burn injury was that of normalization. Areas of normalization as well as the process of adjustment were the major themes. Areas of normalization were found in play and other activities, in school and work, and in family relations with their siblings and their parents. The process of adjustment in the sibling relationship was described as varied, involved school and work re-entry, and might even change their life perspective. Further analysis included an examination of the cases in which more than one person were interviewed and completed the SRQ-R. Participants from five ( n = 11) of six cases (n = 14), scored above 3.0 on the five-point scale on the Warmth/Closeness construct, indicating they perceived the sibling relationship as close. Five participants scored high on the Conflict construct and four participants scored high on the Rivalry construct. Finally, Relative Status/Power was low or negative in the six cases (n = 13). ^ Conclusions/implications. These findings suggest the importance of returning to normalcy for many of the families and the significance of sibling relationships on the process. Some of these families were able to use this major life event in a positive way to promote normalization. ^

A comparative analysis of CHIP Perinatal policy in twelve states

The purpose of this comparative analysis of CHIP Perinatal policy (42 CFR § 457) was to provide a basis for understanding the variation in policy outputs across the twelve states that, as of June 2007, implemented the Unborn Child rule. This Department of Health and Human Services regulation expanded in 2002 the definition of “child” to include the period from conception to birth, allowing states to consider an unborn child a “targeted low-income child” and therefore eligible for SCHIP coverage. ^ Specific study aims were to (1) describe typologically the structural and contextual features of the twelve states that adopted a CHIP Perinatal policy; (2) describe and differentiate among the various designs of CHIP Perinatal policy implemented in the states; and (3) develop a conceptual model that links the structural and contextual features of the adopting states to differences in the forms the policy assumed, once it was implemented. ^ Secondary data were collected from publicly available information sources to describe characteristics of states’ political system, health system, economic system, sociodemographic context and implemented policy attributes. I posited that socio-demographic differences, political system differences and health system differences would directly account for the observed differences in policy output among the states. ^ Exploratory data analysis techniques, which included median polishing and multidimensional scaling, were employed to identify compelling patterns in the data. Scaled results across model components showed that economic system was most closely related to policy output, followed by health system. Political system and socio-demographic characteristics were shown to be weakly associated with policy output. Goodness-of-fit measures for MDS solutions implemented across states and model components, in one- and two-dimensions, were very good. ^ This comparative policy analysis of twelve states that adopted and implemented HHS Regulation 42 C.F.R. § 457 contributes to existing knowledge in three areas: CHIP Perinatal policy, public health policy and policy sciences. First, the framework allows for the identification of CHIP Perinatal program design possibilities and provides a basis for future studies that evaluate policy impact or performance. Second, studies of policy determinants are not well represented in the health policy literature. Thus, this study contributes to the development of the literature in public health policy. Finally, the conceptual framework for policy determinants developed in this study suggests new ways for policy makers and practitioners to frame policy arguments, encouraging policy change or reform. ^

A path analysis of an after-school health and exercise program for elementary school children in El Paso, Texas

As schools are pressured to perform on academics and standardized examinations, schools are reluctant to dedicate increased time to physical activity. After-school exercise and health programs may provide an opportunity to engage in more physical activity without taking time away from coursework during the day. The current study is a secondary data analysis of data from a randomized trial of a 10-week after-school program (six schools, n = 903) that implemented an exercise component based on the CATCH physical activity component and health modules based on the culturally-tailored Bienestar health education program. Outcome variables included BMI and aerobic capacity, health knowledge and healthy food intentions as assessed through path analysis techniques. Both the baseline model (χ2 (df = 8) = 16.90, p = .031; RMSEA = .035 (90% CI of .010–.058), NNFI = 0.983 and the CFI = 0.995) and the model incorporating intervention participation proved to be a good fit to the data (χ2 (df = 10) = 11.59, p = .314. RMSEA = .013 (90% CI of .010–.039); NNFI = 0.996 and CFI = 0.999). Experimental group participation was not predictive of changes in health knowledge, intentions to eat healthy foods or changes in Body Mass Index, but it was associated with increased aerobic capacity, β = .067, p < .05. School characteristics including SES and Language proficiency proved to be significantly associated with changes in knowledge and physical indicators. Further effects of school level variables on intervention outcomes are recommended so that tailored interventions can be developed aimed at the specific characteristics of each participating school. ^

Secondary analysis of effectivness of tinidazole-containing-quadruple eradication therapy in asymptomatic H. pylori infected children in El Paso

Helicobacter pylori infection is frequently acquired during childhood. This microorganism is known to cause gastritis, and duodenal ulcer in pediatric patients, however most children remain completely asymptomatic to the infection. Currently there is no consensus in favor of treatment of H. pylori infection in asymptomatic children. The firstline of treatment for this population is triple medication therapy including two antibacterial agents and one proton pump inhibitor for a 2 week duration course. Decreased eradication rate of less than 75% has been documented with the use of this first-line therapy but novel tinidazole-containing quadruple sequential therapies seem worth investigating. None of the previous studies on such therapy has been done in the United States of America. As part of an iron deficiency anemia study in asymptomatic H. pylori infected children of El Paso, Texas, we conducted a secondary data analysis of study data collected in this trial to assess the effectiveness of this tinidazole-containing sequential quadruple therapy compared to placebo on clearing the infection. Subjects were selected from a group of asymptomatic children identified through household visits to 11,365 randomly selected dwelling units. After obtaining parental consent and child assent a total of 1,821 children 3-10 years of age were screened and 235 were positive to a novel urine immunoglobulin class G antibodies test for H. pylori infection and confirmed as infected using a 13C urea breath test, using a hydrolysis urea rate >10 μg/min as cut-off value. Out of those, 119 study subjects had a complete physical exam and baseline blood work and were randomly allocated to four groups, two of which received active H. pylori eradication medication alone or in combination with iron, while the other two received iron only or placebo only. Follow up visits to their houses were done to assess compliance and occurrence of adverse events and at 45+ days post-treatment, a second urea breath test was performed to assess their infection status. The effectiveness was primarily assessed on intent to treat basis (i.e., according to their treatment allocation), and the proportion of those who cleared their infection using a cut-off value >10 μg/min of for urea hydrolysis rate, was the primary outcome. Also we conducted analysis on a per-protocol basis and according to the cytotoxin associated gene A product of the H. pylori infection status. Also we compared the rate of adverse events across the two arms. On intent-to-treat and per-protocol analyses, 44.3% and 52.9%, respectively, of the children receiving the novel quadruple sequential eradication cleared their infection compared to 12.2% and 15.4% in the arms receiving iron or placebo only, respectively. Such differences were statistically significant (p<0.001). The study medications were well accepted and safe. In conclusion, we found in this study population, of mostly asymptomatically H. pylori infected children, living in the US along the border with Mexico, that the quadruple sequential eradication therapy cleared the infection in only half of the children receiving this treatment. Research is needed to assess the antimicrobial susceptibility of the strains of H. pylori infecting this population to formulate more effective therapies. ^

Demographic analysis of Certified Industrial Hygienists (CIH) with the use of survival analysis techniques

Objective. The goal of this study is to characterize the current workforce of CIHs, the lengths of professional practice careers of the past and current CIHs.^ Methods. This is a secondary data analysis of data compiled from all of the nearly 50 annual roster listings of the American Board of Industrial Hygiene (ABIH) for Certified Industrial Hygienists active in each year since 1960. Survival analysis was performed as a technique to measure the primary outcome of interest. The technique which was involved in this study was the Kaplan-Meier method for estimating the survival function.^ Study subjects: The population to be studied is all Certified Industrial Hygienists (CIHs). A CIH is defined by the ABIH as an individual who has achieved the minimum requirements for education, working experience and through examination, has demonstrated a minimum level of knowledge and competency in the prevention of occupational illnesses. ^ Results. A Cox-proportional hazards model analysis was performed by different start-time cohorts of CIHs. In this model we chose cohort 1 as the reference cohort. The estimated relative risk of the event (defined as retirement, or absent from 5 consecutive years of listing) occurred for CIHs for cohorts 2,3,4,5 relative to cohort 1 is 0.385, 0.214, 0.234, 0.299 relatively. The result show that cohort 2 (CIHs issued from 1970-1980) has the lowest hazard ratio which indicates the lowest retirement rate.^ Conclusion. The manpower of CIHs (still actively practicing up to the end of 2009) increased tremendously starting in 1980 and grew into a plateau in recent decades. This indicates that the supply and demand of the profession may have reached equilibrium. More demographic information and variables are needed to actually predict the future number of CIHs needed. ^

BEST SUBSET SELECTION FOR CATEGORICAL DATA

When choosing among models to describe categorical data, the necessity to consider interactions makes selection more difficult. With just four variables, considering all interactions, there are 166 different hierarchical models and many more non-hierarchical models. Two procedures have been developed for categorical data which will produce the "best" subset or subsets of each model size where size refers to the number of effects in the model. Both procedures are patterned after the Leaps and Bounds approach used by Furnival and Wilson for continuous data and do not generally require fitting all models. For hierarchical models, likelihood ratio statistics (G('2)) are computed using iterative proportional fitting and "best" is determined by comparing, among models with the same number of effects, the Pr((chi)(,k)('2) (GREATERTHEQ) G(,ij)('2)) where k is the degrees of freedom for ith model of size j. To fit non-hierarchical as well as hierarchical models, a weighted least squares procedure has been developed.^ The procedures are applied to published occupational data relating to the occurrence of byssinosis. These results are compared to previously published analyses of the same data. Also, the procedures are applied to published data on symptoms in psychiatric patients and again compared to previously published analyses.^ These procedures will make categorical data analysis more accessible to researchers who are not statisticians. The procedures should also encourage more complex exploratory analyses of epidemiologic data and contribute to the development of new hypotheses for study. ^

Descriptive analysis of Ben Taub Pediatric Weight Management Program

The purpose of this study is to descriptively analyze the current program at Ben Taub Pediatric Weight Management Program in Houston, Texas, a program designed to help overweight children ages three to eighteen to lose weight. In Texas, approximately one in every three children is overweight or obese. Obesity is seen at an even greater level within Ben Taub due to the hospital's high rate of service for underserved minority populations (Dehghan et al, 2005; Tyler and Horner, 2008; Hunt, 2009). The weight management program consists of nutritional, behavioral, physical activity, and medical counseling. Analysis will focus on changes in weight, BMI, cholesterol levels, and blood pressure from 2007–2010 for all participants who attended at least two weight management sessions. Recommendations will be given in response to the results of the data analysis.^

Motivational interviewing and feedback for college drinkers: Handling missing values with complete case analysis and multiple imputation

Objective: In this secondary data analysis, three statistical methodologies were implemented to handle cases with missing data in a motivational interviewing and feedback study. The aim was to evaluate the impact that these methodologies have on the data analysis. ^ Methods: We first evaluated whether the assumption of missing completely at random held for this study. We then proceeded to conduct a secondary data analysis using a mixed linear model to handle missing data with three methodologies (a) complete case analysis, (b) multiple imputation with explicit model containing outcome variables, time, and the interaction of time and treatment, and (c) multiple imputation with explicit model containing outcome variables, time, the interaction of time and treatment, and additional covariates (e.g., age, gender, smoke, years in school, marital status, housing, race/ethnicity, and if participants play on athletic team). Several comparisons were conducted including the following ones: 1) the motivation interviewing with feedback group (MIF) vs. the assessment only group (AO), the motivation interviewing group (MIO) vs. AO, and the intervention of the feedback only group (FBO) vs. AO, 2) MIF vs. FBO, and 3) MIF vs. MIO.^ Results: We first evaluated the patterns of missingness in this study, which indicated that about 13% of participants showed monotone missing patterns, and about 3.5% showed non-monotone missing patterns. Then we evaluated the assumption of missing completely at random by Little's missing completely at random (MCAR) test, in which the Chi-Square test statistic was 167.8 with 125 degrees of freedom, and its associated p-value was p=0.006, which indicated that the data could not be assumed to be missing completely at random. After that, we compared if the three different strategies reached the same results. For the comparison between MIF and AO as well as the comparison between MIF and FBO, only the multiple imputation with additional covariates by uncongenial and congenial models reached different results. For the comparison between MIF and MIO, all the methodologies for handling missing values obtained different results. ^ Discussions: The study indicated that, first, missingness was crucial in this study. Second, to understand the assumptions of the model was important since we could not identify if the data were missing at random or missing not at random. Therefore, future researches should focus on exploring more sensitivity analyses under missing not at random assumption.^

The SanaViz: Human Centered Geovisualization to facilitate visual exploration of Public Health data

These three manuscripts are presented as a PhD dissertation for the study of using GeoVis application to evaluate telehealth programs. The primary reason of this research was to understand how the GeoVis applications can be designed and developed using combined approaches of HC approach and cognitive fit theory and in terms utilized to evaluate telehealth program in Brazil. First manuscript The first manuscript in this dissertation presented a background about the use of GeoVisualization to facilitate visual exploration of public health data. The manuscript covered the existing challenges that were associated with an adoption of existing GeoVis applications. The manuscript combines the principles of Human Centered approach and Cognitive Fit Theory and a framework using a combination of these approaches is developed that lays the foundation of this research. The framework is then utilized to propose the design, development and evaluation of “the SanaViz” to evaluate telehealth data in Brazil, as a proof of concept. Second manuscript The second manuscript is a methods paper that describes the approaches that can be employed to design and develop “the SanaViz” based on the proposed framework. By defining the various elements of the HC approach and CFT, a mixed methods approach is utilized for the card sorting and sketching techniques. A representative sample of 20 study participants currently involved in the telehealth program at the NUTES telehealth center at UFPE, Recife, Brazil was enrolled. The findings of this manuscript helped us understand the needs of the diverse group of telehealth users, the tasks that they perform and helped us determine the essential features that might be necessary to be included in the proposed GeoVis application “the SanaViz”. Third manuscript The third manuscript involved mix- methods approach to compare the effectiveness and usefulness of the HC GeoVis application “the SanaViz” against a conventional GeoVis application “Instant Atlas”. The same group of 20 study participants who had earlier participated during Aim 2 was enrolled and a combination of quantitative and qualitative assessments was done. Effectiveness was gauged by the time that the participants took to complete the tasks using both the GeoVis applications, the ease with which they completed the tasks and the number of attempts that were taken to complete each task. Usefulness was assessed by System Usability Scale (SUS), a validated questionnaire tested in prior studies. In-depth interviews were conducted to gather opinions about both the GeoVis applications. This manuscript helped us in the demonstration of the usefulness and effectiveness of HC GeoVis applications to facilitate visual exploration of telehealth data, as a proof of concept. Together, these three manuscripts represent challenges of combining principles of Human Centered approach, Cognitive Fit Theory to design and develop GeoVis applications as a method to evaluate Telehealth data. To our knowledge, this is the first study to explore the usefulness and effectiveness of GeoVis to facilitate visual exploration of telehealth data. The results of the research enabled us to develop a framework for the design and development of GeoVis applications related to the areas of public health and especially telehealth. The results of our study showed that the varied users were involved with the telehealth program and the tasks that they performed. Further it enabled us to identify the components that might be essential to be included in these GeoVis applications. The results of our research answered the following questions; (a) Telehealth users vary in their level of understanding about GeoVis (b) Interaction features such as zooming, sorting, and linking and multiple views and representation features such as bar chart and choropleth maps were considered the most essential features of the GeoVis applications. (c) Comparing and sorting were two important tasks that the telehealth users would perform for exploratory data analysis. (d) A HC GeoVis prototype application is more effective and useful for exploration of telehealth data than a conventional GeoVis application. Future studies should be done to incorporate the proposed HC GeoVis framework to enable comprehensive assessment of the users and the tasks they perform to identify the features that might be necessary to be a part of the GeoVis applications. The results of this study demonstrate a novel approach to comprehensively and systematically enhance the evaluation of telehealth programs using the proposed GeoVis Framework.

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.

Clostridium difficile infection (CDI) in HIV-positive patients

The main aim of this study was to look at the association of Clostridium difficile infection (CDI) and HIV. A secondary goal was to look at the trend of CDI-related deaths in Texas from 1999-2011. To evaluate the coinfection of CDI and HIV, we looked at 2 datasets provided by CHS-TDSHS, for 13 years of study period from 1999-2011: 1) Texas death certificate data and 2) Texas hospital discharge data. An ancillary source of data was national level death data from CDC. We did a secondary data analysis and reported the age-adjusted death rates (mortality) and hospital discharge frequencies (morbidity) for CDI, HIV and for CDI+HIV coinfection.^ Since the turn of the century, CDI has reemerged as an important public health challenge due to the emergence of hypervirulent epidemic strains. From 1999-2011, there has been a significant upward trend in CDI-related death rates; in the state of Texas alone, CDI mortality rate has increased 8.7 fold in this time period at the rate of 0.2 deaths per year per 100,000 individuals. On the contrary, mortality due to HIV has decreased by 46% and has been trending down. The demographic groups in Texas with the highest CDI mortality rates were elderly aged 65+, males, whites and hospital inpatients. The epidemiology of C. difficile has changed in such a way that it is not only staying confined to these traditional high-risk groups, but is also being increasingly reported in low-risk populations such as healthy people in the community (community acquired C. difficile), and most recently immunocompromised patients. Among the latter, HIV can worsen the adverse health outcomes of CDI and vice versa. In patients with CDI and HIV coinfection, higher mortality and morbidity was found in young & middle-aged adults, blacks and males, the same demographic population that is at higher risk for HIV. As with typical CDI, the coinfection was concentrated in the hospital inpatients. Of all the CDI-related deaths in USA from 1999-2010, in the 25-44 year age group, 13% had HIV infection. Of all CDI-related inpatient hospital discharges in Texas from 1999-2011, in patients 44 years and younger, 17% had concomitant HIV infection. Therefore, HIV is a possible novel emerging risk factor for CDI.^