Aedes aegypti, Aedes albopictus, and Dengue virus in Harris county: An estimate of risk

Recent outbreaks of dengue fever (DF) along the United States/Mexico border, coupled with the high number of reported cases in Mexico suggest that there is the possibility for DF emergence in Houston, Texas1,2. To determine the presence of DF, populations of Aedes aegypti and Aedes albopictus were identified and tested for dengue virus. Maps were created to identify "hot spots" (Figure 1) based on historical data on Ae. aegypti and Ae. albopictus, demographic information, and locations of human cases of dengue fever. BG Sentinel Traps®, in conjunction with BG Lure® attractant, octanol and dry ice, were used to collect mosquitoes, which were then tested for presence of dengue virus using ELISA techniques. All samples tested were negative for dengue virus (DV). Survival of DV ultimately comes down to whether or not it will be vectored by a mosquito to a susceptible human host. The presence of infected humans and contact with the mosquito vectors are two critical factors necessary in the establishment of DF. Historical records indicate the presence of Ae. aegypti and Ae. albopictus in Harris County, which would support localized dengue transmission if infected individuals are present.^ (1) Brunkard JM, Robles-Lopez JL, Ramirez J, Cifuentes E, Rothenberg SJ, Hunsperger EA, Moore CG, Brussolo RM, Villarreal NA, Haddad BM, 2007. Dengue fever seroprevalence and risk factors, Texas-Mexico border, 2004. Emerg Infect Dis 13: 1477-1483. (2) Ramos MM, Mohammed H, Zielinski-Gutierrez E, Hayden MH, Lopez JL, Fournier M, Trujillo AR, Burton R, Brunkard JM, Anaya-Lopez L, Banicki AA, Morales PK, Smith B, Munoz JL, Waterman SH, 2008. Epidemic dengue and dengue hemorrhagic fever at the Texas-Mexico Border: results of a household-based seroepidemiologic survey, December 2005. Am J Trop Med Hyg 78: 364-369.^

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.

An ethnographic study of home remedy use for African-American children

Background and significance. The use of herbs and other remedies by adult and elderly African-Americans has been documented. However, little is understood regarding the use of herbs for African-American children. The purpose of this study was to document and describe the historical and present day uses of herbal and other remedies, specifically for the health and illness of African-American children. This information will provide health care providers with a better understanding of their African-American patients. This information may also contribute to the emerging appreciation of indigenous uses of phytotherapeutics. ^ Methods. A focused ethnographic approach was used to describe the cultural context, including the beliefs, values, customs, and behaviors of a particular culture. The use of intensive fieldwork, including participant observation, audiotaped formal interviews, photographs, and specimen collection of plants helped to describe herb use in this population. Information on the growing, harvesting, preparation, and storage of these plant remedies, as well as the amount and dosage of these compounds was collected in a typology. Detailed information was gathered to discern how, when, and under what conditions these remedies were used and their expected results. Further data collection focused on the history of herbal use, and explanations for how and why informants thought the herbs work. ^ Setting and participants. The setting for this study was in East Texas and field work extended over the period of one year. Thirty African-Americans, age 38 to 98, were interviewed for the study. The African-American population in this area has been relatively stable, with roots dating back prior to the reconstruction period, which allowed excellent historical information. Informants were chosen by a nominated sampling technique starting with two key informants knowledgeable about the use of home remedies for children. ^ Findings. The findings of this study suggest that African-American children in East Texas have a long history of receiving herbs and home remedies for health promotion and illness. Data further suggests that there is a strong connection between spirituality and the health beliefs and practices of this community. This spiritual component underlies the accuracy of oral recall for remedies that have been used over many generations and the use of natural folk remedies. A typology of the herbal remedies was developed with folk and Latin name, herb place of origin, known scientific properties, and informant folk usage and dosage information. ^

Usage of Urinary Antigen Test for Legionella on diagnosed community-acquired pneumonia in San Antonio hospitals

Purpose. To evaluate the use of the Legionella Urine Antigen Test as a cost effective method for diagnosing Legionnaires’ disease in five San Antonio Hospitals from January 2007 to December 2009. ^ Methods. The data reported by five San Antonio hospitals to the San Antonio Metropolitan Health District during a 3-year retrospective study (January 2007 to December 2009) were evaluated for the frequency of non-specific pneumonia infections, the number of Legionella Urine Antigen Tests performed, and the percentage of positive cases of Legionnaires’ disease diagnosed by the Legionella Urine Antigen Test.^ Results. There were a total of 7,087 cases of non-specific pneumonias reported across the five San Antonio hospitals studied from 2007 to 2009. A total of 5,371 Legionella Urine Antigen Tests were performed from January, 2007 to December, 2009 across the five San Antonio hospitals in the study. A total of 38 positive cases of Legionnaires’ disease were identified by the use of Legionella Urinary Antigen Test from 2007-2009.^ Conclusions. In spite of the limitations of this study in obtaining sufficient relevant data to evaluate the cost effectiveness of Legionella Urinary Antigen Test in diagnosing Legionnaires’ disease, the Legionella Urinary Antigen Test is simple, accurate, faster, as results can be obtained within minutes to hours; and convenient because it can be performed in emergency room department to any patient who presents with the clinical signs or symptoms of pneumonia. Over the long run, it remains to be shown if this test may decrease mortality, lower total medical costs by decreasing the number of broad-spectrum antibiotics prescribed, shorten patient wait time/hospital stay, and decrease the need for unnecessary ancillary testing, and improve overall patient outcomes.^