AN ECONOMIC ANALYSIS OF THE DEMAND FOR CARE IN A FREE-STANDING EMERGENCY CENTER

Free-standing emergency centers (FECs) represent a new approach to the delivery of health care which are competing for patients with more conventional forms of ambulatory care in many parts of the U.S. Currently, little is known about these centers and their patient populations. The purpose of this study, therefore, was to describe the patients who visited two commonly-owned FECs, and determine the reasons for their visits. An economic model of the demand for FEC care was developed to test its ability to predict the economic and sociodemographic factors of use. Demand analysis of other forms of ambulatory services, such as a regular source of care (RSOC), was also conducted to examine the issues of substitution and complementarity.^ A systematic random sample was chosen from all private patients who used the clinics between July 1 and December 31, 1981. Data were obtained by means of a telephone interview and from clinic records. Five hundred fifty-one patients participated in the study.^ The typical FEC patient was a 26 year old white male with a minimum of a high school education, and a family income exceeding $25,000 a year. He had lived in the area for at least twenty years, and was a professional or a clerical worker. The patients made an average of 1.26 visits to the FECs in 1981. The majority of the visits involved a medical complaint; injuries and preventive care were the next most common reasons for visits.^ The analytic results revealed that time played a relatively important role in the demand for FEC care. As waiting time at the patients' regular source of care increased, the demand for FEC care increased, indicating that the clinic serves as a substitute for the patients' usual means of care. Age and education were inversely related to the demand for FEC care, while those with a RSOC frequented the clinics less than those lacking such a source.^ The patients used the familiar forms of ambulatory care, such as a private physician or an emergency room in a more typical fashion. These visits were directly related to the age and education of the patients, existence of a regular source of care, and disability days, which is a measure of health status. ^

Integrins in human T cell function: Transdominant regulation, conformational constraints, and intracellular effectors

Integrin adhesion molecules have both positive and negative potential in the regulation of peripheral blood T cell (PB T cell) activation, yet their mechanism of action in the mediation of human T lymphocyte function remains largely undefined. The goals of this study then were to elucidate integrin signaling mechanisms in PB T cells.^ By ligating $\beta$1 integrins with mAb 18D3, it was demonstrated that costimulation of PB T cell proliferation induced by coimmobilizing antibodies specific for $\beta$1, $\beta$2, and $\beta$7 integrin subfamilies in conjunction with the anti-CD3 mAb OKT3 was inhibited. Costimulation of T cell proliferation induced by non-integrins CD4, CD26, CD28, CD44, CD45RA, or CD45RO was unaffected. Inhibition of costimulation correlated with diminished IL-2 production. In his manner, $\beta$1 integrins could regulate heterologous integrins of the $\beta$2 and $\beta$7 subfamilies in a transdominant fashion. It was also demonstrated that integrin costimulation of T cell activation was acutely sensitive to the structural conformation of $\beta$1 integrins. Using the cyclic hexapeptide CWLDVC (TBC772, which is based on the $\alpha4\beta1$ integrin binding site in fibronectin) in soluble form, it was shown that integrins locked into a conformation displaying a neo-epitope called the ligand induced binding site (LIBS) recognized by mAb 15/7 were inhibited from sending mitogenic signals to T cells. When BSA-conjugated TBC772 was coimmobilized with anti-CD3 mAb OKT3, costimulation of proliferation occurred. This suggested that temporally uncoupling integrin receptor occupancy from receptor crosslinking inhibited $\beta$1 integrin signaling mechanisms. When subsets of PB T cells were examined to determine those initially activated by integrins within 6 hours of activation, costimulation induced intracellular accumulation of IL-2 predominantly in the CD4$\sp+$ and CD45RO$\sp+$ T cell subsets. This was similar to a number of PB T cell costimulatory molecules including CD26, CD43, CD44. Only CD28 costimulated IL-2 production from both CD45RA$\sp+$ and CD45RO$\sp+$ subpopulations.^ The GTPase Rho has been implicated in regulating integrin mediated stress fiber formation and anchorage dependent growth in fibroblasts, so studies were initiated to determine if Rho played a role in integrin dependent T cell function. In order to perform this, a technique based on scrape-loading was developed to incorporate macromolecules into PB T cells that maintained their functional activity. With this technique, C3 exoenzyme from Clostridium botulinum was incorporated into PB T cells. C3 ADP-ribosylates Rho proteins on Asn$\sp{41},$ which is in close proximity to the Rho effector domain, rendering it inactive. It was demonstrated that functional Rho is not required for basal or upregulated PB T cell adhesion to $\beta$1 integrin substrates, however PB T cell homotypic aggregation induced by PMA, which is an event mediated predominantly by the integrin $\rm\alpha L\beta2,$ was delayed. PB T cells lacking Rho function displayed altered cell morphology on $\beta$1 integrin ligands, producing stellate, dendritic-like pseudopodia. Rho activity was also found to be required for integrin dependent costimulation of proliferation. When intracellular accumulation of IL-2 was measured, inactivation of Rho prevented both integrin and CD28 costimulatory activity. Rho was identified to lie upstream of signals mediating PKC activation and Ca$\sp{++}$ fluxes, as PMA and ionomycin activation of PB T cells was unaffected by the inactivation of Rho. ^

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.