A qualitative assessment of multi-level nutrition strategies for reducing racial and ethnic disparities in diabetes

The purpose of this study is to examine the stages of program realization of the interventions that the Bronx Health REACH program initiated at various levels to improve nutrition as a means for reducing racial and ethnic disparities in diabetes. This study was based on secondary analyses of qualitative data collected through the Bronx Health REACH Nutrition Project, a project conducted under the auspices of the Institute on Urban Family Health, with support from the Centers for Disease Control and Prevention (CDC). Local human subjects' review and approval through the Institute on Urban Family Health was required and obtained in order to conduct the Bronx Health REACH Nutrition Project. ^ The study drew from two theoretical models—Glanz and colleagues' nutrition environments model and Shediac-Rizkallah and Bone's sustainability model. The specific study objectives were two-fold: (1) to categorize each nutrition activity to a specific dimension (i.e. consumer, organizational or community nutrition environment); and (2) to evaluate the stage at which the program has been realized (i.e. development, implementation or sustainability). ^ A case study approach was applied and a constant comparative method was used to analyze the data. Triangulation of data based was also conducted. Qualitative data from this study revealed the following principal findings: (1) communities of color are disproportionately experiencing numerous individual and environmental factors contributing to the disparities in diabetes; (2) multi-level strategies that targeted the individual, organizational and community nutrition environments can appropriately address these contributing factors; (3) the nutrition strategies greatly varied in their ability to appropriately meet criteria for the three program stages; and (4) those nutrition strategies most likely to succeed (a) conveyed consistent and culturally relevant messages, (b) had continued involvement from program staff and partners, (c) were able to adapt over time or setting, (d) had a program champion and a training component, (e) were integrated into partnering organizations, and (f) were perceived to be successful by program staff and partners in their efforts to create individual, organizational and community/policy change. As a result of the criteria-based assessment and qualitative findings, an ecological framework elaborating on Glanz and colleagues model was developed. The qualitative findings and the resulting ecological framework developed from this study will help public health professionals and community leaders to develop and implement sustainable multi-level nutrition strategies for addressing racial and ethnic disparities in diabetes. ^

Encouraging adolescents toward mental health careers via website biographies.

This project designed, developed, implemented and is currently evaluating the effectiveness of an interactive, multi-media website designed to encourage adolescents to consider careers in mental health. This Web-based learning environment features biographies of mental health scientists. Evaluation is conducted in a systematic, structured way using cognitive achievement, usability (ease of use), and affective scales (e.g., fun to use) as outcome measures

Cellular dynamic simulator: an event driven molecular simulation environment for cellular physiology.

In this paper, we present the Cellular Dynamic Simulator (CDS) for simulating diffusion and chemical reactions within crowded molecular environments. CDS is based on a novel event driven algorithm specifically designed for precise calculation of the timing of collisions, reactions and other events for each individual molecule in the environment. Generic mesh based compartments allow the creation / importation of very simple or detailed cellular structures that exist in a 3D environment. Multiple levels of compartments and static obstacles can be used to create a dense environment to mimic cellular boundaries and the intracellular space. The CDS algorithm takes into account volume exclusion and molecular crowding that may impact signaling cascades in small sub-cellular compartments such as dendritic spines. With the CDS, we can simulate simple enzyme reactions; aggregation, channel transport, as well as highly complicated chemical reaction networks of both freely diffusing and membrane bound multi-protein complexes. Components of the CDS are generally defined such that the simulator can be applied to a wide range of environments in terms of scale and level of detail. Through an initialization GUI, a simple simulation environment can be created and populated within minutes yet is powerful enough to design complex 3D cellular architecture. The initialization tool allows visual confirmation of the environment construction prior to execution by the simulator. This paper describes the CDS algorithm, design implementation, and provides an overview of the types of features available and the utility of those features are highlighted in demonstrations.

Preclinical modeling of multi-drug cancer therapies

Anticancer drugs typically are administered in the clinic in the form of mixtures, sometimes called combinations. Only in rare cases, however, are mixtures approved as drugs. Rather, research on mixtures tends to occur after single drugs have been approved. The goal of this research project was to develop modeling approaches that would encourage rational preclinical mixture design. To this end, a series of models were developed. First, several QSAR classification models were constructed to predict the cytotoxicity, oral clearance, and acute systemic toxicity of drugs. The QSAR models were applied to a set of over 115,000 natural compounds in order to identify promising ones for testing in mixtures. Second, an improved method was developed to assess synergistic, antagonistic, and additive effects between drugs in a mixture. This method, dubbed the MixLow method, is similar to the Median-Effect method, the de facto standard for assessing drug interactions. The primary difference between the two is that the MixLow method uses a nonlinear mixed-effects model to estimate parameters of concentration-effect curves, rather than an ordinary least squares procedure. Parameter estimators produced by the MixLow method were more precise than those produced by the Median-Effect Method, and coverage of Loewe index confidence intervals was superior. Third, a model was developed to predict drug interactions based on scores obtained from virtual docking experiments. This represents a novel approach for modeling drug mixtures and was more useful for the data modeled here than competing approaches. The model was applied to cytotoxicity data for 45 mixtures, each composed of up to 10 selected drugs. One drug, doxorubicin, was a standard chemotherapy agent and the others were well-known natural compounds including curcumin, EGCG, quercetin, and rhein. Predictions of synergism/antagonism were made for all possible fixed-ratio mixtures, cytotoxicities of the 10 best-scoring mixtures were tested, and drug interactions were assessed. Predicted and observed responses were highly correlated (r2 = 0.83). Results suggested that some mixtures allowed up to an 11-fold reduction of doxorubicin concentrations without sacrificing efficacy. Taken together, the models developed in this project present a general approach to rational design of mixtures during preclinical drug development. ^

HEALTH MAINTENANCE ORGANIZATION ENVIRONMENT: AN EMPIRICAL STUDY OF THE RELATIONSHIP OF EXTERNAL CONTEXT AND PERFORMANCE

The research project is an extension of a series of administrative science and health care research projects evaluating the influence of external context, organizational strategy, and organizational structure upon organizational success or performance. The research will rely on the assumption that there is not one single best approach to the management of organizations (the contingency theory). As organizational effectiveness is dependent on an appropriate mix of factors, organizations may be equally effective based on differing combinations of factors. The external context of the organization is expected to influence internal organizational strategy and structure and in turn the internal measures affect performance (discriminant theory). The research considers the relationship of external context and organization performance.^ The unit of study for the research will be the health maintenance organization (HMO); an organization the accepts in exchange for a fixed, advance capitation payment, contractual responsibility to assure the delivery of a stated range of health sevices to a voluntary enrolled population. With the current Federal resurgence of interest in the Health Maintenance Organization (HMO) as a major component in the health care system, attention must be directed at maximizing development of HMOs from the limited resources available. Increased skills are needed in both Federal and private evaluation of HMO feasibility in order to prevent resource investment and in projects that will fail while concurrently identifying potentially successful projects that will not be considered using current standards.^ The research considers 192 factors measuring contextual milieu (social, educational, economic, legal, demographic, health and technological factors). Through intercorrelation and principle components data reduction techniques this was reduced to 12 variables. Two measures of HMO performance were identified, they are (1) HMO status (operational or defunct), and (2) a principle components factor score considering eight measures of performance. The relationship between HMO context and performance was analysed using correlation and stepwise multiple regression methods. In each case it has been concluded that the external contextual variables are not predictive of success or failure of study Health Maintenance Organizations. This suggests that performance of an HMO may rely on internal organizational factors. These findings have policy implications as contextual measures are used as a major determinant in HMO feasibility analysis, and as a factor in the allocation of limited Federal funds. ^

A multi-site pilot test study to measure safety climate in the university work setting

Next to leisure, sport, and household activities, the most common activity resulting in medically consulted injuries and poisonings in the United States is work, with an estimated 4 million workplace related episodes reported in 2008 (U.S. Department of Health and Human Services, 2009). To address the risks inherent to various occupations, risk management programs are typically put in place that include worker training, engineering controls, and personal protective equipment. Recent studies have shown that such interventions alone are insufficient to adequately manage workplace risks, and that the climate in which the workers and safety program exist (known as the "safety climate") is an equally important consideration. The organizational safety climate is so important that many studies have focused on developing means of measuring it in various work settings. While safety climate studies have been reported for several industrial settings, published studies on assessing safety climate in the university work setting are largely absent. Universities are particularly unique workplaces because of the potential exposure to a diversity of agents representing both acute and chronic risks. Universities are also unique because readily detectable health and safety outcomes are relatively rare. The ability to measure safety climate in a work setting with rarely observed systemic outcome measures could serve as a powerful means of measure for the evaluation of safety risk management programs. ^ The goal of this research study was the development of a survey tool to measure safety climate specifically in the university work setting. The use of a standardized tool also allows for comparisons among universities throughout the United States. A specific study objective was accomplished to quantitatively assess safety climate at five universities across the United States. At five universities, 971 participants completed an online questionnaire to measure the safety climate. The average safety climate score across the five universities was 3.92 on a scale of 1 to 5, with 5 indicating very high perceptions of safety at these universities. The two lowest overall dimensions of university safety climate were "acknowledgement of safety performance" and "department and supervisor's safety commitment". The results underscore how the perception of safety climate is significantly influenced at the local level. A second study objective regarding evaluating the reliability and validity of the safety climate questionnaire was accomplished. A third objective fulfilled was to provide executive summaries resulting from the questionnaire to the participating universities' health & safety professionals and collect feedback on usefulness, relevance and perceived accuracy. Overall, the professionals found the survey and results to be very useful, relevant and accurate. Finally, the safety climate questionnaire will be offered to other universities for benchmarking purposes at the annual meeting of a nationally recognized university health and safety organization. The ultimate goal of the project was accomplished and was the creation of a standardized tool that can be used for measuring safety climate in the university work setting and can facilitate meaningful comparisons amongst institutions.^

Objective assessment of the built environment and its relationship to physical activity and obesity

The role of physical activity in the promotion of individual and population health has been well documented in research and policy publications. Significant research activities have produced compelling evidence for the support of the positive association between physical activity and improved health. Despite the knowledge about these public health benefits of physical activity, over half of US adults do not engage in physical activity at levels consistent with public health recommendations. Just as physical inactivity is of significant public health concern in the US, the prevalence of obesity (and its attendant co-morbidities) is also increasing among US adults.^ Research suggests racial and ethnic disparities relevant to physical inactivity and obesity in the US. Various studies have shown more favorable outcomes among non-Hispanic whites when compared to other minority groups as far as physical activity and obesity are concerned. The health disparity issue is especially important because Mexican-Americans who are the fastest growing segment of the US population are disproportionately affected by physical inactivity and obesity by a significant margin (when compared to non-Hispanic whites), so addressing the physical inactivity and obesity issues in this group is of significant public health concern. ^ Although the evidence for health benefits of physical activity is substantial, various research questions remain on the potential motivators for engaging in physical activity. One area of emerging interest is the potential role that the built environment may play in facilitating or inhibiting physical activity.^ In this study, based on an ongoing research project of the Department of Epidemiology at the University of Texas M. D. Anderson Cancer Center, we examined the built environment, measured objectively through the use of geographical information systems (GIS), and its association with physical activity and obesity among a cohort of Mexican- Americans living in Harris County, Texas. The overall study hypothesis was that residing in dense and highly connected neighborhoods with mixed land-use is associated with residents’ increased participation in physical activity and lowered prevalence of obesity. We completed the following specific aims: (1) to generate a land-use profile of the study area and create a “walkability index” measure for each block group within the study area; (2) to compare the level of engagement in physical activity between study participants that reside in high walkability index block groups and those from low walkability block groups; (3) to compare the prevalence of obesity between study participants that reside in high walkability index block groups and those from low walkability block groups. ^ We successfully created the walkability index as a form of objective measure of the built environment for portions of Harris County, Texas. We used a variety of spatial and non-spatial dataset to generate the so called walkability index. We are not aware of previous scholastic work of this kind (construction of walkability index) in the Houston area. Our findings from the assessment of relationships among walkability index, physical activity and obesity suggest the following, that: (1) that attempts to convert people to being walkers through health promotion activities may be much easier in high-walkability neighborhoods, and very hard in low-walkability neighborhoods. Therefore, health promotion activities to get people to be active may require supportive environment, walkable in this case, and may not succeed otherwise; and (2) Overall, among individuals with less education, those in the high walkability index areas may be less obese (extreme) than those in the low walkability area. To the extent that this association can be substantiated, we – public health practitioners, urban designers, and policy experts – we may need to start thinking about ways to “retrofit” existing urban forms to conform to more walkable neighborhoods. Also, in this population especially, there may be the need to focus special attention on those with lower educational attainment.^

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.