Expanding cities and expanding waistlines: Urban sprawl and its impact on obesity, how the adoption of smart growth statutes can build healthier and more active communities

For decades, American towns and cities have expanded from their established cores into the surrounding rural areas. U.S. population has grown but the land that we use has grown at an even faster pace, and our country has now become a largely suburban nation. Americans moved and continue to move out to the suburbs in search of better lives – for clean and healthy living, for larger homes, and for better resources. In many ways and for many Americans, the suburban lifestyle has been a great success. However, there are some unintended public health consequences of urban sprawl that must be recognized. As most Americans no longer walk or bicycle, increasingly sedentary lifestyles now contribute to greater levels of obesity, diabetes and other associated chronic diseases. This thesis reviewed the impacts of urban sprawl on the public's health specifically, as sprawl relates to decreased physical activity rates and increased obesity rates. The health effects and their connection with sprawl were identified, and available evidence was reviewed. Finally, this thesis described legal and policy solutions for addressing the health effect through improving the design of our built environment and by recommending that governments adopt and implement Smart Growth statutes that incorporate a public health component and require public health involvement. ^

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.^

Prevalence of depressive symptoms in urban middle and high school Hispanic and African American students

This cross-sectional study examined the prevalence of depressive symptoms in urban Hispanic and African American middle and high school students (N=1,292) using data collected from a multi-component, multi-wave violence and substance use intervention program targeted at a large urban school district in Texas. Chi-square analysis was used to examine differences in race/ethnicity, gender, grade level and whether or not a student had been held back/repeated a grade in school. Univariate and multivariate logistic regression were used to analyze the association between depressive symptoms and demographic variables. Being female and being held back/repeating a grade was significantly associated with depressive symptoms in both univariate and multivariate analyses. Overall 16% of the students reported depressive symptoms; Hispanic youth had a higher prevalence of depressive symptoms (16.8%) than the African American youth (14.8%). Minority females and those who had been held back/repeated a grade reported a prevalence of 19.4% and 21.2%, respectively. Further research is needed to understand why Hispanic youth continue to report a higher prevalence of depressive symptoms than other minorities. Additionally research is required to further explore the association between academic performance and depressive symptoms in urban minorities, specifically the effect of being held back/repeating a grade.^

AN ASSESSMENT OF AFLATOXIN B1, AND OTHER SELECTED VARIABLES, IN A RURAL ENVIRONMENT AND ASSOCIATED CANCER RISK (TEXAS)

A study to assess possible exposure to carcinogenic metabolites (aflatoxins) from a mold Aspergillus flavus has been made in a rice producing area of Brazoria County, Texas. One hundred samples of unmilled rice were analyzed by thin-layer chromatography (TLC) for the amount of aflatoxin produced by the mold during rice growth and storage. Two well water samples and two rice elevator dust samples were also checked for possible aflatoxin content. The mortality rates from gastrointestinal and urinary tract cancers in the rice-growing part of the county were compared with mortality rates in the nonrice-producing areas of the same county.^ This study was an outgrowth of an earlier investigation by Cech and co-workers in Brazoria County which focused on environmental differences, specifically on the quality of drinking water in the former residences of decedents from primary liver cancer. It also compared subjects who died from other causes. The author of this dissertation participated in this phase of the overall investigation by performing some of the chemical analyses and by preparing synographic maps of water quality, and thus, part of those results from the early phase is also included in this manuscript.^ No aflatoxin was detected by TLC methods. However, when extracts of rice dust were checked for mutagenesis by the Ames Salmonella-microsome assay as a supplement to the TLC analysis, the result suggested that these dusts might have contained mutagenic material. The age-adjusted mortality rates in the rice-growing area were higher than those in the comparison area for both male and female gastrointestinal tract cancer and for male urinary tract cancer, but the differences were not statistically significant. ^

RUNOFF FROM COAL PILES: EFFECTS ON THE ENVIRONMENT AND THE PUBLIC'S HEALTH

The use of coal for fuel in place of oil and natural gas has been increasing in the United States. Typically, users store their reserves of coal outdoors in large piles and rainfall on the coal creates runoffs which may contain materials hazardous to the environment and the public's health. To study this hazard, rainfall on model coal piles was simulated, using deionized water and four coals of varying sulfur content. The simulated surface runoffs were collected during 9 rainfall simulations spaced 15 days apart. The runoffs were analyzed for 13 standard water quality parameters, extracted with organic solvents and then analyzed with capillary column GC/MS, and the extracts were tested for mutagenicity with the Ames Salmonella microsomal assay and for clastogenicity with Chinese hamster ovary cells.^ The runoffs from the high-sulfur coals and the lignite exhibited extremes of pH (acidity), specific conductance, chemical oxygen demand, and total suspended solids; the low-sulfur coal runoffs did not exhibit these extremes. Without treatment, effluents from these high-sulfur coals and lignite would not comply with federal water quality guidelines.^ Most extracts of the simulated surface runoffs contained at least 10 organic compounds including polycyclic aromatic hydrocarbons, their methyl and ethyl homologs, olefins, paraffins, and some terpenes. The concentrations of these compounds were generally less than 50 (mu)g/l in most extracts.^ Some of the extracts were weakly mutagenic and affected both a DNA-repair proficient and deficient Salmonella strain. The addition of S9 decreased the effect significantly. Extracts of runoffs from the low-sulfur coal were not mutagenic.^ All extracts were clastogenic. Extracts of runoffs from the high-sulfur coals were both clastogenic and cytotoxic; those from the low-sulfur coal and the lignite were less clastogenic and not cytotoxic. Clastogenicity occurred with and without S9 activation. Chromosomal lesions included gaps, breaks and exchanges. These data suggest a relationship between the sulfur content of a coal, its mutagenicity and also its clastogenicity.^ The runoffs from actual coal piles should be investigated for possible genotoxic effects in view of the data presented in this study.^

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.