The Safe Drinking Water Act (SDWA) was established to protect consumers from potential exposure to over 90 water contaminants. Each contaminant is assigned a health-based standard meant to reflect the maximum level at which an adverse human health outcome is unlikely; measurements beyond that level have greater potential to result in an adverse health outcome. While extensive research has been done on the human health implications of water contaminants, few studies have specifically examined the risk to fetal health under real world monitoring conditions. Therefore, the objective of this study is to assess whether drinking water violations are related to fetal health in the Commonwealth of Virginia, by examining the association between SDWA violations and preterm birth (PTB), low birth weight (LBW), and term-low birth weight (tLBW). Singleton births (n=665,984) occurring between 2007 and 2015 in Virginia were geocoded and assigned to their corresponding water service area. Health-based (HB) and monitoring and reporting (MR) violations for 12 contaminants were acquired from the USEPA Safe Drinking Water System, and exposure to contaminants was defined at the service area level to limit exposure misclassification. A logistic regression model for each birth outcome was performed to evaluate potential relationships with water contaminants. When examining the relationship between individual monitoring and reporting violations and PTB, Nitrate-Nitrite and Disinfectant Byproducts Stage 2 violations were both positively associated with the birth outcome. When examining the relationship between health-based violations and birth outcomes, the total coliform rule was negatively associated with tLBW. These findings indicate that monitoring and reporting requirements may need to be more stringent to reduce MR violation occurrence. / Master of Science / The Safe Drinking Water Act (SDWA) was established to protect consumers from potential exposure to over 90 water contaminants. Each contaminant is assigned a health-based standard, called the maximum contaminant level (MCL), meant to reflect the maximum level at which an adverse human health outcome is unlikely; measurements beyond that level have greater potential to result in an adverse health outcome. If a contaminant exceeds the MCL or if the water system fails to treat contaminants, then a health-based violation is issued. These health-based violations are a good indication of the water quality within a public water system. In addition to meeting these health-based requirements, public water systems are required to perform regular monitoring and reporting. When a system fails to evaluate water samples or report results, a monitoring and reporting violation is issued. While extensive research has been done on the human health implications of water contaminants, few studies have specifically examined the risk to fetal health under real world monitoring conditions. Therefore, the objective of this study is to assess whether drinking water violations (health-based and monitoring and reporting) are related to fetal health in the Commonwealth of Virginia, by examining the association between SDWA violations and preterm birth (PTB), low birth weight (LBW), and term-low birth weight (tLBW). Singleton births (n=665,984) occurring between 2007 and 2015 in Virginia were geocoded and assigned to their corresponding water service area. Health-based and monitoring and reporting (MR) violations for 12 contaminants were acquired from the USEPA Safe Drinking Water Information System, and exposure to contaminants was defined at the service area level to limit exposure misclassification. A logistic regression model for each birth outcome was performed to evaluate potential relationships with water contaminants. When examining the relationship between individual monitoring and reporting violations and PTB, Nitrate-Nitrite and Disinfectant Byproducts Stage 2 violations were both positively associated with PTB. When examining the relationship between health-based violations and birth outcomes, the total coliform rule was negatively associated with tLBW. These findings indicate that monitoring and reporting requirements may need to be more stringent to reduce MR violation occurrence.

Geographic utilization patterns of nursing homes were examined by comparing patient origin with facility location. The 1985 Patient Origin Study produced by the Virginia Department of Health was used to see if there are predictable geographic patterns of patient migration to nursing homes. A random sample of thirty nursing homes was taken from all facilities in Virginia in the 1985 study. The migration data were used to determine median migration distances, to investigate decreases in utilization with distance from a facility, and to distinguish spatial markets of nursing homes. Comparisons were made between urban and rural facilities to see how factors of limited availability and children-as-decision makers affect the migration patterns. The findings show that there is a distinct pattern of decreasing utilization of nursing homes with distance from the facility. Both urban and rural nursing homes primarily serve the immediate environment in which they are located. Maps of patient origins show that there are service areas for nursing homes which are modified by population distribution and physical geography. General findings show that location is crucial for nursing home utilization. The existence of a facility generates need awareness and utilization throughout the immediate population. On the average, twenty-six percent of the patients at a nursing home come from the same zip code in which the nursing home is located. More than half the patients come from within eight miles of a facility. Eighty-three percent of the patients come from within 26 miles. The ratio of out-of-region migration to urban nursing homes is significantly higher than that for rural nursing homes. The results of this thesis can be used to predict utilization patterns of nursing homes. The findings also have implications for Medicaid budgeting because they show geographic, demographic, and economic factors which affect nursing home utilization rates. / Master of Science

Lyme disease has been a growing problem in the United States over the last few decades, and is currently the most common vector-borne disease in the country. This research evaluates the land cover within specified counties of northern Virginia to determine if a correlation exists between forest fragmentation, suburbanization, and cases of human Lyme disease as has been demonstrated in other Lyme endemic regions in the United States. Few studies have focused specifically on northern Virginia when considering the impacts of land cover change on Lyme disease. Discovered through the use of geospatial and statistical analysis, the cluster of Lyme disease cases in northern Virginia are associated with forest fragmentation within the study region, which creates an ideal habitat for black-legged ticks and the white-footed mouse, allowing for an increase in Lyme disease transfer from vector to humans. The goal is for the research findings to be applicable to other regions with similar land cover types. Regions with similar characteristics would then be able to recognize the potential risk of human Lyme disease and implement ways to reduce the Lyme disease risk associated with suburban development. The purpose of this study is to answer the following research questions: 1) How has the spatial distribution of Lyme disease in Northern Virginia changed since 2005 with respect to land cover? 2) Which suburban communities are more at risk for Lyme disease when considering their land cover types and the increasing spatial distribution of Lyme disease? / Master of Science / Lyme disease has been a growing problem in the United States over the last few decades, and is currently the most common vector-borne disease in the country. This research evaluates the land cover within specified counties of northern Virginia to determine if a correlation exists between forest fragmentation, suburbanization, and cases of human Lyme disease as has been demonstrated in other Lyme endemic regions in the United States. Few studies have focused specifically on northern Virginia when considering the impacts of land cover change on Lyme disease. Discovered through the use of geospatial and statistical analysis, the cluster of Lyme disease cases in northern Virginia are associated with forest fragmentation within the study region, which creates an ideal habitat for black-legged ticks and the white-footed mouse, allowing for an increase in Lyme disease transfer from vector to humans. The goal is for the research findings to be applicable to other regions with similar land cover types. Regions with similar characteristics would then be able to recognize the potential risk of human Lyme disease and implement ways to reduce the Lyme disease risk associated with suburban development. The purpose of this study is to answer the following research questions: 1) How has the spatial distribution of Lyme disease in Northern Virginia changed since 2005 with respect to land cover? 2) Which suburban communities are more at risk for Lyme disease when considering their land cover types and the increasing spatial distribution of Lyme disease?

Virginia's temperate climate is suitable for several mosquito species capable of transmitting pathogens to humans. In southwest Virginia, La Crosse encephalitis and West Nile fever are most prominent. The objective of this research, which uses the Health Belief Model (HBM) as a theoretical framework, is to assess knowledge of mosquito-borne disease in southwest Virginia, as well as perceptions and practices of mosquito prevention. Given that several cases of La Crosse encephalitis have been reported in Wise and Tazewell counties, they were selected as study sites to conduct surveys. Five demographic and socioeconomic variables (gender, age, income, education level and length of time one has lived in the county) were used as predictor variables in logistic regression analyses. Gender, age, and length of residence time in the county were found to be statistically significant predictors of specific health-related behaviors. Within the framework of the HBM, barriers to removing standing water around the home and wearing insect repellent were highlighted. Knowledge of mosquito-borne diseases within the area was generally low, with only one individual correctly identifying La Crosse encephalitis as a threat in the region. Higher numbers (6%) were aware of West Nile virus, while 4% reported malaria in the region, demonstrating a disconnect between actual and perceived risk. These results can enhance existing public health programs by increasing knowledge, addressing public uncertainty about insect repellent safety, and addressing ways to make recommended practices more effective with the knowledge of how different aspects are perceived by varying groups within the community. / Master of Science

WN virus has spread for over 60 years creating endemic and epidemic areas throughout Africa, Asia, and Europe, affecting human, bird, and equine populations. Its 1999 appearance in New York shows the ability of the virus to cross barriers and travel great distances, emerging into new territories previously free of infection. Spreading much faster than expected, WN virus has infected thousands of birds, equine, and humans throughout the conterminous United States (US). Case and serological studies performed in the Eastern hemisphere prior to 1999 offer detailed descriptions of endemic and epidemic locations in regards to geography, land cover, land use, population, climate, and weather patterns. Based on the severity of WN activity within each study area, the patterns associated with these environmental factors allow for the identification of values associated with different levels of risk. We can then model the landscape of the disease within the US and identify areas of high risk for infection. State and county public health officials can use this model as a decision-making tool to allocate funding for disease prevention and control. Dynamic factors associated with increased transmission, such as above average temperature and precipitation, can be closely monitored and measures of prevention can be implemented when necessary. In turn, detailed information from higher resolution analyses can be documented to an online GIS (Geographic Information System) that would contribute to a global collaboration on outbreaks and prevention of disease. / Master of Science

This study evaluates the roles of temperature variability and disease awareness in the emergence of Chagas disease (American trypanosomiasis). Chagas disease is endemic in Latin America and primarily spreads to humans directly via the triatomine vector. Hosts for most triatomine species are mainly rodents and occasionally dogs. The disease itself is caused by a parasitic protozoan, Trypanosoma cruzi (T. cruzi) which is found in the triatomine's feces and is often spread while the triatomine is consuming a blood meal. T. cruzi from feces enters the body via an abrasion on the skin, the mucous membranes, conjunctivae, or through consumption. To determine the risk of Chagas disease transmission one must define qualities that make the triatomine an effective disease vector as well as investigate the level of disease awareness among physicians and the population within the vector's range. This thesis maps triatomine species within the U.S. that harbor T. cruzi naturally and that exhibit qualities of domesticity. These qualities are defined by whether the species bites humans and dogs as well as reports that the species has been found in the domestic setting. Ranges illustrating temperature thresholds for increased triatomine activity for 2000 and 2030 are also depicted. Additionally, outcomes of a physician survey are presented to gauge the status of Chagas disease awareness in areas at higher risk for disease transmission. Results reveal limited consideration of Chagas disease in physician diagnosis despite the higher risk range which extends through the southern U.S. and is predicted to expand significantly by 2030. / Master of Science

Disease maps are powerful tools for depicting spatial variations in disease risk and its underlying drivers.  However, producing effective disease maps requires careful consideration of the statistical and spatial properties of the disease data. In fact, the choice of mapping method influences the resulting spatial pattern of the disease, as well as the understanding of its underlying population characteristics. New developments in mapping methods and software in addition to continuing improvements in data quality and quantity are requiring map-makers to make a multitude of decisions before a map of disease burdens can be created. The impact of such decisions on a map, including the choice of appropriate mapping method, not been addressed adequately in the literature. This research demonstrates how choice of mapping method and associated parameters influence the spatial pattern of disease. We use four different disease-mapping methods – unsmoothed choropleth maps, smoothed choropleth maps produced using the headbanging method, smoothed kernel density maps, and smoothed choropleth maps produced using spatial empirical Bayes methods and 5-years of zip code level HIV incidence (2007- 2011) data from Dallas and Tarrant Counties, Texas. For each map, the leading population characteristics and their relative importance with regards to HIV incidence is identified using a regression analysis of a CDC recommended list of socioeconomic determinants of HIV. Our results show that the choice of mapping method leads to different conclusions regarding the associations between HIV disease burden and the underlying demographic and socioeconomic characteristics. Thus, the choice of mapping method influences the patterns of disease we see or fail to see. Accurate depiction of areas of high disease burden is important for developing and targeting appropriate public health interventions.

published_or_final_version / Geography / Master / Master of Philosophy

Pang, Tak Ting Phoebe. / "September 2010." / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 153-161). / Abstracts in English and Chinese. / Acknowledgement --- p.I / Abstract --- p.II / 摘要 --- p.IV / List of Figures --- p.V / List of Tables --- p.VII / Abbreviations --- p.VIII / Chapter CHAPTER ONE --- INTRODUCTION --- p.1 / Chapter 1.1 --- Historical perspective of tuberculosis --- p.1 / Chapter 1.1.1 --- Sanatorium care --- p.2 / Chapter 1.1.2 --- Vaccination --- p.2 / Chapter 1.1.3 --- Drug treatment --- p.3 / Chapter 1.1.4 --- Transmission dynamics of tuberculosis --- p.3 / Chapter 1.1.5 --- Resurgence of tuberculosis --- p.4 / Chapter 1.2 --- Current global and local tuberculosis epidemiology --- p.6 / Chapter 1.2.1 --- "Tuberculosis and HIV/AIDS, drug resistance in the world" --- p.6 / Chapter 1.2.2 --- Global epidemiology of tuberculosis --- p.9 / Chapter 1.2.3 --- Local epidemiology of tuberculosis --- p.9 / Chapter 1.2.4 --- "Tuberculosis, HIV/AIDS and drug resistance in Hong Kong" --- p.14 / Chapter 1.2.5 --- Approaches in studying tuberculosis epidemiology --- p.15 / Chapter 1.3 --- Determinants of tuberculosis epidemiology --- p.17 / Chapter 1.3.1 --- TB determinants in the triad of epidemiology --- p.17 / Chapter 1.3.2 --- Rise of spatial epidemiology --- p.18 / Chapter 1.4 --- Recent developments of spatial epidemiology --- p.21 / Chapter 1.4.1 --- Spatial epidemiology and infectious disease --- p.21 / Chapter 1.4.2 --- Disease mapping --- p.22 / Chapter 1.4.3 --- Geographic information system --- p.22 / Chapter 1.4.4 --- Statistics in spatial epidemiology --- p.23 / Chapter CHAPTER TWO --- LITERATURE REVIEW --- p.24 / Chapter 2.1 --- Objective of literature review --- p.24 / Chapter 2.2 --- Literature search --- p.25 / Chapter 2.2.1 --- Strategy for literature search --- p.25 / Chapter 2.2.2 --- Results for literature search --- p.25 / Chapter 2.3 --- Spatial perspective in tuberculosis epidemiology --- p.31 / Chapter 2.3.1 --- Mapping the spatial pattern --- p.32 / Chapter 2.3.2 --- Understanding the spatial pattern --- p.32 / Chapter 2.3.3 --- Modelling the spatial pattern --- p.33 / Chapter 2.4 --- Neighbourhood determinants of tuberculosis --- p.34 / Chapter 2.4.1 --- TB and demographics --- p.35 / Chapter 2.4.2 --- TB and socioeconomic status --- p.36 / Chapter 2.4.3 --- TB and the environment --- p.38 / Chapter 2.4.4 --- TB and care factors --- p.40 / Chapter 2.5 --- Techniques applied in studying tuberculosis epidemiology --- p.41 / Chapter 2.5.1 --- Constructing spatial data --- p.41 / Chapter 2.5.2 --- Disease maps used --- p.45 / Chapter 2.5.3 --- "Integrated approach using spatial statistics, conventional statistics and molecular analysis" --- p.52 / Chapter 2.6 --- Research gap and thesis objectives --- p.55 / Chapter 2.6.1 --- Research gap --- p.55 / Chapter 2.6.2 --- Thesis objective --- p.56 / Chapter CHAPTER THREE --- METHODOLOGY --- p.57 / Chapter 3.1 --- Rationale and approach --- p.57 / Chapter 3.1.1 --- Logical flow of the study --- p.57 / Chapter 3.1.2 --- Methodological flow of the study --- p.60 / Chapter 3.2 --- Choosing spatial units --- p.63 / Chapter 3.3 --- Data collection --- p.69 / Chapter 3.3.1 --- Tuberculosis data --- p.70 / Chapter 3.3.2 --- Spatial data --- p.70 / Chapter 3.3.3 --- Neighbourhood data --- p.70 / Chapter 3.4 --- Data manipulation --- p.73 / Chapter 3.4.1 --- Tuberculosis data --- p.73 / Chapter 3.4.2 --- Spatial data --- p.74 / Chapter 3.4.3 --- Neighbourhood data --- p.74 / Chapter 3.5 --- Centrographic analysis --- p.76 / Chapter 3.5.1 --- Types of centrographic statistics --- p.76 / Chapter 3.6 --- Exploratory spatial data analysis --- p.78 / Chapter 3.6.1 --- Spatial proximity matrix --- p.78 / Chapter 3.6.2 --- Moran's Index --- p.79 / Chapter 3.6.3 --- Local Indicator of Spatial Association --- p.79 / Chapter 3.7 --- Explanatory analysis --- p.81 / Chapter 3.7.1 --- Selecting variables for modelling --- p.82 / Chapter 3.7.2 --- Ordinary linear regression --- p.82 / Chapter 3.7.3 --- Geographically weighted regression --- p.83 / Chapter CHAPTER FOUR --- RESULTS --- p.85 / Chapter 4.1 --- Overview --- p.85 / Chapter 4.1.1 --- Individual level --- p.85 / Chapter 4.1.2 --- Aggregated level --- p.89 / Chapter 4.2 --- Results for centrographic analysis --- p.97 / Chapter 4.3 --- Results for exploratory spatial data analysis --- p.101 / Chapter 4.3.1 --- Results for Moran's Index --- p.101 / Chapter 4.3.2 --- Results for Local Indicator of Spatial Association --- p.103 / Chapter 4.4 --- Results for explanatory analysis --- p.110 / Chapter 4.4.1 --- Correlation analysis and variables selection --- p.110 / Chapter 4.4.2 --- Results for ordinary linear regression --- p.114 / Chapter 4.4.3 --- Results for geographically weighted regression --- p.116 / Chapter CHAPTER FIVE --- DISCUSSION --- p.131 / Chapter 5.1 --- Preamble --- p.131 / Chapter 5.1.1 --- Methods overview --- p.132 / Chapter 5.1.2 --- Results overview --- p.132 / Chapter 5.1.3 --- Layout of this chapter --- p.134 / Chapter 5.2 --- Neighbourhood determinants in relation to TB --- p.135 / Chapter 5.2.1 --- Crowding and tuberculosis --- p.135 / Chapter 5.2.2 --- Poverty and tuberculosis --- p.137 / Chapter 5.2.3 --- Immigrants and tuberculosis --- p.138 / Chapter 5.2.4 --- Marital status and tuberculosis --- p.139 / Chapter 5.2.5 --- Implication of local parameter estimates of association --- p.140 / Chapter 5.3 --- Study design for spatial epidemiology --- p.142 / Chapter 5.3.1 --- Application of spatial dependence in spatial epidemiology --- p.142 / Chapter 5.3.2 --- Choosing spatial units --- p.144 / Chapter 5.4 --- Methodological concern in this study --- p.146 / Chapter 5.4.1 --- Concern over disease mapping --- p.146 / Chapter 5.4.2 --- Application of geographically weighted regression --- p.148 / Chapter 5.5 --- Limitation of the study --- p.150 / Chapter 5.6 --- Conclusion --- p.152 / REFERENCE --- p.153 / APPENDIX --- p.162 / Appendix 1 How to calculate TB SNR? --- p.162 / Appendix 2 How GWR works? --- p.164 / Appendix 3 What is AIC? --- p.165 / Appendix 4 How Monte Carlo test works? --- p.166 / Appendix 5 List of GWR output --- p.167

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