Bacterial Source Tracking of Nonpoint Source Pollution Using GIS and DNA Fingerprinting Technologies

This study is only the second to use DNA fingerprinting technology in Maryland to identify fecal coliform sources in order to guide the implementation of water pollution control practices in a watershed with bacterial impairment. By combining the use of digital air photos and GPS with GIS, fieldwork is planned and conducted more efficiently because sample sites can be selected that accurately represent the physical environment of the study area. We can also return to the field and find our sample sites or locate new ones, even in the remotest part of the study area. It is also possible to more accurately map the data directly in the context of its physical environment, greatly increasing the quality of analysis. The integration of DNA fingerprinting techniques with GIS shows great promise for extending our capabilities to identify the controls on water quality and point sources of waterborne health hazards.

The Application of Spatial Analysis to the Public Health Understanding of Alcohol and Alcohol-Related Problems

This chapter describes research that uses spatial modeling to address pressing issues related to a public health understanding of alcohol problems and violence. First, we introduce the language of spatial analysis used in prevention work and discuss the details of spatial research that result in useful public health information, particularly in regard to alcohol-related problems. Issues such as geo-mapping, variable selection, and area definition are discussed in regard to community level occurrence of such problems. We then discuss the general context for understanding the geographic relationship between alcohol outlet density and violent crime. Finally, we give a specific example of an analysis focusing on alcohol outlets and violence. This work is related to the major goal of studying the community geography of alcohol problems by mapping the alcohol environment, relating these features of the environment to the spatial distribution of problem events, and analyzing the statistical associations between these measures and drinking behaviors.

Understanding Health Disparities through Geographic Information Systems

The emerging discipline of health geographics uses the concepts and techniques of medical geography (Meade, Florin & Gesler, 1988) together with modern automated Geographic Information Systems (GIS) methods to investigate health issues (Ricketts, Savitz, Gesler & Osborne, 1994). The main aim of this chapter is to bring the exciting potential contributions inherent in this approach to the attention of health practitioners and researchers. With the development of powerful, yet affordable geo-technologies, digital maps and visual displays are produced that can be used for research, practice and/or health policy analysis. One major advantage of this technology is that complex information can be displayed for the consumer in more intuitive, self-explanatory form. This is accomplished by linking and overlaying health data to standard census geographic areas which can be accessed quickly and flexibly from national and state agencies (Devesa, Grauman, Blor, Pennello, Hoover & Fraumeni, 1999; Kim, 1998). This chapter will illustrate how a GIS-based, multi-method approach can be applied to the study of health disparities. Using the pressing public health issue of access to kidney transplantation in California as an example, we will explore the notion of health disparities using a geographic conceptual framework for studying and understanding existing gaps in transplantations conducted. Different GIS techniques to addressing this issue are presented with a discussion of the relative advantages of each approach and a final review on how to most effectively use a GIS-based approach in studying health disparities.

Race, Class and Place

In this chapter we discuss the importance of community in public health science and practice. Community is —first and foremost— place. The science of place and its implication for health has made major strides over the past decade. The first and second international conferences on health geographics marked an achievement in that development. The argument in this chapter draws out the implication of place and community for public health science and practice.

Using GIS with Publicly Available Data for Hospital Market Share Analysis

Publicly available data of all hospital discharges has been available since Medicare changed to a case-based reimbursement system. A non-confidential version of this dataset contains a Zip Code identifier for each discharge, in addition to diagnoses, procedures, payer information, hospital charges and basic demographic data. The method for converting the raw data into a useful marketing database is described. An application of this database in conjunction with GIS is presented here. In this application, the market share of a community hospital is analyzed. A series of maps shows that geography plays an important role in hospital choice, and a linear regression model provides quantitative evidence of this pattern. Finally, bivariate maps are used for more complex analysis.

Using GIS to Unveil Distance Effects on Hospitalizations in Victoria

In this chapter, we examine travel distance and its effect on total and avoidable hospitalizations using data from the capital health region in British Columbia, Canada. We developed a GIS procedure to connect distance-to-hospital with socioeconomic contexts of patient locations. The procedure includes geo-coding hospital locations and patient locations to determine travel distance for each hospitalization, generating several geographic barriers, such as mountain crossing, to assess their impedance, and linking patient neighborhood locations to socioeconomic variables of their locations. It was found that the overall hospitalization rates have an inverse relationship with distance-to-hospital, and living too close to a hospital may encourage utilization of hospital resources. Even though low-income patients are more likely to be hospitalized for avoidable conditions, the income effect influences different dimensions to those affected by the distance effect. Thus, it explicitly confirms the two aspects of the inverse of healthcare law that work simultaneously: those with lower socioeconomic status and those living in greater distance to hospitals tend to be less likely to access hospital care. Furthermore, the inclusion of physical barriers to our evaluation enhanced our understanding of local conditions and how they may affect hospitalizations.

Using Hierarchical Nearest Neighbor Analysis and Animation to Investigate the Spatial and Temporal Patterns of Raccoon Rabies in West Virginia

One of the most powerful uses of GIS in the field of public health is as an exploratory data analysis tool. By combining the three post-input defining components of a GIS (data manipulation, data investigation, data analysis), the spatial understanding of a disease can be furthered by identifying patterns of cases, or associations between disease and other spatial phenomena (such as elevation). This chapter sets the groundwork for one such exploratory tool that could be used to identify the spatial and temporal patterns of an infectious disease. The disease in question is raccoon rabies in West Virginia during 1999-2000. The exploratory tool, animation, has the potential to give insights into an evolving disease pattern that current spatial cluster techniques could miss. The current raccoon rabies epizootic presents a complex spatial surface as multiple disease foci may be present. Added to this could be a residual “background” or enzootic level of rabies. In order to reduce the impact of multiple foci, an appropriate “scale” of animation is needed. This scale has to be of a small enough geographic area that only one disease focus is considered, and is of practical use so that other meaningful spatial information (such as land cover or elevation) can be interpreted. The purpose of this chapter is to decide on an appropriate method of identifying this scale of animation for an infectious disease of this type. This chapter will select one commonly used technique, Nearest Neighbor Hierarchical (NNH) spatial clustering, to identify the correct scale and location on which to perform an animation. NNH spatial clustering will be applied to three combinations of Raccoon Rabies data for West Virginia, for 1999, 2000 and both years combined. NNH cluster analysis will also be performed on a four-county area identified as having the highest intensity of rabies cases in the state. These results will then be compared to a preliminary animation of rabies cases in West Virginia from which subjects were asked to identify dynamically evolving disease clusters. An animation was also run for the same area of high disease intensity. Cluster and animation results were compared for similarities. It was found that a spatial cluster technique, such as NNH spatial clustering, provides an adequate means of identifying the scale and location on which a more sophisticated animation can be based. The chapter concludes with a discussion of how, once a scale has been decided, a more sophisticated animation can be constructed and ultimately used to guide the placement of interventions such as oral vaccine barriers.

Geographical Analysis of Disease in Small Areas Using Hierarchical Bayesian Models

The purpose of this chapter is to review and compare two techniques to map the mortality risk of a disease in small geographical areas. The first one is a classical approach consisting of mapping standardized mortality ratios, which are maximum likelihood estimates of the relative risk under a Poisson model of death counts. In a second step, the authors consider a Bayesian approach that assumes a hierarchical model where the death counts follow a Poisson distribution conditioned by the prior information. These methods have been applied to the study of geographical variation in men’s lung cancer mortality from 1978 to 1998 in Galicia, Spain. Mapping mortality using the first method has important drawbacks, and there are difficulties to distinguish the mortality pattern. The Bayesian methodology produces smoother maps with a clear mortality pattern and has many advantages over the classical approach.

Community Breast Cancer Mapping in Huntington, Long Island

The Huntington Breast Cancer Action Coalition (HBCAC) recently completed a survey of town residents regarding breast cancer. This chapter reviews how this community group relied upon a network of volunteers and community goodwill to survey local breast cancer patterns and the issues HBCAC confronted in mapping those results. The chapter explains how community-mapping projects differ from mapping projects directed by scientists, private corporations and government agencies. Community organizations often approach maps with different perspectives and goals than these traditional mapping agencies. This chapter emphasizes the significance of the community perspective for understanding and addressing breast cancer. HBCAC is using ESRI’s ArcView software to map breast cancer patterns and to overlay various environmental themes, such as local toxic sites, to better understand local breast cancer patterns.

Spatial Cluster Analysis for Etiological Research and Identification of Socio-environmental Risk Factors

This chapter describes the use of disease clustering methods using diarrheal disease data from a rural area of Bangladesh for which the authors created a household-level GIS database. Understanding distributions of diseases in space and time can be useful for etiologic research and socio-environmental risk factor identification. Disease clustering techniques are not only useful as etiological research tools for chronic diseases but also for infectious diseases. The magnitude of clustering in different areas can assist with the generation of hypotheses about the underlying socio-environmental causes of the clusters. Once clusters are identified, studies can then focus on the socio-environmental characteristics of the areas where clusters are found.