stems partly from the fragmentation of data, partly from the fact that many conditions are never reported, and partly from the responsibility of government entities to protect patient confidentiality. There is a fear that revealing specific locations, even to reputable researchers under IRB scrutiny, could compromise the privacy of individual patients. In the case of HIV/AIDS, at least early in the epidemic, the CDC expressed concern that identifying a town as a “hotspot” could result in stigmatization of that town. However, it has never been demonstrated—and is, in fact, implausible—that individuals would be identifiable from data collected at the block scale or the census tract scale. This is particularly the case if data are released only to qualified researchers who have passed appropriate training courses and have no inherent interest in identifying individuals. The Health Insurance Portability and Accountability Act (HIPAA) was designed, in part, to ensure that mandatory standards are established to safeguard the privacy of individually identifiable health information (Hobson, 1997; HHS, 2006)—so far, HIPAA seems to have imposed little constraint on biomedical and epidemiological research. The analytical focus for GIS analysis is on aggregate data patterns rather than on a single data point at a specific location.

The lack of available data and a concern for the environmental sources of disease led to an important report by the Pew Environmental Health Commission (Pew, 2000) that made a strong case for a national environmental health “tracking network” to link environmental sources of disease with resulting health conditions (see Box 7.3). The EPA and the Department of Homeland Security signed a Memorandum of Understanding in 2004 to move in the direction of coordinating data to establish such a system. It is crucial that such a system include geographically referenced health data.


Both infectious and noninfectious diseases vary geographically at scales ranging from very local to global. Some of this variation may be random, and there are inferential tests of spatial randomness. For the variation that is not random, the reasons for that variation include environmental factors. One of the major purposes of GIS, remote sensing, and spatial analysis is not only to describe the variation but also to explain it in terms of environmental variables. This requires that earth and public health scientists collaborate to develop spatially and temporally accurate models for predicting disease distribution that incorporate layers of geological, geographic, and socioeconomic data.

Research to link earth science and public health in the United States is

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