ABSTRACT
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
Dr. John Snow’s studies of cholera mortality in the vicinity of the Broad Street water pump of the Soho neighborhood of London in 1854 represent an early example of assessing how the pattern of disease incidence with respect to suspected sources of risk can reveal insight into the processes driving underlying morbidity or mortality. The rapid proliferation of georeferenced data and advances in geographic information systems offer great opportunities for the quantification and analysis of spatial patterns in epidemiologic data, especially with respect to assessing the potential impact of putative sources of excess risk. Examples include (but certainly are not limited to) assessments of leukemia risk around hazardous waste sites (Lagakos et al. 1986; Waller et al. 1992, 1994) or nuclear sites (Stone 1988; Bithell et al. 1994; Waller et al. 1995; National Research Council 2012), or respiratory outcomes near industrial sites (Lawson and Williams 1994; Diggle et al. 1999) or highways (English et al. 1999).
