There has been some debate over the years regarding the utility of disease cluster analysis. While it is widely recognized that cluster analysis may be part of a public health response to an outbreak of disease, or to reports of a possible cluster by concerned citizens, the contribution and role of cluster analysis in spatial epidemiology is less clear. Applications frequently cited as successful exemplars include Snow’s investigation of cholera (Snow 1855), the discovery of the link between ﬂuoride in drinking water and dental caries (Dean 1938), and the identiﬁcation of mercury poisoning as the cause of Minamata’s disease (Harada 1995). Other etiological connections triggered by clustering of adverse health events include discovery of acquired immune deﬁciency syndrome (Friedman-Kien et al. 1981) and the association between a food allergy and tick bites (Commins et al. 2011). For chronic diseases such as cancer, the link to a putative cause is often unclear, since disease latency is often long, actual exposures are not measured, and the number of cases is often small, making a ﬁnding of a statistically signiﬁcant excess diﬃcult. Chronic disease clusters successfully linked to speciﬁc exposures include the Libby Montana cluster of lung cancer from asbestos exposures related to vermiculite mining (McDonald et al. 2004); the clustering of leukemia, lymphoma, and
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adverse birth outcomes in Camp Lejeune, North Carolina, due to exposures to trichloroethylene, benzene, and other carcinogens from drinking water (ATSDR 2010); and a possible cluster of brain and central nervous systems cancers in Toms River, New Jersey, possibly caused by exposures to styrene, acrylonitrile, and styrene-acrylonitrile (SAN) trimer (ATSDR 1997). Although there have been some positive cluster studies, the majority of them do not ﬁnd a signiﬁcant excess, leading Neutra and others to view cluster analysis as an expensive endeavor that yields little insights into the existence of clusters, let alone their underlying causes (Neutra 1990). What then are the plausible underlying causes of disease clusters? And how have cluster analyses advanced our understanding of disease processes?