Spatial Clustering and Autocorrelation of Health Events
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Since the mid-nineteenth century, scientists have sought ways to quantify observed spatial patterns of disease incidence and prevalence in order to identify clusters of high risk. We review popular methods for identifying clusters and clustering of disease in geographically referenced epidemiologic data. We identify the questions of interest and illustrate how the combination available data and the choice of analytic method often answer a more specific question, i.e., each method tends to focus on specific types, shapes, and scales of clusters and clustering. Recognizing the specification implicit in the choice of data and method provides a critical context for interpreting the results of a spatial epidemiologic analysis accurately and reliably for stakeholders ranging from other spatial analysts to members of general public.
KeywordsDisease clusters Cluster detection Disease mapping Spatial epidemiology
- Abrams B, Anderson H, Blackmore C, Bove FJ, Condon SK, Eheman CR, Fagliano J, Haynes LB, Lewis LS, Major J, McGeehin MA, Simms E, Sircar K, Soler J, Stanbury M, Watkins SM, Wartenberg D (2013) Investigating suspected cancer clusters and responding to community concerns: guidelines from CDC and the Council of State and Territorial Epidemiologists. Morb Mortal Wkly Rep 62(8):1–14Google Scholar
- Besag J, Newell J (1991) The detection of clusters in rare diseases. J Roy Stat Soc, Series A, 154:143–155Google Scholar
- Koch T (2005) Cartography of disease: maps, mapping, and medicine. ESRI Press, RedlandsGoogle Scholar
- Waller LA (2009) Detection of clustering in spatial data. In: Fotheringham AS, Rogerson PA (eds) The SAGE handbook of spatial analysis. SAGE, London, pp 299–320Google Scholar