title: GIS approaches to the problem of disease clusters: a brief commentary.
abstract: This commentary considers issues raised in a recent article on GIS-based approaches to modeling disease clusters. 'Modeling exposure opportunities' (Sabel, Gatrell & Löytönen et al., 2000. Social Science and Medicine, 50, 1121-37) and the general problem of mapping disease clusters. It notes that the authors' advocate a fundamentally statistical approach, Kerneling estimation, to map the occurrence of a specific illness whose etiology is unknown. Epidemiologists, ironically, have advocated a fundamentally cartographic solution, the cartogram, in addressing the general problem of disease clusters. The advantages and limits of both approaches are reviewed and the potential for their comparison in a single study suggested. Most importantly, perhaps, the commentary seeks to join the epidemiological and medical geographic literatures as they pertain to this analytic problem and medical cartography's potential (GIS-based or traditional) to understand disease etiology.
authors.affiliation: Department of Geography, University of British Columbia, Vancouver, Canada. tokoch@attglobal.net
authors.forename: T
authors.initials: T
authors.lastname: Koch
authors.forename: K
authors.initials: K
authors.lastname: Denike
mesh_terms: D000465:Algorithms; D015982:Bias; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D005843:Geography; D006801:Humans; D015994:Incidence; D007256:Information Systems / Q000592:standards*; D008377:Maps as Topic; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D015203:Reproducibility of Results; D013997:Time Factors