title: Adaptations for finding irregularly shaped disease clusters.
abstract: BACKGROUND
  Recent adaptations of the spatial scan approach to detecting disease clusters have addressed the problem of finding clusters that occur in non-compact and non-circular shapes--such as along roads or river networks. Some of these approaches may have difficulty defining cluster boundaries precisely, and tend to over-fit data with very irregular (and implausible) clusters shapes.
  
  
  CONCLUSIONS
  We describe two simple adaptations to these approaches that can be used to improve the effectiveness of irregular disease cluster detection. The first adaptation penalizes very irregular cluster shapes based on a measure of connectivity (non-connectivity penalty). The second adaptation prevents searches from combining smaller clusters into large super-clusters (depth limit). We conduct experiments with simulated data in order to observe the performance of these adaptations on a number of synthetic cluster shapes.
  
  
  CONCLUSIONS
  Our results suggest that the combination of these two adaptations may increase the ability of a cluster detection method to find irregular shapes without affecting its ability to find more regular (i.e., compact) shapes. The depth limit in particular is effective when it is deemed important to distinguish nearby clusters from each other. We suggest that these adaptations of adjacency-constrained spatial scans are particularly well suited to chronic disease and injury surveillance.
authors.affiliation: School of Geography and Earth Sciences, McMaster University, Hamilton, Canada. niwiyi@gmail.com
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Yiannakoulias
authors.forename: Rhonda J
authors.initials: RJ
authors.lastname: Rosychuk
authors.forename: John
authors.initials: J
authors.lastname: Hodgson
mesh_terms: D002170:Canada / Q000453:epidemiology; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D012680:Sensitivity and Specificity