Abstract
BackgroundSpatial and space–time scan statistics are widely used in disease surveillance to identify geographical areas of elevated disease risk and for the early detection of disease outbreaks. With a scan statistic, a scanning window of variable location and size moves across the map to evaluate thousands of overlapping windows as potential clusters, adjusting for the multiple testing. Almost always, the method will find many very similar overlapping clusters, and it is not useful to report all of them. This paper proposes to use the Gini coefficient to help select which of the many overlapping clusters to report.MethodsThe Gini coefficient provides a quick and intuitive way to evaluate the degree of the heterogeneity of the collection of clusters, which is useful to explain how well the cluster collection reveal the underlying true cluster patterns. Using simulation studies and real cancer mortality data, it is compared with the traditional approach for reporting non-overlapping clusters.ResultsThe Gini coefficient can identify a more refined collection of non-overlapping clusters to report. For example, it is able to determine when it makes more sense to report a collection of smaller non-overlapping clusters versus a single large cluster containing all of them. It also fulfils a set of desirable theoretical properties, such as being invariant under a uniform multiplication of the population numbers by the same constant.ConclusionsThe Gini coefficient can be used to determine which set of non-overlapping clusters to report. It has been implemented in the free SaTScan™ software version 9.3 (www.satscan.org).
Highlights
Spatial and space–time scan statistics are widely used in disease surveillance to identify geographical areas of elevated disease risk and for the early detection of disease outbreaks
We focus on the elliptic purely spatial scan statistic but the principles and theory described here applies to other likelihood-based methods and the space–time scan statistics as well
Within a pre-determined maximum cluster size such as 50 % of the population at risk, the goal of the Gini coefficient, which we propose in this paper, is to determine the best collection of non-overlapping statistically significant clusters to report, from among the many thousands of statistically significant and highly overlapping clusters that the spatial scan statistic finds
Summary
Spatial and space–time scan statistics are widely used in disease surveillance to identify geographical areas of elevated disease risk and for the early detection of disease outbreaks. A scanning window of variable location and size moves across the map to evaluate thousands of overlapping windows as potential clusters, adjusting for the multiple testing. A likelihood-based approach allows the scan statistic to identify clusters and evaluate if they are statistically significant, adjusting for the multiple testing inherent in the many potential cluster locations and sizes. Several methods have been proposed in this class This includes circular and elliptic spatial scan statistics [5, 6], as well as non-parametric spatial scan statistics that aim to detect irregular shaped cluster. The latter are nonparametric in terms of the spatial cluster shapes, not in
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
