Disease Cluster Tests for Uncertain Health Event Data

We will develop software can cluster statistics appropriate when the exact space-time location of tknown. Health professionals are investigating an increasing number of possible disease clusters, andimportant role in cluster description and analysis. Existing cluster statistics assume precise data,events are often imprecise (e.g. place-of- residence is known only to the census district or zip-codfirst became ill sometime in 1985'). Most cluster statistics can be written as the cross product ofmatrix reflects nearest neighbor, distance of adjacency relationships and the second matrix is healtcontrol identities). This research will explore a general approach to clustering which incorporatesspace-time locations into the nearest neighbor, distance or adjacency relationship. Since the appromethods can be used with almost all exiting cluster tests. In phase I we will determine feasibilitygeneral approach for Cuzick & Edwards (nearest neighbor-based), Mantel's (distance-based) and Knox'stests. The delivery of the prototype software and Manual at the end of Phase I will be the criterionfeasibility. In Phase II we will extend the approach to 10 other cluster tests and evaluate the fuzzusing statistical power comparisons based on 3 realistic disease simulations.