The cumulative q interval curve as a starting point in disease cluster investigation

Statistical analyses aimed at detection and investigation of clustering are associated with inherent difficulties. Both types of statistical errors ar e large in these analyses. The results of the analyses should indicate whet her or not at least some of the cases are clustered, and if they are, wheth er or not the cluster is related to an exposure. The temporal changes in th e incidence rate of the disease may alleviate the difficulties associated w ith the large statistical errors. Because of the sparse data, estimates of the incidence rates over time are not reliable. In this study we present th e q interval statistic that has the uniform (0,1) distribution. It can be v iewed as a standardized time interval between consecutive diagnoses of the disease. As such, it reflects the reciprocal of the incidence rates. Since it is measured for each diagnosis, it is sensitive to gradual change in the incidence rate, and in general to a true clustering that is due to exposur e, even when the test result is not significant. When clustering is detecte d, it may indicate which of the possible reasons leading to a cluster has a sound basis. As a result, the epidemiological search for exposure is limit ed to situations indicated by the 0 intervals. In addition, the 0 interval presents a useful survival statistic in a follow-up study when no control g roup is available. Software programs in SAS and in SYSTAT are available. Co pyright (C) 1999 John Whey & Sons, Ltd.