A comparison of three tests to detect general clustering of a rare disease in Santa Clara County, California.
Statistical tests to detect clustering of a rare disease investigate whether an observed spatial pattern of cases appears to be due to chance alone. Heterogeneous population density and the geographic structure of the data under consideration complicate the ability to make comparisons of different tests. Further, interpretation of test results depends on the nature of the test used and what feature of the data it is designed to detect. With these issues in mind, we compare three recent tests for assessing general clustering among cases where the population is distributed heterogeneously across the study area, namely those of Besag and Newell, Turnbull et al. and Tango. We compare these methods using 1981 incidence data for severe cardiac birth defects from Santa Clara County, California.
Hill
E G
EG
Department of Biostatistics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322-5730, USA.
Ding
L
L
Waller
L A
LA
D002140:California / Q000453:epidemiology; D003198:Computer Simulation; D006330:Heart Defects, Congenital / Q000453:epidemiology* / Q000209:etiology; D006801:Humans; D009010:Monte Carlo Method; D013029:Space-Time Clustering