A hybrid method for fast detection of spatial disease clusters in irregular shapes

Abstract

Detection of spatial disease clusters in irregular shapes has generated considerable interest among public health researchers and policymakers. The existing methods have varying issues such as enormous computing workloads, peculiar cluster shapes, and high subjectivity of parameters. To support fast detection of irregularly shaped clusters, we are proposing a hybrid method combining Tango’s restricted likelihood ratio as the test statistic and Assuncao et al.’s dynamic Minimum Spanning Tree method as the search strategy. We discuss the advantages and the implementation of the hybrid method, and systematically compare its performance with other three well-known scan-based cluster detection methods, including Tango’s method, Assuncao et al.’s method, and Kulldorff’s circular spatial scan statistic method. Using simulated data of six cluster models combining two disease incidence levels and three true cluster shapes, the performance of the methods is evaluated in terms of statistical power, geographic accuracy, and computational intensity. The experimental results indicate that our hybrid method with 0.2 as the screening level value has the third highest average statistical power and the best average geographic accuracy among the four methods with all of the tested parameters. The four methods are then applied to the county-level lung cancer incidence data of Georgia from 1998 to 2005, and all find a significant cluster in northwestern Georgia but varying in shape and size.