Research

Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers

Monica C Jackson^1* , Lan Huang^2* , Jun Luo^3* , Mark Hachey^3* and Eric Feuer^2*

¹  Department of Mathematics and Statistics, American University, Washington, DC, 20016 USA

²  National Cancer Institute, National Institutes of Health, Rockville, MD, 20852 USA

³  Information Management Services, Inc, Silver Spring, MD, 20904 USA

author email corresponding author email* Contributed equally

International Journal of Health Geographics 2009, 8:55doi:10.1186/1476-072X-8-55

Published:	12 October 2009

Abstract

Background

The ability to evaluate geographic heterogeneity of cancer incidence and mortality is important in cancer surveillance. Many statistical methods for evaluating global clustering and local cluster patterns are developed and have been examined by many simulation studies. However, the performance of these methods on two extreme cases (global clustering evaluation and local anomaly (outlier) detection) has not been thoroughly investigated.

Methods

We compare methods for global clustering evaluation including Tango's Index, Moran's I, and Oden's I*_pop; and cluster detection methods such as local Moran's I and SaTScan elliptic version on simulated count data that mimic global clustering patterns and outliers for cancer cases in the continental United States. We examine the power and precision of the selected methods in the purely spatial analysis. We illustrate Tango's MEET and SaTScan elliptic version on a 1987-2004 HIV and a 1950-1969 lung cancer mortality data in the United States.

Results

For simulated data with outlier patterns, Tango's MEET, Moran's I and I*_pop had powers less than 0.2, and SaTScan had powers around 0.97. For simulated data with global clustering patterns, Tango's MEET and I*_pop (with 50% of total population as the maximum search window) had powers close to 1. SaTScan had powers around 0.7-0.8 and Moran's I has powers around 0.2-0.3. In the real data example, Tango's MEET indicated the existence of global clustering patterns in both the HIV and lung cancer mortality data. SaTScan found a large cluster for HIV mortality rates, which is consistent with the finding from Tango's MEET. SaTScan also found clusters and outliers in the lung cancer mortality data.

Conclusion

SaTScan elliptic version is more efficient for outlier detection compared with the other methods evaluated in this article. Tango's MEET and Oden's I*_pop perform best in global clustering scenarios among the selected methods. The use of SaTScan for data with global clustering patterns should be used with caution since SatScan may reveal an incorrect spatial pattern even though it has enough power to reject a null hypothesis of homogeneous relative risk. Tango's method should be used for global clustering evaluation instead of SaTScan.