A comparison of spatial clustering and cluster detection techniques for childhood leukemia incidence in Ohio, 1996-2003

Abstract

Background: Spatial cluster detection is an important tool in cancer surveillance to identify areas of elevated risk and to generate hypotheses about cancer etiology. There are many cluster detection methods used in spatial epidemiology to investigate suspicious groupings of cancer occurrences in regional count data and case-control data, where controls are sampled from the at-risk population. Numerous studies in the literature have focused on childhood leukemia because of its relatively large incidence among children compared with other malignant diseases and substantial public concern over elevated leukemia incidence. The main focus of this paper is an analysis of the spatial distribution of leukemia incidence among children from 0 to 14 years of age in Ohio from 1996-2003 using individual case data from the Ohio Cancer Incidence Surveillance System (OCISS).Specifically, we explore whether there is statistically significant global clustering and if there are statistically significant local clusters of individual leukemia cases in Ohio using numerous published methods of spatial cluster detection, including spatial point process summary methods, a nearest neighbor method, and a local rate scanning method. We use the K function, Cuzick and Edward's method, and the kernel intensity function to test for significant global clustering and the kernel intensity function and Kulldorff's spatial scan statistic in SaTScan to test for significant local clusters.

Results: We found some evidence, although inconclusive, of significant local clusters in childhood leukemia in Ohio, but no significant overall clustering. The findings from the local cluster detection analyses are not consistent for the different cluster detection techniques, where the spatial scan method in SaTScan does not find statistically significant local clusters, while the kernel intensity function method suggests statistically significant clusters in areas of central, southern, and eastern Ohio. The findings are consistent for the different tests of global clustering, where no significant clustering is demonstrated with any of the techniques when all age cases are considered together.

Conclusion: This comparative study for childhood leukemia clustering and clusters in Ohio revealed several research issues in practical spatial cluster detection. Among them, flexibility in cluster shape detection should be an issue for consideration.

Figure 1

Childhood leukemia cases (unfilled circles) and controls (filled circles) for years 1996–2003.

Figure 2

K functions (solid) for cases and controls with confidence bands (dashed) and distance in meters.

Figure 3

Difference in case and control K functions with confidence bands (dashed) and distance in meters.

Figure 4

Contours of estimated kernel density functions for cases and controls with UTM coordinates.

Figure 5

Log relative risk surface using kernel density functions with kernel bandwidth = 22,647 meters. The significant risk areas according to Monte Carlo simulation are indicated on the right plot using "-" for points below the 2.5% simulation value and "+" for points above the 97.5% value. The contour lines on this plot indicate the log relative risk.

Figure 6

Simulated values for the test of global clustering using kernel density functions (p-value = 0.27).

Figure 7

Log relative risk surface using kernel density functions with kernel bandwidth = 27,701 meters.

Figure 8

Overall Bonferroni p-value for Cuzick and Edwards' method versus number of Monte Carlo randomizations.

Figure 9

Most likely SaTScan cluster for all cases (43 cases, p-value 0.81).