Cluster morphology analysis

Abstract

Most disease clustering methods assume specific shapes and do not evaluate statistical power using the applicable geography, at-risk population, and covariates. Cluster Morphology Analysis (CMA) conducts power analyses of alternative techniques assuming clusters of different relative risks and shapes. Results are ranked by statistical power and false positives, under the rationale that surveillance should (1) find true clusters while (2) avoiding false clusters. CMA then synthesizes results of the most powerful methods. CMA was evaluated in simulation studies and applied to pancreatic cancer mortality in Michigan, and finds clusters of flexible shape while routinely evaluating statistical power.

Keywords: Clustering methods; medical geography; meta-analysis; statistical power.

Figure 1

Lung cancer incidence in New York. SatScan analysis found circular “clusters” that include ZIP codes with rates 50% below the state average (http://www.health.state.ny.us/nysdoh/cancer/csii/nyscsii.htm).

Figure 2

Cluster model construction, pancreatic cancer mortality in white males in Michigan counties, 1970 through 1994. North and south clusters (outlined in gold) are superimposed on the white male population size (upper left map) with low population size in green and large population size shown in dark brown. The relative risk RR model (center top) shows the background RR in green, RR=2.0 in purple in the North cluster and RR=1.5 as white in the South cluster. One realization from the simulation model (top right) shows pancreatic cancer mortality rates ranging from 6.44/100,000 (pale yellow) to 25/100,000 (dark red). The user has brush selected the north and south clusters and they are shown in gold. Screen capture from the TerraSeer STIS software. See text.

Figure 3

Cluster Morphology Analysis (CMA) of pancreatic cancer mortality in white males, 1970–1994, for simulated clusters (above) and observed data (below). Intersection of the cluster members for the statistically most powerful methods results in a CMA. CMA of the simulated data (above, lower right map) found all 10 clustered counties (gold borders) with 1 false positive (blue border). CMA of observed mortality data found two clusters, one in the north and one in the southeast (below). The southeast cluster in Macomb and Wayne County has been validated using SEER data (see text). Different clustering methods are sensitive to different aspects of clustering, a characteristic exploited by the new approach. Screen capture from the TerraSeer STIS software.