Local clustering in breast, lung and colorectal cancer in Long Island, New York

Abstract

BACKGROUND: Analyses of spatial disease patterns usually employ a univariate approach that uses one technique to identify disease clusters. Because different methods are sensitive to different aspects of spatial pattern, an approach employing a battery of techniques is expected to describe geographic variation in human health more fully. This two-part study employs a multi-method approach to elucidate geographic variation in cancer incidence in Long Island, New York, and to evaluate spatial association with air-borne toxics. This first paper uses the local Moran statistic to identify cancer hotspots and spatial outliers. We evaluated the geographic distributions of breast cancer in females and colorectal and lung cancer in males and females in Nassau, Queens, and Suffolk counties, New York, USA. We calculated standardized morbidity ratios (SMR values) from New York State Department of Health (NYSDOH) data. RESULTS: We identified significant local clusters of high and low SMR and significant spatial outliers for each cancer-gender combination. We then compared our results with the study conducted by NYSDOH using Kulldorff's spatial scan statistic. We identified patterns on a smaller spatial scale with different cluster shapes than the NYSDOH analysis did, a consequence of different statistical methods and analysis scale. CONCLUSION: This is a methodological and comparative study to evaluate whether there is substantial benefit added by using a variety of techniques for geographic pattern detection at different spatial scales. We located significant spatial pattern in cancer morbidity in Nassau, Queens, and Suffolk counties. These results broadly agree with the results of other studies that used different techniques, but differ in specifics. The differences in our results and that of the NYSDOH underscore the need for an exploratory, integrative, and multi-scalar approach to assessing geographic patterns of disease, as different methods identify different patterns. We recommend that future studies of geographic patterns use a concordance of evidence from a multiscalar integrative geographic approach to assure that 1) different aspects of spatial pattern are fully identified and 2) the results from the suite of analyses are logically consistent.

Figure 1

Geographic distribution of female colorectal cancer. The fill color in each ZIP code represents the SMR, with green indicating relatively low SMR and purple representing relatively high SMR. White indicates SMR near 1 (observed and expected equivalent). The ZIP codes outlined and cross-hatched in orange had significantly high incidences and formed local clusters under Moran's test. The ZIP code outlined and cross-hatched in yellow was a significant spatial outlier by the local Moran test, though its SMR is not significantly different from 1. The black outlines describe ZIP code boundaries. Labels identify the centering ZIP codes for each cluster or outlier. The strip of Fire Island outlined in orange is part of ZIP code 11782, the main portion of which is a neighbor of 11705 on Long Island proper.

Figure 2

Geographic distribution of male colorectal cancer. The fill color in each ZIP code represents the SMR, with green indicating relatively low SMR and purple representing relatively high SMR. White ZIP codes indicate SMR near 1 (observed and expected equivalent). The ZIP codes outlined and cross-hatched in orange had significantly high incidences and formed local clusters under Moran's test. The ZIP code outlined and cross-hatched in yellow is a significant spatial outlier in the local Moran analysis, though its confidence interval is not significantly different from 1. The black outlines describe ZIP code boundaries. Labels identify the centering ZIP codes for each cluster or outlier.

Figure 3

Geographic distribution of female breast cancer. The fill color in each ZIP code represents the SMR, with green indicating relatively low SMR and purple represent relatively high SMR. White ZIP codes indicate SMR near 1 (observed and expected equivalent). Orange and yellow hatching of ZIP codes indicate significant clusters and outliers according to the local Moran test. The black outlines describe ZIP code boundaries. Labels identify the centering ZIP codes for each significant cluster or outlier.

Figure 4

Geographic distribution of lung cancer in females. The black outlines describe ZIP code boundaries. The fill color in each ZIP code represents the SMR, with green indicating relatively low SMR and purple representing relatively high SMR. White ZIP codes indicate SMR near 1 (observed and expected equivalent). Orange and yellow hatching of ZIP codes indicate significant clusters and outliers according to the Local Moran test. Labels identify the centering ZIP codes for each significant cluster or outlier.

Figure 5

Lung cancer in males. The fill color in each ZIP code represents the SMR, with green indicating relatively low SMR and purple representing relatively high SMR. White ZIP codes indicate SMR near 1 (observed and expected equivalent). Orange and yellow hatching of ZIP codes indicate significant clusters and outliers according to the Local Moran test. The blue outlines describe ZIP code boundaries. Labels identify the centering ZIP codes