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

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.