Breast Cancer Clusters in the Northeast United States: A Geographic Analysis

Abstract

High breast cancer mortality rates have been reported in the northeastern part of the United States, with recent attention focused on Long Island, New York. In this study, the authors investigate whether the high breast cancer mortality is evenly spread over the Northeast, in the sense that any observed clusters of deaths can be explained by chance alone, or whether there are clusters of statistical significance. Demographic data and age-specific breast cancer mortality rates for women were obtained for all 244 counties in 11 northeastern states and for the District of Columbia for 1988-1992. A recently developed spatial scan statistic is used, which searches for clusters of cases without specifying their size or location ahead of time, and which tests for their statistical significance while adjusting for the multiple testing inherent in such a procedure. The basic analysis is adjusted for age, with further analyses examining how the results are affected by incorporating race, urbanicity, and parity as confounding variables. There is a statistically significant and geographically broad cluster of breast cancer deaths in the New York City-Philadelphia, Pennsylvania, metropolitan area (p = 0.0001), which has a 7.4% higher mortality rate than the rest of the Northeast. The cluster remains significant when race, urbanicity, and/or parity are included as confounding variables. Four smaller subclusters within this area are also significant on their own strength: Philadelphia with suburbs (p = 0.0001), Long Island (p = 0.0001), central New Jersey (p = 0.0001), and northeastern New Jersey (p = 0.0001). The elevated breast cancer mortality on Long Island might be viewed less as a unique local phenomenon and more as part of a more general situation involving large parts of the New York City-Philadelphia metropolitan area. The several known and hypothesized risk factors for which we could not adjust and that may explain the detected cluster are most notably age at first birth, age at menarche, age at menopause, breastfeeding, genetic mutations, and environmental factors.

Spatial Clustering of HIV Prevalence in Atlanta, Georgia and Population Characteristics Associated with Case Concentrations

“…30 For this analysis, Kulldorff's spatial scan method, which has been widely used to detect clustering in a variety of health-related fields, was used to determine which census tracts fell in clusters of high HIV prevalence. [27][28][29][31][32][33][34][35][36] Kulldorff's spatial scan method provides the number and location of spatial clusters.…”

Residential Racial Composition, Spatial Access to Care, and Breast Cancer Mortality among Women in Georgia

“…To date, existing research on exogenous environmental risk factors for breast cancer has focused on exposure to pollutants and other toxins, [7][8][9] urban vs. rural differences in risk, 10 and US regional differences in risk. [10][11][12] Virtually nothing, however, is known about how the social environment creates and perpetuates disparities in breast cancer survival and mortality.…”

Multivariate scan statistics for disease surveillance

“…Let c st be the observed number of cases in the geographical location s during time period t. Let n st be either the population or the expected number of cases in location s during time period t. Let C = s,t c st be the total number of cases and let N = s,t n st be the total population/expected cases. The expected may be calculated in different ways adjusting for various covariates such as age, gender, urbanicity, day-of-week or seasonal effects [2,17,18].…”