Kernel density analysis reveals a halo pattern of breast cancer incidence in Connecticut. (August 2018)

• Record Type: Journal Article
• Authors: Rybnikova, Natalya; Stevens, Richard G.; Gregorio, David I.; Samociuk, Holly; Portnov, Boris A.
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 26(2018)
• Page Start: 143
• Page End: 151
• Publication Date: 2018-08
• Subjects: Breast cancer (BC) -- Artificial light-at-night (ALAN) -- Double kernel density (DKD) analysis -- Connecticut; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2018.06.003
• Languages: English
• ISSNs: 1877-5845

Abstract

Highlights: Connecticut has among the highest incidence of breast cancer in the U.S. Kernal density smoothing of incidence by census tract yielded two observations. Highest incidence was between 5 and 15 km from centers of large cities. Community nighttime light level was significantly associated with incidence. Abstract: Breast cancer (BC) incidence rates in Connecticut are among the highest in the United States, and are unevenly distributed within the state. Our goal was to determine whether artificial light at night (ALAN) played a role. Using BC records obtained from the Connecticut Tumor Registry, we applied the double kernel density (DKD) estimator to produce a continuous relative risk surface of a disease throughout the State. A multi-variate analysis compared DKD and census track estimates with population density, fertility rate, percent of non-white population, population below poverty level, and ALAN levels. The analysis identified a "halo" geographic pattern of BC incidence, with the highest rates of the disease observed at distances 5-15 km from the state's major cities. The "halo" was of high-income communities, with high ALAN, located in suburban fringes of the state's main cities.