Rybnikova Natalya, Stevens Richard G, Gregorio David I, Samociuk Holly, Portnov Boris A
Department of Natural Resources and Environment Management, Faculty of Management, University of Haifa, Haifa, Israel.
Department of Community Medicine, School of Medicine, University of Connecticut, Farmington, CT 06030, United States.
Spat Spatiotemporal Epidemiol. 2018 Aug;26:143-151. doi: 10.1016/j.sste.2018.06.003. Epub 2018 Jun 19.
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.
康涅狄格州的乳腺癌发病率在美国位居前列，且在该州内分布不均。我们的目标是确定夜间人造光（ALAN）是否起到了作用。利用从康涅狄格肿瘤登记处获得的乳腺癌记录，我们应用双核密度（DKD）估计器来生成该州疾病的连续相对风险表面。多变量分析将DKD和人口普查轨迹估计值与人口密度、生育率、非白人人口百分比、贫困线以下人口以及ALAN水平进行了比较。分析确定了乳腺癌发病率的“光环”地理模式，在距离该州主要城市5至15公里处观察到该病的最高发病率。“光环”区域是高收入社区，具有高ALAN水平，位于该州主要城市的郊区边缘。
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