Kernel density analysis reveals a halo pattern of breast cancer incidence in Connecticut.

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.

Spatial identification of potential health hazards: a systematic areal search approach.

BACKGROUND AND AIMS: Large metropolitan areas often exhibit multiple morbidity hotspots. However, the identification of specific health hazards, associated with the observed morbidity patterns, is not always straightforward. In this study, we suggest an empirical approach to the identification of specific health hazards, which have the highest probability of association with the observed morbidity patterns. METHODS: The morbidity effect of a particular health hazard is expected to weaken with distance. To account for this effect, we estimate distance decay gradients for alternative locations and then rank these locations based on the strength of association between the observed morbidity and wind-direction weighted proximities to these locations. To validate this approach, we use both theoretical examples and a case study of the Greater Haifa Metropolitan Area (GHMA) in Israel, which is characterized by multiple health hazards. RESULTS: In our theoretical examples, the proposed approach helped to identify correctly the predefined locations of health hazards, while in the real-world case study, the main health hazard was identified as a spot in the industrial zone, which hosts several petrochemical facilities. CONCLUSION: The proposed approach does not require extensive input information and can be used as a preliminary risk assessment tool in a wide range of environmental settings, helping to identify potential environmental risk factors behind the observed population morbidity patterns.

Application of the double kernel density approach to the analysis of cancer incidence in a major metropolitan area.

RATIONALE: Although cancer is a main cause of human morbidity worldwide, relatively small numbers of new cancer cases are recorded annually in single urban areas. This makes the association between cancer morbidity and environmental risk factors, such as ambient air pollution, difficult to detect using traditional methods of analysis based on age standardized rates and zonal estimates. STUDY GOAL: The present study investigates the association between air pollution and cancer morbidity in the Greater Haifa Metropolitan Area in Israel by comparing two analytical techniques: the traditional zonal approach and more recently developed Double Kernel Density (DKD) tools. While the first approach uses age adjusted Standardized Incidence Ratios (SIRs) for small census areas, the second approach estimates the areal density of cancer cases, normalized by the areal density of background population in which cancer events occurred. Both analyses control for several potential confounders, including air pollution, proximities to main industrial facilities and socio-demographic attributes. RESULTS: Air pollution variables and distances to industrial facilities emerged as statistically significant predictors of lung and NHL cancer morbidity in the DKD-based models (p<0.05) but not in the models based on SIRs estimates (p>0.2). CONCLUSION: DKD models appear to be a more sensitive tool for assessing potential environmental risks than traditional SIR-based models, because DKD estimates do not depend on a priory geographic delineations of statistical zones and produce a smooth and continuous disease 'risk surface' covering the entire study area. We suggest using the DKD method in similar studies of the effect of ambient air pollution on chronic morbidity, especially in cases in which the number of statistical areas available for aggregation and comparison is small and recorded morbidity events are relatively rare.