![Sample our Environment & Sustainability journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5935/TOC-Subject-Sample-2021-Environment-Sustainability.jpg"})
Abstract
Cancer cluster studies in North Carolina identified several communities in which there existed an elevated risk of brain cancer. These findings prompted a series of case-control studies. The current article, which originated from the results of the 3rd of such studies, is focused on inclusion of the earth's own geomagnetic fields that interact with electromagnetic fields generated from distribution power lines. This article also contains an assessment of the contribution of confounding by residential (e.g., urban, rural) and case characteristics (e.g., age, race, gender). Newly diagnosed brain cancer cases were identified for a 4-county region of central North Carolina, which the authors chose on the basis of the results of earlier observations. A 3:1 matched series of cancer cases from the same hospitals in which the cases were diagnosed served as the comparison group. Extensive geographic information was collected and was based on an exact place of residence at the time of cancer diagnosis, thus providing several strategic geophysical elements for assessment. The model for this assessment was based on the effects of these two sources of electromagnetic fields for an ion cyclotron resonance mechanism of disease risk. The authors used logistic regression models that contained the predicted value for the parallel component of the earth's magnetic field; these models were somewhat erratic, and the elements were not merged productively into a single statistical model. Interpretation of these values was difficult; therefore, the modeled values for the model elements, at progressive distances from the nearest power-line segments, are provided. The results of this study demonstrate the merits of using large, population-based databases, as well as using rigorous Geographic Information System techniques, for the assessment of ecologic environmental risks. The results also suggest promise for exposure classification that is compatible with the theoretical biological mechanisms posited for electromagnetic fields.
