脑癌风险和电磁场:评估地磁成分

T. Aldrich, K. W. Andrews, A. Liboff
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引用次数: 18

摘要

在北卡罗来纳州进行的癌症集群研究确定了几个存在脑癌高风险的社区。这些发现促使了一系列的病例对照研究。目前的这篇文章源于此类研究的第三个结果,重点是包括地球自身的地磁场,它与配电线路产生的电磁场相互作用。这篇文章还包含了由居住(例如,城市,农村)和病例特征(例如,年龄,种族,性别)混淆的贡献的评估。新诊断的脑癌病例是在北卡罗来纳州中部的4个县地区发现的,作者根据早期观察的结果选择了这个地区。来自同一医院的3:1匹配的癌症病例系列作为对照组。收集了广泛的地理信息,并以癌症诊断时的确切居住地为基础,从而为评估提供了若干战略地球物理要素。该评估模型是基于这两种电磁场源对离子回旋共振机制疾病风险的影响。作者使用了包含地球磁场平行分量预测值的逻辑回归模型;这些模型有些不稳定,而且这些元素没有有效地合并到一个单一的统计模型中。解释这些价值是困难的;因此,在离最近的电力线段逐渐的距离上,提供了模型元件的模拟值。本研究的结果证明了使用大型、基于人口的数据库以及使用严格的地理信息系统技术来评估生态环境风险的优点。研究结果还表明,暴露分类有望与电磁场的理论生物学机制相兼容。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain Cancer Risk and Electromagnetic Fields (EMFs): Assessing the Geomagnetic Component
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.
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