Exposimeters are increasingly applied in bioelectromagnetic research to determine personal radiofrequency electromagnetic field (RF-EMF) exposure. The main advantages of exposimeter measurements are their convenient handling for study participants and the large amount of personal exposure data, which can be obtained for several RF-EMF sources. However, the large proportion of measurements below the detection limit is a challenge for data analysis. With the robust ROS (regression on order statistics) method, summary statistics can be calculated by fitting an assumed distribution to the observed data. We used a preliminary sample of 109 weekly exposimeter measurements from the QUALIFEX study to compare summary statistics computed by robust ROS with a naïve approach, where values below the detection limit were replaced by the value of the detection limit. For the total RF-EMF exposure, differences between the naïve approach and the robust ROS were moderate for the 90th percentile and the arithmetic mean. However, exposure contributions from minor RF-EMF sources were considerably overestimated with the naïve approach. This results in an underestimation of the exposure range in the population, which may bias the evaluation of potential exposure-response associations. We conclude from our analyses that summary statistics of exposimeter data calculated by robust ROS are more reliable and more informative than estimates based on a naïve approach. Nevertheless, estimates of source-specific medians or even lower percentiles depend on the assumed data distribution and should be considered with caution.

译文

exposimeter越来越多地应用于生物电磁研究中,以确定个人射频电磁场 (rf-emf) 暴露。exposimeter测量的主要优点是它们对研究参与者的方便处理以及大量的个人暴露数据,这些数据可以针对多个rf-emf源获得。但是,低于检测限的大部分测量值对数据分析是一个挑战。使用稳健的ROS (顺序统计回归) 方法,可以通过将假定的分布拟合到观察到的数据来计算汇总统计。我们使用了来自QUALIFEX研究的每周109次exposition测量的初步样本,将由稳健的ROS计算的汇总统计数据与天真方法进行了比较,其中低于检测极限的值被检测极限的值代替。对于总rf-emf暴露,对于第90个百分位数和算术平均值,天真方法和稳健ROS之间的差异适中。但是,天真的方法大大高估了较小的RF-EMF源的暴露贡献。这导致低估了人群中的暴露范围,这可能会使对潜在暴露-反应关联的评估产生偏差。我们从分析中得出的结论是,与基于幼稚方法的估计相比,由稳健的ROS计算出的exposition数据的汇总统计信息更可靠,更有用。尽管如此,特定于源的中位数甚至更低的百分位数的估计取决于假定的数据分布,应谨慎考虑。

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