Statistical analysis of personal radiofrequency electromagnetic field measurements with nondetects

Abstract

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.

Assessment of personal exposure from radiofrequency-electromagnetic fields in Australia and Belgium using on-body calibrated exposimeters

“…means, medians, 25 th , 75 th and 95 th percentiles) compared to the former method . Röösli et al (2008) also observed that the larger the proportion of censored data, the larger was the difference between the substitution (by LOD) and the ROS mean. The frequency-specific LODs for the exposimeter used in our study were lower compared to those employed in previous studies (EME SPY) (Bhatt et al, 2016b), which suggest that our devices were more sensitive than EME SPY.…”

“…The performance of these methods has been evaluated using simulations on single and contaminated lognormal data and it was found that LOD/√2 substitution method provided slightly positively biased means and negatively biased 95 th percentiles, yet plausible results compared to more advanced approaches (Hewett & Ganser, 2007). Substitution methods have been used in RF-EMF personal exposure assessment elsewhere (Ibrani et al, 2016;Thomas et al, 2008;Röösli et al, 2008).…”

Temporal trends of radio-frequency electromagnetic field (RF-EMF) exposure in everyday environments across European cities

“…To take into account that a large proportion of data points were below the lower detection limit of the exposimeter, arithmetic mean values have been calculated for each measurement day per frequency and per environment with the robust regression on order statistics (ROS) algorithm using the statistical software R Version 3.0.1 (www.r-project.org) (Röösli et al, 2008). A full description of the analysis method can be found in Helsel (2005).…”

Radio-frequency electromagnetic field (RF-EMF) exposure levels in different European outdoor urban environments in comparison with regulatory limits

“…The lower were calculated using the robust regression on order statistics (ROS) algorithm (Röösli et al 2008). If less than three measurements were above the detection limit for a given area and frequency band, the arithmetic mean value was set to 0.000265 mW/m 2 (0.01 V/m).…”