Personal exposure to mobile communication networks and well‐being in children—A statistical analysis based on a functional approach

Abstract

The MobilEe-study was the first cross-sectional population-based study to investigate possible health effects of mobile communication networks on children using personal dosimetry. Exposure was assessed every second resulting in 86,400 measurements over 24 h for each participant. Therefore, a functional approach to analyze the exposure data was considered appropriate. The aim was to categorize exposure taking into account the course of the measurements over 24 h. The analyses were based on the 480 maxima of each 3 min time interval. Exposure was classified using a nonparametric functional method. Heterogeneity of a sample of functional data was assessed by comparing the functional mode and mean of the distribution of a functional variable. The partition was built within a descending hierarchical method. The resulting exposure groups were compared with categories derived from a standard method, which used the average exposure over 24 h and set the cut-off at the 90th percentile. The functional classification resulted in a splitting of the exposure data into two groups. Plots of the mean curves showed that the groups could be interpreted as children with "low exposure" (88%) and "higher exposure" (12%). These groups were comparable with categories of the standard method. No association between the categorized exposure and well-being was observed in logistic regression models. The functional classification approach yielded a plausible partition of the exposure data. The comparability with the standard approach might be due to the data structure and should not be generalized to other exposures.

Systematic review on the health effects of exposure to radiofrequency electromagnetic fields from mobile phone base stations

“…19 However, further analysis indicated that this change was due to a lower baseline score before UMTS exposure rather than to a higher score after exposure. The remaining four epidemiological studies 28,31,35,39 and one laboratory trial 17 did not indicate any association between MPBS exposure and headache. With respect to self-reported sleep measures, only an Egyptian study 27 reported greater daytime fatigue in exposed individuals.…”

“…Most of these studies were cross-sectional, and the magnitude of the exposure was based on the distance between place of residence and the nearest MPBS, 1,27 or on spot measurements of MPBS radiation in the bedroom, 24,35 or on personal measurements of exposure to radiofrequency electromagnetic fields over a 24-hour period. 31,39 Four epidemiological studies applied an experimental approach (field intervention) in which exposure was modified either by turning on and off an MPBS 28,29 or by using shielding curtains. 30,33 Sample size ranged from 43 to 26 039 participants.…”

“…None of the other studies found any association between MPBS exposure and fatigue or self-reported sleep disturbances (Table 4). 20,24,[29][30][31]35,39 Many other non-specific symptoms have been evaluated, such as concentration difficulties or dizziness. Generally, no association with exposure was observed ( Table 1 and Table 2).…”

Classification of personal exposure to radio frequency electromagnetic fields (RF-EMF) for epidemiological research: Evaluation of different exposure assessment methods

“…The use of personal exposure meters (exposimeters) has been recommended in order to characterize personal exposure to RFEMFs (Neubauer et al, 2007). Several exposure assessment studies have been conducted so far using exposimeters, (Joseph et al, 2008;Kühnlein et al, 2009;Thomas et al, 2008;Thuróczy et al, 2008;Viel et al, 2009), which allow capture of exposure from all relevant RF-EMF sources in the different environments where a study participant spends time (Neubauer et al, 2007;Radon et al, 2006). They are suitable for measuring RF-EMF from environmental far-field sources like mobile phone base stations, but are less apt to accurately measure exposure to personal mobile or cordless phones (Inyang et al, 2008) because measurements during personal phone calls are dependent on the distance between the emitting device and the exposimeter.…”

Statistical perturbations in personal exposure meters caused by the human body in dynamic outdoor environments

“…They have been employed in epidemiological research in recent years [Kühnlein et al, 2009;Viel et al, 2009;Frei et al, 2009a]. Nevertheless, some constraints have been reported in the scientific community, such as the shadowing effect caused by the subject's body [Blas et al, 2007], false summation of signals within the same band, out-of-band responses, and high calibration factors in a few frequency bands [Neubauer et al, 2010].…”