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The Importance of Quantification of Data in Studies on the Health Effects of Exposure to Electromagnetic Fields Generated by Mobile Base Stations

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Soft Computing Applications (SOFA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 633))

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Abstract

This study aimed at developing simple methods for quantification of the data in studies on the health effects of exposure to electromagnetic fields of mobile base stations to prevent different interpretations of the findings. We have previously conducted a cross-sectional study on people living in 10 different districts of Shiraz city. Based on how frequent the symptoms were (always/usually/occasionally/never), a score was determined for each self-reported symptom and the total score for each individual was calculated. In the next stage, instead of reporting the effect of RF-EMF exposure on the occurrence of each symptom, the impact of exposures on the human health were assessed by evaluation of the total scores. Moreover, principal component analysis (PCA) was used for assessing the factors with greatest correlation. We found that exposure to RF-EMF emitted by mobile base stations significantly affected the residents’ health. Furthermore, the distance between the mobile phone base stations and the homes had a significant effect on the residents’ health. The frequency of self-reported symptoms of myalgia, palpitation, early fatigue, nervousness and low back pain decreased with increasing the distance from mobile phone base stations.

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Acknowledgement

This study was supported by the Ionizing and Non-ionizing Radiation Protection Research Center (INIRPRC), Shiraz University of Medical Sciences (SUMS), Shiraz, Iran and Aurel Vlaicu University, Arad, Romania.

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Correspondence to Valentina Emilia Balas .

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Mortazavi, S.M.J. et al. (2018). The Importance of Quantification of Data in Studies on the Health Effects of Exposure to Electromagnetic Fields Generated by Mobile Base Stations. In: Balas, V., Jain, L., Balas, M. (eds) Soft Computing Applications. SOFA 2016. Advances in Intelligent Systems and Computing, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-319-62521-8_26

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