The very rapid worldwide increase in mobile phone use in the last decade has generated considerable interest in the possible health effects of exposure to radio frequency (RF) fields. A multinational case-control study, INTERPHONE, was set-up to investigate whether mobile phone use increases the risk of cancer and, more specifiBaruch Modan is deceased. 123Eur J Epidemiol (2007) 22: 647-664 DOI 10.1007/s10654-007-9152-z cally, whether the RF fields emitted by mobile phones are carcinogenic. The study focused on tumours arising in the tissues most exposed to RF fields from mobile phones: glioma, meningioma, acoustic neurinoma and parotid gland tumours. In addition to a detailed history of mobile phone use, information was collected on a number of known and potential risk factors for these tumours. The study was conducted in 13 countries. Australia, Canada, Denmark, Finland, France, Germany, Israel, Italy, Japan, New Zealand, Norway, Sweden, and the UK using a common core protocol. This paper describes the study design and methods and the main characteristics of the study population. INTERPHONE is the largest case-control study to date investigating risks related to mobile phone use and to other potential risk factors for the tumours of interest and includes 2,765 glioma, 2,425 meningioma, 1,121 acoustic neurinoma, 109 malignant parotid gland tumour cases and 7,658 controls. Particular attention was paid to estimating the amount and direction of potential recall and participation biases and their impact on the study results.

Computer simulation has become the standard tool in many engineering fields for designing and optimizing systems, as well as for assessing their reliability. Optimization and uncertainty quantification problems typically require a large number of runs of the computational model at hand, which may not be feasible with high-fidelity models directly. Thus surrogate models (a.k.a metamodels) have been increasingly investigated in the last decade. Polynomial Chaos Expansions (PCE) and Kriging are two popular non-intrusive metamodelling techniques. PCE surrogates the computational model with a series of orthonormal polynomials in the input variables where polynomials are chosen in coherency with the probability distributions of those input variables. A least-square minimization technique may be used to determine the coefficients of the PCE. On the other hand, Kriging assumes that the computer model behaves as a realization of a Gaussian random process whose parameters are estimated from the available computer runs, i.e. input vectors and response values. These two techniques have been developed more or less in parallel so far with little interaction between the researchers in the two fields. In this paper, PC-Kriging is derived as a new non-intrusive meta-modeling approach combining PCE and Kriging. A sparse set of orthonormal polynomials (PCE) approximates the global behavior of the computational model whereas Kriging manages the local variability of the model output. An adaptive algorithm similar to the least angle regression algorithm determines the optimal sparse set of polynomials. PC-Kriging is validated on various benchmark analytical functions which are easy to sample for reference results. From the numerical investigations it is concluded that PC-Kriging performs better than or at least as good as the two distinct meta-modeling techniques. A larger gain in accuracy is obtained when the experimental design has a limited size, which is an asset when dealing with demanding computational models.

The rapid worldwide increase in mobile phone use in the last decade has generated considerable interest in possible carcinogenic effects of radio frequency (RF). Because exposure to RF from phones is localized, if a risk exists it is likely to be greatest for tumours in regions with greatest energy absorption. The objective of the current paper was to characterize the spatial distribution of RF energy in the brain, using results of measurements made in two laboratories on 110 phones used in Europe or Japan. Most (97-99% depending on frequency) appears to be absorbed in the brain hemisphere on the side where the phone is used, mainly (50-60%) in the temporal lobe. The average relative SAR is highest in the temporal lobe (6-15%, depending on frequency, of the spatial peak SAR in the most exposed region of the brain) and the cerebellum (2-10%) and decreases very rapidly with increasing depth, particularly at higher frequencies. The SAR distribution appears to be fairly similar across phone models, between older and newer phones and between phones with different antenna types and positions. Analyses of risk by location of tumour are therefore important for the interpretation of results of studies of brain tumours in relation to mobile phone use.

The specific absorption rates (SAR) determined computationally in the specific anthropomorphic mannequin (SAM) and anatomically correct models of the human head when exposed to a mobile phone model are compared as part of a study organized by IEEE Standards Coordinating Committee 34, SubCommittee 2, and Working Group 2, and carried out by an international task force comprising 14 government, academic, and industrial research institutions. The detailed study protocol defined the computational head and mobile phone models. The participants used different finite-difference time-domain software and independently positioned the mobile phone and head models in accordance with the protocol. The results show that when the pinna SAR is calculated separately from the head SAR, SAM produced a higher SAR in the head than the anatomically correct head models. Also the larger (adult) head produced a statistically significant higher peak SAR for both the 1- and 10-g averages than did the smaller (child) head for all conditions of frequency and position.

The increasing deployment of mobile communication base stations led to an increasing demand for epidemiological studies on possible health effects of radio frequency emissions. The methodological challenges of such studies have been critically evaluated by a panel of scientists in the fields of radiofrequency engineering/dosimetry and epidemiology. Strengths and weaknesses of previous studies have been identified. Dosimetric concepts and crucial aspects in exposure assessment were evaluated in terms of epidemiological studies on different types of outcomes. We conclude that in principle base station epidemiological studies are feasible. However, the exposure contributions from all relevant radio frequency sources have to be taken into account. The applied exposure assessment method should be piloted and validated. Short to medium term effects on physiology or health related quality of life are best investigated by cohort studies. For long term effects, groups with a potential for high exposure need to first be identified; for immediate effect, human laboratory studies are the preferred approach.