Research - Exposure Assessment

It is clear that many factors can affect an individual's exposure to RFR associated with cellular telephone use. This makes exposure assessment in a risk study very difficult to interpret. Ideally the exact exposure dose should be measured, but no specific measure is available. In experimental situations the Specific Absorption Rate (SAR) is used. This is the amount of energy that is deposited in tissue, and is measured in W/kg. SAR has been developed for quantification of thermal effects of RFR. It is assumed that it may serve as an adequate measure of other effects, although no biological mechanism has been established by which possible health effects could be induced (Auvinen et al., 2006). In epidemiological studies, similar SAR levels have been assumed for all telephone models, although results are occasionally presented separately for users of analogue and digital phones. Since SAR measures are not available, the validity of a proxy exposure measure is critical. Cumulative exposure is usually used as a measure of exposure dose, but this does not take into account variations in the signal for the reasons discussed above. In long-term studies, estimation of a dose-response relationship is important for assessment of causality.

A group of papers published in Epidemiology in 1996 attempted to address some of the issues affecting exposure assessment. One paper (Funch) described the accuracy and feasibility of using telephone company records as an index for exposure by conducting a correlation analysis with responses from a questionnaire survey to account holders. Another paper (Rothman) attempted to link noncorporate, single-phone customer account information, credit bureau information and social security administration data in order to conduct a mortality comparison between portable and mobile phone users. The linkage proved to have some major limitations, including the potential for bias as a result of losing about two-thirds of the target population due to linkage problems. These papers provided some insight into the difficulties in initiating epidemiological studies of cell phone use.

Exposure assessment in studies of cellular telephones is most often done by self-report. Parslow (2003) reported that users of mobile phones tended to under-report their use. Unfortunately, there was a very low participation rate in this study, which casts doubt on the validity of the findings. Studies by Cooper (2004) and by Ardoino (2004) described the development of specially adapted cell phones that were able to measure various aspects of long-term use. Technology such as this may help to overcome the difficulties of determining RF exposure from cell phones. Morrisey (2006) used software-modified phones (SMPs) that record talk time and dynamically changing transmit power levels. He enlisted the help of Motorola employees in different sites around the world. These volunteers used the SMPs for 2 weeks, and the data was later analyzed. Each volunteer was sent a questionnaire within 2 weeks that included questions on their phone use. He found considerable variability in transmit power within a single call, in separate calls, between individuals in the same study region, and between averaged values from different study groups. Morrisey also found that there was significant inaccuracy (45-60%) in recalling "time of use". Erdreich (2007) also used SMPs to study factors affecting the energy output of GSM phones during operation, and found that the largest factor was study area, followed by user movement and location (inside or outside), use of a hands-free device, and urbanicity. Blas (2007) showed that personal exposure meters are subject to errors associated with perturbations of the electric fields by the presence of the human body.

Mild (2005) proposed a method that would enable combining the use of different mobile (e.g. analogue and digital) and cordless phones by weighting factors. This would take account of the fact that analogue phones operate with a maximum power greater than digital phones, which in turn operate at a greater power than cordless ones. Auvinen et al. (2006) suggest that an appropriate measure of exposure would be a weighted average of the cumulative time of cellular telephone use with weighting by power, stratified by side and excluding hands-free use. They further suggest that power can be estimated from the hours of use by adjusting for characteristics of the telephone and network.SC Kim (2006) proposed a new method to estimate quantitative and relative RF exposure levels using a neural network model. The parameters that were used to develop this model were average usage time per day, total period of usage in years, SAR of the specific phone, hands-free usage, antenna extraction, and the type of phone (flip or folder). Bürgi and colleagues (2007), recognizing that personal measurements to assess long-term exposure are expensive, developed a geospatial model that allowed the estimation of ambient HF-EMF strengths with spatial resolution. They included cell phone base stations and broadcast transmitters in their model, which considers the location and transmission patterns of the transmitters, the three-dimensional topography, and shielding effects of buildings. In an evaluation of their method in the region of Basel in Switzerland, they found good correlation between modeling and measurements.

The case-control design is the most common study design used in this research question, and presents additional difficulties in exposure assessment. Clear biases may exist in reporting past cellular telephone use in cases compared to controls. Bias due to differential recall of exposure by cases and controls usually tends to overestimate the true effects. These problems may be accentuated if different procedures are used to obtain information from cases and controls. Different interviewers might be used, or the location of the interview may be different e.g. home or hospital. Potential cognitive impairment among brain tumour cases is an important consideration in exposure misclassification, and difficult to assess. For glioma cases in particular, due to the highly fatal nature of the tumour, proxy respondents are often used to report exposure information on behalf of the patient. The possibility of introducing misclassification or bias due to the use of proxy respondents is real. The Interphone study is a series of multinational case-control studies that examine the risk of brain and salivary gland tumours with exposure to cell phones (for more on the Interphone study, go to "Research Programs - Interphone study"). Lahkola et al. (2005) analyzed responses from complete and incomplete participants in the Finnish arm of the Interphone study. The incomplete participants gave only a brief phone interview. The Odds Ratio (OR) was calculated for the full, the incomplete, and the combined participants. Full participants were more likely to have used a cell phone regularly. The effect of this selection bias was to distort the effect estimates below unity, while analyses based on more comprehensive material gave results closer to unity, although the distortion of results was slight.

The Interphone study group carried out a validation study of short-term recall of phone use (Vrijheid 2006). There were moderate to high correlations between recalled and actual use, as measured by operators or through the use of software modified phones. The authors found that there was moderate systematic error and substantial random error. The latter error would tend to reduce the power of the Interphone study to detect an increase in brain tumour risk, if one exists.

Auvinen and colleagues reviewed the factors that can affect the validity of epidemiological studies on health effects of cell phones. They emphasize the imprecision of exposure assessment in the studies. They also point out that this is particularly likely to occur in case-control studies, and that prospective studies afford the best opportunity to improve the quality of evidence. A group of German researchers published two papers (Samkange-Zeeb et al; Berg et al.), in which they assessed the validity of self-reported use, from a questionnaire used in the Interphone study. They found good correlation between self-reported use and information provided by network providers in terms of the number of calls per day. The correlation was moderate with regard to cumulative use. Schuz (who is an author in the Berg paper)and Johansen (2007) also compared self-reported cell phone use with subscriber data. This information had been obtained in separate studies. They found "fair" agreement between the two sources of exposure assessment. They contend that both measures have limitations and may lead to a potential underestimation of an association. Like Auvinen et al., they are of the opinion that these limitations can be minimized in prospective follow-up studies, where exposure estimation would be based on records of subscriber use. SC Kim (2006) proposed a new method to estimate quantitative and relative RF exposure levels using a neural network model. The parameters that were used to develop this model were average usage time per day, total period of usage in years, SAR of the specific phone, hands-free usage, antenna extraction, and the type of phone (flip or folder).

Inyang et al. (2007) reviewed the different methods of exposure assessment in epidemiological studies of cell phones. They conclude that hardware-modified phones offer considerable advantages. They automatically log the call duration and number of calls, and thus avoid the potential for faulty recall by the study participant. These phones also capture the various tilts and rotations that occur in everyday use, and record power fluctuations of each call.

The above is concerned mainly with cell phone studies. In other radiofrequency radiation studies exposure is usually based either on geographic location (in the case of studies of radio and TV transmitters), or on job title (in occupational studies). Clearly, these methods of exposure assessment are imprecise. Some use death certificates as the source of information on occupation, something that has been shown to be quite inaccurate (Andrews KA, et al., Bioelectromagnetics 1999;20:512-518).

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