Time course of health complaints attributed to RF-EMF exposure and predictors of electromagnetic hypersensitivity over 10 years in a prospective cohort of Dutch adults.

BACKGROUND: Some individuals attribute health complaints to radiofrequency electromagnetic field (RF-EMF) exposure. This condition, known as idiopathic environmental intolerance attributed to RF-EMFs (IEI-RF) or electromagnetic hypersensitivity (EHS), can be disabling for those who are affected. In this study we assessed factors related to developing, maintaining, or discarding IEI-RF over the course of 10 years, and predictors of developing EHS at follow-up using a targeted question without the condition of reporting health complaints attributed to RF-EMF exposure. METHODS: Participants (n = 892, mean age 50 at baseline, 52 % women) from the Dutch Occupational and Environmental Health Cohort Study AMIGO filled in questionnaires in 2011/2012 (T0), 2013 (T1), and 2021 (T4) where information pertaining to perceived RF-EMF exposure and risk, non-specific symptoms, sleep problems, IEI-RF, and EHS was collected. We fitted multi-state Markov models to represent how individuals transitioned between states ("yes", "no") of IEI-RF. RESULTS: At each time point, about 1 % of study participants reported health complaints that they attributed to RF-EMF exposure. While this percentage remained stable, the individuals who reported such complaints changed over time: of nine persons reporting health complaints at T0, only one reported IEI-RF at both T1 and T4, and two newly reported health complaints at T4. Overall, participants had a 95 % chance of transitioning from "yes" to "no" over a time course of 10 years, and a chance of 1 % of transitioning from "no" to "yes". Participants with high perceived RF-EMF exposure and risk had a general tendency to move more frequently between states. CONCLUSIONS: We observed a low prevalence of IEI-RF in our population. Prevalence did not vary strongly over time but there was a strong aspect of change: over 10 years, there was a high probability of not attributing symptoms to RF-EMF exposure anymore. IEI-RF appears to be a more transient condition than previously assumed.

Longitudinal associations between risk appraisal of base stations for mobile phones, radio or television and non-specific symptoms.

INTRODUCTION: Studies found that higher risk appraisal of radiofrequency electromagnetic fields is associated with reporting more non-specific symptoms such as headache and back pain. There is limited data available on the longitudinal nature of such associations and what aspects of risk appraisal and characteristics of subjects are relevant. OBJECTIVE: To examine cross-sectional and longitudinal associations between risk appraisal measures and non-specific symptoms, and assess the role of subject characteristics (sex, age, education, trait negative affect) in a general population cohort. METHODS: This study was nested in the Dutch general population AMIGO cohort that was established in 2011/2012, when participants were 31-65 years old. We studied a sample of participants (n = 1720) who filled in two follow-up questionnaires in 2013 and 2014, including questions about perceived exposure, perceived risk, and health concerns as indicators of risk appraisal of base stations, and non-specific symptoms. RESULTS: Perceived exposure, perceived risk, and health concerns, respectively, were associated with higher symptom scores in cross-sectional and longitudinal analyses. Only health concerns (not perceived exposure and perceived risk) temporally preceded high symptom scores and vice versa. Female sex, younger age, higher education, and higher trait negative affect were associated with higher risk appraisal of mobile phone base stations. DISCUSSION: The findings in this study strengthen the evidence base for cross-sectional and longitudinal associations between higher risk appraisal and non-specific symptoms in the general population. However, the directionality of potential causal relations in non-sensitive general population samples should be examined further in future studies, providing information to the benefit of risk communication strategies.

Modeled and perceived RF-EMF, noise and air pollution and symptoms in a population cohort. Is perception key in predicting symptoms?

BACKGROUND: Psychosocial research has shown that perceived exposure can influence symptom reporting, regardless of actual exposure. The impact of this phenomenon on the interpretation of results from epidemiological research on environmental determinants of symptoms is unclear. OBJECTIVE: Our aim was to compare associations between modeled exposures, the perceived level of these exposures and reported symptoms (non-specific symptoms, sleep disturbances, and respiratory symptoms) for three different environmental exposures (radiofrequency electromagnetic fields (RF-EMF), noise, and air pollution). These environmental exposures vary in the degree to which they can be sensorially observed. METHODS: Participant characteristics, perceived exposures, and self-reported health were assessed with a baseline (n = 14,829, 2011/2012) and follow-up (n = 7905, 2015) questionnaire in the Dutch population-based Occupational and Environmental Health Cohort (AMIGO). Environmental exposures were estimated at the home address using spatial models. Cross-sectional and longitudinal regression models were used to examine the associations between modeled and perceived exposures, and reported symptoms. RESULTS: The extent to which exposure sources could be observed by participants likely influenced correlations between modeled and perceived exposure as correlations were moderate for air pollution (rSp = 0.34) and noise (rSp = 0.40), but less so for RF-EMF (rSp = 0.11). Perceived exposures were consistently associated with increased symptom scores (respiratory, sleep, non-specific). Modeled exposures, except RF-EMF, were associated with increased symptom scores, but these associations disappeared or strongly diminished when accounted for perceived exposure in the analyses. DISCUSSION: Perceived exposure has an important role in symptom reporting. When environmental determinants of symptoms are studied without acknowledging the potential role of both modeled and perceived exposures, there is a risk of bias in health risk assessment. However, the etiological role of exposure perceptions in relation to symptom reporting requires further research.

Modeled and Perceived Exposure to Radiofrequency Electromagnetic Fields From Mobile-Phone Base Stations and the Development of Symptoms Over Time in a General Population Cohort.

We assessed associations between modeled and perceived exposure to radiofrequency electromagnetic fields (RF-EMF) from mobile-phone base stations and the development of nonspecific symptoms and sleep disturbances over time. A population-based Dutch cohort study, the Occupational and Environmental Health Cohort Study (AMIGO) (n = 14,829; ages 31-65 years), was established in 2011/2012 (T0), with follow-up of a subgroup (n = 3,992 invited) in 2013 (T1; n = 2,228) and 2014 (T2; n = 1,740). We modeled far-field RF-EMF exposure from mobile-phone base stations at the home addresses of the participants using a 3-dimensional geospatial model (NISMap). Perceived exposure (0 = not at all; 6 = very much), nonspecific symptoms, and sleep disturbances were assessed by questionnaire. We performed cross-sectional and longitudinal analyses, including fixed-effects regression. We found small correlations between modeled and perceived exposure in AMIGO participants at baseline (n = 14,309; rSpearman = 0.10). For 222 follow-up participants, modeled exposure increased substantially (>0.030 mW/m2) between T0 and T1. This increase in modeled exposure was associated with an increase in perceived exposure during the same time period. In contrast to modeled RF-EMF exposure from mobile-phone base stations, perceived exposure was associated with higher symptom reporting scores in both cross-sectional and longitudinal analyses, as well as with sleep disturbances in cross-sectional analyses.

Residential exposure to RF-EMF from mobile phone base stations: Model predictions versus personal and home measurements.

INTRODUCTION: Geospatial models have been demonstrated to reliably and efficiently estimate RF-EMF exposure from mobile phone base stations (downlink) at stationary locations with the implicit assumption that this reflects personal exposure. In this study we evaluated whether RF-EMF model predictions at the home address are a good proxy of personal 48h exposure. We furthermore studied potential modification of this association by degree of urbanisation. METHOD: We first used an initial NISMap estimation (at an assumed height of 4.5m) for 9563 randomly selected addresses in order to oversample addresses with higher exposure levels and achieve exposure contrast. We included 47 individuals across the range of potential RF-EMF exposure and used NISMap to re-assess downlink exposure at the home address (at bedroom height). We computed several indicators to determine the accuracy of the NISMap model predictions. We compared residential RF-EMF model predictions with personal 48h, at home, and night-time (0:00-8:00AM) ExpoM3 measurements, and with EME-SPY 140 spot measurements in the bedroom. We obtained information about urbanisation degree and compared the accuracy of model predictions in high and low urbanised areas. RESULTS: We found a moderate Spearman correlation between model predictions and personal 48h (rSp=0.47), at home (rSp=0.49), at night (rSp=0.51) and spot measurements (rSp=0.54). We found no clear differences between high and low urbanised areas (48h: high rSp=0.38, low rSp=0.55, bedroom spot measurements: high rSp=0.55, low rSp=0.50). DISCUSSION: We achieved a meaningful ranking of personal downlink exposure irrespective of degree of urbanisation, indicating that these models can provide a good proxy of personal exposure in areas with varying build-up.

Somatic symptom reports in the general population: Application of a bi-factor model to the analysis of change.

OBJECTIVE: To investigate the latent structure of somatic symptom reports in the general population with a bi-factor model and apply the structure to the analysis of change in reported symptoms after the emergence of an uncertain environmental health risk. METHODS: Somatic symptoms were assessed in two general population environmental health cohorts (AMIGO, n=14,829 & POWER, n=951) using the somatization scale of the four-dimensional symptom questionnaire (4DSQ-S). Exploratory bi-factor analysis was used to determine the factor structure in the AMIGO cohort. Multi-group and longitudinal models were applied to assess measurement invariance. For a subsample of residents living close to a newly introduced power line (n=224), we compared a uni- and multidimensional method for the analysis of change in reported symptoms after the power line was put into operation. RESULTS: We found a good fit (RMSEA=0.03, CFI=0.98) for a bi-factor model with one general and three symptom specific factors (musculoskeletal, gastrointestinal, cardiopulmonary). The latent structure was found to be invariant between cohorts and over time. A significant increase (p<.05) was found only for musculoskeletal and gastrointestinal symptoms after the power line was put into operation. CONCLUSIONS: In our study we found that a bi-factor structure of somatic symptoms reports was equivalent between cohorts and over time. Our findings suggest that taking this structure into account can lead to a more informative interpretation of a change in symptom reports compared to a unidimensional approach.

Validity of at home model predictions as a proxy for personal exposure to radiofrequency electromagnetic fields from mobile phone base stations.

BACKGROUND: Epidemiological studies on the potential health effects of RF-EMF from mobile phone base stations require efficient and accurate exposure assessment methods. Previous studies have demonstrated that the 3D geospatial model NISMap is able to rank locations by indoor and outdoor RF-EMF exposure levels. This study extends on previous work by evaluating the suitability of using NISMap to estimate indoor RF-EMF exposure levels at home as a proxy for personal exposure to RF-EMF from mobile phone base stations. METHODS: For 93 individuals in the Netherlands we measured personal exposure to RF-EMF from mobile phone base stations during a 24h period using an EME-SPY 121 exposimeter. Each individual kept a diary from which we extracted the time spent at home and in the bedroom. We used NISMap to model exposure at the home address of the participant (at bedroom height). We then compared model predictions with measurements for the 24h period, when at home, and in the bedroom by the Spearman correlation coefficient (rsp) and by calculating specificity and sensitivity using the 90th percentile of the exposure distribution as a cutpoint for high exposure. RESULTS: We found a low to moderate rsp of 0.36 for the 24h period, 0.51 for measurements at home, and 0.41 for measurements in the bedroom. The specificity was high (0.9) but with a low sensitivity (0.3). DISCUSSION: These results indicate that a meaningful ranking of personal RF-EMF can be achieved, even though the correlation between model predictions and 24h personal RF-EMF measurements is lower than with at home measurements. However, the use of at home RF-EMF field predictions from mobile phone base stations in epidemiological studies leads to significant exposure misclassification that will result in a loss of statistical power to detect health effects.