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Does exposure to environmental radiofrequency electromagnetic fields cause cognitive and behavioral effects in 10-year-old boys?

  1. Irene Calvente1,
  2. Rocío Pérez-Lobato1,
  3. María-Isabel Núñez1,2,3,
  4. Rosa Ramos1,
  5. Mònica Guxens3,4,5,
  6. Juan Villalba2,
  7. Nicolás Olea1,2,3 and
  8. Mariana F. Fernández1,2,3,*

Article first published online: 18 DEC 2015

DOI: 10.1002/bem.21951

Issue

Bioelectromagnetics

Bioelectromagnetics

Volume 37, Issue 1, pages 25–36, January 2016


Additional Information

How to Cite

Calvente, I., Pérez-Lobato, R., Núñez, M.-I., Ramos, R., Guxens, M., Villalba, J., Olea, N. and Fernández, M. F. (2016), Does exposure to environmental radiofrequency electromagnetic fields cause cognitive and behavioral effects in 10-year-old boys?. Bioelectromagnetics, 37: 25–36. doi: 10.1002/bem.21951

Author Information

  1. 1

    Instituto de Investigación Biosanitaria (ibs.GRANADA), Granada, Spain

  2. 2

    Department of Radiology, University of Granada, Spain

  3. 3

    CIBER de Epidemiología y Salud Pública (CIBERESP), Granada, Madrid, Spain

  4. 4

    Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain

  5. 5

    Pompeu Fabra University, Barcelona, Spain

* Correspondence to: Mariana F. Fernández, University of Granada;, Av. Madrid s/n; 18012, Granada, Spain. E-mail: marieta@ugr.es

  1. Conflict of interests: None.

Publication History

  1. Issue published online: 18 DEC 2015
  2. Article first published online: 18 DEC 2015
  3. Manuscript Accepted: 4 DEC 2015
  4. Manuscript Received: 13 NOV 2014

Funded by

  • Spanish Ministry of Health. Grant Numbers: FIS PI11/0610, FIS PI13/02406
  • Andalusia Regional Government, Consejería de Salud. Grant Number: SAS PI-0675-2010

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Keywords:

  • RF measurements;
  • exposure;
  • power density;
  • childhood;
  • cognitive effects;
  • behavior effects

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

The relationship between exposure to electromagnetic fields from non-ionizing radiation and adverse human health effects remains controversial. We aimed to explore the association of environmental radiofrequency-electromagnetic fields (RF-EMFs) exposure with neurobehavioral function of children. A subsample of 123 boys belonging to the Environment and Childhood cohort from Granada (Spain), recruited at birth from 2000 through 2002, were evaluated at the age of 9–11 years. Spot electric field measurements within the 100 kHz to 6 GHz frequency range, expressed as both root mean-square (SRMS) and maximum power density (SMAX) magnitudes, were performed in the immediate surrounds of childreńs dwellings. Neurocognitive and behavioral functions were assessed with a comprehensive battery of tests. Multivariate linear and logistic regression models were used, adjusting for potential confounders. All measurements were lower than reference guideline limits, with median SRMS and SMAX values of 285.94 and 2759.68 μW/m2, respectively. Most of the cognitive and behavioral parameters did not show any effect, but children living in higher RF exposure areas (above median SRMS levels) had lower scores for verbal expression/comprehension and higher scores for internalizing and total problems, and obsessive-compulsive and post-traumatic stress disorders, in comparison to those living in areas with lower exposure. These associations were stronger when SMAX values were considered. Although some of our results may suggest that low-level environmental RF-EMF exposure has a negative impact on cognitive and/or behavior development in children; given limitations in the study design and that the majority of neurobehavioral functioning tasks were not affected, definitive conclusions cannot be drawn. Bioelectromagnetics. 37:25–36, 2016. © 2015 Wiley Periodicals, Inc.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Environmental exposure to radio-frequency electromagnetic fields (RF-EMFs) has increased rapidly in recent years. Children are exposed to them in their daily activities, whether at home or in its surrounds, at school, or in parks, among other places [Ortega-Garcia et al., 2009; Urbinello et al., 2014]. Authors have pointed to the increased use of new technologies by children and teenagers, which can start at an early age, and to the rise in in utero exposure from this cause [WHO, 2005; Divan et al., 2008; Rosenberg, 2013]. There is particular concern about the potential effects of exposure to RF-EMF on children [Bakker et al., 2011], who may be more vulnerable than adults, although this question remains under debate [van Rongen et al., 2009]. It has been proposed that children could be more sensitive to RF-EMF because they are still in a physiological and psychological development period [Kheifets et al., 2005; Schüz, 2005]. Studies investigating potential causal relationships between RF-EMF exposure and adverse health outcomes have mostly focused on childhood cancers [Calvente et al., 2010; Teepen and van Dijck, 2012] and brain neoplasms [Li et al., 2012]. There has been little exploration of its effects on behavioral problems [Divan et al., 2008, 2012; Thomas et al., 2010a,b; Guxens et al., 2013], or psychosocial risk [Sansone and Sansone, 2013]. In fact, research on the potential effects of exposure to RF-EMFs on neurobehavioral function in children is scant and has largely focused on the association between cell phone use and behavioral problems [Divan et al., 2008, 2012; Thomas et al., 2010b; Feychting, 2011; Guxens et al., 2013]. Findings from the Danish National Birth Cohort showed a positive and dose-dependent relationship between cell phone use by mothers during pregnancy and behavioral problems in their offspring [Divan et al., 2008, 2012; Sudan et al., 2013]; however, this association was not supported by others [Guxens et al., 2013]. This discrepancy may be attributable to differences in outcome reporting (e.g., by parents and/or teachers) or exposure assessment (typically based on questionnaires) or to the presence of unmeasured confounding factors.

A critical review of 41 studies addressing the effects of RF-EMF exposure on human cognitive development concluded that state-of-the-art methods have not been fully implemented in bio-electromagnetic research into the effects of RF-EMF on cognition [Regel and Achermann, 2011]. The lack of a standardized protocol for reliably assessing RF-EMF induced changes in neurobehavioral performance may in part explain discrepancies among studies on the cognitive and behavioral effects of exposure. The wide variety of findings may also be attributable to methodological differences (e.g., in sample size, study group composition, experimental design, exposure setup, exposure conditions, and/or selection bias) [Hareuveny et al., 2011].

The aim of this study was to explore the association of environmental RF-EMF exposure with the neurobehavioral function of boys belonging to the Spanish Environment and Childhood “Infancia y Medio Ambiente-INMA” mother–child cohort study, at the age of 9–11 years. Exposure in the immediate surrounds of the dwellings of their families was assessed with spot electric field measurements in the 100 kHz to 6 GHz frequency range.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Study Population

The recruitment and characteristics of the study population were previously reported [Calvente et al., 2014, 2015]. Briefly, the study sample was drawn from the INMA cohort study, a population-based study in seven regions of Spain, which aims to explore the role of environmental pollutants in air, water, and diet during pregnancy and early childhood in relation to child growth and development. The INMA study protocol includes medical follow-ups of the children from birth through childhood as well as epidemiological questionnaires and biological sample collections. The present study included the INMA cohort set up in Granada (a province in Southern Spain). From October 2000 through July 2002, 668 mother–child pairs were recruited at delivery in the San Cecilio University Hospital of Granada (Spain) with the initial aim of investigating the association of chronic exposure to endocrine disrupting chemicals with urogenital malformations in newborn boys. The inclusion and exclusion criteria were published elsewhere [Freire et al., 2011]. When the children reached the age of 9–11 years (2011–2012), all families in the cohort were contacted and invited to participate in the new follow-up. Three hundred (44.9%) families gave their consent and completed an ad hoc questionnaire on their home environment, including a specific RF-EMF questionnaire. The follow-up also included assessment of the children's growth (by a single pediatrician) and neuropsychological and behavioral status (by a single psychologist). The study was approved by the Ethics Committee of San Cecilio University Hospital (Granada), and signed informed consent was obtained from the participants' families.

The setting of the INMA-Granada cohort is the health district of the San Cecilio University Hospital, including part of the city of Granada (236,000 inhabitants) and 50 towns and villages. Out of the 300 children/families enrolled in the study, the present work included the 123 (41%) families/dwellings for which outdoor RF-EMF measurements (surrounds of the dwellings) were finally carried out. Half of the dwellings (44.7%) were in an urban area (city of Granada), 45.5% in semi-urban areas (towns of >20,000 inhabitants in city residential belt), and 9.8% in rural areas (<20,000 inhabitants).

Environmental Exposure Assessment

Spot electric field measurements were performed in wideband mode between 2 and 4 p.m., recording the average measurement during 6-min periods according to national regulations. All measurements were considered to correspond to a far-field regime and free space. Measurements were made using a TS/001/UB Taoma base unit (Tecnoservizi, Rome, Italy) with a TS/004/EHF isotropic electric field probe. The frequency range analyzed was from 100 kHz to 6 GHz. The measurement range was from 0.2 to 340 V/m, and the quantification limit was 0.2 V/m (for the sum of all frequencies), well below even the most cautious guideline levels of the International Commission on Non-Ionizing Radiation Protection [ICNRP, 1998]. The probe incorporated a Global Positioning System (GPS) module and was equipped with sensors for recording temperature and humidity. The probe, connected to the base unit, was placed on an insulating tripod in the immediate surroundings of the dwelling (at a height of 1.45 m and at a mean distance of 2 m from childreńs houses), based on recommendations of the Institute of Electrical and Electronics Engineers for power frequency magnetic fields [IEEE, 1987]. RF-EMFs are usually expressed in terms of electric field or power density. In the present study, power density (S) magnitude was obtained from direct measurements, and the root mean-square of power density (SRMS) and maximum power density (SMAX) were calculated.

The most important sources of RF-EMF exposure to the general public are within the frequency range 100 kHz-6 GHz. Possible sources of RF-EMF exposure within this range include radio and TV stations and communication networks used by emergency services, the police, and transport management systems, among others (Supplementary Table 1).

Neuropsychological Measures

Neuropsychological function was evaluated with a comprehensive battery of tests at the Monitoring and Early Stimulation Unit of the San Cecilio University hospital by a neuropsychologist trained to administer the tests and interpret scores for the neuropsychological domains. Completion of these tests generally takes around 1 h, a sufficiently short period to sustain the attention of children of this age (9–11 years) and avoid fatigue.

Briefly, the cognitive battery includes [Pérez-Lobato et al., 2015]: (i) general cognitive intelligence, based on the composite Intelligence Quotient (IQ), assessed with the Kaufman Brief Intelligence Test (K-BIT); (ii) language, evaluated with the verbal scale of the K-BIT, which includes two subtests, verbal knowledge and general knowledge and riddles; (iii) attention, assessed with the Continuous Performance Test (CPT), which measures sustained and selective attention and impulsivity; (iv) verbal memory, evaluated with the Complutense-Spain Madrid Verbal Learning Test (TAVECI), which assesses different memory and learning processes, including immediate recall, short- and long-term recall, and recognition; (v) visual-motor coordination, assessed with part A of the Trail Making Test (TMT), which involves connecting consecutive numbers in an alternating sequence as quickly as possible; (vi) processing speed, measured by the sum of the results of two subtests (symbol search and coding) from the Wechsler Intelligence Scales for Children (WISC-IV); and (vii) executive function, divided into four components: (i) updating measures, with two components: working memory and verbal fluency; (ii) inhibition, with two components: the Spanish childreńs version of the Stroop Color and Word Test (STROOP), which measures cognition inhibition; and the go/no-go task, which measures motor inhibition, (iii) flexibility, measured by part B of the TMT, and (iv) abstract reasoning (matrix analogies test), measured with the non-verbal scale of the K-BIT.

Behavioral Problems

Behavioral function was evaluated with the Child Behavior Checklist (CBCL/6-18), a standardized parent report questionnaire. The CBCL provides eight syndrome scales grouped into three composite scales (Internalizing, Externalizing, and Total Problems), six DSM-IV oriented scales, and four competence scales, reported as both raw scores and sex- and age-normalized T-scores. Children with CBCL/6–18 T-scores ≥60 on internalizing or externalizing problem scales, T-scores ≥65 on diagnostic scales, and T-scores ≤30 on competence scales, were classified as normal or borderline/clinical cases, respectively.

Covariates

Information was gathered at the follow-up visit on parental and children socio-demographic characteristics, including marital status, maternal schooling (up to primary/secondary/university studies), smoking during pregnancy, and the age, weight, and height of the children, calculating their body mass index (BMI). Parents reported Wi-Fi coverage at home (yes/no) and whether their children had a cell phone (yes/no) and, if so, whether it was used (e.g., voice calls against the head, in speaker mode, or for data, etc.) or never used by the children. Rural and semi-urban areas were grouped together because they shared similar features in terms of the number of emission sources and frequency ranges. Thus, the number of substations/antennas (bands) never exceeded one in the semi-urban or rural areas, whereas more than two were always observed in the urban areas. Nevertheless, the studied zones do not fully represent the birth cohort study area.

Out of the 123 participating children, 4 were excluded because of the presence of chronic disease, related to attention deficit hyperactivity disorder (ADHD) (n = 1), language disorder (n = 1), Asperger syndrome (n = 1), or spina bifida (n = 1). Data on RF-EMF exposure, covariates, and neuropsychological and behavioral test scores were finally available for 119 (96.75%) participants.

Statistical Analysis

Descriptive analysis of neuropsychological and behavioral test results yielded arithmetic means and standard deviations (SDs), median, minimum, maximum values, and 25th and 75th percentiles, stratified by median power density (above or below 285.9 μW/m2). Frequencies for categorical variables were also calculated.

The Spearman correlation test was used for bivariate analyses of quantitative variables. The association between quantitative and categorical variables was analyzed with the Mann–Whitney test or Kruskal–Wallis test (for >2 variables), and the association between categorical variables with Pearson's χ2 test.

Exposure to RF-EMFs (SRMS and SMAX) was categorized into two groups, using the median value as cut-off point, and was also analyzed in tertiles. Exposure could not be treated as a continuous variable because many values were below the limit of quantification [Calvente et al., 2015]. All models were adjusted for potential confounders, selected a priori on the basis of previous studies, including smoking during pregnancy, maternal schooling (up to primary/secondary /university studies), place of residence (urban/suburban-rural), and internet/Wi-Fi access at home. These covariates were selected using a backward procedure.

Neuropsychological test results were analyzed as continuous variables based on the raw scores, because standardized scores for the Spanish child population were not available for all tests. Behavioral test results were analyzed as continuous variables based on standardized scores for the Spanish population. Linear and logistic regression models were constructed to explore the association of RF-EMF exposure with neuropsychological and behavioral test scores. Logistic regression models were used to estimate the risk of obtaining scores above the 80th percentile (TMTA, TMTB) or below the 20th percentile (other tests) as a function of exposure levels. These percentiles were selected to enhance the detection of low or borderline/clinical performance, as proposed by Jacobson and Jacobson [2005]. Logistic models were also constructed to estimate the risk (OR; 95%CI) of obtaining borderline/clinical scores (as explained above) [Donders, 1969].

We assessed collinearity between independent variables, linearity of independent quantitative variables, and homoscedasticity of linear models. Significance level was set at P ≤ 0.05, following recommendations for the evaluation of exposure-outcome relationships in the public health setting. Data analyses were performed using SPSS v20.0 (IBM, Chicago, IL) and R-Commander free software (R i386 3.0.1 version; http://www.r-project.org).

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Table 1 shows characteristics of the study population by exposure category. The mean age (± standard deviation) of the children was 9.89 ± 0.32 yrs; 25.21% of mothers had higher education and 44.54% primary schooling; 43.7% of participating families lived in urban areas; 24.79% of mothers reported smoking during pregnancy, and 89.3% had a stable partner. A cell phone was possessed by 97.5% of the children but only 6.0% of the children used it. At the time of the study, a higher percentage of fathers vs. mothers was employed (83.1% vs. 65.8%, respectively), and the percentage of families with a low income, defined according to the Organization for Economic Co-operation and Development (OECD), was 61.9% (data not shown).

Table 1. Characteristics of Study Population (n = 119)
 Mean ± SDMinMedianp25p75Max
Children age (years)9.9 ± 0.39.09.89.710.011.2
Children BMI (Kg/m2)18.7 ± 3.31319162129
  SRMS < 285.9 (μW/m2)SRMS ≥ 285.9 (μW/m2)SMAX ≤ 2759.68 (μW/m2)SMAX > 2759.68 (μW/m2)
 n (%)n (%)n (%)n (%)n (%)

  1. BMI, body mass index (Kg/m2); Mean, arithmetic mean; SD, standard deviation; p, percentile; Min, minimum; Max, maximum. *In reference to children; #No, never; SRMS, Root mean-square power density; SMAX, Maximum power density.

Smoking during pregnancy     
No89 (75.2)47 (53.4)41 (46.6)48 (54.5)40 (45.5)
Yes30 (24.8)12 (41.4)17 (58.6)12 (41.4)17 (58.6)
Maternal schooling     
Up to primary53 (44.5)27 (50.9)26 (49.1)23 (43.4)30 (56.6)
Secondary studies36 (30.2)17 (47.2)19 (52.8)21 (58.3)15 (41.7)
University studies30 (25.2)15 (50.0)15 (50.0)16 (53.3)14 (46.7)
Place of residence     
Urban52 (43.7)28 (53.8)24 (46.2)31 (59.6)21 (40.4)
Semi-urban/rural67 (56.3)31 (46.3)36 (53.7)29 (43.3)38 (56.7)
Have mobile phone*     
Yes116 (97.5)————
No3 (2.5)————
Use mobile phone*     
Yes10 (8.4)————
No#109 (91.6)————

Median SRMS and SMAX values in the immediate surrounds of the childreńs dwellings were 285.94 μW/m2 and 2759.68 μW/m2, respectively, with a range of 5.51–11559.55 μW/m2 and 2.39–150001.06 μW/m2, respectively. Maximum SRMS and SMAX values were 11559.55 μW/m2 and 150001.06 μW/m2, respectively. All measurements obtained were below the reference limit. The mean distance from the dwellings to mobile phone base stations/aerials emitting GSM 900 and GSM 1800 was 660.87 ± 717.48 m, with a minimum distance of 35 m and maximum of 5000 m; 50% of the dwellings were within 500 m.

Most of the children appeared highly motivated to complete the cognitive test battery and showed no inattention or fatigue symptoms. Mean (SD) standardized IQ score was 108.20 (11.80) points. No differences were found between the children with and without RF-EMF exposure measurements (119 vs. 181 subjects) in cognitive or behavioral function scores or in parent characteristics. Supplementary Tables 2 and 3 exhibit raw cognitive function scores and standardized behavioral function scores according to the median RF-EMF exposure level. Figure 1 depicts the association between cognitive functions and some behavioral problems as a function of Power Density (SRMS μW/m2).

image

Figure 1. Association of Radiofrequency Electromagnetic Field [Root Mean Square Power Density (SRMS μW/m2)] exposure with two cognitive functions (IQ and verbal expression/comprehension) and some behavioral problems (anxious/depressed symptoms; internalizing symptoms, obsessive compulsive disorder, and post-traumatic stress disorder).

Download figure to PowerPoint

The association between RF-EMF exposure and cognitive functioning was examined using multivariable linear regression models. Unadjusted analysis showed a negative relationship between children in higher exposure areas (SRMS ≥ 285.94 μW/m2) and several neuropsychological test scores in comparison to children in lower exposure areas, which was statistically significant for IQ (P = 0.05) and verbal expression and comprehension (P = 0.03). However, after adjustment for covariates (child́s place of residence, maternal schooling, maternal smoking during pregnancy, and Wi-Fi), only verbal expression and comprehension remained significant (Table 2). The results for exposure in tertiles were consistent with the findings obtained with the dichotomous categorization of exposure in relation to verbal expression and comprehension (Supplementary Table 4). Multivariable logistic regression analysis of the association between RF-EMF exposure and cognitive functioning revealed a higher risk of worse flexibility [OR = 3.90; 95%CI = (1.37–12.95); P = 0.01] in the children with SRMS ≥ 285.94 μW/m2 (data not shown).

Table 2. Association Between RF-EMF Exposure Levels and Cognitive Development in Children From INMA-Granada Cohort (n = 119)
 SRMS ≥ 285.9 (μW/m2)SMAX ≥ 2759.68 (μW/m2)
 Crude modelAdjusted modelCrude modelAdjusted model
 βSEPβSEPβSEPβSEP

  • β, linear regression coefficient; SE, standard error; RF-EMFs, radiofrequency electromagnetic fields given as power density (S); RMS, root mean-square; MAX, maximum. Adjusted for child́s place of residence, smoking during pregnancy, maternal schooling and Wi-Fi.

  • Direct scores were used for all tests.

  • Bolded values signify P ≤ 0.05.

  • a

    Higher score indicates better cognitive function.

  • b

    Higher score indicates worse cognitive function.

Intelligence quotienta−7.834.030.05−7.193.740.06−11.033.96<0.01−8.493.770.03
Verbal expression and comprehensiona−2.080.940.03−1.910.880.03−2.760.92<0.01−2.340.880.01
Attention            
Impulsivityb3.222.380.183.222.380.182.952.300.202.862.420.24
Attention Indexa−0.010.050.82−0.010.050.80−0.020.050.63−0.020.050.74
Verbal memorya            
Short-term recall−0.260.400.52−0.350.410.38−0.450.390.26−0.500.410.22
Long-term recall−0.100.430.82−0.160.440.72−0.540.430.21−0.520.450.25
Visual-motor coordinationb1.681.890.381.451.900.451.921.890.311.181.930.54
Processing speeda−0.682.300.77−0.682.300.77−2.972.240.19−2.752.320.24
Executive functions            
Working memorya0.290.470.540.290.470.540.020.490.960.230.480.63
Verbal fluencya−0.480.740.51−0.480.740.51−0.230.710.75−0.160.750.84
Impulsivity/inhibition            
Interferencea−0.530.990.60−0.530.990.59−0.410.960.67−0.581.010.57
Hit ratea<0.01<0.010.56<0.01<0.010.56<−0.01<0.010.82<0.01<0.010.95
False-alarm rateb<0.01<0.010.65<0.010.010.650.010.010.150.010.010.15
Flexibilityb12.356.420.0611.426.630.0912.616.410.0510.706.730.12
Abstract reasoninga−0.590.810.47−0.590.810.47−1.430.830.09−1.060.820.20

Multivariable linear regression models were also used to examine the relationship between cognitive functioning and RF-EMF exposure considered as the maximum power density (SMAX). As shown in Table 2, unadjusted analysis showed a negative relationship between children in higher exposure areas [SMAX ≥ 2759.68 μW/m2 (median value)] and certain neuropsychological test scores in comparison to children in lower exposure areas (SMAX < 2759.68 μW/m2); this negative association was statistically significant for IQ score (P = 0.03) and verbal expression and comprehension ability (P = 0.01) in the adjusted model. The results for exposure in tertiles were consistent with those obtained for the dichotomous categorization of exposure in relation to internalizing and total problems and obsessive-compulsive and post-traumatic stress disorders (Supplementary Table 5). Multivariable logistic regression models revealed a significantly higher risk of a score < P20 in verbal expression and comprehension test in children from higher exposure areas (OR = 3.37; 95%CI = 1.34–9.08; P = 0.01) (data not shown).

The relationship between RF-EMF exposure and behavioral functioning was also explored. Unadjusted multivariable linear regression analysis showed that anxious-depressed behaviors, social problems, rule-breaking, total problems, obsessive compulsive disorder (OCD), and posttraumatic stress disorder (PTSD) were positively and significantly associated with higher (SRMS ≥ median) vs. lower exposure (Table 3). When the model was adjusted for the childreńs place of residence, maternal schooling, maternal smoking during pregnancy, and Wi-Fi, the associations with anxious-depressed behaviors, social problems, OCD, and PTSD remained statistically significant (Table 3). No significant results were obtained in the multivariable logistic regression analysis (data not shown).

Table 3. Association Between RF-EMFs Exposure Levels and Behavioral Tests in Children From INMA-Granada Cohort (n = 119)
 SRMS ≥ 285.9 (μW/m2)SMAX ≥ 2759.68 (μW/m2)
 Crude modelAdjusted modelCrude modelAdjusted model
 βSEPβSEPβSEPβSEP

  1. * Typical scores were used for all tests. Internalizing problems include anxious/depressed, withdrawn/depressed, and somatic complains; externalizing problems include rule breaking and aggressive behavior; total problems include eight individual scores. Linear regression model adjusted for child's place of residence, smoking during pregnancy, maternal schooling, and Wi-Fi; β: linear regression coefficient; SE, standard error; RF-EMFs, radiofrequency electromagnetic fields are given as power density (S); RMS, root mean-square; MAX, maximum; DSM, diagnostic and statistical manual of mental disorders; ADHD, attention deficit hyperactivity disorder.

  2. Bolded values signify P ≤ 0.05.

Individual scores (typical scores)*            
Anxious/depressed2.481.230.052.511.240.053.341.210.013.531.240.01
Withdrawn/depressed1.891.280.141.781.300.171.801.280.161.681.320.21
Somatic complaints0.911.160.440.621.130.581.041.160.370.521.150.65
Social problems1.860.860.031.690.850.051.870.850.031.790.870.04
Thought problems1.311.160.260.861.170.461.871.150.111.561.180.19
Attention problems1.951.030.061.711.020.102.221.030.031.931.040.07
Rule Breaking1.991.010.051.730.980.083.540.98<0.013.010.97<0.01
Aggressive behavior1.521.040.151.281.040.222.591.020.012.221.040.04
Composite scores            
Internalizing problems2.861.490.062.711.450.063.091.480.042.961.470.05
Externalizing problems1.611.750.361.311.720.453.351.730.062.971.740.09
Total problems3.221.580.042.871.520.064.171.560.013.831.530.01
DSM-oriented scales            
Affective problems1.601.170.171.281.150.272.021.160.081.791.170.13
Anxiety problems2.181.390.122.081.390.142.851.370.043.011.400.03
Somatic problems-0.361.180.76-0.621.120.580.361.180.76-0.071.140.95
ADHD problems1.621.070.131.241.050.242.651.050.012.201.060.04
Oppositional-defiant0.970.950.310.730.940.441.600.940.091.370.950.15
Conduct problems1.610.980.101.340.950.162.840.95<0.012.280.950.02
Obsessive compulsive disorder2.381.160.042.521.190.042.681.160.022.931.200.02
Posttraumatic stress disorder3.231.210.012.871.190.023.541.21<0.013.131.210.01
Competences            
School competence−2.021.110.071−1.831.100.10−2.731.090.01−2.351.110.04
Social competence−0.811.510.592−0.621.490.68−0.341.510.820.071.520.96

The relationship between SMAX and behavioral functioning was examined with multivariable linear regression models. Adjusted analysis showed a positive association between children with SMAX ≥ 2759.68 μW/m2 and several behavior scores, which was statistically significant for anxious/depressed behaviors (P < 0.01), social problems (P = 0.04), rule-breaking (P < 0.01), aggressive behavior (P = 0.04), internalizing (P = 0.05), total problems (P = 0.01), anxiety problems (P = 0.03), ADHD (P = 0.04), conduct problems (P = 0.02), OCD (P = 0.02), and PTSD (P = 0.01). A negative association was found with school competence (P = 0.04) (Table 3).

Multivariable logistic regression analysis found no significant relationship between RF-EMF exposure (SMAX) and behavioral functioning.

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Environmental exposure to RF-EMF appeared to be associated with worse verbal expression/comprehension and with a few behavioral problems (internalizing and total problems, obsessive-compulsive and post-traumatic stress disorders) in the children in this study; however, the majority of neurobehavioral functioning tasks were not affected. Thus, children with higher exposure levels (SRMS ≥ 285.9 μW/m2) in the immediate surrounds of their dwellings had lower verbal expression/comprehension scores and higher behavioral and emotional problems, including anxious-depressed behaviors, OCD, and PTSD, in comparison to those in lower exposure areas levels (SRMS < 285.9 μW/m2). When exposure was measured as SMAX, similar results were observed for cognitive functions but worse results for internalizing and total behavior problems, finding lower IQ and school competence scores and higher ADHD and social and conduct problems (aggressive and rule-breaking behaviors) in children from higher (SMAX ≥ 2759.68 μW/m2) vs. lower exposure areas. The prevalence of total behavior problems was greater with higher exposure. Overall, 8.6% of the children were classified as borderline/clinical in the lower exposure group vs. 20.3% of those in the higher exposure group. Nevertheless, there are a number of issues that need to be critically considered.

In the present study, direct measurements of environmental exposure were conducted in the immediate surrounds of the children's dwellings. Most researchers have analyzed the association between RF-EMF exposure and effects on neurobehavioral function in children by considering self-reported cell phone use by pregnant mothers or children as proxies of exposure. Only a few studies have directly measured environmental or individual exposure, for example, with spot measurements or personal dosimeters [Barth et al., 2008; Berg-Beckhoff et al., 2009; Frei et al., 2010; Heinrich et al., 2010, 2011; Thomas et al., 2010].

Although it is difficult to compare results among studies, environmental exposure levels in the present study were within the range of RF levels described in Europe [Calvente et al., 2015]. Thus, mean Savg values were lower but median Savg values higher than those reported by Tomitsch and Dechant [2012, 2015]. Power density levels were also lower than those from mobile phone base station antennas measured by Abdel-Rassoul et al. [2007]. Moreover, all of measured exposure values in our study were several orders below current ICNIRP guideline limit [1998] recommended for the general population, in line with reports by Heinrich et al., [2010, 2011] and Thomas et al. [2010].

Few studies have investigated the possible adverse health effects of RF-EMF exposure in children, who may be more vulnerable than adults to EMF-NIR [Kheifets et al., 2005; Schüz, 2005]. For instance, the lower bone density and lesser amount of fluid in the brains of children vs. adults may result in a deeper cerebral absorption of larger amounts of RFs [Christ et al., 2010]. However, evidence that children are indeed more vulnerable to this exposure remains scant [Otto and von Mühlendahl, 2007; Leung et al., 2011; BioInitiative, 2012], and some studies have shown that effects in children did not differ from those in healthy adults [van Rongen et al., 2009; Croft et al., 2010; Segalowitz et al., 2010; Feychting, 2011; Valentini et al., 2011; Loughran et al., 2013]. The direct impact of RF exposure on neurodevelopment remains unknown, and the mechanisms that may be involved are poorly understood [Regel and Achermann, 2011; Loughran et al., 2013]; nevertheless, its negative health effects cannot be ruled out [Wiedemann and Schütz, 2011], and some scientific reports of adverse effects may indicate that a reduction in exposure is warranted as a preventive measure, especially for children [Hardell and Sage, 2008; Divan et al., 2010; Rosenberg, 2013; Redmayne, 2015].

Some cognitive effects of short-term experimental exposure to RF-EMF fields were previously reported in a meta-analysis that found small but significant pooled effects of RF exposure on attention and working memory [Barth et al., 2008]. A possible cognitive effect of mobile phone use was also investigated among Australian young adolescents (12–13 years), taking into account both total voice calls and short message service (SMS) messages made and received per week. Poorer accuracy of working memory, shorter response time on learning tasks, and poorer inhibitory function were observed among students with greater exposure [Abramson et al., 2009]. Nevertheless, the follow-up of participants at one year showed changes in reaction times but not in accuracy [Thomas et al., 2010b]. Finally, according to a critical review of 41 studies addressing the effects of RF-EMF exposure on human cognitive development, no specific cognitive task appears especially susceptible to RF EMF exposure [Regel and Achermann, 2011].

A possible association between measured exposure to RF-EMF fields and behavioral problems was investigated among Bavarian children and adolescents using personal dosimeters. The highest quartile of exposure was associated with overall behavioral problems for adolescents (OR 2.2; 95%CI 1.1–4.5) but not for children (1.3; 95%CI 0.7–2.6) [Thomas et al., 2010].

A study in Egypt based on power density values provided by the National Telecommunications Institute reported that adults living near mobile phone base stations and exposed to higher RF-EMF evidenced a significantly lower performance in attention and short-term auditory memory. However, the authors concluded that further research was required to establish a causal relationship between exposure to RF-EMF emitted by mobile phone base stations and neurobehavioral dysfunction [Abdel-Rassoul et al., 2007].

The strengths of our study include the direct measurement of environmental exposure to RF-EMF and the analysis of its relationship with cognitive and behavioral functioning in healthy school children, on which few published data are available. The children belonged to a prospective birth cohort that has been followed over ten years, yielding data on multiple covariates since birth. The programming, measurement, and analysis were performed by the same person, reducing the potential variability in measurements and improving the comparability of results; and the evaluation of neurodevelopment was performed by a single psychologist blinded to the RF exposure status of the children, using a wide battery of tests. There is no consensus on the most appropriate instruments for identifying cognitive and behavioral problems in children. We used a comprehensive battery of neuropsychological tests and behavioral assessments at the age of 9–11 years, a time window that allows a wide range of cognitive and behavioral functions to be examined with sensitive and specific tests [Ramos et al., 2013].

Ideally, exposure assessment combines personal dosimeter readings with exposure data on the multiple indoor and outdoor locations in which subjects spend time [Martens et al., 2015]. Personal dosimeters are considered to provide the best assessment of individual RF-EMF exposure, although this may be underestimated by these devices [Heinrich et al., 2010, 2011; Neubauer et al., 2010]. A relevant issue for the present study design is that the utilization of personal dosimeters is especially challenging in children [Juhász et al., 2011]. We used spot measurements as a proxy of exposure because they did not rely on the compliance of our young study population and were less costly. Spot measurements have also been described as more accurate and less prone to bias in comparison to self-reported exposure [Heinrich et al., 2010], and Gryz et al. [2015] concluded that the uncertainty of exposure assessments was significantly higher with the use of a single exposimeter in comparison to spot measurements. Nevertheless, spot measurements outside the home have been described as inadequate surrogates of individual exposure [Martens et al., 2015], and this study limitation should be taken into account in interpreting our results.

The present findings should also be interpreted with caution because statistical significance was only reached for one cognitive function and a few behavioral tasks, which may be due to chance or to the performance of multiple comparisons. Furthermore, as the design of the study was cross-sectional and the exposure and neurodevelopment were only assessed at one time point, it is not possible to determine whether the RF-EMF exposure had affected cognitive function or whether these findings represented pre-existing cognitive and/or behavioral development. Other methodological limitations that need to be taken into consideration include the lack of control for potential confounders, for example, pubertal development or maturity of the child. In addition, the influence of individual variability in development may play a role in exposure-related effects, as highlighted by other authors [Croft et al., 2010, Segalowitz et al., 2010]. Our population only comprised boys; hence, these results cannot be extrapolated to girls, given the gender differences in social and cultural factors and their relationship to psychological disorders. Finally, it should also be taken into account that the observed effects on cognitive and behavioral abilities may have been mediated by other socio-cultural, economic, or genetic variables that were not controlled for in this study, such as breastfeeding, paternal psychological problems, or exposure to environmental contaminants, among others.

CONCLUSIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Some of the present findings may suggest that low-level environmental exposure to RF-EMFs has a negative impact on cognitive and/or behavioral development in children; however, given limitations in the study design and that the majority of neurobehavioral functioning tasks were not affected, definitive conclusions cannot be drawn. Further research is warranted to elucidate the potential risks of long-term exposure and to investigate the underlying mechanisms. A more standardized research approach is needed to reveal meaningful results on which risk assessment can be soundly based after evaluation of the relevance of any effects.

ACKNOWLEDGMENTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

The authors are grateful to all participating INMA families for their cooperation and to Richard Davies for editorial assistance. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Confinanciado por Fondos Feder.

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  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Additional supporting information may be found in the online version of this article at the publisher's web-site.

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bem21951-sup-0005-SuppTable-S5.docx29KSupporting Table S5.

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