subjective symptoms related to gsm radiation from mobile ... · symptoms. 3) this work presents a...

For peer review only

Subjective Symptoms Related to GSM Radiation from Mobile Phone Base Stations

Journal: BMJ Open

Manuscript ID: bmjopen-2013-003836

Article Type: Research

Date Submitted by the Author: 16-Aug-2013

Complete List of Authors: Gomez-Perretta, Claudio; La Fe, Hospital, Research Center Navarro, Enrique; University of Valencia, Applied Physics Segura, Jaume; University of Valencia, Computer Science Portolés, Manuel; La Fe, Hospital, Research Center

<b>Primary Subject Heading</b>:

Public health

Secondary Subject Heading: Occupational and environmental medicine, Epidemiology

Keywords: PUBLIC HEALTH, OCCUPATIONAL & INDUSTRIAL MEDICINE, EPIDEMIOLOGY, Health & safety < HEALTH SERVICES ADMINISTRATION & MANAGEMENT

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

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Subjective Symptoms Related to GSM Radiation

1

Subjective Symptoms Related to GSM Radiation from Mobile Phone

Base Stations

Corresponding autor:

Enrique A. Navarro

Departamento de Física Aplicada, Universitat de València

C/. Dr. Moliner 50,

46100 Burjassot, Valencia, Spain

Email: [email protected]

Authors

Claudio Gómez-Perretta (1)

Enrique A. Navarro (2)

Jaume Segura (3)

Manuel Portolés (1)

(1) Research Center, Universitary Hospital La Fe, Avda Campanar s/n, 46009 Valencia, Spain

(2) Departament of Applied Physics, Universitat de València, C/. Dr. Moliner 50, 46100 Burjassot

(València), Spain.

(3) Departament of Computer Sciences, ETSE- Universitat de València, Avda Universitat s/n,

46100 Burjassot (València), Spain.

Running title


Keywords: Public health; GSM; Microwave Sickness.

Word Count: 3674

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Abstract

Objectives

This article focuses on the possible effects of exposure to radiofrequencies emitted by mobile

phone base stations. The possible influence of other variables such as demographic data and use of

electronic devices was also analysed. Since concerns have been raised about possible health

consequences caused by the emitted microwaves, in the present work we analyse if these

symptoms might be related to fear to the exposure, and therefore having a subjective basis.

Method

Binary logistic regression was used as the main statistical model because our target variables were

dichotomised variables (with or without symptoms) and the independents were either categorical or

continuous. This analysis was carried out on a regular basis and bootstrapping [1000 bootstrap

replications, 95% percentile method, and simple sampling] was used to provide more accurate

standard errors and confidence intervals. Various covariates were taken into account: age; gender;

radiofrequency electromagnetic fields (RF EMFs) from GSM technology; use of computers; use of

mobile (cellular) phones as well as the level of worry about the mobile phone base stations (BSs).

Results

The univariate logistic regression indicated that most of the symptoms were from weak to moderate

related with GSM exposure. Within the moderately affected were: lack of appetite and

concentration, irritability and sleep troubles. Change in -2 log likelihood showed similar results.

Moreover, gender did not influence the results, with the oldest having lighter difficulties for

hearing and walking. Besides, the use of mobile phones was strongly associated with lack of

appetite and moderately with vertigo, while worry about the BSs was strongly related with sleep

troubles.

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The GSM variable (RF EMF exposure) remained significant in the multivariate analysis, this

means that the incidence of the symptoms was due to GSM exposure independently of the co-

analyzed variables.

Changes in -2 log likelihood showed similar results. The bootstrapped values were similar to the

asymptotic standard errors and confidence intervals.

Conclusion

This new study partially confirms our preliminary results. Accordingly, we observe that incidence

of most of the symptoms was related to GSM levels – independently of the demographical

variables and some possible risk factors. Also, anxiety about adverse effects from exposure to RF

EMF, despite it is strongly related with sleep disturbances, does not influence the direct

association between GSM exposure and sleep problems. Other covariates such as age and use of

cellular were associated with hearing difficulties, walking difficulties and lack of appetite,

respectively. Lack of appetite, concentration, irritability and sleep troubles, are found moderately

associated with GSM exposure. Finally, the use of mobile phones was strongly associated with

lack of appetite and moderately with vertigo. The bootstrap results were similar – thereby

confirming the adequacy of our conventional analysis.

ARTICLE SUMMARY:

Article Focus: 1) It was stated by some authors that microwave syndrome would be related to anxiety

associated to fear and visual perception of cellular phone towers. In this manuscript we demonstrated that

although anxiety per se perturbs sleep, sleep disorder continued to be related with GSM exposure after

adjusting for these concerns. 2) It was showed that symptoms of the microwave sickness were moderately

related with this radiation, reinforcing that may exists some underlying biological mechanism behind these

symptoms. 3) This work presents a re-analysis of our previous study [4], using a more robust statistical

method.

Key Messages: 1) Existence of the microwave syndrome. 2) The symptoms were related to very low GSM

levels against Spanish and international guidelines [35], [36].

Strengths: We use a robust statistical analysis with a highly homogeneous sample in a homogeneous

environment. Limitations: We work with old data. The environmental radiation at that time (2001) was

mainly associated to GSM.

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Authors' contributions

CGP was one of the principle researchers responsible for design, conduct, and writing the

manuscript. CGP also made the main statistical contributions. EAN was also one of the main

researchers responsible for design and acquisition of RF EMF data, writing, and final review of

manuscript. JS contributed mainly to processing of RF EMF data, while MP was responsible for

design and final review of manuscript.

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The health risk due to exposure to microwave (MW), continues to be discussed today. The study

that led to this debate was initiated after verification that the U.S. embassy in Moscow was being

subjected to such radiation from 1953 to May 1975 [1]. Recently, a review of that episode [2]

reopened the debate about the potential harmfulness of these MW. In 2010, a review about this

topic [3], found that eight of the 10 studies evaluated through PubMed, reported increased

prevalence of adverse neurobehavioral symptoms or cancer in populations living at distances < 500

meters from base stations.

None of the studies reported exposure above accepted international guidelines, suggesting that

current guidelines may be inadequate in protecting the health of human populations. Thus, emerges

the need to reevaluate our pioneering work in this field, trying to improve it, adding new

procedures and data. To our understanding, few articles have addressed the possible association

between microwave sickness and microwave exposure from GSM (Global System for Mobile

Communications) base stations (BSs) since the publication of our first study [4]. Chronologically,

Santini et al., [5] and Gadzicka et al., [6] reported differences in the distance-dependent prevalence

of symptoms such as headache, impaired concentration, and irritability. Later, a study carried out

in Austria [7] showed a positive association between the measured electric field (GSM 900/1800)

in bedrooms and headaches, cold hands and feet, and difficulties in concentrating. An Egyptian

study [8] showed a prevalence of neurological complaints, such as headache, memory changes,

dizziness, tremors, depressive symptoms, and sleep disturbances among subjects more directly

exposed to GSM signals from BSs.

The symptoms reported by all the above cited authors belong to those attributed to the

microwave syndrome [9]. However, one article [10] using personal monitored data from GSM-

UMTS frequency bands did not find any statistical association in adults. More recently, the same

authors observed no association in children [11], contradictory results in children and adolescents

[12], and concluded that the few observed significant associations were not causal but rather

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occurred by chance. Blettner et al. [13] reported in Phase 1 of their study more health problems

closer to base stations, but in Phase 2 [14] they concluded that measured EMF emissions were not

related to adverse health effects.

Other researchers focused their work on the possible existence of subjects with sensitivity to

GSM or UMTS signals according to psychological, cognitive, or autonomic assessment. These

researchers used short-term exposure (only 30-50 minutes) under laboratory conditions [15-19]

and revealed a large disparity between subjects. Recently, a study measuring several biological

stress markers [20] found that RF-EMF emitted by mobile phone base stations from 5.2 µW/m2 to

2126.8 µW/m2 increased cortisol and salivary alpha-amylase, while IgA concentration was not

significantly modified

In 2010, the Selbitz study [21], described a significant dose-response relationship in symptoms

related with sleep, mood, joints, infections, skin condition, as well as neurological cardiovascular,

visual, and auditory systems, and the gastrointestinal tract.

In the work of Danker-Hopfe et al. [22] it was not evident the existence of short-term

physiological effects of EMF on sleep quality, however it was stated that the presence of BSs

per se (not the EMF) may have a negative impact on sleep quality.

In 2012, a Polish study did not present correlation between the electric field strength and the

frequency of subjective symptoms however showed a correlation between subjective symptoms

and the distance to BSs, [23] . A study carried out in Egypt [24] revealed that exposure to EMF

emitted either from mobile phones or BSs had significant effects on pituitary-adrenal axis. More

recently, the work developed in Iran [25] indicated that symptoms such as nausea, headache,

dizziness, irritability, discomfort, nervousness, depression, sleep disturbance, memory loss and

lowering of libido were statistically significant in the inhabitants living near the BSs (<300m

distances) compared to those living far from the BSs (>300m).

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In our cross-sectional analysis [4], 11 out of 16 symptoms showed significantly higher scores in

the group with the maximum exposure level. The symptoms are included in the microwave

syndrome. We also reported significant correlation coefficients between the measured electric

field and fourteen out of sixteen symptoms.

A review [26] recently established several conditions for epidemiological studies to be eligible

for introduction in general analysis: eligible studies must quantify exposure using objective

measures (such as distance to the nearest BS, spot, or personal exposure measurements in a

specific frequency range); possible confounders must be considered; and the selection of the

study population must be clearly free of bias in terms of exposure and outcomes.

Accordingly, in this re-analysis of our previous study [4], possible confounders were included in

addition to the specific RF EMF measurements made in 2001 (covering the specific range between

900 and 1800 MHz). So, we co-analyzed the effects of other variables such as socio-demographic

data and use of electronic devices. Lately, concern about being damaged by the radiation of the

antenna was also analyzed.

In the present work a more robust statistical method indifferent to the assumption of normality was

employed. Re-sample method or bootstrapping is based on inferences rather than making

assumptions about the population, treating a given sample as the population. Consequently, we

could extrapolate our results to the entire population from which the sample was obtained and

reduce sample size effect.

The present study re-evaluates our initial work using a new methodology, analyzing confounding

effects, in order to test more deeply the reliability of those results. Interestingly, this period was

free of other sources of RF such as WIFI or UMTS etc. or the massive use of mobile phones,

enabling a specific study of GSM technology.

Finally, the suitability of the size of the sample was analyzed.

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METHODS

Study design

The study was carried out in La Ñora, a small village in the south-east of Spain. Two BS masts,

each about 30 m height were sited at different positions to provide GSM-900-1800 coverage. The

GSM 900 BS was positioned not earlier than 1997 while the GSM 1800 BS was built in December

1999.

The main demographic characteristics of the sample and the use of electronic devices was

recollected through a Spanish-language questionnaire [5]. All of the subjects were of the same

ethnic origin, shared similar family income levels and general standard of living, and were born in

La Ñora or nearby. All the residents in the study were living in the village before the erection of

both BSs. All of the residents were at home for more than eight hours a day for at least six days a

week and normally slept at home.

The core of the questionnaire was a symptom checklist for estimating the frequency of 15 health-

related symptoms attributed to microwave sickness. These symptoms are: fatigue; irritability;

headaches; nausea; loss of appetite; sleep disorders; depressive tendency; dizziness; concentration

difficulties; memory loss; skin lesions; visual and hearing deficiencies; walking difficulties; and

cardiovascular problems. The frequency was quantified as: never suffer = 0; sometimes = 1; often

= 2; and very often = 3.

The percentage of residents who reported electrical transformers less than 10 m from their home

was 21.6%; while a 42% reported high voltage power lines less than 100 m from home. Finally,

40% of residents reported a TV transmitter within a radius of around 4 km.

The questionnaire included a statement that its purpose was health research, that the data gathered

would be confidential, and that the study was approved by the Ethical Committee of the University

of Valencia in accordance with the Declaration of Helsinki (http://www.wma.net/e/policy/b3.htm).

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215 questionnaires were randomly distributed through seventeen streets which represented

practically the entire village. 150 were collected being the difference due of not being at home or

refusal to fulfil the questionnaire.

Then, 101 RF EMF measurements in bedrooms were made in 2001. The 49 missing residents were

due mainly because, refusal to give admittance for taking measurements, residents not being at

home for the scheduled measurement appointment and having serious healthy problems.

A further review was conducted in 2010. Consequently, two residents were removed from the

primitive survey, one because of alcohol abuse and another because pregnancy. Moreover, eleven

subjects were eliminated because there was a change (it was raining) in the usual dry weather

conditions when registering their respective broadband measurements.

So, the reanalysis of the dataset, which is the main focus of this paper, was finally performed with

88 subjects (45 female and 43 male) instead of the 101 analysed in 2001.

Concerns about microwave exposure

66 subjects out of 88 were reached by telephone in February 2012 following two questions:

a) ¿Were you worry about the masts (BSs) at the time when it was erected?

b) ¿Did you believe its radiation (BSs) could damage your health?

In all cases those who were worried about the masts were concerned about health consequences. 27

subjects (40, 9 %) responded no and 39 (59, 1%) responded yes. Responses were not related to age

(ANOVA test), sex (lambda statistic) or subjective distance to BS (Somers´d statistic).

Exposure assessment

Broadband measurements were made on two Saturdays in February and March 2001 from 11 am to

7pm with a portable electrical field (400 MHz – 3 GHz) detector (Nuova Elettronica Model LX-

1435). This meter was calibrated with an HP-8510C network analyser inside an anechoic chamber

at the University of Valencia. During the bedroom exposure assessment the electric field probe was

held for approximately five minutes about one meter from the walls and 1.2 meters above the

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ground – and moved around a circle of 0.25 m radius, orientating the antenna in different directions

to obtain the maximum electrical field strength above the bed.

To check the intensity of TV and radio channels, as well as the number of working channels for the

GSM-900-1800 BSs, measurements of the spectral power density were carried out with a probe

antenna and a portable spectrum analyser.

The probe was mounted on a linen phenolic tripod 1.2 m above the ground. The location of the

probe was the same on both days – on a hill next to the village and 20 m from the BS. With the

spectrum analyser we scanned the frequency bands and the levels were averaged for six minutes.

The measurement of the spectrum was similar on both days – with a difference in the peak

estimation (channel carriers) of about 1 dB.

Statistical analysis

Demographical data was analysed using the Mann-Whitney one-way variance analysis and Chi

square test.

The main statistical analysis was made using binary logistic regression (mode enter) carried out on

a regular basis and subsequent bootstrapping [1000 bootstrap replications, 95% percentile method

and simple sampling] [27] that could provide more accurate standard errors and confidence

intervals. After producing (1000) bootstrap replicates θ b of an estimator θ, the bootstrap standard

error was the standard deviation of the bootstrap replicates:

SE (θ) = √ [∑ (θb - θ) **2 / (r-1)] b=1� r where θ is the mean of the θb. Because of our small sample size, a non-parametric confidence

interval for the estimate (mean) was constructed from the quartiles of the bootstrap sampling

distribution of θ. The 95% percentile interval (θ (lower) <θ< θ (upper) is shown, and where the θb

are the r-ordered bootstrap replicates: lower = 0.025 × r (sample 25) and upper = 0.975 × r (sample

975).

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The 15 health-related symptoms described above constituted the dichotomous dependent variables.

Then, univariate analysis was performed for each symptom and for each of the predictors variables:

exposure to base station (µW/m2 as a natural logarithmic) and age were used as continuous

variables; while gender, computer use >2 h/ day, mobile phone use > 20 minutes per day and worry

about the antennae were used as dichotomous variables. The covariates with predictive value were

considered for the multivariate analysis. Thus possible confounder effects were evaluated.

In all cases, change in -2 log likelihood, odds ratio, 95%-confidence intervals (95%-CI), as well as

the probability value (p-value) were calculated. For all tests, a p-value below 0.05 was considered

significant.

The dependent variables (health-related symptoms) given in four ordinal categories (0=never,

1=sometimes, 2=often; 3=very often) were dichotomised (0,1 = 0 versus 2, 3 = 1).

We used the GSM exposure (the measurement of RF EMF in the bedroom) as a continuous

variable because it is well recognised that categorisation of continuous variables introduces major

problems in the analysis and interpretation of models derived in a data-dependent fashion [28, 29,

30].

We chose exposure values in the logarithmic form because these values are well grouped around

their median; while the raw values showed a high dispersion of values, with two outliers and ten

extreme values (data not shown).

Confounding was assessed by adding the potential confounding variable to the model and making a

subjective decision as to whether or not the coefficient of the variable of interest, ORs of GSM

exposure, had changed substantially. A 10 per cent variation was accepted as a considerable change

Possible interactions between covariates were also evaluated.

The maximum number of covariates included in each multivariate analysis was calculated

following this formula [31]. Let π be the smallest of the proportions of negative or positive cases

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in the population and k the number of covariates, then the minimum number of cases to include

is:

N = 10 k / π

Goodness-of-fit tests such as the classification table, the Hosmer-Lemeshow statistic, ROC

curves, Cox and Snell’s, and Nagelkerke’s Pseudo R square measures were used. The Wald

statistic was also evaluated to test the significance of individual independent variables.

Moreover, possible multicolinearity was also tested.

For statistical analysis SPSSTM for Windows was used.

Because an exposure assessment for transformers, high voltage power lines, and radio or TV

transmitters based on self-estimated distances would not produce a reliable exposure estimate, it

was decided to omit these covariates in the analysis.

RESULTS

Demographical data and the percentage of users of personal computers and mobile phones were

analysed. The sample age distribution was 42, 17 ± 17, 61 and [15–81] interval, while 13,6 % used

regularly computers (6,6 % were women and 7,0% men) and 23,9 % mobile phones (8,8 %

corresponded to women and 15,1 % to men). In the total sample, 51,1 % were women (mean age =

45.08 years (SD =17.98) ; [interval=15-81] and 48,9% men (39.12 (16.88) [15-75]. No differences

related with age, use of mobile phones or computers between sexes were found.

The univariate logistic regression indicated that age was inversely associated with irritability [OR =

0.97 (0.95-0.99)] and that the oldest had the greatest difficulties hearing [1.03 (1.01-1.06)] and

walking [1.04 (1.01-1.07)] . However, gender clearly did not influence the outcome of any

dependent variable. Use of mobile phones was linked with lack of appetite and vertigo, while

worry about the radiation from BSs was associated with sleep troubles (Table 1).

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Most of the symptoms were related with GSM exposure, especially: fatigue, irritability, lack of

appetite, sleep troubles, depression, and lack of concentration. Change in -2 log likelihood showed

similar results (Table 2).

ROC curves of each one of the logistic regression models (GSM exposure vs. each symptom)

oscillated between 0.65 and 0.87 (Table 3). Headaches (0, 84), nausea (0,86), appetite (0,87), and

vascular problems (0,85) showed the highest values, while memory (0,67), skin (0,67), and visual

disturbances (0,65) showed the lowest ones. The Hosmer and Lemeshow test indicated that most

analyses showed no significance p- values. The exceptions were fatigue (0,003), depression (0,003)

and vertigo (0,03). In the majority of the cases the models predicted better specificity than

sensitivity. Only in the case of headaches and sleep disorder, sensitivity prevailed over specificity

(Table 3 – classification table). In the extreme case, skin and vascular problems showed null or

minimum sensitivity, but 100 % specificity. Nagelkerke pseudo R-squared, showed acceptable

coefficients with the exception of the symptoms related with vertigo and skin problems (Table 3).

The lowest threshold cut-off values of GSM were for headaches, sleep, irritability and attention;

while vascular and skin problems showed the highest threshold cut-off values (Table 3).

The influence of other covariates on the GSM ORs coefficients, such as age, cellular use and

concern about the antennae were always less than 10% (Tables 4,).

There was no observed multicollinearity among variables. Kappa values according to factor

analysis were always lower than 2 and well below the critical value of 30.

Finally, no interactions between covariates were observed.

Standard errors and confidence intervals obtained by re-sampling were similar to those calculated

from the asymptotic approximation (Tables 4, 5). There was a small bias or difference between

the average bootstrap coefficients (not shown) and the respective estimates obtained from the

original sample.

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DISCUSSION

This new study partially confirms our preliminary results. So, the most of the symptoms were

related to GSM levels – independently of the demographical variables and some possible risk

factors.

Related to microwave radiation, the spectral power density analysis maintained that the most

important contribution to broadband measurements was from GSM 900/1800, and the main

variability of the measurements between different places was due to a different coverage of the

GSM 900/1800 signals, i.e. spatial variability. This was further supported by the fact that the

antenna used was fairly insensitive to frequencies below 400 MHz. Therefore, the radio channels

80-110 MHz were not a significant part of the broadband measurements. Moreover, the narrow

band measurements showed TV channels with substantially lower intensities than the GSM

900/1800 signals. The effects from these exposures will therefore not confound the effects of BSs.

Moreover, some authors [7] found that the only relevant contribution to the variance of the high

microwave exposure was from BSs – up to 93% of variance. Moreover, at the time of our study the

GSM signal was almost invariable in time because there were very few calls. The main

contribution was made from the broadcast channels working almost constantly throughout the day.

Short-range evaluations of exposure could be acceptable for describing a 24-hour period and the

measurements were made in bedrooms – a location where the subjects were assumed to spend

significant periods of time.

However, some subjects were mobile phone users at the time of this study and exposure from a

mobile phone during a phone call is much higher than that received from BSs. Nevertheless, some

authors [7] stated about that there is no a priori argument why these lower levels should have no

effect on the presence of a widespread use of mobile telephones. Exposure from a BS will be at a

low but almost constant level for many hours a day and especially at night.

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While GSM exposure was associated with most of the symptoms, walking difficulties and hearing

loss was correlated only with age. Also, age remained slightly inversely associated with irritability.

Those who were users of cellular phones were more prone to complain of loss of appetite and

vertigo, while those who expressed worry about the BSs, were associated with having sleep

problems. This later finding was in concordance with two other articles [7, 14, 22]. However,

worry about the BSs was unrelated with age, gender or subjective distance to BS. This agrees with

article [32] claiming that there was no significant association between symptom occurrence and

actual distance to BSs or powerlines.

Contrarily, some authors indicated that opponents of mobile phone towers generally do not express

anxieties about electromagnetic field exposure, indicating that the risk rating is comparable with

other perceived common hazards in the modern world [33].

None of the analysed covariates behaved as confounders. So, the relationship between GSM

exposure with irritability, sleep troubles, lack of appetite and vertigo remained significant despite

the introduction of the above covariates.

When the conventional multivariate analysis was tested using bootstrapping it was observed that

the standard error and confidence intervals obtained by re-sampling were similar to those

calculated from asymptotic approximation and this supports the adequacy of our conventional

analysis. Our sample, chosen at random, would qualify to represent the population from which it

came.

The model appeared generally well adjusted while the cut-off values could constitute good

guidance for predicting the threshold of symptom appearance.

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CONCLUSIONS

This new study partially confirms our preliminary results about microwave sickness resulting from

exposure to emissions from GSM mobile phone BSs. So, fatigue, irritability, lack of appetite, sleep

troubles, depression, and lack of concentration were especially related with GSM exposure.

These results were independent of the main socio-demographical variables, other EMF exposures

and the worry about being irradiated. Nevertheless, we confirm that apprehension about modern

technology could predict some symptom, specially related with sleep problems.

Our results agree with those who claimed that by distorting perceptions of risk, disproportionate

precaution might paradoxically lead to illness that would not otherwise occur [34]. However,

health changes related with GSM exposure seems to occur unrelated with those fears. Finally, this

exposure was very low in that period and also very low against Spanish recommendations [35] and

international guidelines [36].

Recommendations

We subscribe the guidelines observed by other authors [37] to follow the principle of prevention

as long as the non-thermal effects are not considered in any official standard. This includes

exposure minimization in the frame of the technical feasibility to guarantee a significantly

reduction in the long term radiation exposures of cellular phone towers in residential areas.

Epidemiological and clinical studies should be continuing to observe possible health changes in

the population. Finally, clear information about the correct use of the newer electronic devices

should be implemented.

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Competing interests

The authors declare that they have no competing interests.

ACKNOWLEDGEMENTS

We would like to express our gratitude to Angeles Martinez Gomez for her assistance during the

field work in La Ñora; as well as the Spanish Ministry of Science and Technology for grant FIT

number 070000-2002-58.

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TABLE LEGENDS

Table 1. . Univariate ORs & 95% CIs of all clinical symptoms related with various possible confounders.

Table 2. ORs & 95% CIs of GSM exposure for predicting clinical symptoms. Change in -2 log likelihood is also shown.

Table 3. Goodness-of-fit of the outcome binary response variable related to GSM exposure (ln). Cut-off values of exposure to microwaves when the predicted probability of symptoms is more than 50%. The data are presented in ascending order. Table 4. Adjusted GSM variable coefficients: Asymptotic standard errors (SE) and 95% confidence intervals (CI). Statistics for r = 1000 bootstrapped binary logistic regression and 95% percentile confidence intervals are also shown for comparison. Univariate coefficients for worry about BSs (a) and GSM exposure (b); and (c) Multivariate GSM exposure coefficient adjusted for concern about the BSs.

Table 5. Statistics for r = 1000 bootstrapped binary logistic regression (GSM exposure coefficients). Asymptotic standard errors (SE) and 95% confidence intervals (CI) are also shown for comparison.

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Table 1. Univariate ORs & 95% CIs of all clinical symptoms related with various possible confounders.

Symptom/

Variable

Worry about BSs (1) Computer (not use) Mobile (not use) OR (95%CI) OR (95%CI) OR (95%CI)

Fatigue 0.67 0.23-1.90 2.62 0.76-9.04 1.56 0.56-4.35 Irritability 1.13 0.43-3.03 1.56 0.45-5.34 2.62 0.94-7.33 Headaches 1.75 0.62-4.94 1.39 0.34-5.58 1.56 0.51-4.83 Nausea 0.68 0.18-2.24 0.34 0.04-2.84 1.43 0.44-4.67 Lack of appetite 1.05 0.33-3.40 3.16 0.87-11.44 4.28** 1.43-12.78 Trouble sleeping 3.12* 1.10-8.86 0.55 0.16-1.88 0.74 0.27-2.02 Depression 1.06 0.39-2.93 0.81 0.22-2.93 1.03 0.38-2.84 Lack of concentration 0.92 0.35-2.47 1.11 0.33-3.76 2.79 0.99-7.80 Memory loss 1.71 0.62-4.75 0.41 0.10-1.64 1.35 0.50-3.61 Skin alterations 0.74 0.23-2.35 φ φ 0.63 0.16-2.45

Visual disturbances 1.31 0.48-3.60 0.77 0.21-2.77 1.63 0.60-4.39 Vertigo 0.61 0.20-1.91 0.77 0.19-3.10 2.90* 1.04-8.07 Vascular alteration 0.96 0.27-3.43 1.48 0.35-6.17 2.04 0.65-6.41 Hearing problems 0.59 0.20-1.70 0.77 0.19-3.10 0.48 0.15-1.60 Walking difficulty 0.60 0.20-1.79 φ φ 0.42 0.11-1.60 * p<0.05; ** p<0.01; (1) Not worried, as reference code (2) & (3) Not device use, as reference code, φ any subject affected using computer Table 2. ORs & 95% CIs of GSM exposure for predicting clinical symptoms. Change in -2 log likelihood is also shown.

Symptom

OR / CI

Change in -2 Log likelihood

Fatigue 1.39*** (1.14-1.70) 11.74***

Irritability 1.51*** (1.23-1.85) 19.36***

Headaches 1.43** (1.15-1.78) 12.32***

Nausea 1.38** (1.09-1.73) 8.3**

Appetite 1.58*** (1.23-2.03) 16.31***

Sleep 1.49*** (1.20-1.84) 16.38***

Depression 1.41*** (1.16-1.72) 13.99***

Concentration 1.54*** (1.25- 1.89) 20.75***

Memory 1.27** (1.06- 1.52) 7.29**

Skin 1.24* (1.001-1.54) 4.08*

Visual 1.23 * (1.03-1.46) 5.30*

Vertigo 1.36** (1.11-1.66) 10.14***

Vascular 1.32* (1.05-1.64) 6.30*

Hearing 0.96 (0.80-1.15)

Walking 0.95 (0.78-1.15)

* p<0.05; ** p<0.01; *** p<0.001

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Table 3. Goodness-of-fit of the outcome binary response variable related to GSM exposure (ln). Cut-off values of exposure to microwaves when the predicted probability of symptoms is more than 50%. The data are presented in ascending order. Symptom ROC curves

Area

Classification table

Pseudo-R**2

(1)

Cut-off (Ln GSM)

Cut-off GSM (µW/m2)

SSV SPF AV

Headaches 0.84*** 0.90 0.23 0.72 0.41 0.57 1.77 Sleep 0.78*** 0.82 0.66 0.76 0.28 1.66 5.26

Attention 0.78*** 0.67 0.72 0.69 0.28 3.61 36.97 Irritability 0.76*** 0.67 0.73 0.71 0.26 3.61 36.97 Memory 0.67** 0.54 0.77 0.67 0.11 4.99 146.94

Depression 0.75*** 0.46 0.76 0.65 0.20 5.22 184.93 Visual 0.65* 0.24 0.83 0.60 0.08 5.91 368.71 Fatigue 0.73*** 0.22 0.90 0.69 0.18 6.53 685.4 Vertigo 0.74*** 0.16 0.87 0.67 0.19 6.53 685.4 Appetite 0.87*** 0.40 0.94 0.85 0.43 7.31 1495.18 Nausea 0.86*** 0.46 0.93 0.87 0.38 7.31 1495.18

Vascular 0.85*** 0.20 1.0 0.90 0.34 8.02 3041.18 Skin 0.67* 0.00 1.00 0.81 0.072 9.06 8604.15

* p<0.05, ** p<0.01, *** p<0.001 (1) Nagelkerke

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Table 4. Adjusted GSM variable coefficients: Asymptotic standard errors (SE) and 95% confidence intervals (CI). Statistics for r = 1000 bootstrapped binary logistic regression and 95% percentile confidence intervals are also shown for comparison. Univariate coefficients for worry about BSs (a) and GSM exposure (b); and (c) Multivariate GSM exposure coefficient adjusted for concern about the BSs.

Symptom Covariates Normal (φ)))) Bootstrap

OR SE 95% CI

Bias SE Sig. 95% percentile

Lower Upper Lower Upper Irritability Age & Mw 1.47*** 0.1 1.20 1.81 0.02 0.1 .002 1.23 1.94

Appetite UCel & Mw 1.53** 0.1 1.19 1.99 0.03 0.1 .001 1.24 2.22

Vertigo UCel & Mw 1.32** 0.1 1.08 1.62 0.01 0.1 .003 1.11 1.68

Sleepless Afraid (a) 3.12* 0.5 1.10 8.86 0.02 0.6 .03 1.12 10.87

Sleepless Mw (b) 1.69*** 0.1 1.27 2.24 0.05 0.2 .001 1.34 2.83

Sleepless Affraid -Mw (c) 1.64*** 0.1 1.22 2.19 0.06 0.7 .001 1.28 2.59

φ Binary logistic regression (enter) Ucel: cellular use; Mw: GSM exposure

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Table 5. Statistics for r = 1000 bootstrapped binary logistic regression (GSM exposure coefficients). Asymptotic standard errors (SE) and 95% confidence intervals (CI) are also shown for comparison.

Symptom B

Bootstrap Normal

Bias SE 95% percentile

intervals SE 95% CI

Lower Upper Lower Upper Fatigue 0.329 0.012 0.097 0.155 0.539 0.102 0.128 0.529 Irritability 0.411 0.016 0.110 0.241 0.670 0.104 0.207 0.615 Headache 0.358 0.022 0.139 0.149 0.688 0.113 0.137 0.578 Nausea 0.319 0.013 0.124 0.099 0.590 0.118 0.088 0.550 Appetite 0.456 0.026 0.134 0.264 0.784 0.128 0.205 0.707 Sleep 0.396 0.022 0.124 0.193 0.690 0.109 0.181 0.610 Depression 0.346 0.012 0.102 0.174 0.583 0.100 0.151 0.541 Attention 0.429 0.020 0.118 0.254 0.711 0.106 0.222 0.636 Memory 0.237 0.009 0.098 0.057 0.448 0.091 0.058 0.415 Skin 0.217 0.008 0.110 0.011 0.451 0.110 0.001 0.433 Visual 0.203 0.004 0.093 0.037 0.398 0.090 0.026 0.379 Hearing -0.05 -0.002 0.089 -0.219 0.143 0.093 -0.228 0.135 Vertigo 0.306 0.010 0.101 0.127 0.530 0.102 0.107 0.505 Walking -0.05 -0.006 0.098 -0.265 0.120 0.098 -0.246 0.138 Vascular 0.274 0.010 0.109 0.084 0.520 0.114 0.051 0.497

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Subjective Symptoms Related to GSM Radiation from Mobile Phone Base Stations: A cross-sectional study

Journal: BMJ Open

Manuscript ID: bmjopen-2013-003836.R1


Date Submitted by the Author: 29-Oct-2013



Public health




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Base Stations: A cross-sectional study


Enrique A. Navarro


C/. Dr. Moliner 50,



Authors



Jaume Segura (3)




(València), Spain.



Running title



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Abstract

Objectives: This paper is a cross-sectional study, a reanalysis of the data from Navarro et al,

2003 (10), in which health symptoms related to microwave exposure from mobile phone base

stations (BS) were explored. We analyze if these symptoms might be related to fear to the

exposure, and therefore having a subjective basis.

Method: Since weather circumstances could influence the exposure, we restricted the data to

those measured under similar weather conditions. The participants with known illness in 2003 were

at this time disregarded: 88 subjects instead of 101 (in 2003) were analyzed. A statistical method

indifferent to the assumption of normality was now employed: Binary logistic regression for

modelling a binary response, e.g. having fatigue (1) or not (0), introducing the exposure as a

predictor variable. This analysis was carried out on a regular basis and bootstrapping [95%

percentile method] to provide more accurate confidence intervals.

Results: The symptoms more related with exposure were: Lack of appetite [odds ratio (OR)] =

1.6, 95% confidence interval (95%CI) = 1.2-2.0, lack of concentration [OR = 1.5, 95% CI = 1.25-

1.89], irritability [OR = 1.51, 95% CI = 1.23-1.85] and sleep troubles [OR = 1.5, 95% CI = 1.2-1.8].

Change in -2 log likelihood showed similar results. Concerns about the BS were strongly related

with sleep troubles [OR = 3.1, 95% CI = 1.1-8.9]. The exposure variable remained statistically

significant in the multivariate analysis. The bootstrapped values were similar to the asymptotic

confidence intervals

Conclusion: This study confirms our preliminary results. We observe that incidence of most of

the symptoms was related to exposure levels – independently of the demographical variables and

some possible risk factors. Also, concern about adverse effects from exposure, despite it is strongly

related with sleep disturbances, does not influence the direct association between exposure and

sleep.

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ARTICLE SUMMARY:

Article Focus: 1) It was stated by some authors that microwave syndrome would be related to

anxiety associated to fear and visual perception of cellular phone towers. In this manuscript we

demonstrated that although anxiety per se perturbs sleep, sleep disorder continued to be related

with GSM exposure after adjusting for these concerns. 2) It was showed that symptoms of the

microwave sickness were moderately related with this radiation, reinforcing that may exists some

underlying biological mechanism behind these symptoms. 3) This work presents a re-analysis of

our previous cross-sectional study [10], using a more robust statistical method.

Key Messages: 1) Existence of the microwave syndrome. 2) The symptoms were related to very

low GSM levels against Spanish and international guidelines [40], [41].

Strengths: We use a robust statistical analysis with a highly homogeneous sample in a

homogeneous environment. Limitations: We work with old data. The environmental radiation at

that time (2001) was mainly associated to GSM.

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Introduction

The health risk due to exposure to radiofrequency electromagnetic fields (RF EMF) continues to be

discussed today. The study that led to this debate was initiated after verification that the U.S.

embassy in Moscow was being subjected to such radiation from 1953 to May 1975 [1]. Recently, a

review of that episode [2] reopened the debate about the potential harmfulness of RF EMF. The

increasing number of base stations (BSs) on masts and buildings increased public awareness. This

issue promoted scientific research in order to know to what extent the low-intensity

electromagnetic fields might affect the health of humans and other organisms [3, 4]. Furthermore,

the term electromagnetic hypersensitivity (EHS) has been introduced recently, the term EHS was

related to subjects attributing symptoms to exposure to electromagnetic fields (EMFs) [5-8]. In

2010, a review about this topic [9], found that eight of the 10 studies evaluated through PubMed,

reported increased prevalence of adverse neurobehavioral symptoms or cancer in populations

living at distances < 500 meters from base stations.







Santini et al., [11] and Gadzicka et al., [12] reported differences in the distance-dependent

prevalence of symptoms such as headache, impaired concentration, and irritability. Later, a study

carried out in Austria [13] showed a positive association between the measured electric field

(GSM 900/1800) in bedrooms and headaches, cold hands and feet, and difficulties in

concentrating. An Egyptian study [14] showed a prevalence of neurological complaints, such as

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headache, memory changes, dizziness, tremors, depressive symptoms, and sleep disturbances

among subjects more directly exposed to GSM signals from BSs.















significantly modified.









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In our cross-sectional analysis [10], 11 out of 16 symptoms showed statistically significant

higher scores in the group with the maximum exposure level. The symptoms are included in the

microwave syndrome. We also reported statistically significant correlation coefficients between

the measured electric field and fourteen out of sixteen symptoms.


for introduction in general analysis: Eligible studies must quantify exposure using objective









The new statistical approach tested the possible influences of other variables, such as demographic

data, and the use of electronic devices. Moreover, since some concerns have been raised about

possible health consequences caused by the emitted microwaves, we analysed if these symptoms

might be related to fear to the exposure. Also, since some subjects refused to allow measurements

at their homes, we analyzed if symptoms status or subjective distance to the BS, could be a bias of

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participation in the study. Interestingly, this period was free of other sources of RF such as WIFI or

UMTS etc. or the massive use of mobile phones, enabling a specific study of GSM technology.


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METHODS

Study design

We chose a small urban area with mixed rural characteristics, with low environmental pollution

(more agricultural than industrial), with no major differences in socio-economic characteristics

through the region (excluding big cities), same ethnicity (white Caucasian) and language (Spanish);

and having mobile phone communication for at least two years. Therefore, La Nora was chosen

because it met these requirements, common with the rest of this region of the south-east of Spain,

being a place with features of small city, near the capital (Murcia) and rural environment, without

any particular issue, such as a relevant health problem or particular environmental risk.

Consequently, La Ñora could be representative of small urban areas in east Spain with fewer than

20,000 inhabitants, and rural areas, which account for 19.8% of the population and 35.9% of the

territory.

Two BS masts, each about 30 m height were sited at different positions to provide GSM-900-1800

coverage. The GSM 900 BS was positioned not earlier than 1997 while the GSM 1800 BS was

built in December 1999.











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practically the entire village. Election of the subjects' residences was performed using a Street Map

of the village. 150 were collected being the difference due of not being at home (31) or refusal to

fulfil the questionnaire (34).

Then, 101 RF EMF measurements in bedrooms were made in 2001. The missing (49) residents

were due to refusal to give admittance for taking measurements (16), residents not being at home

for the scheduled measurement appointment (10) and having serious health problems (23).

However, some changes are now being introduced in this re-analysis. 13 subjects included in the

original study have now been eliminated, two subjects (one because of alcohol abuse and another

because pregnancy) to increase the requirement on health criteria and eleven to increase the

homogeneity of the RF EMFs measurements, there was a change (it was raining) in the usual dry

weather conditions when their respective broadband measurements were registered.

The reanalysis of the dataset, which is the main focus of this paper, was finally performed with 88

subjects (45 female and 43 male) instead of the 101 analysed in 2001.

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a) Were you worried about the masts (BSs) at the time when they were erected?

b) Did you believe its radiation (BSs) could damage your health?


subjects (40, 9 %) responded no and 39 (59, 1%) responded yes. Responses were analyzed related

to age (ANOVA test), sex (lambda statistic), and subjective distance to BS (Somers´d statistic).

Exposure assessment








To check the intensity of TV and radio channels, as well as the intensity of working channels and

broadcast channels for the GSM-900-1800 BSs, measurements of the spectral power density were

carried out with a probe antenna and a portable spectrum analyser.

The probe was mounted on a linen phenolic tripod 1.2 m above the ground. The position of the





The broadband measured exposure was almost invariable during the time interval of the

measurements. It changed with the position or place but it did not change in time, this could be

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related with a low intensity of the traffic channels (a very low number of phone calls) related

with the higher and constant intensity of the broadcast channel [10].



square test. Also, differences between groups were performed through the analysis of variance

(ANOVA), and the analysis of covariance (ANCOVA).










975).







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statistically significant.






34].











is:

N = 10 k / π





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With the predicted probability scores derived from the regression analysis, receiver operating

characteristic (ROC) curves were constructed for all symptoms or modalities in order to analyze

sensitivity and specificity levels. For each curve, the best cut-offs for GSM exposure that

maximizes (sensitivity + specificity) were also calculated.

For statistical analysis we used the Statistical Package for Social Sciences, version 21.0 (IBM

SPSS Inc., Chicago, IL, USA) for Windows.




RESULTS


analysed. The mean age was 42, 17 years (standard deviation ± 17, 61, interval 15–81), while 13,6

% used regularly computers (6,6 % were women and 7,0% men) and 23,9 % mobile phones (8,8 %





0.97, 95%CI = 0.95-0.99] and that the oldest had the greatest difficulties hearing [OR= 1.03, 95%

CI=1.01-1.06] and walking [OR= 1.04, 95%CI = 1.01-1.07]. However, gender clearly did not

influence the outcome of any dependent variable. Use of mobile phones was linked with lack of

appetite and vertigo, while worry about the radiation from BSs was associated with sleep troubles

(Table 1). However, concerns about the radiation from BSs were not related to age (ANOVA test),

sex (lambda statistic) or subjective distance to BS (Somers´d statistic).

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from the asymptotic approximation (Table 4). There was a small bias or difference between the

average bootstrap coefficients (not shown) and the respective estimates obtained from the

original sample.

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There were not health differences between those who permitted an exposure measurement (88 in

our last model) and those uninterested in RFs measures (26). ANOVA showed that the group with

RF EMFs measures were more prone to complain of memory loss ( F = 5,07 ; p = 0,027); while

subjects without EMF measures showed more skin problems ( F = 10,66; p = 0,001). There were

not health differences between those groups when using age as covariate. Square partial eta

measured 0 % contribution of the willingness participation variable to symptoms that correlated

with age such as: irritability, headaches, walking difficulties and hearing loss. Also, there was not

relationship between subjective distance to the BS and willingness to participate (Pearson Chi-

square = 2,80, df = 1, p=0,094).

DISCUSSION

In the present re-analysis a more robust statistical method was employed, indifferent to the

assumption of normality. In order to reduce the limitation of sample size effect and extrapolate

our results to the entire population from which the sample was obtained, re-sample method or

bootstrapping was used.

This new study partially confirms our preliminary results, being the most of the symptoms related

to GSM levels – independently of the demographical variables and some possible risk factors.









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worry about the BSs was unrelated with age, gender or subjective distance to BSs. This agrees

with an article [36] claiming that there was no statistically significant association between

symptom occurrence associated with perceived proximity to BSs, psychological components,

socio-demographic characteristics, and actual distance to BSs or powerlines.

Some authors indicated that opponents of mobile phone towers generally do not express anxieties

about electromagnetic field exposure, indicating that the risk rating is comparable with other

perceived common hazards in the modern world [37].

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exposure with irritability, sleep troubles, lack of appetite and vertigo remained statistically

significant despite the introduction of the above covariates.





came.



We cannot truly rule out that their inhabitants were more; equal or less worried than elsewhere in

this region, since we cannot provide the percentage of worriers about the BS masts in La Ñora

compared with other places of its environment. However, information about this issue spread to

all over this region at this time and La Ñora circumstances are common with the rest of small

urban and rural areas. The sample was randomly selected but a participation bias cannot be rule

out since the most part of our participants expressed fear for the existence of the BSs and this could

contribute to their participation in the study. Also, it is also possible to speculate that the percentage

of the subjects who refused to participate were for opposite reasons (indifference about the BSs). In

this regard, neither health status nor subjective distance to the BS explained willingness to

participate in the study.

Concerns about the radiation from BSs were not related to age, sex, or subjective distance to BSs.

This agrees with statements from some authors [13] that living near a base station does not make

more people generally fearful, but people that generally worry about the fields would express

stronger fears when they live close by.

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Nevertheless, irrespective of these explanations, there seem to be effects of exposure that occur

independent of the fear of the subjects, since controlling for fear did not change the association

between exposure and symptoms. However, biological grounds explaining non-thermal effects

have not been clearly established. Recently, it has been described that voltage-gated calcium

channels are essential to the beneficial or adverse responses to microwave EMFs, nanosecond EMF

pulses, and static electrical and magnetic fields [38].

In summary, the results of this study indicate that effects of very low but long lasting exposures to

emissions from mobile telephone base-stations on well-being cannot be ruled out. The effects

fulfilled almost completely the symptoms described within the microwave syndrome. Finally,

unraveling the causal pathways would be better done with experimental study design.

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CONCLUSIONS












Recommendations











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ACKNOWLEDGEMENTS



number 070000-2002-58.







Funding

This work was supported by Spanish Ministry of Science and Technology for grant FIT number

070000-2002-58.

Competing Interest

There are no competing interests

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REFERENCES







3. Belyaev IY. Non-thermal Biological Effects of Microwaves. Microwave Review. 2005a;

11:13-29

4. Belyaev IY. Non-thermal Biological Effects of Microwaves: Current Knowledge,

Further Perspective, and Urgent Needs. Electromagn. Biol. Med. 2005b ; 24:375-403.

5. Röösli M, Moser M, Baldinini Y, Meier M, Braun-Fahrländer C. Symptoms of ill health

ascribed to electromagnetic field exposure—a questionnaire survey. Int. J. Hyg. Environ.

Health. 2004 ; 207:141–150.

6. Frick U, Mayer M, Hauser S, Binder H , Rosner R , Eichhammer P. Entwicklung eines

deutschprachigen Messinsntrumentes für ‘‘Elektrosmog-Beschwerden’’. Umwelt Med.

Forsch. Prax. 2006 ; 11:103–113.

7. Eltiti S, Wallace D, Ridgewell A, Zougkou K, Russo R, Sepulveda F, et al. Does short-term

exposure to mobile phone base station signals increase symptoms in individuals who report

sensitivity to electromagnetic fields? A double-blind randomized provocation study. Environ

Health Perspect 2007;115:1603-1608.



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2009;30:556-63.







50:369 – 373.






Med 2006; 63:307–313.



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Environ Med.2009;66:118-123.












(TNO) 2003;148:1-89.




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Am J Hum Biol. 2010; 5:613-8


near mobile phone base stations in Poland. Int J Occup Med Environ Health 2012;25:31-40





Med. 2013 Jun 19.



Organ 2010; 88: 887–896.











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Health 2011; 11:421



38. Pall ML. Electromagnetic fields act via activation of voltage-gated calcium channels to

produce beneficial or adverse effects. J. Cell. Mol. Med 2013; 7:. 958-965


63:298–299.





Health Phys 1998; 74: 494–522.


phone towers in residential areas. 2nd International Workshop on Biological effects of EMFS.

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Symptom/

Variable


Fatigue 0.67 0.23-1.90 2.62 0.76-9.04 1.56 0.56-4.35

Irritability 1.13 0.43-3.03 1.56 0.45-5.34 2.62 0.94-7.33

Headaches 1.75 0.62-4.94 1.39 0.34-5.58 1.56 0.51-4.83

Nausea 0.68 0.18-2.24 0.34 0.04-2.84 1.43 0.44-4.67

Lack of appetite 1.05 0.33-3.40 3.16 0.87-11.44 4.28** 1.43-12.78

Trouble sleeping 3.12* 1.10-8.86 0.55 0.16-1.88 0.74 0.27-2.02

Depression 1.06 0.39-2.93 0.81 0.22-2.93 1.03 0.38-2.84

Lack of concentration 0.92 0.35-2.47 1.11 0.33-3.76 2.79 0.99-7.80

Memory loss 1.71 0.62-4.75 0.41 0.10-1.64 1.35 0.50-3.61

Skin alterations 0.74 0.23-2.35 φ φ 0.63 0.16-2.45

Visual disturbances 1.31 0.48-3.60 0.77 0.21-2.77 1.63 0.60-4.39

Vertigo 0.61 0.20-1.91 0.77 0.19-3.10 2.90* 1.04-8.07

Vascular alteration 0.96 0.27-3.43 1.48 0.35-6.17 2.04 0.65-6.41

Hearing problems 0.59 0.20-1.70 0.77 0.19-3.10 0.48 0.15-1.60

Walking difficulty 0.60 0.20-1.79 φ φ 0.42 0.11-1.60 * p<0.05; ** p<0.01; (1) Not worried, as reference code (2) & (3) Not device use, as reference code,φ any subject affected using computer

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Table 2. ORs & 95% CIs for GSM exposure: increase in risk per increase in ln GSM (µW/m2). Change in -2 log likelihood is also shown.

Symptom

OR (95%CI) Change in

-2 Log likelihood

Fatigue 1.39***(1.14-1.70) 11.74***

Irritability 1.51***(1.23-1.85) 19.36***

Irritability (adjusted with age) 1.47***(1.20-1.81) -

Headaches 1.43** (1.15-1.78) 12.32***

Nausea 1.38** (1.09-1.73) 8.3**

Lack of Appetite 1.58** (1.23-2.03) 16.31***

Lack of Appetite (adjusted to use of cellular ) 1.53** (1.19-1.99) -

Trouble sleeping 1.49***(1.20-1.84) 16.38***

Trouble sleeping (adjusted to worry to BSs) 1.64***(1.22-2.19) -

Depression 1.41***(1.16-1.72) 13.99***

Concentration 1.54***(1.25- 1.89) 20.75***

Memory 1.27** (1.06- 1.52) 7.29**

Skin 1.24* (1.001-1.54) 4.08*

Visual 1.23 * (1.03-1.46) 5.30*

Vertigo 1.36** (1.11-1.66) 10.14***

Vertigo (adjusted to use of cellular ) 1.32** (1.08-1.62) -

Vascular 1.32* (1.05-1.64) 6.30*

Hearing 0.96 (0.80-1.15)

Walking 0.95 (0.78-1.15)

* p<0.05; ** p<0.01; *** p<0.001

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Table 3. Goodness-of-fit of the outcome binary response variable related to GSM exposure (ln). Cut-off values of exposure to microwaves according to ROC analysis. The data are presented in ascending order. Symptom ROC curves

Area

Classification Table (a)

Pseudo-R**2

(1)

Cut-off (2) (Ln GSM)

Cut-off (2) GSM (µW/m2)

SSV SPF AV

Headaches 0.84*** 0.90 0.23 0.72 0.41 0.57 1.77 Sleep 0.78*** 0.82 0.66 0.76 0.28 1.66 5.26



Vascular 0.85*** 0.20 1.0 0.90 0.34 8.02 3041.18 Skin 0.67* 0.00 1.00 0.81 0.072 9.06 8604.15

* p<0.05, ** p<0.01, *** p<0.001 (1) Nagelkerke (2) Cut-off (ROC curve) (a) SSV=sensitivity, SPF =specificity, AV=average

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Table 4. Statistics for r = 1000 bootstrapped binary logistic regression (GSM exposure coefficients: Increase in risk per increase in ln GSM (µW/m2). Asymptotic standard errors (SE) and 95% confidence intervals (CI) are also shown for comparison.

Symptom B*

Bootstrap Normal


intervals SE 95% CI

Lower Upper Lower Upper Fatigue 0.329 0.012 0.097 0.155 0.539 0.102 0.128 0.529 Irritability 0.411 0.016 0.110 0.241 0.670 0.104 0.207 0.615 Headache 0.358 0.022 0.139 0.149 0.688 0.113 0.137 0.578 Nausea 0.319 0.013 0.124 0.099 0.590 0.118 0.088 0.550 Appetite 0.456 0.026 0.134 0.264 0.784 0.128 0.205 0.707 Sleep 0.396 0.022 0.124 0.193 0.690 0.109 0.181 0.610 Depression 0.346 0.012 0.102 0.174 0.583 0.100 0.151 0.541 Attention 0.429 0.020 0.118 0.254 0.711 0.106 0.222 0.636 Memory 0.237 0.009 0.098 0.057 0.448 0.091 0.058 0.415 Skin 0.217 0.008 0.110 0.011 0.451 0.110 0.001 0.433 Visual 0.203 0.004 0.093 0.037 0.398 0.090 0.026 0.379 Hearing -0.05 -0.002 0.089 -0.219 0.143 0.093 -0.228 0.135 Vertigo 0.306 0.010 0.101 0.127 0.530 0.102 0.107 0.505 Walking -0.05 -0.006 0.098 -0.265 0.120 0.098 -0.246 0.138 Vascular 0.274 0.010 0.109 0.084 0.520 0.114 0.051 0.497 * beta coefficent (log OR)

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Base Stations: A cross-sectional study


Enrique A. Navarro


C/. Dr. Moliner 50,



Authors



Jaume Segura (3)




(València), Spain.



Running title



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Abstract

Objectives: This paper is a cross-sectional study, a reanalysis of the data from Navarro et al,

2003 (10), in which health symptoms related to microwave exposure from mobile phone base

stations (BS) were explored. We analyze if these symptoms might be related to fear to the

exposure, and therefore having a subjective basis.

Method: Since weather circumstances could influence the exposure, we restricted the data to

those measured under similar weather conditions. The participants with known illness in 2003 were

at this time disregarded: 88 subjects instead of 101 (in 2003) were analyzed. A statistical method

indifferent to the assumption of normality was now employed: Binary logistic regression for

modelling a binary response, e.g. having fatigue (1) or not (0), introducing the exposure as a

predictor variable. This analysis was carried out on a regular basis and bootstrapping [95%

percentile method] to provide more accurate confidence intervals.

Results: The symptoms more related with exposure were: Lack of appetite [odds ratio (OR)] =

1.6, 95% confidence interval (95%CI) = 1.2-2.0, lack of concentration [OR = 1.5, 95% CI = 1.25-

1.89], irritability [OR = 1.51, 95% CI = 1.23-1.85] and sleep troubles [OR = 1.5, 95% CI = 1.2-1.8].

Change in -2 log likelihood showed similar results. Concerns about the BS were strongly related

with sleep troubles [OR = 3.1, 95% CI = 1.1-8.9]. The exposure variable remained statistically

significant in the multivariate analysis. The bootstrapped values were similar to the asymptotic

confidence intervals

Conclusion: This study confirms our preliminary results. We observe that incidence of most of

the symptoms was related to exposure levels – independently of the demographical variables and

some possible risk factors. Also, concern about adverse effects from exposure, despite it is strongly

related with sleep disturbances, does not influence the direct association between exposure and

sleep.

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ARTICLE SUMMARY:

Article Focus: 1) It was stated by some authors that microwave syndrome would be related to

anxiety associated to fear and visual perception of cellular phone towers. In this manuscript we

demonstrated that although anxiety per se perturbs sleep, sleep disorder continued to be related

with GSM exposure after adjusting for these concerns. 2) It was showed that symptoms of the

microwave sickness were moderately related with this radiation, reinforcing that may exists some

underlying biological mechanism behind these symptoms. 3) This work presents a re-analysis of

our previous cross-sectional study [10], using a more robust statistical method.

Key Messages: 1) Existence of the microwave syndrome. 2) The symptoms were related to very

low GSM levels against Spanish and international guidelines [40], [41].

Strengths: We use a robust statistical analysis with a highly homogeneous sample in a

homogeneous environment. Limitations: We work with old data. The environmental radiation at

that time (2001) was mainly associated to GSM.

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The health risk due to exposure to radiofrequency electromagnetic fields (RF EMF) continues to be

discussed today. The study that led to this debate was initiated after verification that the U.S.

embassy in Moscow was being subjected to such radiation from 1953 to May 1975 [1]. Recently, a

review of that episode [2] reopened the debate about the potential harmfulness of RF EMF. The

increasing number of base stations (BSs) on masts and buildings increased public awareness. This

issue promoted scientific research in order to know to what extent the low-intensity

electromagnetic fields might affect the health of humans and other organisms [3, 4]. Furthermore,

the term electromagnetic hypersensitivity (EHS) has been introduced recently, the term EHS was

related to subjects attributing symptoms to exposure to electromagnetic fields (EMFs) [5-8]. In

2010, a review about this topic [9], found that eight of the 10 studies evaluated through PubMed,

reported increased prevalence of adverse neurobehavioral symptoms or cancer in populations

living at distances < 500 meters from base stations.







Santini et al., [11] and Gadzicka et al., [12] reported differences in the distance-dependent

prevalence of symptoms such as headache, impaired concentration, and irritability. Later, a study

carried out in Austria [13] showed a positive association between the measured electric field

(GSM 900/1800) in bedrooms and headaches, cold hands and feet, and difficulties in

concentrating. An Egyptian study [14] showed a prevalence of neurological complaints, such as

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headache, memory changes, dizziness, tremors, depressive symptoms, and sleep disturbances

among subjects more directly exposed to GSM signals from BSs.
























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In our cross-sectional analysis [10], 11 out of 16 symptoms showed statistically significant

higher scores in the group with the maximum exposure level. The symptoms are included in the

microwave syndrome. We also reported statistically significant correlation coefficients between

the measured electric field and fourteen out of sixteen symptoms.


for introduction in general analysis: Eligible studies must quantify exposure using objective









The new statistical approach tested the possible influences of other variables, such as demographic

data, and the use of electronic devices. Moreover, since some concerns have been raised about

possible health consequences caused by the emitted microwaves, we analysed if these symptoms

might be related to fear to the exposure. Also, since some subjects refused to allow measurements

at their homes, we analyzed if symptoms status or subjective distance to the BS, could be a bias of

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participation in the study. Interestingly, this period was free of other sources of RF such as WIFI or

UMTS etc. or the massive use of mobile phones, enabling a specific study of GSM technology.


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METHODS

Study design

We chose a small urban area with mixed rural characteristics, with low environmental pollution

(more agricultural than industrial), with no major differences in socio-economic characteristics

through the region (excluding big cities), same ethnicity (white Caucasian) and language (Spanish);

and having mobile phone communication for at least two years. Therefore, La Nora was chosen

because it met these requirements, common with the rest of this region of the south-east of Spain,

being a place with features of small city, near the capital (Murcia) and rural environment, without

any particular issue, such as a relevant health problem or particular environmental risk.

Consequently, La Ñora could be representative of small urban areas in east Spain with fewer than

20,000 inhabitants, and rural areas, which account for 19.8% of the population and 35.9% of the

territory.

Two BS masts, each about 30 m height were sited at different positions to provide GSM-900-1800

coverage. The GSM 900 BS was positioned not earlier than 1997 while the GSM 1800 BS was

built in December 1999.











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practically the entire village. Election of the subjects' residences was performed using a Street Map

of the village. 150 were collected being the difference due of not being at home (31) or refusal to

fulfil the questionnaire (34).

Then, 101 RF EMF measurements in bedrooms were made in 2001. The missing (49) residents

were due to refusal to give admittance for taking measurements (16), residents not being at home

for the scheduled measurement appointment (10) and having serious health problems (23).

However, some changes are now being introduced in this re-analysis. 13 subjects included in the

original study have now been eliminated, two subjects (one because of alcohol abuse and another

because pregnancy) to increase the requirement on health criteria and eleven to increase the

homogeneity of the RF EMFs measurements, there was a change (it was raining) in the usual dry

weather conditions when their respective broadband measurements were registered.

The reanalysis of the dataset, which is the main focus of this paper, was finally performed with 88

subjects (45 female and 43 male) instead of the 101 analysed in 2001.

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a) Were you worried about the masts (BSs) at the time when they were erected?

b) Did you believe its radiation (BSs) could damage your health?


subjects (40, 9 %) responded no and 39 (59, 1%) responded yes. Responses were analyzed related

to age (ANOVA test), sex (lambda statistic), and subjective distance to BS (Somers´d statistic).

Exposure assessment








To check the intensity of TV and radio channels, as well as the intensity of working channels and

broadcast channels for the GSM-900-1800 BSs, measurements of the spectral power density were

carried out with a probe antenna and a portable spectrum analyser.

The probe was mounted on a linen phenolic tripod 1.2 m above the ground. The position of the





The broadband measured exposure was almost invariable during the time interval of the

measurements. It changed with the position or place but it did not change in time, this could be

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related with a low intensity of the traffic channels (a very low number of phone calls) related

with the higher and constant intensity of the broadcast channel [10].



square test. Also, differences between groups were performed through the analysis of variance

(ANOVA), and the analysis of covariance (ANCOVA).










975).







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statistically significant.






34].











is:

N = 10 k / π





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With the predicted probability scores derived from the regression analysis, receiver operating

characteristic (ROC) curves were constructed for all symptoms or modalities in order to analyze

sensitivity and specificity levels. For each curve, the best cut-offs for GSM exposure that

maximizes (sensitivity + specificity) were also calculated.

For statistical analysis we used the Statistical Package for Social Sciences, version 21.0 (IBM

SPSS Inc., Chicago, IL, USA) for Windows.




RESULTS


analysed. The mean age was 42, 17 years (standard deviation ± 17, 61, interval 15–81), while 13,6

% used regularly computers (6,6 % were women and 7,0% men) and 23,9 % mobile phones (8,8 %





0.97, 95%CI = 0.95-0.99] and that the oldest had the greatest difficulties hearing [OR= 1.03, 95%

CI=1.01-1.06] and walking [OR= 1.04, 95%CI = 1.01-1.07]. However, gender clearly did not

influence the outcome of any dependent variable. Use of mobile phones was linked with lack of

appetite and vertigo, while worry about the radiation from BSs was associated with sleep troubles

(Table 1). However, concerns about the radiation from BSs were not related to age (ANOVA test),

sex (lambda statistic) or subjective distance to BS (Somers´d statistic).

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from the asymptotic approximation (Table 4). There was a small bias or difference between the

average bootstrap coefficients (not shown) and the respective estimates obtained from the

original sample.

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There were not health differences between those who permitted an exposure measurement (88 in

our last model) and those uninterested in RFs measures (26). ANOVA showed that the group with

RF EMFs measures were more prone to complain of memory loss ( F = 5,07 ; p = 0,027); while

subjects without EMF measures showed more skin problems ( F = 10,66; p = 0,001). There were

not health differences between those groups when using age as covariate. Square partial eta

measured 0 % contribution of the willingness participation variable to symptoms that correlated

with age such as: irritability, headaches, walking difficulties and hearing loss. Also, there was not

relationship between subjective distance to the BS and willingness to participate (Pearson Chi-

square = 2,80, df = 1, p=0,094).

DISCUSSION

In the present re-analysis a more robust statistical method was employed, indifferent to the

assumption of normality. In order to reduce the limitation of sample size effect and extrapolate

our results to the entire population from which the sample was obtained, re-sample method or

bootstrapping was used.

This new study partially confirms our preliminary results, being the most of the symptoms related

to GSM levels – independently of the demographical variables and some possible risk factors.









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worry about the BSs was unrelated with age, gender or subjective distance to BSs. This agrees

with an article [36] claiming that there was no statistically significant association between

symptom occurrence associated with perceived proximity to BSs, psychological components,

socio-demographic characteristics, and actual distance to BSs or powerlines.

Some authors indicated that opponents of mobile phone towers generally do not express anxieties

about electromagnetic field exposure, indicating that the risk rating is comparable with other

perceived common hazards in the modern world [37].

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exposure with irritability, sleep troubles, lack of appetite and vertigo remained statistically

significant despite the introduction of the above covariates.





came.



We cannot truly rule out that their inhabitants were more; equal or less worried than elsewhere in

this region, since we cannot provide the percentage of worriers about the BS masts in La Ñora

compared with other places of its environment. However, information about this issue spread to

all over this region at this time and La Ñora circumstances are common with the rest of small

urban and rural areas. The sample was randomly selected but a participation bias cannot be rule

out since the most part of our participants expressed fear for the existence of the BSs and this could

contribute to their participation in the study. Also, it is also possible to speculate that the percentage

of the subjects who refused to participate were for opposite reasons (indifference about the BSs). In

this regard, neither health status nor subjective distance to the BS explained willingness to


Concerns about the radiation from BSs were not related to age, sex, or subjective distance to BSs.

This agrees with statements from some authors [13] that living near a base station does not make

more people generally fearful, but people that generally worry about the fields would express

stronger fears when they live close by.

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Nevertheless, irrespective of these explanations, there seem to be effects of exposure that occur

independent of the fear of the subjects, since controlling for fear did not change the association

between exposure and symptoms. However, biological grounds explaining non-thermal effects

have not been clearly established. Recently, it has been described that voltage-gated calcium

channels are essential to the beneficial or adverse responses to microwave EMFs, nanosecond EMF


In summary, the results of this study indicate that effects of very low but long lasting exposures to

emissions from mobile telephone base-stations on well-being cannot be ruled out. The effects

fulfilled almost completely the symptoms described within the microwave syndrome. Finally,

unraveling the causal pathways would be better done with experimental study design.

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CONCLUSIONS












Recommendations








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Competing interests


ACKNOWLEDGEMENTS



number 070000-2002-58.

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REFERENCES







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11:13-29


Further Perspective, and Urgent Needs. Electromagn. Biol. Med. 2005b ; 24:375-403.

5. Röösli M, Moser M, Baldinini Y, Meier M, Braun-Fahrländer C. Symptoms of ill health

ascribed to electromagnetic field exposure—a questionnaire survey. Int. J. Hyg. Environ.

Health. 2004 ; 207:141–150.

6. Frick U, Mayer M, Hauser S, Binder H , Rosner R , Eichhammer P. Entwicklung eines

deutschprachigen Messinsntrumentes für ‘‘Elektrosmog-Beschwerden’’. Umwelt Med.

Forsch. Prax. 2006 ; 11:103–113.

7. Eltiti S, Wallace D, Ridgewell A, Zougkou K, Russo R, Sepulveda F, et al. Does short-term

exposure to mobile phone base station signals increase symptoms in individuals who report

sensitivity to electromagnetic fields? A double-blind randomized provocation study. Environ




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2009;30:556-63.







50:369 – 373.






Med 2006; 63:307–313.



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Environ Med.2009;66:118-123.












(TNO) 2003;148:1-89.




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Am J Hum Biol. 2010; 5:613-8


near mobile phone base stations in Poland. Int J Occup Med Environ Health 2012;25:31-40





Med. 2013 Jun 19.



Organ 2010; 88: 887–896.











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Health 2011; 11:421



38. Pall ML. Electromagnetic fields act via activation of voltage-gated calcium channels to

produce beneficial or adverse effects. J. Cell. Mol. Med 2013; 7:. 958-965


63:298–299.





Health Phys 1998; 74: 494–522.


phone towers in residential areas. 2nd International Workshop on Biological effects of EMFS.

October 2002; Rhodes (Greece) Vol 1:327-333.

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Symptom/

Variable



Visual disturbances 1.31 0.48-3.60 0.77 0.21-2.77 1.63 0.60-4.39 Vertigo 0.61 0.20-1.91 0.77 0.19-3.10 2.90* 1.04-8.07 Vascular alteration 0.96 0.27-3.43 1.48 0.35-6.17 2.04 0.65-6.41 Hearing problems 0.59 0.20-1.70 0.77 0.19-3.10 0.48 0.15-1.60 Walking difficulty 0.60 0.20-1.79 φ φ 0.42 0.11-1.60 * p<0.05; ** p<0.01; (1) Not worried, as reference code (2) & (3) Not device use, as reference code,φ any subject affected using computer

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Table 2. ORs & 95% CIs for GSM exposure: increase in risk per increase in ln GSM (µW/m2). Change in -2 log likelihood is also shown.

Symptom

OR (95%CI) Change in

-2 Log likelihood

Fatigue 1.39***(1.14-1.70) 11.74***

Irritability 1.51***(1.23-1.85) 19.36***

Irritability (adjusted with age) 1.47***(1.20-1.81) -

Headaches 1.43** (1.15-1.78) 12.32***

Nausea 1.38** (1.09-1.73) 8.3**

Lack of Appetite 1.58** (1.23-2.03) 16.31*** Lack of Appetite (adjusted to use of cellular ) 1.53** (1.19-1.99) -

Trouble sleeping 1.49***(1.20-1.84) 16.38***

Trouble sleeping (adjusted to worry to BSs) 1.64***(1.22-2.19) -

Depression 1.41***(1.16-1.72) 13.99***

Concentration 1.54***(1.25- 1.89) 20.75***

Memory 1.27** (1.06- 1.52) 7.29**

Skin 1.24* (1.001-1.54) 4.08*

Visual 1.23 * (1.03-1.46) 5.30*

Vertigo 1.36** (1.11-1.66) 10.14***

Vertigo (adjusted to use of cellular ) 1.32** (1.08-1.62) -

Vascular 1.32* (1.05-1.64) 6.30*

Hearing 0.96 (0.80-1.15)

Walking 0.95 (0.78-1.15)

* p<0.05; ** p<0.01; *** p<0.001

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Table 3. Goodness-of-fit of the outcome binary response variable related to GSM exposure (ln). Cut-off values of exposure to microwaves according to ROC analysis. The data are presented in ascending order. Symptom ROC curves

Area


Pseudo-R**2

(1)

Cut-off (2) (Ln GSM)


SSV SPF AV

Headaches 0.84*** 0.90 0.23 0.72 0.41 0.57 1.77 Sleep 0.78*** 0.82 0.66 0.76 0.28 1.66 5.26



Vascular 0.85*** 0.20 1.0 0.90 0.34 8.02 3041.18 Skin 0.67* 0.00 1.00 0.81 0.072 9.06 8604.15

* p<0.05, ** p<0.01, *** p<0.001 (1) Nagelkerke (2) Cut-off (ROC curve) (a) SSV=sensitivity, SPF =specificity, AV=average

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Table 4. Statistics for r = 1000 bootstrapped binary logistic regression (GSM exposure coefficients: Increase in risk per increase in ln GSM (µW/m2). Asymptotic standard errors (SE) and 95% confidence intervals (CI) are also shown for comparison.

Symptom B*

Bootstrap Normal


intervals SE 95% CI

Lower Upper Lower Upper Fatigue 0.329 0.012 0.097 0.155 0.539 0.102 0.128 0.529 Irritability 0.411 0.016 0.110 0.241 0.670 0.104 0.207 0.615 Headache 0.358 0.022 0.139 0.149 0.688 0.113 0.137 0.578 Nausea 0.319 0.013 0.124 0.099 0.590 0.118 0.088 0.550 Appetite 0.456 0.026 0.134 0.264 0.784 0.128 0.205 0.707 Sleep 0.396 0.022 0.124 0.193 0.690 0.109 0.181 0.610 Depression 0.346 0.012 0.102 0.174 0.583 0.100 0.151 0.541 Attention 0.429 0.020 0.118 0.254 0.711 0.106 0.222 0.636 Memory 0.237 0.009 0.098 0.057 0.448 0.091 0.058 0.415 Skin 0.217 0.008 0.110 0.011 0.451 0.110 0.001 0.433 Visual 0.203 0.004 0.093 0.037 0.398 0.090 0.026 0.379 Hearing -0.05 -0.002 0.089 -0.219 0.143 0.093 -0.228 0.135 Vertigo 0.306 0.010 0.101 0.127 0.530 0.102 0.107 0.505 Walking -0.05 -0.006 0.098 -0.265 0.120 0.098 -0.246 0.138 Vascular 0.274 0.010 0.109 0.084 0.520 0.114 0.051 0.497 * beta coefficent (log OR)

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Subjective Symptoms Related to GSM Radiation from Mobile Phone Base Stations: a cross-sectional study

Journal: BMJ Open

Manuscript ID: bmjopen-2013-003836.R2


Date Submitted by the Author: 16-Nov-2013



Public health




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1

Subjective Symptoms Related to GSM Radiation from Mobile

Phone Base Stations: a cross-sectional study

Corresponding author:

Enrique A. Navarro


C/. Dr. Moliner 50,



Authors



Jaume Segura (3)


(1) Research Center, University Hospital La Fe, Avda Campanar s/n, 46009 Valencia,

Spain

(2) Department of Applied Physics, Universitat de València, C/. Dr. Moliner 50, 46100

Burjassot (Valencia), Spain.

(3) Department of Computer Sciences, ETSE- Universitat de València, Avda Universitat

s/n, 46100 Burjassot (Valencia), Spain.

Running title


Keywords: public health; GSM; microwave sickness.

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Abstract

Objectives: We performed a reanalysis of the data from Navarro et al., 2003, in which

health symptoms related to microwave exposure from mobile phone base stations (BS)

were explored, including data obtained in a retrospective inquiry about fear of exposure

from BS.

Method: Since weather circumstances can influence exposure, we restricted data to

measurements made under similar weather conditions. Participants with known illness in

2003 were subsequently disregarded: 88 participants instead of 101 (in 2003) were

analysed. A statistical method indifferent to the assumption of normality was employed:

namely, binary logistic regression for modelling a binary response (e.g. suffering fatigue

(1) or not (0)), and so exposure was introduced as a predictor variable. This analysis was

carried out on a regular basis and bootstrapping [95% percentile method] was used to

provide more accurate confidence intervals.

Results: The symptoms most related to exposure were: lack of appetite [odds ratio

(OR)] = 1.58, 95% confidence interval (95%CI) = 1.23-2.03; lack of concentration

[OR = 1.54, 95% CI = 1.25- 1.89]; irritability [OR = 1.51, 95% CI = 1.23-1.85]; and

trouble sleeping [OR = 1.49, 95% CI = 1.20-1.84]. Changes in -2 log likelihood showed

similar results. Concerns about the BS were strongly related with trouble sleeping

[OR = 3.12, 95% CI = 1.10-8.86]. The exposure variable remained statistically significant

in the multivariate analysis. The bootstrapped values were similar to the asymptotic

confidence intervals.

Conclusion: This study confirms our preliminary results. We observed that the

incidence of most of the symptoms was related to exposure levels – independently of the

demographic variables and some possible risk factors. Concerns about adverse effects

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from exposure, despite being strongly related with sleep disturbances, do not influence the

direct association between exposure and sleep.

ARTICLE SUMMARY:

Article Focus: 1) Some authors stated that the microwave syndrome could be related to anxiety

associated with fear and the visual perception of cell phone towers. In this manuscript we

demonstrate that although anxiety per se disturbs sleep, sleep disorder continued to be related

with GSM exposure after adjusting for these concerns. 2) It is shown that symptoms of

microwave sickness are moderately related with this radiation, reinforcing the idea that some

underlying biological mechanism may be behind these symptoms. 3) This work presents a re-

analysis of our previous cross-sectional study [10] that uses a more robust statistical method.

Key messages: 1) Existence of the microwave syndrome. 2) Symptoms related to very low GSM

levels in comparison with Spanish and international guidelines [40], [41].

Strengths: We used a robust statistical analysis with a highly homogeneous sample in a

homogeneous environment. Limitations: A participation bias cannot be ruled out. The late

query about concerns (as a possible confounder) may render the results less valid.

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The health risk due to exposure to radiofrequency electromagnetic fields (RF EMF)

continues to be discussed today. The study that led to this debate was initiated after

verification that the U.S. embassy in Moscow was being subjected to such radiation from

1953 to May 1975 [1]. Recently, a review of that episode [2] reopened the debate about

the potential harmfulness of RF EMF. The increasing number of base stations (BSs) on

masts and buildings has increased public awareness. This issue has prompted scientific

research to establish to what extent low-intensity electromagnetic fields may affect the

health of humans and other organisms [3, 4]. Furthermore, the term electromagnetic

hypersensitivity (EHS) has been recently introduced in discussions attributing symptoms

to exposure to electromagnetic fields (EMFs) [5-8]. A review of this topic [9] in 2010

found that 8 of the 10 studies evaluated through PubMed had reported increased

prevalence of adverse neurobehavioral symptoms or cancer in populations living at

distances < 500 meters from base stations.

None of the studies reported exposure above accepted international guidelines, suggesting

that current guidelines may be inadequate in protecting health. Thus, the need emerges to

revaluate our pioneering work in this field in order to add new procedures and data. Few

articles have addressed the possible association between microwave sickness and

microwave exposure from GSM (Global System for Mobile Communications) base

stations (BSs) since the publication of our first study [10]. Chronologically, Santini et al.,

[11] and Gadzicka et al., [12] reported differences in the distance-dependent prevalence of

symptoms such as headache, impaired concentration, and irritability. A later Austrian

study [13] showed a positive association between the measured electrical field (GSM

900/1800) in bedrooms – and headaches, cold hands and feet, and difficulties in

concentration. An Egyptian study [14] showed a prevalence of neurological complaints,

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such as headache, memory changes, dizziness, tremors, depressive symptoms, and sleep

disturbances among participants directly exposed to GSM signals from BSs.


microwave syndrome [15]. However, one article [16] using personal monitored data

from GSM-UMTS frequency bands found no statistical association in adults. More

recently, the same authors observed no association in children [17], contradictory results

in children and adolescents [18], and concluded that the few observed significant

associations were not causal but rather occurred by chance. Blettner et al. [19] reported

in Phase 1 of their study more health problems closer to base stations, but in Phase 2 [20]

they concluded that measured EMF emissions were not related to adverse health effects.

Other researchers focused their work on the possible existence of participants with

sensitivity to GSM or UMTS signals according to psychological, cognitive, or

autonomic assessment. These researchers used short-term exposure (only 30-50

minutes) under laboratory conditions [21-23] and revealed a large disparity between

participants. Recently, a study measuring several biological stress markers [24] found that

RF-EMF emitted by mobile phone base stations from 5.2 µW/m2 to 2126.8 µW/m2

increased cortisol and salivary alpha-amylase, while IgA concentration was not


The Selbitz study [25] in 2010 described a significant dose-response relationship in

symptoms related with sleep, mood, joints, infections, skin condition, as well as

neurological, cardiovascular, visual, and auditory systems, and the gastrointestinal tract.

The existence of short-term physiological effects of EMF on sleep quality was not

evident in the work of Danker-Hopfe et al. [26]; however, it was stated that the presence

of BSs per se (not the EMF) may have a negative impact on sleep quality.

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A Polish study in 2012 did not show correlation between electrical field strength and

frequency of subjective symptoms; however, it showed a correlation between subjective

symptoms and the distance to BSs [27]. A study carried out in Egypt [28] revealed that

exposure to EMF emitted either from mobile phones or BSs had significant effects on the

pituitary-adrenal axis. More recently, work developed in Iran [29] indicated that

symptoms such as nausea, headache, dizziness, irritability, discomfort, nervousness,

depression, sleep disturbance, memory loss and lowering of libido were statistically

significant in people living near BSs (<300m distances) compared to those living far from

the BSs (>300m).

In our cross-sectional analysis [10], 11 out of 16 symptoms showed statistically

significant higher scores in the group with the maximum exposure level. The symptoms

are included in the microwave syndrome. We also reported statistically significant

correlation coefficients between the measured electrical field and 14 out of 16

symptoms.

A review [30] recently established several conditions for epidemiological studies to be

eligible for introduction in general analysis: eligible studies must quantify exposure

using objective measures (such as distance to the nearest BS, spot, or personal exposure

measurements in a specific frequency range); possible confounders must be considered;

and the selection of the study population must be clearly free of bias in terms of

exposure and outcomes.

Accordingly, in this re-analysis of our previous study [10], possible confounders were

included in addition to the specific RF EMF measurements made in 2001 (covering the

specific range between 900 and 1800 MHz). Therefore, we co-analysed the effects of

other variables such as socio-demographic data and the use of electronic devices. Concern

about being damaged by radiation from antennas was also analysed.

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The new statistical approach tested the possible influences of other variables, such as

demographic data, and the use of electronic devices. Moreover, since some concerns have

been raised about possible health consequences caused by the emitted microwaves, we

analysed if these symptoms might be related to fear of exposure. As some participants

refused to allow measurements in their homes, we analysed if symptom status or

subjective distance to the BS could be a bias of participation in the study. Interestingly,

this period was free of other sources of RF such as WIFI or UMTS etc. or the massive use

of mobile phones, enabling a specific study of GSM technology. Finally, the suitability of

the size of the sample was analysed.

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METHODS

Study design

We chose a small urban area with mixed rural characteristics: low levels of environmental

pollution (more agricultural than industrial); no major differences in socio-economic

characteristics throughout the region (excluding large cities); similar ethnicity (white

Caucasian) and language (Spanish); and with mobile phone communication operative for

at least two years. La Ñora was chosen because it had the features of a small city, and was

located near the capital (Murcia) in a rural environment without any particular health or

environmental problems. Consequently, La Ñora was representative of small urban areas

in eastern Spain with fewer than 20,000 inhabitants – such rural areas accounting for

19.8% of the population and 35.9% of the territory in Spain.

Two BS masts, each about 30 m height were sited at different positions to provide GSM-

900-1800 coverage. The GSM 900 BS was positioned not before 1997 while the GSM

1800 BS was built in December 1999.

Data regarding the main demographic characteristics of the sample and their use of

electronic devices was collected through a Spanish-language questionnaire [11]. All of

the participants were of the same ethnic origin, shared similar family income levels and

general standard of living, and were born in La Ñora or nearby. All the residents in the

study were living in the village before the erection of both BSs. All of the residents were

at home for more than eight hours a day for at least six days a week and normally slept at

home.

The core of the questionnaire was a symptom checklist for estimating the frequency of 15

health-related symptoms attributed to microwave sickness. These symptoms were:

fatigue; irritability; headaches; nausea; loss of appetite; sleep disorders; depressive

tendency; dizziness; concentration difficulties; memory loss; skin lesions; visual and

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hearing deficiencies; walking difficulties; and cardiovascular problems. The frequency

was quantified as: never suffer = 0; sometimes = 1; often = 2; and very often = 3.

The percentage of residents who reported electrical transformers less than 10 m from their

home was 21.6%; while 42% reported high voltage power lines less than 100 m from

home. Finally, 40% of residents reported a TV transmitter within a radius of around 4 km.

The questionnaire included a statement that its purpose was health research, that the data

gathered would be confidential, and that the study was approved by the ethical committee

of the University of Valencia in accordance with the Declaration of Helsinki

(http://www.wma.net/e/policy/b3.htm).

Some 215 questionnaires were randomly distributed through 17 streets representing

practically the entire village. The houses were selected using a street map of the village.

In total 150 questionnaires were collected with the remainder being uncollected because

nobody was at home (31); or there was a refusal by the householder to complete the

questionnaire (34).

During 2001, 101 RF EMF measurements in bedrooms were made. The other (49)

residents refused admittance for taking the measurements (16), were not at home for the

scheduled measurement appointment (10), or had serious health problems (23).

However, some changes are now being introduced in this re-analysis. Thirteen of the

participants included in the original study have now been eliminated: two participants

were eliminated (one regarding alcohol abuse and another regarding pregnancy) to

increase the requirement on health criteria; and 11 to increase the homogeneity of the RF

EMFs measurements because there was a change (it was raining) in the usual dry weather

conditions when the respective broadband measurements were registered.

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The re-analysis of the dataset, which is the main focus of this paper, was finally

performed with 88 participants (45 female and 43 male) instead of the 101 analysed in

2001.


Sixty-six of the 88 participants were reached by telephone in February 2012 and asked

two questions:

a) Were you worried about the masts (BSs) when they were erected?

b) Did you believe their radiation (BSs) could damage your health?

In all cases, those who were worried about the masts were concerned about health

consequences. Twenty-seven participants (40. 9 %) responded no, and 39 (59.1%)

responded yes. Responses were analysed relative to age (ANOVA test), sex (lambda

statistic), and subjective distance to BS (Somers’ D statistic).

Exposure assessment

Broadband measurements were made on two Saturdays in February and March 2001 from

11 am to 7pm with a portable electrical field (400 MHz – 3 GHz) detector (Nuova

Elettronica Model LX-1435). This meter was calibrated with an HP-8510C network

analyser inside an anechoic chamber at the University of Valencia. During the bedroom

exposure assessment the electric field probe was held for approximately five minutes

about one meter from the walls and 1.2 meters above the ground – and moved around a

circle of 0.25 m radius, orientating the antenna in different directions to obtain the

maximum electrical field strength above the bed.

To check the intensity of TV and radio channels, as well as the intensity of working

channels and broadcast channels for the GSM-900-1800 BSs, measurements of the

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spectral power density were carried out with a probe antenna and a portable spectrum

analyser.

The probe was mounted on a linen phenolic tripod 1.2 m above the ground. The position

of the probe was the same on both days – on a hill next to the village and 20 m from the

BS. With the spectrum analyser we scanned the frequency bands and the levels were

averaged for six minutes. The measurement of the spectrum was similar on both days –

with a difference in the peak estimation (channel carriers) of about 1 dB.

The measured broadband exposure was almost invariable during the time interval of the

measurements. Exposure changed with the position or place but it did not change over

time, and this could be related with a low intensity of traffic (few phone calls) and the

high and constant intensity of the broadcast channel [10].


Demographic data was analysed using the Mann-Whitney one-way variance analysis and

Chi square test. Differences between groups were performed through variance

(ANOVA) and covariance analysis (ANCOVA).

The main statistical analysis was made using binary logistic regression (mode enter)

carried out on a regular basis with subsequent bootstrapping [1000 bootstrap replications,

95% percentile method and simple sampling] [31] to provide more accurate standard error

and confidence intervals. After producing (1000) bootstrap replicates θ b of an estimator

θ, the bootstrap standard error was the standard deviation of the bootstrap replicates:

SE (θ) = √ [∑ (θb - θ) **2 / (r-1)] b=1� r where θ is the mean of the θb. Because of our small sample size, a non-parametric

confidence interval for the estimate (mean) was constructed from the quartiles of the

bootstrap sampling distribution of θ. The 95% percentile interval (θ (lower) <θ< θ (upper)

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is shown, and where the θb are the r-ordered bootstrap replicates: lower = 0.025 × r

(sample 25) and upper = 0.975 × r (sample 975).

The dependent variables (health-related symptoms) given in four ordinal categories

(0=never, 1=sometimes, 2=often; 3=very often) were dichotomised (0,1 = 0 versus 2, 3 =

1).

The 15 health-related symptoms described above constituted the dichotomous dependent

variables. Univariate analysis was then performed for each symptom and for each of the

predictor variables: exposure to base station (µW/m2 as a natural logarithmic) and age

were used as continuous variables; while gender, computer use >2 h/ day, mobile phone

use > 20 minutes per day, and worry about the antennae were used as dichotomous

variables. The covariates with predictive value were considered for the multivariate

analysis. Thus possible confounder effects were evaluated.

In all cases, changes in -2 Log likelihood, odds ratio, 95%-confidence intervals (95%-CI),

as well as the probability value (p-value) were calculated. For all tests, a p-value below

0.05 was considered statistically significant.

We used the GSM exposure (the measurement of RF EMF in the bedroom) as a

continuous variable because it is recognised that categorisation of continuous variables

introduces major problems in the analysis and interpretation of models derived in a data-

dependent fashion [32, 33, 34].

We chose exposure values in the logarithmic form because these values are well grouped

around their median; while the raw values showed a high dispersion of values, with two

outliers and ten extreme values (data not shown).

Confounding was assessed by adding the potentially confounding variable to the model

and making a subjective decision as to whether or not the coefficient of the variable of

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interest, ORs of GSM exposure, had changed substantially. A 10 per cent variation was

accepted as a considerable change.


The maximum number of covariates included in each multivariate analysis was

calculated following this formula [35]. Let π be the smallest of the proportions of

negative or positive cases in the population and k the number of covariates, then the

minimum number of cases to include is:

N = 10 k / π

Goodness-of-fit tests such as the classification table, the Hosmer-Lemeshow statistic,

ROC curves, Cox and Snell’s, and Nagelkerke’s Pseudo R square measures were used.

The Wald statistic was also evaluated to test the significance of individual independent

variables. Moreover, possible multicolinearity was also tested.

With the predicted probability scores derived from the regression analysis, receiver

operating characteristic (ROC) curves were constructed for all symptoms or modalities in

order to analyse sensitivity and specificity levels. For each curve, the best cut-offs for

GSM exposure that maximises (sensitivity + specificity) were also calculated.

For statistical analysis we used the Statistical Package for Social Sciences, version 21.0

(IBM SPSS Inc., Chicago, IL, USA) for Windows.

Because an exposure assessment for transformers, high voltage power lines, and radio or

TV transmitters based on self-estimated distances would not produce a reliable exposure

estimate, it was decided to omit these covariates in the analysis.

RESULTS

Demographic data and the percentage of users of personal computers and mobile phones

were analysed. The mean age was 42. 17 years (standard deviation ± 17. 61, interval 15–

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81). Women totalled 51.1 % (mean age = 45.08 years (SD =17.98) ; [interval=15-81] and

48.9% were men (mean age = 39.12 years, SD=16.88) [15-75]. Some 13.6 % regularly

used computers and 23.9 % mobile phones.

No differences related with age, and use of mobile phones or computers were found

between the sexes.

The univariate logistic regression indicated that age was inversely associated with

irritability [OR = 0.97, 95%CI = 0.95-0.99] and that the oldest had the greatest difficulties

hearing [OR= 1.03, 95% CI=1.01-1.06] and walking [OR= 1.04, 95%CI = 1.01-1.07].

However, gender clearly did not influence the outcome of any dependent variable. Use of

mobile phones was linked with lack of appetite and vertigo, while worry about the

radiation from BSs was associated with trouble sleeping (Table 1). However, concern

about radiation from BSs was unrelated to age (ANOVA test), sex (lambda statistic), or

subjective distance to BS (Somers’ D statistic).

Most of the symptoms were related with GSM exposure, especially: fatigue, irritability,

lack of appetite, trouble sleeping, depression, and lack of concentration. Change in -2 log

likelihood showed similar results (Table 2). Figure 1 shows the distribution of EMF

measurements throughout the sample.

ROC curves for each of the logistic regression models (GSM exposure vs. each symptom)

oscillated between 0.65 and 0.87 (Table 3). Headaches (0.84), nausea (0.86), appetite

(0.87), and vascular problems (0.85) showed the highest values, while memory (0.67),

skin (0.67), and visual disturbances (0.65) showed the lowest values. The Hosmer and

Lemeshow test indicated that most analyses showed no significant p-values. The

exceptions were fatigue (0.003), depression (0.003) and vertigo (0.03). In the majority of

the cases, the models predicted better specificity than sensitivity. Only in the case of

headaches and sleep disorder, did sensitivity prevail over specificity (Table 3 –

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classification table). In the extreme case, skin and vascular problems showed null or

minimum sensitivity and100 % specificity. Nagelkerke pseudo R-squared, showed

acceptable coefficients with the exception of the symptoms related with vertigo and skin

problems (Table 3).

Threshold cut-off values of GSM for sleep, attention, irritability, and memory are also

shown (Table 3). The remaining cut-off values were not considered since sensitivity or

specificity was reported at below 0.50 %.

The influence of other covariates on the GSM ORs coefficients, such as age, cellular

use, and concern about the BS were always less than 10% (Tables 2).

There was no observed multi-collinearity among variables. Kappa values according to

factor analysis were always lower than 2 and well below the critical value of 30.


Standard errors and confidence intervals obtained by re-sampling were similar to those

calculated from the asymptotic approximation (Table 4). There was a small bias or

difference between the average bootstrap coefficients (not shown) and the respective

estimates obtained from the original sample.

There were no global health differences between those who permitted a bedroom

exposure measurement (88 in our last model) and those who refused RF measurements

(26), and these results were unaltered when using age as a covariate. Square partial eta

measured a 0% contribution of the willing participation variable to symptoms that

correlated with age such as: irritability, headaches, walking difficulties, and hearing loss.

There was no relationship between subjective distance to the BS and willingness to

participate (Pearson Chi- square = 2.80, df = 1, p=0.094).

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However, ANOVA showed that the group with recorded RF EMF levels was more prone

to complain of memory loss ( F = 5.07 ; p = 0.027); while participants without EMF

measures showed more skin problems ( F = 10.66; p = 0.001).

DISCUSSION

In the present re-analysis a more robust statistical method was employed that was

indifferent to the assumption of normality. To reduce the limitation of the sample size

effect and extrapolate our results to the entire population from which the sample was

obtained, a re-sample method or bootstrapping was used.

This new study partially confirms our preliminary results – namely, that most of the

symptoms related to GSM levels independently of the demographical variables and some

possible risk factors. Related to microwave radiation, the spectral power density analysis

maintained that the most important contribution to broadband measurements was from

GSM 900/1800, and the main variability of the measurements between different places

was due to a different coverage of the GSM 900/1800 signals, i.e. spatial variability. This

was further supported by the fact that the antenna used was fairly insensitive to

frequencies below 400 MHz. Therefore, the radio channels 80-110 MHz were not a

significant part of the broadband measurements. Moreover, the narrow band

measurements showed TV channels with substantially lower intensities than the GSM

900/1800 signals. The effects from these exposures will therefore not confound the effects

of BSs. Moreover, some authors [13] found that the only relevant contribution to the

variance of the high microwave exposure was from BSs – up to 93% of variance.

Moreover, at the time of our study the GSM signal was almost invariable in time because

there were very few calls. The main contribution was made from the broadcast channels

working almost constantly throughout the day. Short-range evaluations of exposure could

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be acceptable for describing a 24-hour period and the measurements were made in

bedrooms – a location where the participants were assumed to spend significant periods

of time.

However, some participants were mobile phone users at the time of this study and

exposure to a mobile phone during a phone call is much higher than that received from

BSs. Nevertheless, some authors [13] stated about that there is no a priori argument why

these lower levels should have no effect on the presence of a widespread use of mobile

telephones. Exposure to a BS will be at a low but almost constant level for many hours of

the day and especially at night.

While GSM exposure was associated with most of the symptoms, walking difficulties and

hearing loss was correlated only with age. Age also remained slightly inversely associated

with irritability. Users of cellular phones were more prone to complain of loss of appetite

and vertigo, while those who expressed worry about the BSs, were associated with sleep

problems. This later finding was in concordance with two other articles [13, 20, 26].

However, worry about the BSs was unrelated with age, gender, or subjective distance to

BSs. This agrees with an article [36] claiming that there was no statistically significant

association between symptom occurrence associated with perceived proximity to BSs,

psychological components, socio-demographic characteristics, and distance to BSs or

power lines.

Some authors indicated that opponents of mobile phone towers generally do not express

anxieties about electromagnetic field exposure, indicating that the risk rating is

comparable with other commonly perceived hazards in the modern world [37].

None of the analysed covariates behaved as confounders. The relationship of GSM

exposure with irritability, sleep troubles, lack of appetite, and vertigo remained

statistically significant despite the introduction of the above covariates.

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When the conventional multivariate analysis was tested using bootstrapping it was

observed that the standard error and confidence intervals obtained by re-sampling were

similar to those calculated from asymptotic approximation and this supports the

adequacy of our conventional analysis. Our sample, chosen at random, represents the

population from which it came.

The model appeared generally well adjusted while the cut-off values could constitute

good guidance for predicting the threshold of symptom appearance.

We cannot truly state that residents were more worried; equally worried, or less worried

than elsewhere in this region, since we cannot provide the percentage of those worried

about the BS masts in La Ñora compared with other nearby places. However,

information about this issue was widespread in this region at the time, and the

circumstances at La Ñora were shared with most other small urban and rural areas. The

sample was randomly selected but a participation bias cannot be ruled out since most of

our participants expressed fear regarding BSs and this could contribute to their

participation in the study. It is also possible to speculate that the percentage of participants

who refused to participate did so for the opposite reasons (indifference about BSs). In this

regard, neither health status nor subjective distance to the BS explained a willingness to


Concerns about radiation from BSs were not related to age, sex, or subjective distance to

BSs. This agrees with statements from several authors [13] that living near a base

station does not make people generally fearful, but people that generally worry about

fields express stronger fears when they live close to a station.

Nevertheless, irrespective of these explanations, there seems to be effects of exposure that

occur independently of the fear felt by the participants, since controlling for fear did not

change the association between exposure and symptoms. However, the late query about

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concerns (as a possible confounder) may render the results less valid. In contrast to our

findings, note that biological grounds explaining non-thermal effects have not been

clearly established. Recently, it has been stated that voltage-gated calcium channels are

essential to the beneficial or adverse responses to microwave EMFs, nanosecond EMF


In summary, the results of this study indicate that effects of very low but long lasting

exposure to emissions from mobile telephone base stations on well-being cannot be ruled

out. The effects almost completely matched the symptoms described within the

microwave syndrome. Finally, unravelling the causal pathways would be best performed

with an experimental study design.

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CONCLUSIONS

This new study partially confirms our preliminary results about microwave sickness

resulting from exposure to emissions from GSM mobile phone BSs. Fatigue, irritability,

lack of appetite, sleep troubles, depression, and lack of concentration were especially

related with GSM exposure.

These results were independent of the main socio-demographic variables, other EMF

exposures, and anxiety about being irradiated. Nevertheless, we confirm that

apprehension about modern technology could predict some symptoms, especially those

related with sleep problems.

Our results agree with those who claimed that by distorting perceptions of risk,

disproportionate precaution might paradoxically lead to illness that would not otherwise

occur [39]. However, health changes related with GSM exposure seem to occur in a

manner unrelated with those fears. Finally, exposure was very low during the period and

also very low in comparison with Spanish recommendations [40] and international

guidelines [41].

Recommendations

We subscribe to the guidelines observed by other authors [42] in following the principle

of prevention while the non-thermal effects are not considered in any official standard.

This includes exposure minimisation within the limits of technical feasibility to

guarantee a significant reduction in long term radiation exposure to cellular phone

towers in residential areas. Epidemiological and clinical studies should continue to

observe possible health changes in the population. Finally, clear information about the

correct use of newer electronic devices should be implemented.

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ACKNOWLEDGEMENTS

We would like to express our gratitude to Angeles Martinez Gomez for her assistance

during the fieldwork in La Ñora; as well as the Spanish Ministry of Science and

Technology for grant FIT number 070000-2002-58.


CGP was mainly responsible for designing and writing the manuscript. CGP also made

the main statistical contribution. EAN was one of the researchers responsible for the

design and acquisition of RF EMF data, as well as writing and reviewing the

manuscript. JS contributed mainly to processing the RF EMF data, while MP was

responsible for the design and final review of the manuscript.

Funding

This work was funded by the Spanish Ministry of Science and Technology for grant

FIT number 070000-2002-58.

Competing interests


Data Sharing

The data used in this statistical analysis can be obtained from Dr. Claudio Gómez-

Perretta upon request by email ([email protected]).

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Symptom/

Variable



Visual disturbances 1.31 0.48-3.60 0.77 0.21-2.77 1.63 0.60-4.39 Vertigo 0.61 0.20-1.91 0.77 0.19-3.10 2.90* 1.04-8.07 Vascular alteration 0.96 0.27-3.43 1.48 0.35-6.17 2.04 0.65-6.41 Hearing problems 0.59 0.20-1.70 0.77 0.19-3.10 0.48 0.15-1.60 Walking difficulty 0.60 0.20-1.79 φ φ 0.42 0.11-1.60 * p<0.05; ** p<0.01; (1) Not worried, as reference code (2) & (3). No device use, as reference code,φ any subject affected using computer

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Table 2. ORs & 95% CIs for GSM exposure: increase in risk per increase in Log GSM (µW/m2). Changes in -2 Log likelihood is also shown. The third column represents the ORs for a 10 fold increase in GSM (Log10 GSM)

Symptom OR (95%CI)

Change in -2 Log likelihood OR (95%CI)

Fatigue 1.39***(1.14-1.70) 11.74*** 2.13***(1.34-3.83) Irritability 1.51***(1.23-1.85) 19.36*** 2.58***(1.61-4.12) Irritability (adjusted with age)

1.47***(1.20-1.81) - 2.44***(1.52-3.94)

Headaches 1.43** (1.15-1.78) 12.32*** 2.28**(1.37-3.78) Nausea 1.38** (1.09-1.73) 8.3** 2.09**(1.23-3.55) Lack of Appetite 1.58** (1.23-2.03) 16.31*** 2.86***(1.60-5.09) Lack of Appetite (adjusted to cellular use)

1.53** (1.19-1.99) - 2.68***(1.48-4.84)

Trouble sleeping 1.49***(1.20-1.84) 16.38*** 2.49***(1.52-4.08) Trouble sleeping (adjusted to worry to BSs)

1.64***(1.22-2.19) - 3.11***(1.59-6.09)

Depression 1.41***(1.16-1.72) 13.99*** 2.22***(1.42-3.48) Concentration 1.54***(1.25- 1.89) 20.75*** 2.68***(1.67-4.32) Memory 1.27** (1.06- 1.52) 7.29** 1.73**(1.14-2.60) Skin 1.24* (1.001-1.54) 4.08* 1.65*(1.01-2.71) Visual 1.23 * (1.03-1.46) 5.30* 1.59*(1.06-2.40) Vertigo 1.36** (1.11-1.66) 10.14*** 2.02**(1.28-3.20) Vertigo (adjusted to cellular use)

1.32** (1.08-1.62) - 1.91**(1.20-3.04)

Vascular 1.32* (1.05-1.64) 6.30* 1.88*(1.12-3.14) Hearing 0.96 (0.80-1.15) 0.90(0.59-1.37) Walking 0.95 (0.78-1.15) 0.88(0.57-1.37) * p<0.05; ** p<0.01; *** p<0.001

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Table 3. Goodness-of-fit of the outcome binary response variable related to GSM exposure (Log=Ln). Cut-off values of exposure to microwaves according to ROC analysis. The data is presented in ascending order. Symptom ROC curves

Area


Pseudo-R**2

(1)

Cut-off (2) (Log GSM)


SSV SPF AV

Headaches 0.84*** 0.90 0.23 0.72 0.41 - 1.77 Sleep 0.78*** 0.82 0.66 0.76 0.28 1.66 5.26 Attention 0.78*** 0.67 0.72 0.69 0.28 3.61 36.97 Irritability 0.76*** 0.67 0.73 0.71 0.26 3.61 36.97 Memory 0.67** 0.54 0.77 0.67 0.11 4.99 146.94 Depression 0.75*** 0.46 0.76 0.65 0.20 - 184.93 Visual 0.65* 0.24 0.83 0.60 0.08 - 368.71 Fatigue 0.73*** 0.22 0.90 0.69 0.18 - 685.4 Vertigo 0.74*** 0.16 0.87 0.67 0.19 - 685.4 Appetite 0.87*** 0.40 0.94 0.85 0.43 - 1495.18 Nausea 0.86*** 0.46 0.93 0.87 0.38 - 1495.18 Vascular 0.85*** 0.20 1.0 0.90 0.34 - 3041.18 Skin 0.67* 0.00 1.00 0.81 0.072 - 8604.15 * p<0.05, ** p<0.01, *** p<0.001 (1) Nagelkerke (2) Cut-off (ROC curve): Only values showing SSV and SPF above 0.5 are reported (a) SSV=sensitivity, SPF =specificity, AV=average

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Table 4. Statistics for r = 1000 bootstrapped binary logistic regression (GSM exposure coefficients: Increase in risk per increase in Log GSM (µW/m2). Asymptotic standard errors (SE) and 95% confidence intervals (CI) are also shown for comparison.

Symptom B*

Bootstrap Normal


intervals SE 95% CI

Lower Upper Lower Upper Fatigue 0.329 0.012 0.097 0.155 0.539 0.102 0.128 0.529 Irritability 0.411 0.016 0.110 0.241 0.670 0.104 0.207 0.615 Headache 0.358 0.022 0.139 0.149 0.688 0.113 0.137 0.578 Nausea 0.319 0.013 0.124 0.099 0.590 0.118 0.088 0.550 Appetite 0.456 0.026 0.134 0.264 0.784 0.128 0.205 0.707 Sleep 0.396 0.022 0.124 0.193 0.690 0.109 0.181 0.610 Depression 0.346 0.012 0.102 0.174 0.583 0.100 0.151 0.541 Attention 0.429 0.020 0.118 0.254 0.711 0.106 0.222 0.636 Memory 0.237 0.009 0.098 0.057 0.448 0.091 0.058 0.415 Skin 0.217 0.008 0.110 0.011 0.451 0.110 0.001 0.433 Visual 0.203 0.004 0.093 0.037 0.398 0.090 0.026 0.379 Hearing -0.05 -0.002 0.089 -0.219 0.143 0.093 -0.228 0.135 Vertigo 0.306 0.010 0.101 0.127 0.530 0.102 0.107 0.505 Walking -0.05 -0.006 0.098 -0.265 0.120 0.098 -0.246 0.138 Vascular 0.274 0.010 0.109 0.084 0.520 0.114 0.051 0.497 * beta coefficent (Log OR)

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FIGURE CAPTIONS

Figure 1. Distribution of EMF measurement throughout the sample.

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1

Subjective Symptoms Related to GSM Radiation from Mobile

Phone Base Stations: a cross-sectional study

Corresponding author:

Enrique A. Navarro


C/. Dr. Moliner 50,



Authors



Jaume Segura (3)


(1) Research Center, University Hospital La Fe, Avda Campanar s/n, 46009 Valencia,

Spain

(2) Department of Applied Physics, Universitat de València, C/. Dr. Moliner 50, 46100

Burjassot (Valencia), Spain.

(3) Department of Computer Sciences, ETSE- Universitat de València, Avda Universitat

s/n, 46100 Burjassot (Valencia), Spain.

Running title


Keywords: public health; GSM; microwave sickness.

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Abstract

Objectives: We performed a reanalysis of the data from Navarro et al., 2003, in which

health symptoms related to microwave exposure from mobile phone base stations (BS)

were explored, including data obtained in a retrospective inquiry about fear of exposure

from BS.

Method: Since weather circumstances can influence exposure, we restricted data to

measurements made under similar weather conditions. Participants with known illness in

2003 were subsequently disregarded: 88 participants instead of 101 (in 2003) were

analysed. A statistical method indifferent to the assumption of normality was employed:

namely, binary logistic regression for modelling a binary response (e.g. suffering fatigue

(1) or not (0)), and so exposure was introduced as a predictor variable. This analysis was

carried out on a regular basis and bootstrapping [95% percentile method] was used to

provide more accurate confidence intervals.

Results: The symptoms most related to exposure were: lack of appetite [odds ratio

(OR)] = 1.58, 95% confidence interval (95%CI) = 1.23-2.03; lack of concentration

[OR = 1.54, 95% CI = 1.25- 1.89]; irritability [OR = 1.51, 95% CI = 1.23-1.85]; and

trouble sleeping [OR = 1.49, 95% CI = 1.20-1.84]. Changes in -2 log likelihood showed

similar results. Concerns about the BS were strongly related with trouble sleeping

[OR = 3.12, 95% CI = 1.10-8.86]. The exposure variable remained statistically significant

in the multivariate analysis. The bootstrapped values were similar to the asymptotic

confidence intervals.

Conclusion: This study confirms our preliminary results. We observed that the

incidence of most of the symptoms was related to exposure levels – independently of the

demographic variables and some possible risk factors. Concerns about adverse effects

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from exposure, despite being strongly related with sleep disturbances, do not influence the

direct association between exposure and sleep.


CGP was mainly responsible for designing and writing the manuscript. CGP also made

the main statistical contribution. EAN was one of the researchers responsible for the

design and acquisition of RF EMF data, as well as writing and reviewing the

manuscript. JS contributed mainly to processing the RF EMF data, while MP was

responsible for the design and final review of the manuscript.

ARTICLE SUMMARY:

Article Focus: 1) Some authors stated that the microwave syndrome could be related to anxiety

associated with fear and the visual perception of cell phone towers. In this manuscript we

demonstrate that although anxiety per se disturbs sleep, sleep disorder continued to be related

with GSM exposure after adjusting for these concerns. 2) It is shown that symptoms of

microwave sickness are moderately related with this radiation, reinforcing the idea that some

underlying biological mechanism may be behind these symptoms. 3) This work presents a re-

analysis of our previous cross-sectional study [10] that uses a more robust statistical method.

Key messages: 1) Existence of the microwave syndrome. 2) Symptoms related to very low GSM

levels in comparison with Spanish and international guidelines [40], [41].

Strengths: We used a robust statistical analysis with a highly homogeneous sample in a

homogeneous environment. Limitations: A participation bias cannot be ruled out. The late

query about concerns (as a possible confounder) may render the results less valid.

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The health risk due to exposure to radiofrequency electromagnetic fields (RF EMF)

continues to be discussed today. The study that led to this debate was initiated after

verification that the U.S. embassy in Moscow was being subjected to such radiation from

1953 to May 1975 [1]. Recently, a review of that episode [2] reopened the debate about

the potential harmfulness of RF EMF. The increasing number of base stations (BSs) on

masts and buildings has increased public awareness. This issue has prompted scientific

research to establish to what extent low-intensity electromagnetic fields may affect the

health of humans and other organisms [3, 4]. Furthermore, the term electromagnetic

hypersensitivity (EHS) has been recently introduced in discussions attributing symptoms

to exposure to electromagnetic fields (EMFs) [5-8]. A review of this topic [9] in 2010

found that 8 of the 10 studies evaluated through PubMed had reported increased

prevalence of adverse neurobehavioral symptoms or cancer in populations living at

distances < 500 meters from base stations.

None of the studies reported exposure above accepted international guidelines, suggesting

that current guidelines may be inadequate in protecting health. Thus, the need emerges to

revaluate our pioneering work in this field in order to add new procedures and data. Few

articles have addressed the possible association between microwave sickness and

microwave exposure from GSM (Global System for Mobile Communications) base

stations (BSs) since the publication of our first study [10]. Chronologically, Santini et al.,

[11] and Gadzicka et al., [12] reported differences in the distance-dependent prevalence of

symptoms such as headache, impaired concentration, and irritability. A later Austrian

study [13] showed a positive association between the measured electrical field (GSM

900/1800) in bedrooms – and headaches, cold hands and feet, and difficulties in

concentration. An Egyptian study [14] showed a prevalence of neurological complaints,

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such as headache, memory changes, dizziness, tremors, depressive symptoms, and sleep

disturbances among participants directly exposed to GSM signals from BSs.


microwave syndrome [15]. However, one article [16] using personal monitored data

from GSM-UMTS frequency bands found no statistical association in adults. More

recently, the same authors observed no association in children [17], contradictory results

in children and adolescents [18], and concluded that the few observed significant

associations were not causal but rather occurred by chance. Blettner et al. [19] reported

in Phase 1 of their study more health problems closer to base stations, but in Phase 2 [20]

they concluded that measured EMF emissions were not related to adverse health effects.

Other researchers focused their work on the possible existence of participants with

sensitivity to GSM or UMTS signals according to psychological, cognitive, or

autonomic assessment. These researchers used short-term exposure (only 30-50

minutes) under laboratory conditions [21-23] and revealed a large disparity between

participants. Recently, a study measuring several biological stress markers [24] found that

RF-EMF emitted by mobile phone base stations from 5.2 µW/m2 to 2126.8 µW/m2

increased cortisol and salivary alpha-amylase, while IgA concentration was not


The Selbitz study [25] in 2010 described a significant dose-response relationship in

symptoms related with sleep, mood, joints, infections, skin condition, as well as

neurological, cardiovascular, visual, and auditory systems, and the gastrointestinal tract.

The existence of short-term physiological effects of EMF on sleep quality was not

evident in the work of Danker-Hopfe et al. [26]; however, it was stated that the presence

of BSs per se (not the EMF) may have a negative impact on sleep quality.

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A Polish study in 2012 did not show correlation between electrical field strength and

frequency of subjective symptoms; however, it showed a correlation between subjective

symptoms and the distance to BSs [27]. A study carried out in Egypt [28] revealed that

exposure to EMF emitted either from mobile phones or BSs had significant effects on the

pituitary-adrenal axis. More recently, work developed in Iran [29] indicated that

symptoms such as nausea, headache, dizziness, irritability, discomfort, nervousness,

depression, sleep disturbance, memory loss and lowering of libido were statistically

significant in people living near BSs (<300m distances) compared to those living far from

the BSs (>300m).

In our cross-sectional analysis [10], 11 out of 16 symptoms showed statistically

significant higher scores in the group with the maximum exposure level. The symptoms

are included in the microwave syndrome. We also reported statistically significant

correlation coefficients between the measured electrical field and 14 out of 16

symptoms.

A review [30] recently established several conditions for epidemiological studies to be

eligible for introduction in general analysis: eligible studies must quantify exposure

using objective measures (such as distance to the nearest BS, spot, or personal exposure

measurements in a specific frequency range); possible confounders must be considered;

and the selection of the study population must be clearly free of bias in terms of

exposure and outcomes.

Accordingly, in this re-analysis of our previous study [10], possible confounders were

included in addition to the specific RF EMF measurements made in 2001 (covering the

specific range between 900 and 1800 MHz). Therefore, we co-analysed the effects of

other variables such as socio-demographic data and the use of electronic devices. Concern

about being damaged by radiation from antennas was also analysed.

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The new statistical approach tested the possible influences of other variables, such as

demographic data, and the use of electronic devices. Moreover, since some concerns have

been raised about possible health consequences caused by the emitted microwaves, we

analysed if these symptoms might be related to fear of exposure. As some participants

refused to allow measurements in their homes, we analysed if symptom status or

subjective distance to the BS could be a bias of participation in the study. Interestingly,

this period was free of other sources of RF such as WIFI or UMTS etc. or the massive use

of mobile phones, enabling a specific study of GSM technology. Finally, the suitability of

the size of the sample was analysed.

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METHODS

Study design

We chose a small urban area with mixed rural characteristics: low levels of environmental

pollution (more agricultural than industrial); no major differences in socio-economic

characteristics throughout the region (excluding large cities); similar ethnicity (white

Caucasian) and language (Spanish); and with mobile phone communication operative for

at least two years. La Ñora was chosen because it had the features of a small city, and was

located near the capital (Murcia) in a rural environment without any particular health or

environmental problems. Consequently, La Ñora was representative of small urban areas

in eastern Spain with fewer than 20,000 inhabitants – such rural areas accounting for

19.8% of the population and 35.9% of the territory in Spain.

Two BS masts, each about 30 m height were sited at different positions to provide GSM-

900-1800 coverage. The GSM 900 BS was positioned not before 1997 while the GSM

1800 BS was built in December 1999.

Data regarding the main demographic characteristics of the sample and their use of

electronic devices was collected through a Spanish-language questionnaire [11]. All of

the participants were of the same ethnic origin, shared similar family income levels and

general standard of living, and were born in La Ñora or nearby. All the residents in the

study were living in the village before the erection of both BSs. All of the residents were

at home for more than eight hours a day for at least six days a week and normally slept at

home.

The core of the questionnaire was a symptom checklist for estimating the frequency of 15

health-related symptoms attributed to microwave sickness. These symptoms were:

fatigue; irritability; headaches; nausea; loss of appetite; sleep disorders; depressive

tendency; dizziness; concentration difficulties; memory loss; skin lesions; visual and

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hearing deficiencies; walking difficulties; and cardiovascular problems. The frequency

was quantified as: never suffer = 0; sometimes = 1; often = 2; and very often = 3.

The percentage of residents who reported electrical transformers less than 10 m from their

home was 21.6%; while 42% reported high voltage power lines less than 100 m from

home. Finally, 40% of residents reported a TV transmitter within a radius of around 4 km.

The questionnaire included a statement that its purpose was health research, that the data

gathered would be confidential, and that the study was approved by the ethical committee

of the University of Valencia in accordance with the Declaration of Helsinki

(http://www.wma.net/e/policy/b3.htm).

Some 215 questionnaires were randomly distributed through 17 streets representing

practically the entire village. The houses were selected using a street map of the village.

In total 150 questionnaires were collected with the remainder being uncollected because

nobody was at home (31); or there was a refusal by the householder to complete the

questionnaire (34).

During 2001, 101 RF EMF measurements in bedrooms were made. The other (49)

residents refused admittance for taking the measurements (16), were not at home for the

scheduled measurement appointment (10), or had serious health problems (23).

However, some changes are now being introduced in this re-analysis. Thirteen of the

participants included in the original study have now been eliminated: two participants

were eliminated (one regarding alcohol abuse and another regarding pregnancy) to

increase the requirement on health criteria; and 11 to increase the homogeneity of the RF

EMFs measurements because there was a change (it was raining) in the usual dry weather

conditions when the respective broadband measurements were registered.

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The re-analysis of the dataset, which is the main focus of this paper, was finally

performed with 88 participants (45 female and 43 male) instead of the 101 analysed in

2001.


Sixty-six of the 88 participants were reached by telephone in February 2012 and asked

two questions:

a) Were you worried about the masts (BSs) when they were erected?

b) Did you believe their radiation (BSs) could damage your health?

In all cases, those who were worried about the masts were concerned about health

consequences. Twenty-seven participants (40. 9 %) responded no, and 39 (59.1%)

responded yes. Responses were analysed relative to age (ANOVA test), sex (lambda

statistic), and subjective distance to BS (Somers’ D statistic).

Exposure assessment

Broadband measurements were made on two Saturdays in February and March 2001 from

11 am to 7pm with a portable electrical field (400 MHz – 3 GHz) detector (Nuova

Elettronica Model LX-1435). This meter was calibrated with an HP-8510C network

analyser inside an anechoic chamber at the University of Valencia. During the bedroom

exposure assessment the electric field probe was held for approximately five minutes

about one meter from the walls and 1.2 meters above the ground – and moved around a

circle of 0.25 m radius, orientating the antenna in different directions to obtain the

maximum electrical field strength above the bed.

To check the intensity of TV and radio channels, as well as the intensity of working

channels and broadcast channels for the GSM-900-1800 BSs, measurements of the

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spectral power density were carried out with a probe antenna and a portable spectrum

analyser.

The probe was mounted on a linen phenolic tripod 1.2 m above the ground. The position

of the probe was the same on both days – on a hill next to the village and 20 m from the

BS. With the spectrum analyser we scanned the frequency bands and the levels were

averaged for six minutes. The measurement of the spectrum was similar on both days –

with a difference in the peak estimation (channel carriers) of about 1 dB.

The measured broadband exposure was almost invariable during the time interval of the

measurements. Exposure changed with the position or place but it did not change over

time, and this could be related with a low intensity of traffic (few phone calls) and the

high and constant intensity of the broadcast channel [10].


Demographic data was analysed using the Mann-Whitney one-way variance analysis and

Chi square test. Differences between groups were performed through variance

(ANOVA) and covariance analysis (ANCOVA).

The main statistical analysis was made using binary logistic regression (mode enter)

carried out on a regular basis with subsequent bootstrapping [1000 bootstrap replications,

95% percentile method and simple sampling] [31] to provide more accurate standard error

and confidence intervals. After producing (1000) bootstrap replicates θ b of an estimator

θ, the bootstrap standard error was the standard deviation of the bootstrap replicates:

SE (θ) = √ [∑ (θb - θ) **2 / (r-1)] b=1� r where θ is the mean of the θb. Because of our small sample size, a non-parametric

confidence interval for the estimate (mean) was constructed from the quartiles of the

bootstrap sampling distribution of θ. The 95% percentile interval (θ (lower) <θ< θ (upper)

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is shown, and where the θb are the r-ordered bootstrap replicates: lower = 0.025 × r

(sample 25) and upper = 0.975 × r (sample 975).

The dependent variables (health-related symptoms) given in four ordinal categories

(0=never, 1=sometimes, 2=often; 3=very often) were dichotomised (0,1 = 0 versus 2, 3 =

1).

The 15 health-related symptoms described above constituted the dichotomous dependent

variables. Univariate analysis was then performed for each symptom and for each of the

predictor variables: exposure to base station (µW/m2 as a natural logarithmic) and age

were used as continuous variables; while gender, computer use >2 h/ day, mobile phone

use > 20 minutes per day, and worry about the antennae were used as dichotomous

variables. The covariates with predictive value were considered for the multivariate

analysis. Thus possible confounder effects were evaluated.

In all cases, changes in -2 Log likelihood, odds ratio, 95%-confidence intervals (95%-CI),

as well as the probability value (p-value) were calculated. For all tests, a p-value below

0.05 was considered statistically significant.

We used the GSM exposure (the measurement of RF EMF in the bedroom) as a

continuous variable because it is recognised that categorisation of continuous variables

introduces major problems in the analysis and interpretation of models derived in a data-

dependent fashion [32, 33, 34].

We chose exposure values in the logarithmic form because these values are well grouped

around their median; while the raw values showed a high dispersion of values, with two

outliers and ten extreme values (data not shown).

Confounding was assessed by adding the potentially confounding variable to the model

and making a subjective decision as to whether or not the coefficient of the variable of

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interest, ORs of GSM exposure, had changed substantially. A 10 per cent variation was

accepted as a considerable change.


The maximum number of covariates included in each multivariate analysis was

calculated following this formula [35]. Let π be the smallest of the proportions of

negative or positive cases in the population and k the number of covariates, then the

minimum number of cases to include is:

N = 10 k / π

Goodness-of-fit tests such as the classification table, the Hosmer-Lemeshow statistic,

ROC curves, Cox and Snell’s, and Nagelkerke’s Pseudo R square measures were used.

The Wald statistic was also evaluated to test the significance of individual independent

variables. Moreover, possible multicolinearity was also tested.

With the predicted probability scores derived from the regression analysis, receiver

operating characteristic (ROC) curves were constructed for all symptoms or modalities in

order to analyse sensitivity and specificity levels. For each curve, the best cut-offs for

GSM exposure that maximises (sensitivity + specificity) were also calculated.

For statistical analysis we used the Statistical Package for Social Sciences, version 21.0

(IBM SPSS Inc., Chicago, IL, USA) for Windows.

Because an exposure assessment for transformers, high voltage power lines, and radio or

TV transmitters based on self-estimated distances would not produce a reliable exposure

estimate, it was decided to omit these covariates in the analysis.

RESULTS

Demographic data and the percentage of users of personal computers and mobile phones

were analysed. The mean age was 42. 17 years (standard deviation ± 17. 61, interval 15–

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81). Women totalled 51.1 % (mean age = 45.08 years (SD =17.98) ; [interval=15-81] and

48.9% were men (mean age = 39.12 years, SD=16.88) [15-75]. Some 13.6 % regularly

used computers and 23.9 % mobile phones.

No differences related with age, and use of mobile phones or computers were found

between the sexes.

The univariate logistic regression indicated that age was inversely associated with

irritability [OR = 0.97, 95%CI = 0.95-0.99] and that the oldest had the greatest difficulties

hearing [OR= 1.03, 95% CI=1.01-1.06] and walking [OR= 1.04, 95%CI = 1.01-1.07].

However, gender clearly did not influence the outcome of any dependent variable. Use of

mobile phones was linked with lack of appetite and vertigo, while worry about the

radiation from BSs was associated with trouble sleeping (Table 1). However, concern

about radiation from BSs was unrelated to age (ANOVA test), sex (lambda statistic), or

subjective distance to BS (Somers’ D statistic).

Most of the symptoms were related with GSM exposure, especially: fatigue, irritability,

lack of appetite, trouble sleeping, depression, and lack of concentration. Change in -2 log

likelihood showed similar results (Table 2). Figure 1 shows the distribution of EMF

measurements throughout the sample.

ROC curves for each of the logistic regression models (GSM exposure vs. each symptom)

oscillated between 0.65 and 0.87 (Table 3). Headaches (0.84), nausea (0.86), appetite

(0.87), and vascular problems (0.85) showed the highest values, while memory (0.67),

skin (0.67), and visual disturbances (0.65) showed the lowest values. The Hosmer and

Lemeshow test indicated that most analyses showed no significant p-values. The

exceptions were fatigue (0.003), depression (0.003) and vertigo (0.03). In the majority of

the cases, the models predicted better specificity than sensitivity. Only in the case of

headaches and sleep disorder, did sensitivity prevail over specificity (Table 3 –

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classification table). In the extreme case, skin and vascular problems showed null or

minimum sensitivity and100 % specificity. Nagelkerke pseudo R-squared, showed

acceptable coefficients with the exception of the symptoms related with vertigo and skin

problems (Table 3).

Threshold cut-off values of GSM for sleep, attention, irritability, and memory are also

shown (Table 3). The remaining cut-off values were not considered since sensitivity or

specificity was reported at below 0.50 %.

The influence of other covariates on the GSM ORs coefficients, such as age, cellular

use, and concern about the BS were always less than 10% (Tables 2).

There was no observed multi-collinearity among variables. Kappa values according to

factor analysis were always lower than 2 and well below the critical value of 30.


Standard errors and confidence intervals obtained by re-sampling were similar to those

calculated from the asymptotic approximation (Table 4). There was a small bias or

difference between the average bootstrap coefficients (not shown) and the respective

estimates obtained from the original sample.

There were no global health differences between those who permitted a bedroom

exposure measurement (88 in our last model) and those who refused RF measurements

(26), and these results were unaltered when using age as a covariate. Square partial eta

measured a 0% contribution of the willing participation variable to symptoms that

correlated with age such as: irritability, headaches, walking difficulties, and hearing loss.

There was no relationship between subjective distance to the BS and willingness to

participate (Pearson Chi- square = 2.80, df = 1, p=0.094).

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However, ANOVA showed that the group with recorded RF EMF levels was more prone

to complain of memory loss ( F = 5.07 ; p = 0.027); while participants without EMF

measures showed more skin problems ( F = 10.66; p = 0.001).

DISCUSSION

In the present re-analysis a more robust statistical method was employed that was

indifferent to the assumption of normality. To reduce the limitation of the sample size

effect and extrapolate our results to the entire population from which the sample was

obtained, a re-sample method or bootstrapping was used.

This new study partially confirms our preliminary results – namely, that most of the

symptoms related to GSM levels independently of the demographical variables and some

possible risk factors. Related to microwave radiation, the spectral power density analysis

maintained that the most important contribution to broadband measurements was from

GSM 900/1800, and the main variability of the measurements between different places

was due to a different coverage of the GSM 900/1800 signals, i.e. spatial variability. This

was further supported by the fact that the antenna used was fairly insensitive to

frequencies below 400 MHz. Therefore, the radio channels 80-110 MHz were not a

significant part of the broadband measurements. Moreover, the narrow band

measurements showed TV channels with substantially lower intensities than the GSM

900/1800 signals. The effects from these exposures will therefore not confound the effects

of BSs. Moreover, some authors [13] found that the only relevant contribution to the

variance of the high microwave exposure was from BSs – up to 93% of variance.

Moreover, at the time of our study the GSM signal was almost invariable in time because

there were very few calls. The main contribution was made from the broadcast channels

working almost constantly throughout the day. Short-range evaluations of exposure could

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be acceptable for describing a 24-hour period and the measurements were made in

bedrooms – a location where the participants were assumed to spend significant periods

of time.

However, some participants were mobile phone users at the time of this study and

exposure to a mobile phone during a phone call is much higher than that received from

BSs. Nevertheless, some authors [13] stated about that there is no a priori argument why

these lower levels should have no effect on the presence of a widespread use of mobile

telephones. Exposure to a BS will be at a low but almost constant level for many hours of

the day and especially at night.

While GSM exposure was associated with most of the symptoms, walking difficulties and

hearing loss was correlated only with age. Age also remained slightly inversely associated

with irritability. Users of cellular phones were more prone to complain of loss of appetite

and vertigo, while those who expressed worry about the BSs, were associated with sleep

problems. This later finding was in concordance with two other articles [13, 20, 26].

However, worry about the BSs was unrelated with age, gender, or subjective distance to

BSs. This agrees with an article [36] claiming that there was no statistically significant

association between symptom occurrence associated with perceived proximity to BSs,

psychological components, socio-demographic characteristics, and distance to BSs or

power lines.

Some authors indicated that opponents of mobile phone towers generally do not express

anxieties about electromagnetic field exposure, indicating that the risk rating is

comparable with other commonly perceived hazards in the modern world [37].

None of the analysed covariates behaved as confounders. The relationship of GSM

exposure with irritability, sleep troubles, lack of appetite, and vertigo remained

statistically significant despite the introduction of the above covariates.

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When the conventional multivariate analysis was tested using bootstrapping it was

observed that the standard error and confidence intervals obtained by re-sampling were

similar to those calculated from asymptotic approximation and this supports the

adequacy of our conventional analysis. Our sample, chosen at random, represents the

population from which it came.

The model appeared generally well adjusted while the cut-off values could constitute

good guidance for predicting the threshold of symptom appearance.

We cannot truly state that residents were more worried; equally worried, or less worried

than elsewhere in this region, since we cannot provide the percentage of those worried

about the BS masts in La Ñora compared with other nearby places. However,

information about this issue was widespread in this region at the time, and the

circumstances at La Ñora were shared with most other small urban and rural areas. The

sample was randomly selected but a participation bias cannot be ruled out since most of

our participants expressed fear regarding BSs and this could contribute to their

participation in the study. It is also possible to speculate that the percentage of participants

who refused to participate did so for the opposite reasons (indifference about BSs). In this

regard, neither health status nor subjective distance to the BS explained a willingness to


Concerns about radiation from BSs were not related to age, sex, or subjective distance to

BSs. This agrees with statements from several authors [13] that living near a base

station does not make people generally fearful, but people that generally worry about

fields express stronger fears when they live close to a station.

Nevertheless, irrespective of these explanations, there seems to be effects of exposure that

occur independently of the fear felt by the participants, since controlling for fear did not

change the association between exposure and symptoms. However, the late query about

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concerns (as a possible confounder) may render the results less valid. In contrast to our

findings, note that biological grounds explaining non-thermal effects have not been

clearly established. Recently, it has been stated that voltage-gated calcium channels are

essential to the beneficial or adverse responses to microwave EMFs, nanosecond EMF


In summary, the results of this study indicate that effects of very low but long lasting

exposure to emissions from mobile telephone base stations on well-being cannot be ruled

out. The effects almost completely matched the symptoms described within the

microwave syndrome. Finally, unravelling the causal pathways would be best performed

with an experimental study design.

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CONCLUSIONS

This new study partially confirms our preliminary results about microwave sickness

resulting from exposure to emissions from GSM mobile phone BSs. Fatigue, irritability,

lack of appetite, sleep troubles, depression, and lack of concentration were especially

related with GSM exposure.

These results were independent of the main socio-demographic variables, other EMF

exposures, and anxiety about being irradiated. Nevertheless, we confirm that

apprehension about modern technology could predict some symptoms, especially those

related with sleep problems.

Our results agree with those who claimed that by distorting perceptions of risk,

disproportionate precaution might paradoxically lead to illness that would not otherwise

occur [39]. However, health changes related with GSM exposure seem to occur in a

manner unrelated with those fears. Finally, exposure was very low during the period and

also very low in comparison with Spanish recommendations [40] and international

guidelines [41].

Recommendations

We subscribe to the guidelines observed by other authors [42] in following the principle

of prevention while the non-thermal effects are not considered in any official standard.

This includes exposure minimisation within the limits of technical feasibility to

guarantee a significant reduction in long term radiation exposure to cellular phone

towers in residential areas. Epidemiological and clinical studies should continue to

observe possible health changes in the population. Finally, clear information about the

correct use of newer electronic devices should be implemented.

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Competing interests


ACKNOWLEDGEMENTS

We would like to express our gratitude to Angeles Martinez Gomez for her assistance

during the fieldwork in La Ñora; as well as the Spanish Ministry of Science and

Technology for grant FIT number 070000-2002-58.

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Symptom/

Variable



Visual disturbances 1.31 0.48-3.60 0.77 0.21-2.77 1.63 0.60-4.39 Vertigo 0.61 0.20-1.91 0.77 0.19-3.10 2.90* 1.04-8.07 Vascular alteration 0.96 0.27-3.43 1.48 0.35-6.17 2.04 0.65-6.41 Hearing problems 0.59 0.20-1.70 0.77 0.19-3.10 0.48 0.15-1.60 Walking difficulty 0.60 0.20-1.79 φ φ 0.42 0.11-1.60 * p<0.05; ** p<0.01; (1) Not worried, as reference code (2) & (3). No device use, as reference code,φ any subject affected using computer

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Table 2. ORs & 95% CIs for GSM exposure: increase in risk per increase in Log GSM (µW/m2). Changes in -2 Log likelihood is also shown. The third column represents the ORs for a 10 fold increase in GSM (Log10 GSM)

Symptom OR (95%CI)

Change in -2 Log likelihood OR (95%CI)

Fatigue 1.39***(1.14-1.70) 11.74*** 2.13***(1.34-3.83) Irritability 1.51***(1.23-1.85) 19.36*** 2.58***(1.61-4.12) Irritability (adjusted with age)

1.47***(1.20-1.81) - 2.44***(1.52-3.94)

Headaches 1.43** (1.15-1.78) 12.32*** 2.28**(1.37-3.78) Nausea 1.38** (1.09-1.73) 8.3** 2.09**(1.23-3.55) Lack of Appetite 1.58** (1.23-2.03) 16.31*** 2.86***(1.60-5.09) Lack of Appetite (adjusted to cellular use)

1.53** (1.19-1.99) - 2.68***(1.48-4.84)

Trouble sleeping 1.49***(1.20-1.84) 16.38*** 2.49***(1.52-4.08) Trouble sleeping (adjusted to worry to BSs)

1.64***(1.22-2.19) - 3.11***(1.59-6.09)

Depression 1.41***(1.16-1.72) 13.99*** 2.22***(1.42-3.48) Concentration 1.54***(1.25- 1.89) 20.75*** 2.68***(1.67-4.32) Memory 1.27** (1.06- 1.52) 7.29** 1.73**(1.14-2.60) Skin 1.24* (1.001-1.54) 4.08* 1.65*(1.01-2.71) Visual 1.23 * (1.03-1.46) 5.30* 1.59*(1.06-2.40) Vertigo 1.36** (1.11-1.66) 10.14*** 2.02**(1.28-3.20) Vertigo (adjusted to cellular use)

1.32** (1.08-1.62) - 1.91**(1.20-3.04)

Vascular 1.32* (1.05-1.64) 6.30* 1.88*(1.12-3.14) Hearing 0.96 (0.80-1.15) 0.90(0.59-1.37) Walking 0.95 (0.78-1.15) 0.88(0.57-1.37) * p<0.05; ** p<0.01; *** p<0.001

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Table 3. Goodness-of-fit of the outcome binary response variable related to GSM exposure (Log=Ln). Cut-off values of exposure to microwaves according to ROC analysis. The data is presented in ascending order. Symptom ROC curves

Area


Pseudo-R**2

(1)

Cut-off (2) (Log GSM)


SSV SPF AV

Headaches 0.84*** 0.90 0.23 0.72 0.41 - 1.77 Sleep 0.78*** 0.82 0.66 0.76 0.28 1.66 5.26 Attention 0.78*** 0.67 0.72 0.69 0.28 3.61 36.97 Irritability 0.76*** 0.67 0.73 0.71 0.26 3.61 36.97 Memory 0.67** 0.54 0.77 0.67 0.11 4.99 146.94 Depression 0.75*** 0.46 0.76 0.65 0.20 - 184.93 Visual 0.65* 0.24 0.83 0.60 0.08 - 368.71 Fatigue 0.73*** 0.22 0.90 0.69 0.18 - 685.4 Vertigo 0.74*** 0.16 0.87 0.67 0.19 - 685.4 Appetite 0.87*** 0.40 0.94 0.85 0.43 - 1495.18 Nausea 0.86*** 0.46 0.93 0.87 0.38 - 1495.18 Vascular 0.85*** 0.20 1.0 0.90 0.34 - 3041.18 Skin 0.67* 0.00 1.00 0.81 0.072 - 8604.15 * p<0.05, ** p<0.01, *** p<0.001 (1) Nagelkerke (2) Cut-off (ROC curve): Only values showing SSV and SPF above 0.5 are reported (a) SSV=sensitivity, SPF =specificity, AV=average

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Table 4. Statistics for r = 1000 bootstrapped binary logistic regression (GSM exposure coefficients: Increase in risk per increase in Log GSM (µW/m2). Asymptotic standard errors (SE) and 95% confidence intervals (CI) are also shown for comparison.

Symptom B*

Bootstrap Normal


intervals SE 95% CI

Lower Upper Lower Upper Fatigue 0.329 0.012 0.097 0.155 0.539 0.102 0.128 0.529 Irritability 0.411 0.016 0.110 0.241 0.670 0.104 0.207 0.615 Headache 0.358 0.022 0.139 0.149 0.688 0.113 0.137 0.578 Nausea 0.319 0.013 0.124 0.099 0.590 0.118 0.088 0.550 Appetite 0.456 0.026 0.134 0.264 0.784 0.128 0.205 0.707 Sleep 0.396 0.022 0.124 0.193 0.690 0.109 0.181 0.610 Depression 0.346 0.012 0.102 0.174 0.583 0.100 0.151 0.541 Attention 0.429 0.020 0.118 0.254 0.711 0.106 0.222 0.636 Memory 0.237 0.009 0.098 0.057 0.448 0.091 0.058 0.415 Skin 0.217 0.008 0.110 0.011 0.451 0.110 0.001 0.433 Visual 0.203 0.004 0.093 0.037 0.398 0.090 0.026 0.379 Hearing -0.05 -0.002 0.089 -0.219 0.143 0.093 -0.228 0.135 Vertigo 0.306 0.010 0.101 0.127 0.530 0.102 0.107 0.505 Walking -0.05 -0.006 0.098 -0.265 0.120 0.098 -0.246 0.138 Vascular 0.274 0.010 0.109 0.084 0.520 0.114 0.051 0.497 * beta coefficent (Log OR)

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FIGURE CAPTIONS

Figure 1. Distribution of EMF measurement throughout the sample.

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Distribution of EMF measurement throughout the sample 106x90mm (300 x 300 DPI)

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