Mobile Phone Base Stations do not cause cancer in babies of pregnant women

Mobile phone base stations and early childhood cancers: case-control study

Paul Elliott, professor of epidemiology and public health medicine, head of
department, director, MRC-HPA centre for environment and health, Mireille B
Toledano, senior lecturer in epidemiology, J Bennett, research fellow, L
Beale, research fellow, K de Hoogh, senior research officer, N Best,
professor of statistics and epidemiology, D J Briggs, professor, chair in
environmental and health sciences, school of public health

1 Small Area Health Statistics Unit, MRC-HPA Centre for Environment and
Health, Department of Epidemiology and Biostatistics, School of Public
Health, Imperial College London, St Mary’s Campus, London W2 1PG

Correspondence to: P Elliott

Objective To investigate the risk of early childhood cancers associated
with the mother’s exposure to radiofrequency from and proximity to
macrocell mobile phone base stations (masts) during pregnancy.


Design Case-control study.

Setting Cancer registry and national birth register data in Great Britain.

Participants 1397 cases of cancer in children aged 0-4 from national cancer
registry 1999-2001 and 5588 birth controls from national birth register,
individually matched by sex and date of birth (four controls per case).

Main outcome measures Incidence of cancers of the brain and central nervous
system, leukaemia, and non-Hodgkin’s lymphomas, and all cancers combined,
adjusted for small area measures of education level, socioeconomic
deprivation, population density, and population mixing.

Results Mean distance of registered address at birth from a macrocell base
station, based on a national database of 76 890 base station antennas in
1996-2001, was similar for cases and controls (1107 (SD 1131) m v 1073 (SD
1130) m, P=0.31), as was total power output of base stations within 700 m
of the address (2.89 (SD 5.9) kW v 3.00 (SD 6.0) kW, P=0.54) and modelled
power density (–30.3 (SD 21.7) dBm v –29.7 (SD 21.5) dBm, P=0.41). For
modelled power density at the address at birth, compared with the lowest
exposure category the adjusted odds ratios were 1.01 (95% confidence
interval 0.87 to 1.18) in the intermediate and 1.02 (0.88 to 1.20) in the
highest exposure category for all cancers (P=0.79 for trend), 0.97 (0.69 to
1.37) and 0.76 (0.51 to 1.12), respectively, for brain and central nervous
system cancers (P=0.33 for trend), and 1.16 (0.90 to 1.48) and 1.03 (0.79
to 1.34) for leukaemia and non-Hodgkin’s lymphoma (P=0.51 for trend).

Conclusions There is no association between risk of early childhood cancers
and estimates of the mother’s exposure to mobile phone base stations during


Use of mobile (cellular) phones has increased markedly in recent years. In
the United Kingdom, the number of mobile connections has risen from just
under nine million in 1997 to almost 74 million in 2007, and there are over
four billion connections worldwide. Alongside the well recognised benefits
of mobile phones,1 questions have been raised about possible health
effects, including incidence of brain and other cancers, especially after
prolonged use.1 2 3 In addition there have been suggestions of an increased
risk of neurological conditions such as migraine and vertigo.4 There have
also been concerns about possible developmental and other health effects
associated with exposures to mobile phone base stations (masts),5 6 and
surveys of the general public indicate high levels of concern about the
potential risks of living near mobile phone base stations.7 8 The few
reports of apparent cancer clusters near a mobile phone base station9 10 11
are difficult to interpret because of small numbers and possible selection
and reporting biases.12 13 Also, there is no known radiobiological
explanation,14 though low level exposures from base stations are widespread
15 and more or less continuous, and it is possible that cumulative
exposures are important.16

Particular concerns have been raised about exposures of young children to
mobile telephony, as the developing brain and other tissues might be more
susceptible than in adults to potential effects of low level exposures to
radiofrequency electromagnetic fields.17 We carried out a case-control
study of early childhood cancers and estimated maternal exposures to
radiofrequency during pregnancy from macrocell base stations in Great

Selection of cases and controls
We obtained data on all registered cases of cancer in children aged 0-4 in
Great Britain in 1999-2001. We included brain and central nervous system
(ICD (international classification of disease, tenth revision) codes
C71-C72), leukaemia and non-Hodgkin’s lymphomas (C91-95, C82-85), and all
cancers combined (C00-C96). From a total of 1926 cases, we obtained
coordinates for the registered address at birth for 1792 (93%) using
ADDRESS-POINT (1 m accuracy)18 and for 38 (2%) from the postcode centroid,
which typically represents about 12 homes. We excluded 96 cases (5%) with
no valid birth address or postcode. For 433 cases (22%), exposure data
(based on birth address) were unavailable for the pregnancy period, leaving
1397 (73%) for study. We obtained four controls per case (5588 (90%) with
complete addresses from 6222 randomly selected) from the national register
of all births in Great Britain, individually matched to the cases by sex
and date of birth.

Data on mobile phone base stations
The four national mobile phone operators (Vodafone, O2, Orange, and
T-Mobile) provided data on all 81 781 antennas (Global System for Mobile
communications 900 and 1800) for the period 1 January 1996 to 31 December
2001. The data included site identifier, coordinates (to an accuracy of
about 10 m), start and decommission dates (month, year), number of antennas
per base station, antenna orientation (azimuth), type (sectoral,
omni-directional), height above ground level, total (electrical and
mechanical) tilt, lateral and vertical beam width (degrees), total power
output (effective isotropic radiated power), and frequency (MHz). As start
and decommission dates were unavailable for one operator, we assumed those
base stations were operational throughout the study period. For one
operator, macrocells and microcells were not separately delimited; 4891
(6%) antennas were identified as microcells (by use of discriminant
analysis based on height, equivalently isotropic radiated power, frequency,
and percentage of urban land within 500 m of the antenna site) and were
excluded. For the remaining 76 890 antennas, 66 790 (87%) had complete
data, 5081 (7%) had missing data on tilt, beam widths, or frequency
(replaced by company specific medians), and 5019 (7%) had missing data on
height or effective isotropic radiated power, which we imputed from a
regression model calibrated using antennas with complete data.

Exposure assessment
We estimated three exposure metrics for the birth address of each case and
control. First was the distance (m) from the nearest mobile phone base
station. Second was the total power output (kW) from summation across all
base stations within 700 m (because power density at ground level typically
peaks at a distance of 200-500 m from the base station, and then falls off
rapidly with distance19 20). Finally, we computed modelled power density
(dBm) at each birth address for base stations within 1400 m, using a
purpose designed propagation model. This took the form of the equation in
the figureGo. Exposures beyond 1400 m were considered to be at background

Figure 1
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[PowerPoint Slide for Teaching]
Derivation of modelled power density

We used measurements from two field campaigns, in a rural and urban area,
to set values for the user defined parameters in the power density model.
Geographic coordinates of each measurement location (1 m accuracy) were
obtained with the global positioning system. Data on surface height and
distance and azimuth (compass direction) to the base station were derived
in a geographical information system (ArcGIS). The rural survey comprised
151 sites (1510 antenna specific measurements) around a group of four base
stations in an isolated and relatively flat rural location near Bowes,
County Durham; the urban survey covered 50 sites (658 antenna specific
measurements) in Cheltenham, Gloucestershire. Measurements were made of
field strength in the broadcast control channel (BCCH) carrier frequency
for each base station, which provides a temporally stable signal
proportional to total power output, with a Narda SRM 3000 spectrum analyser
and isotropic probe (a technical appendix is available from the authors).
Zero values were set to –70 dBm, the limit of detection of the measurements
using the settings we applied in the field.

Model parameters were optimised by a path following search technique, in
which the settings were sought that gave the maximum R2 and minimum root
mean squared error (RMSE) while satisfying the criteria that the regression
slope was 0.8-<1.2 and the fractional bias was –0.2-<0.2. Different
parameter settings were found to be necessary for urban and rural areas: R2
for measured versus modelled power densities was 0.57 for the 1510 antenna
specific rural measurements (0.65 for total power density at the 151 sites)
and 0.30 (0.40) for the urban area (see fig A on

The models were validated with data from two further surveys, following the
same measurement protocol. Data for validation of the rural model were
collected from 145 sites along transects radiating from five groups of base
stations (1044 antenna specific readings) in locations across Oxfordshire
and Gloucestershire. Urban validation was done with measurements at 13
sites (234 antenna specific measurements) in Banbury, Oxfordshire.
Comparison of the antenna specific measurements with modelled estimates
gave R2=0.56 for the rural sites and 0.24 for the urban area. Comparisons
using all the data from all four surveys (calibration and validation) gave
R2=0.64 at the antenna level and 0.62 at site level.

Further fieldwork was undertaken to assess the performance of the model in
“real world” settings. Total power densities across all frequencies used
for Global System for Mobile communications 900 and 1800 were measured at
620 locations across the country by geometric averaging of 64 sweeps of the
frequency range with the Narda spectrum analyser. The locations were
selected in a random stratified framework to represent urban, suburban, and
rural areas and different micro-environments, from open to densely
cluttered by buildings or vegetation. Fig B on shows boxplots of
measured power density at these locations.

Spearman’s r correlation between measured and modelled power density was
0.66. The correlation of measured power density with distance from nearest
base station was –0.72 and 0.66 with total power output.

Statistical methods
Statistical analyses were carried out in the statistical package R21 with
conditional logistic regression. The three exposure metrics estimated at
the birth address for each case and control were time averaged across
monthly estimates for the duration of the pregnancy (assumed to be nine
months) because exposures in utero might be the most relevant period for
early onset childhood leukaemia22 (when the birth occurred after the 15th
of the month, the birth month was included in the nine month period). The
temporal resolution of the exposure data was not considered sufficient to
allow reliable estimates for shorter periods, such as month of birth, and
because among the 1397 affected children, 528 (38%) moved residence between
birth and diagnosis, analyses for exposure periods that extended beyond
birth were not considered meaningful. Each exposure metric was divided into
three categories: we used thirds (based on values for all cases and
controls) for distance from nearest base station and modelled power
density; for total power output, because of the large number (58%) of cases
and controls with no base stations within 700 metres, we used a zero group
together with two equal sized groups for non-zero values. We also carried
out regression analyses using continuous measures of the exposure metrics.

We present unadjusted analyses and analyses after sequential adjustment for
the main potential sociodemographic confounders affecting the geographical
distribution of brain cancer and leukaemia,23 24 measured at small area
(census output area) level and categorised into fifths. These comprised the
percentage of population with education to degree level or higher and
Carstairs score (a composite area deprivation measure25) and the population
density and population mixing (percentage inward migration to the output
area over the previous year). Potential confounding data were all sourced
from the 2001 census. We report 95% confidence intervals and two sided P
values, with no correction for multiple testing.


Of the 1397 cases, there were 251 brain and central nervous system cancers
and 527 cases of leukaemia and non-Hodgkin’s lymphoma. Mean age at
diagnosis was 2.0 (SD 1.2) years. The mean distance of registered address
at birth from a base station was 1107 (SD 1131) m for cases and 1073 (SD
1130) m for controls (P=0.31); the mean total power output of base stations
within 700 m was 2.89 (SD 5.9) kW for cases and 3.00 (SD 6.0) kW for
controls (P=0.54); and the mean modelled power density was –30.3 (SD 21.7)
dBm for cases and –29.7 (SD 21.5) dBm for controls (P=0.41) (table 1)Go.
Mean values of the sociodemographic measures were similar for cases and
controls (see table A on Total power output and modelled power
density were positively correlated (Spearman’s r=0.62), and they were
inversely correlated with distance from nearest base station (r=–0.82 and
–0.74, respectively). Correlations between the exposure metrics and
sociodemographic measures ranged up toboxv0.36boxv(distance and population
density) (see table B on Spearman’s correlations between
estimated exposures of cases in the nine months before and nine months
after birth (based on birth address) were 0.95, 0.96, and 0.94 for
distance, total power output, and modelled power density, respectively.
Mean distance of birth address from nearest FM, television, and VHF
broadcast antennas in England and Wales was similar for cases and controls
(see table C on

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Table 1 Mean (SD) of exposure metrics for cases (children aged 0-4 with
specified cancer) and controls matched for sex and age by cancer site

Table 2 shows the results for the categorical analysesGo and table 3 for
the continuous analysesGo. We found no association between mobile phone
base stations and risk of cancer. In the fully adjusted analyses, for
modelled power density at birth address, compared with the lowest exposure
category, the odds ratio for brain and central nervous system cancer was
0.97 (95% confidence interval 0.69 to 1.37) in the intermediate and 0.76
(0.51 to 1.12) in the highest exposure category (P=0.33 for trend); for
leukaemia and non-Hodgkin’s lymphoma, odds ratios were 1.16 (0.90 to 1.48)
and 1.03 (0.79 to 1.34), respectively (P=0.51 for trend) (table 2).Go

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Table 2 Odds ratios (95% confidence intervals) for categories of three
exposure metrics by site of cancer in children aged 0-4

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Table 3 Odds ratios (95% confidence intervals) for 15th to 85th centile
change in continuous measures of three exposure metrics by site of cancer
in children aged 0-4

In analyses of the exposure metrics as continuous variables, for brain and
central nervous system cancer, fully adjusted odds ratios for a 15th to
85th centile difference in the exposure variable ranged from 0.82 (0.55 to
1.22; modelled power density) to 1.12 (0.91 to 1.39; distance); and for
leukaemia and non-Hodgkin’s lymphoma, from 0.97 (0.87 to 1.08; distance) to
1.03 (0.91 to 1.16; total power output) (table 3)Go. Addition of a
quadratic term to the continuous exposure models was of borderline
significance (P=0.05) for brain and central nervous system cancer, for
which risk was lower with higher estimated levels of exposure.

In this systematic national investigation we found no association between
risk of cancer in young children and estimated exposures to radiofrequency
from mobile phone base stations during pregnancy.

Comparison with other studies
To date, there is no convincing or consistent evidence from cellular or
animal studies to suggest that exposure to radiofrequency electromagnetic
fields is associated with brain tumours or risk of other cancers26
27.Furthermore, uptake of mobile telephony has not been mirrored by trends
in the incidence of brain tumours or acoustic neuromas.4 28 Radiofrequency
exposures in the general population from mobile phone base stations are
extremely low, in the order 1000 to 10 000 times lower than values in the
guidelines of the International Commission on Non-Ionizing Radiation
Protection (ICNIRP).29 For example, in the 900 MHz frequency band, the
reference level (based on heating effects) is 36.6 dBm (4.5W/m2;
dBm=10*log10 mW/m2), which compares with a median modelled power density
among controls in our study of –4.9 dBm (0.32 mW/m2) and maximum modelled
power density of 9.4 dBm (8.6 mW/m2). For comparison, estimated power
density at a distance of 18 m from a 100 W incandescent light bulb is –2.22
dBm (0.6 mW/m2).30 It has been estimated that one day’s exposure from a
base station at an incident level of 1-2 V/m (about 4.2-10 dBm or 2-10
mW/m2) corresponds to about the first 4 seconds of local exposure to the
head and about 30 minutes of whole body exposure arising from the use of a
mobile phone.14 15 Even at low levels of exposure, however, there are
theoretical concerns about the effects on children because of relatively
greater dose (per kg body mass), the potential greater susceptibility of
children compared with adults,17 and potential effects of lifelong,
cumulative exposures.16 17 For these reasons, in the UK it has been
recommended that exposure of children to mobile telephony should be

The few previous reports of excess risks of cancer near mobile phone base
stations were based on apparent clusters of small numbers of affected
people living nearby.9 10 11 Such reports of individual clusters are
difficult to evaluate as they are subject to possible selection and
reporting biases. Problems include “boundary shrinkage,” whereby estimates
of risk are exaggerated by choice of areas/time periods/demographic strata
for study, variable ascertainment or duplication of cases, denominator
errors, and publication bias.12 There are also reports of weak31 32 33 34
35 36 37 and inconsistent38 39 40 41 geographical associations between risk
of leukaemia (including childhood leukaemia) and proximity to radio and
television broadcast antennas, another source of low level radiofrequency
exposures to the population. Two recent case-control studies—which used
propagation models to estimate total radiofrequency exposure from radio and
television transmitters at the home address of each child36 37 41—found no
association with childhood leukaemia, though one study did report an excess
risk of lymphocytic leukaemia associated with peak exposures.37

Strengths and limitations of study
Major strengths of our study include its size and national coverage, hence
avoiding selection and reporting effects (bias) in the choice of cases and
areas for study. Our use of modelling and validation represents an advance
on the assessment of exposure for studying risk of cancer near mobile phone
base stations because previous studies relied on distance measures only.9
10 11 In comparison with purely distance based methods, modelled estimates
of exposure are able to take into account the characteristics of and
contribution from multiple transmitters.42 Also, because we focused on
early childhood cancers, we avoided problems of long latency that affect
interpretation of mobile phone studies in adults.2

Our focus on early childhood cancers, however, meant that we did not
include longer term or other potential health effects that have been
associated with mobile telephony.1 3 4 6 We assumed that radiofrequency
exposures from mobile phone base stations estimated at registered birth
address are representative of true individual exposures during pregnancy.
We were unable to account for any attenuation of radiofrequency exposures
within the home nor could we obtain personal measurements of individual
exposures of the mothers from mobile phone base stations43 as cases and
controls were identified from national registers, without individual
contact. In addition, our models did not include information on other
sources of radiofrequency exposure, such as from microcells or picocells,
cordless phone base stations, maternal use of mobile/DECT phones during
pregnancy, or radio and television transmitters (though distance from
nearest radio and television transmitters was similar for cases and
controls). Neither were we able to take account of migration of the mother
during pregnancy.44 Despite the large study size, such potential
misclassification of exposure and migratory effects could have reduced the
ability of the study to detect any true excess in risk. In addition, we
were unable to investigate possible health effects from exposures to mobile
phone base stations after birth; whereas we had address at birth and
diagnosis for cases, data on controls, obtained from the national births
register, were available only at birth. Based on birth address,
correlations of the exposure metrics in the nine months before and after
birth were high (≥0.94), but this does not take account of migration
before or after birth. The focus on prenatal exposures could be an
important limitation to the extent that postnatal exposures might be
relevant to the incidence of early childhood cancers.22

Conclusions and policy implications
In summary, we found no association between risk of childhood cancers and
mobile phone base station exposures during pregnancy. The results of our
study should help to place any future reports of cancer clusters near
mobile phone base stations in a wider public health context.

What is already known on this topic

Previous reports of apparent cancer clusters near mobile phone base
stations are difficult to interpret because of small numbers and possible
selection and reporting biases
There is no known radiobiological explanation for such cancer excesses

What this study adds

This study used national registers of cancers and births and available
data on macrocell mobile phone base stations, thus avoiding selection and
reporting biases
There was no association between risk of early childhood cancers and
estimates of exposure to mobile phone base stations during pregnancy

Cite this as: BMJ 2010;340:c3077

We are grateful to Les Barclay, David Bacon, and Michael Willis who advised
on exposure aspects of the study; Clair Chilvers for advice on study
design; Margaret Douglass, Peter Hambly, Catherine Keshishian and Chloe
Morris for their help with data extraction and checking; Nirupa Dattani,
Gloria Brackett, Nicola Cooper (Office for National Statistics), Ian Brown
(General Register Office Scotland), Fiona Campbell, and Lesley Bhatti
(Scottish Information and Statistics Division) for their help with checking
cancer cases, obtaining birth controls, and data linkage.

Contributors: PE and DJB conceived the study and obtained funding. DJB was
responsible for the fieldwork. LB and KdH carried out the geographical
information system analysis. MBT was responsible for the data extractions,
linkage, and checking. JB and MBT carried out the data analysis under the
supervision of NB, DJB, and PE. PE, MT, JB, KdH, and DJB interpreted the
results and drafted the paper. All authors approve the final draft of the
paper. PE is guarantor.

Funding: The study was funded through the UK Mobile Telecommunications
Health Research (MTHR) Programme (, an independent body set
up to provide funding for research into the possible health effects of
mobile telecommunications. The MTHR is jointly funded by the UK Department
of Health and the mobile telecommunications industry. Members of the
independent MTHR programme management committee approved the study design
and commented on a draft manuscript. Data collection and analysis,
interpretation of data, and the decision to submit the paper for
publication were the sole responsibility of the authors.

Competing interests: MBT, JB, LB, KdH, and NB declare that the answers to
the questions on the Unified Competing interest form at are all No and therefore declare that they
have no conflicts of interest. PE and DJB obtained funding in support of
this work from MTHR. PE was a member of the MTHR programme management

Ethical approval: The study was approved by the St Mary’s NHS Trust local
research ethics committee.

Data sharing: A technical appendix describing the derivation of modelled
power density in more detail is available from the corresponding author.


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(Accepted 23 April 2010)

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