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Research Article
Brain Tumours: Rise in Glioblastoma Multiforme Incidence in
England 1995–2015 Suggests an Adverse Environmental or
Lifestyle Factor
Alasdair Philips ,1,2 Denis L. Henshaw,1,3 Graham Lamburn,2 and Michael J. O’Carroll4
1 Children with Cancer UK, 51 Great Ormond Street, London, WC1N 3JQ, UK
2Powerwatch, Cambridgeshire, UK
3Professor Emeritus, University of Bristol, UK
4Professor Emeritus, Vice–Chancellor’s Office, University of Sunderland, UK
Correspondence should be addressed to Alasdair Philips; alasdair@brambling.info
Received 19 December 2017; Revised 14 March 2018; Accepted 21 March 2018; Published 24 June 2018
Academic Editor: Evelyn O. Talbott
Copyright © 2018 Alasdair Philips et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Objective. To investigate detailed trends in malignant brain tumour incidence over a recent time period. Methods. UK Oce of
National Statistics (ONS) data covering 81,135 ICD10 C71 brain tumours diagnosed in England (1995–2015) were used to calculate
incidence rates (ASR) per 100k person–years, age–standardised to the European Standard Population (ESP–2013). Results.We
report a sustained and highly statistically signicant ASR rise in glioblastoma multiforme (GBM) across all ages. e ASR for GBM
more than doubled from 2.4 to 5.0, with annual case numbers rising from 983 to 2531. Overall, this rise is mostly hidden in the
overall data by a reduced incidence of lower-grade tumours. Conclusions. e rise is of importance for clinical resources and brain
tumour aetiology. e rise cannot be fully accounted for by promotion of lower–grade tumours, random chance or improvement
in diagnostic techniques as it aects specic areas of the brain and only one type of brain tumour. Despite the large variation in
case numbers by age, the percentage rise is similar across the age groups, which suggests widespread environmental or lifestyle
factors may be responsible. is article reports incidence data trends and does not provide additional evidence for the role of any
particular risk factor.
1. Introduction
e causes of brain tumours in adults remain largely
unknown [1]. In 2011, the World Health Organisation (WHO)
prioritised the monitoring of detailed brain tumour incidence
trends through population–based cancer registries [2]. is
article reports recent changes in malignant brain tumour
incidence in England that include age, sex, morphology and
tumour location.
2. Materials and Methods
2.1. Data. e International Classication of Diseases for
Oncology (ICD–O) is a dual classication, with coding sys-
tems for both topography and morphology [3]. e relevant
topology codes are listed in Table 1, along with the number of
tumours diagnosed in 1995 and 2015.
ere are 102 dierent ICD–O–3.1 morphology codes
usedinthedataset,thoughmanyhavefewcases.e
morphology code describes the cell type and its biological
activity / tumour behaviour.
WHO last updated their classications in 2016, but their
changes have minimal impact on our analysis of the data [4,
5]. Malignant brain neoplasms without histology are recorded
as ICD–10 D43 (D43.0 & D43.2 supratentorial).
We used anonymised individual–level national cancer
registration case data from the UK Oce of National
Statistics (ONS) for all 81,135 ICD10–C71 category primary
malignant brain tumours diagnosed in England for the years
from 1995 to 2015, plus 8,008 ICD10–D43 supratentorial
malignant tumours without histology/morphology data from
1998–2015. e initial data is supplied by the National Cancer
Registration Service (NCRS). e ONS then apply further
Hindawi
Journal of Environmental and Public Health
Volume 2018, Article ID 7910754, 10 pages
https://doi.org/10.1155/2018/7910754
Journal of Environmental and Public Health
T : ONS WHO ICD brain tumour data for England.
C Malignant primary neoplasm of brain cases cases
C. Cerebrum except lobes & ventricles
C. Frontal lobe
C. Temporal lobe
C. Parietal lobe
C. Occipital lobe
C. Cerebral ventricle
C. Cerebellum
C. Brain stem
C. Overlapping lesion of brain
C. Brain, unspecied site
C All topology sites
D Uncertain behaviour (no histology data)
D.-. Unspecied tumour details - cases
validation checks and the UK Department of Health use the
ONS data to inform policy making. e ONS state their
cancer data are generally within % of the correct values [].
Until about , some cases in the oldest age–groups will
not have been recorded in the cancer registries. Since
this error is likely to be small.
Glioblastoma Multiforme (GBM), the most common and
most malignant primary tumour of the brain, is associated
with one of the worst ve–year survival rates among all
humancancers,withanaveragesurvivalfromdiagnosis
of only about year. is ensures that few cases will be
unrecorded in the ONS database and we show that their
number of GBM tumours is similar to NHS hospital inpatient
numbers. e data include the year of diagnosis, age at
diagnosis, sex of patient, primary site and morphology
code. National population estimates of age and gender
by calendar year were also obtained from ONS data []
and age–specic incidence rates per , person–years
and for a wide variety of tumour types were calculated
in -year age group bins for males and females sepa-
rately.
Some published incidence analyses have used dierent
criteria as to which glioma and astrocytoma should be
considered malignant. WHO considers Grades I to IV as
biologically malignant even if they have not been graded
histologically malignant. We have taken the WHO/IARC
morphology behaviour codes /, / and / as being histologi-
cally malignant which means that Grade I and II tumours are
classed as low–grade malignancies.
We are not aware of any specic bias in the ONS data.
ere is a slight data–lag in cancer registry data, which are
regularly checked and updated if necessary, but are generally
stable aer to years. Our ONS data extract is dated 4th July
.
Brodbelt et al. () [] reported an analysis of treatment
and survival for , GBM cases in England over the period
–, which had an overall median survival of only
. months, rising to . months with maximal treatment.
Brodbelt et.al.’s GBM case total from English hospital data
is only .% higher that our ONS GBM total of , cases
for the same time period; this suggests that a very complete
UK cancer diagnosis and registration system is now in place.
In contrast, Ostrom et al. () [] reporting on USA SEER
brain tumour data provide a scatter–plot that shows a median
complete registration and histological conrmation level of
only about %, with the best examples returning less than
% full completion in .
2.2. Confounding. We h a d a l a r ge number of catego r i e s an d
sub–categories in the data. It was necessary to combine some
of these to increase the resolving power. We ran analyses
separately for each site (C. to C.), for each main type of
tumour, and for tumour grade (I to IV). It was immediately
obvious that the most signicant change was in the incidence
of GBM in frontal and temporal lobes. e obvious potential
confounders would be the C. (overlapping) and C.
(unspecied) categories due to better imaging techniques and
we discuss this later.
2.3. Standardisation. Incidence rates rise dramatically with
age and standardisation is necessary as population age pro-
les are changing with time. We calculated age–standardised
incidence rates (ASR) per k person–years to the current
recommended European Standard Population (ESP–), as
it best represents the reality of the case burden on society [].
Adjusting European cancer incidence to the World Standard
Population is not helpful as the age-spectra are so dierent.
Table lists the morphology codes with the highest case
numbers, totalling tumours. Included in our analyses
are an additional cases in other categories, each with
fewer than cases over the years. A full listing of all the
cases in the data set is provided in the Supplementary File
[S].
We needed to group data to improve resolution and
reduce random data noise. We examined infant and child
Journal of Environmental and Public Health
T : ICD-O- morphology codes with more than cases between - inclusive. (A full listing of all the morphology codes
and cases is present in the Supplementary le).
Morphology Grade All cases Group Sub-group WHO/IARC summary description
NOS unclassied, malignant, blastoma, NOS
carcinoma carcinoma, metastatic, NOS
carcinoma epithelial tumour, carcinoma, malignant
carcinoma carcinoma, metastatic, NOS
sarcoma rhabdoid sarcoma
germ cell neoplasia
glioma NOS glioma, malignant, NOS, not neoplastic
glioma astrocytic gliomatosis cerebri
glioma astrocytic mixed glioma / oligoastrocytoma
glioma ependymal ependymoma
glioma ependymal anaplastic ependymoma
glioma astrocytic astrocytoma, NOS, diuse
glioma astrocytic anaplastic astrocytoma (high grade)
glioma astrocytic germistocytic astrocytoma, diuse
glioma astrocytic brillary astrocytoma, diuse
glioma astrocytic pilocytic astrocytoma
glioma astrocytic pleomorphic xantoastrocytoma
glioma GBM-IV glioblastoma multiforme
glioma GBM-IV giant cell glioblastoma
glioma GBM-IV gliosarcoma
glioma oligodendrial oligodendroglioma
glioma oligodendrial anaplastic oligodendroglioma
glioma embryonal medulloblastoma
glioma embryonal desmoplastic medulloblastoma
glioma embryonal primitive neuroectodermal tumour
neoplasms separately, but did not nd any statistically signif-
icant time–trends. ree age-groups seemed reasonable. We
chose a child, teenage and young-adult group (-), a main
middle-age group (-) and an older age group (over
years of age). ese reasonably split the population into three
roughly equal (, and million) groups of people. e
case totals in the three groups were about .k, .k and k
respectively. We tested moving the cut-point boundaries by
years in both directions and it made little dierence to the
overall results.
2.4. Analysis. e cases were analysed by morphology, topol-
ogy, sex, age, age–specic and age–standardised incidence.
e Annual Average Percentage Change (AAPC) and cor-
responding % CI and p–values were calculated using
Stata SE. (StataCorp). A linear model on the log of the
age–standardised rates, which tests for a constant rate of
change (e(ln(rate))), best tted the data. See Supplementary File
sections [S]and [S].
2.5. Background. In a major review article, Hiroko
Ohgaki and Paul Kleihues [] wrote “Glioblastoma is the
most frequent and malignant brain tumor. e vast majority
of glioblastomas (∼%) develop rapidly de novo in elderly
patients, without clinical or histologic evidence of a less
malignant precursor lesion (primary glioblastomas). Sec-
ondary glioblastomas progress from low-grade diuse astro-
cytoma or anaplastic astrocytoma. ey manifest in younger
patients, have a lesser degree of necrosis, are preferentially
located in the frontal lobe, and carry a signicantly better
prognosis.”
Overall primary malignant brain tumour ASRs are only
rising slowly and are oen considered fairly static. Figure
shows the age–standardised trends from to . From
the s to about there was a fairly steady rise in
recorded overall incidence, however since then the rise has
slowed, though clinicians have been reporting a rise in high-
grade, aggressive tumours.
Overall adult survival for all malignant brain tumours
aer diagnosis during – was about % for one
year and % for ve years, falling to about % for aggres-
sive grades–III and IV tumours. ONS data show age-
standardised death rates from malignant brain tumours (C)
have increased by % between and , showing that
improvements in treatment alone are inadequate and that
there is a need to nd ways of preventing brain cancer [].
3. Results
Comparing new case numbers in with shows an
extra aggressive GBM tumour cases annually. Figure
Journal of Environmental and Public Health
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
ASR ESP2013 Rate / 100k people
ICD10 C71 Male
ICD10 C71 all
ICD10 C71 Female
ICD10 D43.0 & D43.2 (see text)
Primary brain tumour mortality
F : Age–standardised overall trends from to using
data in ONS MB series, including a smaller number of supratento-
rial neoplasms without histology or morphology data coded D.
& D.. e data table for this gure is in the SI le as [S].
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
ASR ESP2013 Rate / 100k people
(1) GBM (2) astrocytic non_GBM
(3) glioma 93803 (4) other glioma
(5) = (1)+(2)+(3)
F : Age–standardised incidence rates for all C glioma
cases diagnosed between and analysed by type and year
(Data in Table ). Grouping details: () = – () =
, – () = () = , , –,
–.
and Table show that up to about the overall rise
in GBM incidence (Annual Average Percentage Change
(AAPC) .%, % CI .–., p <⋅) could be mostly
compensated for by the fall in incidence of all lower grade
astrocytoma and “glioma, malignant, NOS, ICD–”.
is leaves a fairly steady rise in the GBM ASR from
to (AAPC .%, % CI .–., p <.).
Ohgaki and Kleihues [] reported that most secondary
GBMs are found in younger middle-age people and most
primary GBMs are in over s. We tested our (–) and
(>) age group data, splitting the total GBM into de novo
and promoted tumours. We estimated the maximum possible
number of promoted tumours using the change in the grades
II and III diuse and anaplastic astrocytomas. e results are
shown in Figures (a) and (b). ese are discussed later.
We found a large decrease of ASR over time for Grade–II
diuse astrocytoma, a slight rise in ASR for WHO Grade–III
anaplastic astrocytoma (; cases). ere was little
change in rates of anaplastic oligodendroglioma (;
cases), anaplastic ependymoma (; cases)
Grade–II oligodendroglioma (; cases), embryonal,
or ependymal tumours.
Figure shows the relative increase in age-specic GBM
incidence between the averaged periods (–) and
(–) for –year age–groups. is .-fold change
is remarkably similar across the age–groups, suggesting a
universal factor.
Figure shows ASR GBM rates for frontal lobe, temporal
lobe, unspecied & overlapping (C. & C.) and ‘all other
brain regions’. Most of the rise is in the frontal and temporal
lobes, and most of the cases are in people over years of age,
with a highly statistically signicant overall AAPC of .%
(see Table ). ere was an extra rise in frontal and temporal
GBM incidence between and , which coincided
with a slight reduction in the GBM ASR in overlapping and
unspecied regions and may be due to improved imaging.
4. Discussion
Using suciently high–quality data, we present a clearer
picture of the changing pattern in incidence of brain tumour
types than any previously published. We report a sustained
and highly statistically signicant ASR rise in GBM across
all ages and throughout the years (–), which is
of importance both for clinical resources and brain tumour
aetiology.
Dobes et al. () [] reported a signicant increase
in malignant tumour incidence from to in the
≥–year age group. In a second article they noted an
increasing incidence of GBM (APC, .; % CI, .–.) in
patients in the same age group, especially in temporal and
frontal lobes []. De Vocht et al. () [] reported a rise in
temporal lobe tumour incidence in ONS data, but dismissed
its signicance. In a paper he claimed no increase in
GBM incidence, but later published a major correction to the
paper that shows an increase [].
Zada et al. () [] using USA SEER data for –
reported a rising trend in frontal and temporal lobe tumours,
the majority of which were GBM, with a decreased incidence
of tumours across all other anatomical sub–sites. Ho et
al. () [] reported a .–fold increase in glioblastoma
incidence in the Netherlands over the period – (APC
., p<.).
ere were no material classication changes over the
analysis period that might explain our ndings [], though
multidisciplinary team working was strengthened (
onwards) and better imaging has resulted in improved
diagnosis along with a more complete registration of brain
tumours in the elderly. We analysed our data in -year age
group categories to look for evidence of improved diagnosis;
the data do suggest diagnosis and registration have improved
Journal of Environmental and Public Health
T : ICD-C and (D. + D.) cases and age-standardised (ESP-) incidence rates.
Typ e ->GBM astro-c
non GBM
glioma
Other
glioma
other
C
D.
+D. GBM astro-c
non GBM
glioma
Other
glioma
other
C
all
C
D.
+D.
Ye a r Case numbers Age-standardised (ESP-) incidence rates
n/a . . . . . . n/a
n/a . . . . . . n/a
n/a . . . . . . n/a
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
. . . . . . .
Journal of Environmental and Public Health
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
Age-standardised GBM rates (30-54)
promoted
de novo
0
1
2
3
4
Incidence rate per 100,000 people
(a)
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
Age-standardised GBM rates ( >54)
promoted
de novo
0
2
4
6
8
10
12
14
Incidence rate per 100,000 people
(b)
F : Age–standardised ratesfor two age groups. e possiblesplit between de novo and se condary promoted GB Ms is based on inci dence
change of Grades II and III diuse and anaplastic astrocytoma.
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
GBM ASpR (2011-2015) relative to that in (1995-1999)
Male Female
0.00
0.50
1.00
1.50
2.00
2.50
3.00
Relative change in Age Specic Rate
F : Relative change in GBM age–specic incidence rates
(ASpR) averaged over two ve-year periods - and -
in -year age bands and gender.
in people aged over . However, at earlier ages the incidence
rate of ‘all’ glioma (and all C) registrations have remained
almost constant, whereas the rates for lower–grade tumours
fell until about and have since remained fairly static as
the rate for GBM has risen steadily.
Most GBM cases seem to originate without any known
genetic predisposition. GBMs from promoted lower–grade
gliomas usually have dierent molecular genetic markers
from de novo GBMs []. e revision of the WHO
classication of CNS tumours [, ] highlights the need for
recording molecular genetic markers and divides glioblas-
tomas into two main groups. e IDH–wildtype mostly
corresponds to clinically dened primary or de novo glioblas-
toma and accounts for about % of cases. e remaining
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Frontal & temporal lobes ASR
All other brain sites ASR
Frontal lobe ASR
Temporal lobe ASR
C71.8 + C71.9 ASR
0.0
0.5
1.0
1.5
2.0
2.5
3.0
ASR ESP2013 Rate / 100k people
F : Frontal and temporal lobe GBM age–standardised inci-
dence rates by tumour site and year (data table in the SI as [S]).
% are IDH–mutant cases, which usually arise in younger
patients and mostly correspond to secondary or promoted
lower–grade diuse glioma [, ]. Figures (a) and (b)
support the conclusion of Ohgaki and Kleihues [] that
promoted (secondary) tumours mainly occur in younger
people and that de novo GBMs dominate in the over- age
group. It is important that this pattern is monitored using
modern genetic techniques.
GBM tumours are almost always fatal and are not likely
to have been undiagnosed in the time-frame of our data. It
ispossiblethatsomeelderlycaseswerenotfullyclassied,
Journal of Environmental and Public Health
T : Age standardised incidence rates to ESP- (/k people).
Ye a r G BMallbrainsites all ages all ages GBM frontal and temporal lobes all ages all ages
age->< - + all ages M F < - + all ages M F
AAPC . . . . . . . . . . . .
CI . . . . . . . . . . . . . . . . . . . . . . . .
p. <. <. <. <. <. <. <. <. <. <. <.
. . . . . . . . . . . .
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. . . . . . . . . . . .
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. . . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . . .
Journal of Environmental and Public Health
but then they should have been recorded as ICD–D.
However, as D rates have remained very constant over this
time period (see Figure ), this is unlikely to have been a
signicant confounder.
4.1. Possible Causal Factors. We cite examples of some
possible causal factors that have been discussed in the
literature that could contribute changes in GBM incidence.
In an important “state of science” review of glioma
epidemiology, Ostrom et al. [] list and discuss a number
of potential factors that have been associated with glioma
incidence, some of which we list below.
Ionising radiation, especially from X-rays used in CT
scans, has the most supportive evidence as a causal factor.
Due to the easy availability of CT imaging and relative lack
and higher cost of MRI imaging in UK NHS hospitals, CT
scans are oen used, especially for initial investigations. eir
use over the period - is shown in the Supplementary
File [S]. Given the time-frame of the trend that we have
identied, we suggest that CT imaging X-ray exposures
should be further investigated for both the promotion and
initiation of the rising incidence of GBM tumours that we
have identied.
Preston et al. () [] concluded that radiation–
associated cancer persists throughout life regardless of age
at exposure and that glioma incidence shows a statistically
signicant dose response. Our oldest age group also expe-
rienced atmospheric atomic bomb testing fallout and some
association with ingested and inhaled radionuclides should
not be dismissed as a possible factor. England was in one of
the highest exposed regions for atmospheric testing fallout
as determined by the United Nations Scientic Committee
on the Eects of Atomic Radiation, UNSCEAR Report
[]. Further information is given in Supplementary File S.
If only some of the population were susceptible and received
a signicant dose, any resulting extra cancers would show up
in the ONS data.
e European Study of Cohorts for Air Pollution Eects
by Andersen et al. () [] found suggestive evidence
of an association between trac-related air pollution and
malignant brain tumours.
ere is increasing evidence literature that many cancers
including glioma have a metabolic driver due to mitochon-
drial dysfunction resulting in downstream genetic changes in
the nucleus [–].
e International Agency for Research on Cancer
(IARC) judged both power–frequency ELF () [] and
radio–frequency RF () [] electromagnetic elds as
Group B ‘possible human carcinogens’. Villeneuve et al.
() [] concluded that occupational (ELF) magnetic eld
exposure increases the risk of GBM with an OR = .
(% CI: . – .). Hardell and Carlberg () [] have
reported an increase in high–grade glioma associated with
mobilephoneuse.emulti-countryInterphonestudy[]
collected data from to and included few people
over years of age and would have been unable to resolve
any association involving older–aged people. Volkow et al.
() [] found that, in healthy participants and compared
with no exposure, a -minute cell phone exposure produced
a statistically signicant increase in brain glucose metabolism
in the orbitofrontal cortex and temporal pole regions closest
to the handset.
5. Conclusions
() We show a linear, large and highly statistically signi-
cant increase in primary GBM tumours over years
from –, especially in frontal and temporal
lobes of the brain. is has aetiological and resource
implications.
() Although most of the cases are in the group over
years of age, the age–standardised AAPC rise is
strongly statistically signicant in all our three main
analysis age groups.
() e rise in age–standardised incidence cannot be fully
accounted for by improved diagnosis, as it aects
specic areas of the brain and just one type of
brain tumour that is generally fatal. We suggest that
widespread environmental or lifestyle factors may be
responsible, although these results do not provide
additional evidence for the role of any particular risk
factor.
() Our results highlight an urgent need for funding more
research into the initiation and promotion of GBM
tumours. is should include the use of CT imaging
for diagnosis and also modern lifestyle factors that
may aect tumour metabolism.
Data Availability
e data were obtained from the UK Oce for National
Statistics (ONS), who are the legal owners of the data. Some
data are publicly available in the ONS annual MB data series,
which are freely downloadable from the ONS website, but this
article uses the latest updated data, plus ICD–O– morphol-
ogy codes, extracted under personal researcher contract from
the ONS database in July . ONS Data Guardian approval
was required for the supply, control and use of the data. A
nominal charge is made by the ONS for such data extraction.
We are not permitted to supply the raw ONS extracted data
to anyone else. Other researchers can obtain the latest data
directly from the ONS in a similar manner. e authors
provide some extra tables and gures in the Supplementary
File downloadable from the journal website. Supplementary
data can be obtained from the corresponding author upon
request.
Conflicts of Interest
Alasdair Philips: Independent Engineer and Scientist. (a)
Trustee of Children with Cancer UK (unpaid); (b) On a
voluntary unpaid basis, has run Powerwatch for years (a
small UK NGO providing free information on possible health
associations with EMF/RF exposure); (c) Technical Director
Journal of Environmental and Public Health
and shareholder of EMFields Solutions Ltd., who design
and sell EMF/RF measuring instruments and protective
shielding items; (d) Shareholder of Sensory Perspective Ltd.;
(e) Occasional voluntary advisor to the Radiation Research
Trust (Registered Charity). Denis L. Henshaw: (a) Scientic
Director of Children with Cancer UK (honorarium basis);
(b) Shareholder of Track Analysis Systems Ltd., a company
oering radon measurement services; (c) Voluntary scien-
tic advisor for Electrosensitivity UK (Registered Charity).
Michael J. O’Carroll: (a) Chairman of Rural England against
Overhead Line Transmission group; (b) Occasional advisor
to the Radiation Research Trust. Graham Lamburn: (a) Acts
as voluntary unpaid ‘Technical Manager’ for Powerwatch.
Authors’ Contributions
Alasdair Philips and Graham Lamburn conceived the study
and rst–draed most of the manuscript with signicant
input from Denis L. Henshaw and Michael J. O’Carroll.
Graham Lamburn organised the data obtained from the UK
ONS and wrote the database analysis scripts. All authors had
full access to the results of all analyses and have provided
strategic input over several years of following the ONS brain
tumour data. All authors have approved the nal manuscript.
Alasdair Philips is the guarantor for the ONS data.
Funding
is research received no funding from any external agency
or body. e ONS data extracts were paid for personally
by Alasdair Philips. Administration costs were paid for
personally by the authors.
Acknowledgments
We are very grateful to Professor Georey Pilkington and
Professor Annie Sasco for their invaluable comments on early
dras of this paper. We thank the ONS for providing the data
and Michael Carlberg, MSc for advice regarding statistical
analysis.
Supplementary Materials
S. Table of data morphology coding and the case numbers
usedinthestudy.S.GBMcasenumbersandage-specic
incidence rate data used in the study. S. Sample STATA data
and DO script. S. Data table for Figure . S. Data table for
Figure . S. CT and MRI use in the UK NHS. S. Some notes
on atomic bomb testing and other nuclear fallout in England.
(Supplementary Materials)
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