ARTICLE IN PRESSE U RO P E A N J O U R NA L O F CA N C E R x x x ( 2 0 0 7 ) x x x –x x x

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Case–control study on childhood cancer in the vicinityof nuclear power plants in Germany 1980–2003
Claudia Spixa,*, Sven Schmiedela, Peter Kaatscha, Renate Schulze-Ratha, Maria BlettnerbaGerman Childhood Cancer Registry, Institute for Medical Biostatistics, Epidemiology and Informatics, University Mainz,
55101 Mainz, GermanybInstitute for Medical Biostatistics, Epidemiology and Informatics, University Mainz, 55101 Mainz, Germany

A R T I C L E I N F O

A B S T R A C T

Article history:

The 1984 Windscale study raised concern about a possible association between living in the

Received 31 July 2007

vicinity of nuclear power plants and childhood cancer. No such effect for all cancers was

Received in revised

seen in ecological studies in Germany (1980–1995). Results from exploratory analyses led

form 11 October 2007

to a new study.

Accepted 29 October 2007

Pre-selected areas around all 16 major nuclear power plants in Germany formed thestudy area. The design is a matched case–control study; cases are all cancers under five
years diagnosed in 1980–2003: 1592 cases, and 4735 controls. Inverse distance of place of

Keywords:

residence to the nearest nuclear power plant at the time of diagnosis was used as the inde-

Infantpendent variable in a conditional logistic regression model.

Preschool child

Results show an increased risk for childhood cancer under five years when living near

Neoplasms

nuclear power plants in Germany. The inner 5-km zone shows an increased risk (odds ratio

Leukaemia

1.47; lower one-sided 95% confidence limit 1.16). The effect was largely restricted to leukae-

Nuclear reactors

mia.

Case–control study
Germany

The results are compatible with the corresponding subgroups in the previous German
ecological studies, with which this study shares most of the cases. They contrast withthe lack of an effect observed or expected from other studies due to low doses from routine
nuclear power plant operation.
Ó 2007 Elsevier Ltd. All rights reserved.

1.

Introduction

The German population has long been worried about the potential dangers and health effects of nuclear power. In 1984,
the public was frightened by reports of elevated childhood
cancer rates within a 10-mile zone of the Windscale (Sellafield) nuclear power plant in England, other investigations followed shortly.1–5 The German Childhood Cancer Registry,founded in 1980, investigated whether there had been a similar increase in Germany. In an ecological study with a similar
design to the UK (United Kingdom) studies,1–4,6 the incidence

rates of all cancers in children under 15 years of age during
1980–1990 in communities within a 15-km zone of all West
German nuclear power plants (812 cases) were compared withthose in reference communities with similar population
densities and degrees of urbanisation. No statistically significant increase in risk was found (relative risk [RR] 0.97; 95%
confidence interval [CI] [0.87;1.08]).7 Nevertheless, exploratory
analyses of subsets showed statistically significant results
particularly for acute leukaemia in children under five yearsof age living in the inner 5-km zone (RR 3.01; 95%CI[1.25;10.31]). When five more years of data had been accrued* Corresponding author: Tel.: +49 6131 17 3252; fax:+49 6131 17 2968.
E-mail address: kinderkrebsregister@imbei.uni-mainz.de (C. Spix).
URL: http://www.kinderkrebsregister.de (C. Spix).
0959-8049/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ejca.2007.10.024

Please cite this article in press as: Spix C et al., Case–control study on childhood cancer in the vicinity ..., Eur J Cancer (2007),
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E U R O P E A N J O U R N A L O F CA N C E R x x x ( 2 0 0 7 ) x x x –x x x(1991–1995) the study was repeated: the RR for all cancers
amongst children under 15 living within a 15-km zone was
1.05 (95% CI [0.92;1.20]) and the RR for acute leukaemia
amongst children under 5 living within a 5-km zone was
1.39 (95% CI [0.69;2.57]).8
In the late 1990s, a third party obtained data up to 1998
from the German Childhood Cancer Registry (GCCR) by
county via the Bundesamt für Strahlenschutz (Federal Officefor Radiation Protection) for the State of Bavaria. The data
were analysed in an exploratory manner applying linear
regression to standardised incidence ratios (SIR’s) by county.
Elevated SIR’s were observed for selected combinations ofyears, counties, and disease subgroups around Bavarian nuclear power plants. The GCCR criticised the methods usedin this analysis.9 Nevertheless the results, published overthe Internet but never in a peer-reviewed journal and quoted
briefly by the Deutsches Ärzteblatt,10 were sufficiently alarming to the public to induce the German Federal Ministry for
the Environment, Nature Conservation and Nuclear Safety
to call for applications for another study. The design originated from discussions with a Bundesamt für Strahlenschutz(Federal Office for Radiation Protection)-expert committee.
The design was influenced by the exploratory results of theprevious studies.11 The study is a matched case–control study
in which the exposure surrogate is the distance of individual
residences at the date of diagnosis from the nearest nuclear
power plant. Data from 1996 to 2003 are now included.
The main question of the investigation presented here is:
Is the risk of childhood cancer associated with living in theproximity of nuclear power stations? The distance measure,previously based on community midpoints, is now determined by the place of residence at the date of diagnosis. A
subset of cases and controls was to be interviewed with regard to potential confounders.
Since the emissions from a nuclear power plant add only
minimally to the background radiation level, no effect would
be expected on the basis of the usual models for the effects oflow levels of radiation, as presented by the biological effectsof ionizing radiation (BEIR) – Committee and the international
commission on radiological protection (ICRP).12,13 However,these models are based mainly on data from adults, as childhood cancer is very rare. The BEIR Committee reviewed studies on leukaemia/childhood cancer of populations living
around nuclear facilities but did not draw any conclusions
from them, as they generally do not include individual estimates of radiation dose.12
This paper presents the overall results of the recent study
conducted by the GCCR. Another paper presents the results
for leukaemia and the comparison with the previous ecological studies in more detail.14

2.

Materials and methods

2.1.

Nuclear power plants

The study covered the data available at the GCCR for 1980–
2003. The expert committee selected all 16 sufficiently large
and long running German nuclear power plants, resulting in
the inclusion of only West German nuclear power plants. A
power plant was considered relevant for the study from 1 year

after it started producing energy until 5 years after ceasing tooperate (Table 1). The committee then selected areas aroundthese power plants, with an emphasis on the east side because of the predominant west winds in Germany. For eachnuclear power plant, the corresponding county, its next
neighbour and usually one more county east of it were to be
included. These counties define the area for this specific nuclear power plant. These areas overlap for several nuclear
power plants. The total study area is shown in Fig. 1. The borders shown are county borders. As can be seen, nuclear power
plants tend to sit close to district borders. A county in Germany consists either of one large city (community) or of a larger mixed/rural area with a varying number of smaller towns
and villages (communities).11

2.2.

Participants

One thousand five hundred and ninety two cases of cancer
amongst children under 5 years of age, with oncologic diseases included in the International Classification of Childhood Cancer (ICCC)15 resident in the study area at the dateof diagnosis with known address and diagnosed in the relevant study period of the nearest nuclear power plant were included. All cases were matched with controls selected fromthe records of the appropriate registrar’s offices. The controls
were matched for date of birth (as closely as possible but atleast within 1.5 years), age, sex and nuclear power plant area
(at the date of diagnosis). Per control, a community was selected randomly out of the respective area according to thecase-corresponding population (by sex, age and year of diagnosis). This community was asked to make available addresses and names of children with the matching criteria.
From this address list the control closest to the date of birthof the case was selected.
Not all communities complied with our request to provide
the addresses of controls. Six controls per case were requested and three of these were selected randomly. Finally,
4735 controls were used in the analysis.
For all case and control children, the geo-code of the placeof residence at the date of diagnosis was obtained from theLand register.16 For 9.9% of the case children and 8.4% of thecontrols, the address could not be coded and was replacedby the street mid-point (140 cases, 359 controls) or by thecommunity or zip-code area mid-point (20 cases, 40 controls).
The position of the chimney of each nuclear power plant wascoded in the same way from high-resolution maps. All distances were given in metres.

2.3.

Control for potential confounders

To assess potential confounding, the families of a subset of
all cases and controls were invited to participate in a telephone interview covering other potential risk factors forchildhood cancer.17,18 The subset included all cases withselected diagnoses (leukaemia, lymphoma or a central nervous system tumour) diagnosed in 1993–2003 and their controls. The questions were summarised to a total of 20
potential confounders: social status, information on additional radiation exposure (parents, child), other risk factors(such as pesticides, mother’s hormone intake), immune sys-

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E U RO P E A N J O U R NA L O F CA N C E R x x x ( 2 0 0 7 ) x x x –x x x

Table 1 – Relevant nuclear power plants and their operation periods and study periods
Name
Brunsbüttel
BrokdorfKrümmel
Stade
Unterweser
LingenEmslandGrohndeWürgassen
Grafenrheinfeld
BiblisObrigheim
Neckarwestheim
Philippsburg
Isar
Gundremmingen

Operating period

Study period

23.06.1976 – 31.12.2003
08.10.1986 – 31.12.2003
14.09.1983 – 31.12.2003
08.01.1972 – 31.12.2003
16.09.1978 – 31.12.2003
31.01.1968 – 05.01.1977
14.04.1988 – 31.12.2003
01.09.1984 – 31.12.2003
10.10.1971 – 26.08.1994
09.12.1981 – 31.12.2003
16.07.1974 – 31.12.2003
22.09.1968 – 31.12.2003
26.05.1976 – 31.12.2003
09.03.1979 – 31.12.2003
20.11.1977 – 31.12.2003
14.08.1966 – 13.01.1977a09.03.1984 – 31.12.2003

01.01.1980 – 31.12.2003
08.10.1987 – 31.12.2003
14.09.1984 – 31.12.2003
01.01.1980 – 31.12.2003
01.01.1980 – 31.12.2003
01.01.1980 – 05.01.1982
14.04.1989 – 31.12.2003
01.09.1985 – 31.12.2003
01.01.1980 – 26.08.1999
09.12.1982 – 31.12.2003
01.01.1980 – 31.12.2003
01.01.1980 – 31.12.2003
01.01.1980 – 31.12.2003
09.03.1980 – 31.12.2003
01.01.1980 – 31.12.2003
01.01.1980 – 31.12.2003

All periods right censored at 31.12.2003 (end of study) and study periods left censored at 1.1.1980 (start of childhood cancer registration). The
order is roughly North to South.
a The ‘gap’ was intentionally included in the study period.

tem related issues (such as vaccinations, breast feeding andchild’s social interaction), type of region and folic acid inpregnancy. In addition, we asked about previous residencesof the child.

2.4.

Statistical methods

The main question was whether there is a monotonic
descending relation between proximity of place of residence
at the date of diagnosis to the nearest nuclear power plant included in the study at the time of diagnosis and the risk forchildhood cancer. On the basis of the linear no-thresholdlow-dose effect excess relative risk-models as proposed by
the BEIR Committee, the ICRP and the dispersion models presented by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR), a conditional logistic
regression with 1/(distance) as the continuous independent
variable was used.12,13,19 In the following 1/(distance in km)
is referred to as measure of proximity. We adopted the viewproposed by BEIR that a beneficial effect of radiation cannot
be expected even at extremely low doses.12 This is the basis
for the one-sided analysis.
Additionally, categorical analyses were performed for the
inner 5- and 10-km zones versus the respective outer zones.
The results of the categorical models and the continuous
model were compared by calculating the corresponding odds
ratio (OR) from the continuous model, using the mean proximity of the controls in the respective inner zone. The conditional logistic regression model included one proximity
measure at a time (continuous or categorical) and no othercovariates.
If it is assumed that the estimated odds ratios are approximations of relative risk estimates, the categorical results canbe converted to population attributable risks and to an attributable risk fraction for exposed cases with corresponding
confidence intervals.20

The primary analysis included all cases in children under 5
years of age at diagnosis. The diagnostic groups defined inadvance in the study protocol were leukaemia (ICCC Ia-e),lymphoid leukaemia (ICCC Ia), acute non-lymphocytic
leukaemia (ICCC Ib), central nervous system tumours including medulloblastoma (ICCC IIIa-f) and embryonal tumours
except for medulloblastoma (ICCC IVa, V and VIa). Detailed
results for the leukaemia subgroups are presented elsewhere.14 In further subgroup analyses, we divided theoperating periods of the nuclear power plants by half, andwe analysed only those who were to be interviewed. All
regression results are presented with one-sided lower confidence limits (CL) at a significance level of 5%.

2.5.

Sensitivity analyses

The randomness of the selection of the three matched controls from the maximum of six controls was assessed byrepeating the regression using all available (up to 6) controls.
The appropriateness of the fitted curve was investigated byfractional polynomial and Box–Tidwell-models for assessing
the ‘best fitting’ curve (based on the deviance).21,22
Further sensitivity analyses were required in addition to
those planned in advance. While 10% of the communities
generally refused to provide control addresses, the proportionof refused addresses was higher (16%) amongst the communities situated in the inner 5-km zone. Therefore, the relevant
analyses were repeated only for cases and controls from communities which provided control addresses.
The questionnaire part of the study raised a strong suspicion that communities might have sent the addresses of persons who were never resident in the respective community
before the date of diagnosis of the corresponding case (about5%). We therefore simulated artificial datasets by removing
this 5% of controls from the analysis, assuming these 5% were
either randomly distributed with respect to distance from the

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E U R O P E A N J O U R N A L O F CA N C E R x x x ( 2 0 0 7 ) x x x –x x xFig. 1 – Selected nuclear power plants and study areas in Germany. Each nuclear power plant is identified by name;
Lingen/ Emsland are two reactors 2 km from each other.

nuclear power plant, or more likely to live close to it or far
from it. For a sub sample of the controls (45%) we were able
to check the address information at the date of diagnosis of
the corresponding case. Amongst these we found 15% of controls that had not lived in the indicated place at that time,
though they might have lived there prior to the date of diagnosis of the corresponding case. The analysis was repeatedincluding only controls, where the address could be checkedand excluding those, whose address at the date of diagnosisof the corresponding case had been incorrect.
The previous German studies had shown single nuclear
power plants to influence the results considerably, so the calculations were repeated leaving the nuclear power plants outof the analysis one by one.
As confounder assessment we planned to use a change bymore than one standard deviation (out of the calculation for

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E U RO P E A N J O U R NA L O F CA N C E R x x x ( 2 0 0 7 ) x x x –x x x

the respective subset of cases not including any confoundervariables) of the continuous proximity parameter.
To ensure the correctness of our analyses all relevant computations were repeated independently by the coordinating
centre of clinical trials (KKS) of the University of Mainz.

3.

for interviewing was almost the same as that in the study
as a whole, although it was not statistically significant because of small numbers (b = 1.05; lower one-sided 95% CL)0.30) (Table 3).
When the continuous model was refitted with all available(maximally 6) controls per case (1592 cases, 8527 controls),
the parameter estimate was b = 1.18 (lower one-sided 95%
CL 0.50), which is identical to that obtained with the three selected controls (compare to Table 3). When the model was
refitted after exclusion of communities that did not provide
control addresses (leaving 1310 cases and 3905 controls), a
statistically significant parameter estimate was foundb = 1.01 (lower one-sided 95% CL 0.24) (compare to Table 3).
When 5% of all controls were either excluded randomly
from the dataset with respect to their distances from thenearest nuclear power station, or selectively from close to or
far from the nearest nuclear power station, we found average
statistically significant estimated regression parameters of
1.18, 1.54 or 1.09, respectively, based on 1000 simulations
each. These are all close to the results found with the full data(compare to Table 3). Excluding the controls from the analysis,
which had their address at the date of diagnosis checked andfound incorrect, led to an estimated regression parameter ofResults

Table 2 shows the characteristics of the case children and thecontrols. The age and sex distributions were similar, as thesewere matching criteria. The case children lived 1.2–81.6 km
from the nearest nuclear power plant and the controls between 1.1 km and 92.0 km.
The parameter from the continuous model for the measure of proximity was b = 1.18 (lower one-sided 95% confidence limit [CL] 0.46) (Table 3, Fig. 2). The diagnostic groups
defined in the study protocol showed a statistically significant
effect only for leukaemia, which was stronger than the general effect (Table 2). We also give the complementary calculation beyond the study protocol (non-leukaemia cases, Table
3). No statistically significant difference was found comparing
the first and second half of the respective operating periods ofthe nuclear power plants. The effect in the subgroup eligible

Table 2 – Characteristics of cases of all malignancies in children under 5 years of age, as defined by the ICCC,9 diagnosed in
1980–2003 resident in the study areas, and their matched controls
Cases
N

Controls
%

N

%

All
Boys
Girls

1592
893
699

100.0
56.1
43.9

4735
2656
2079

100.0
56.1
43.9

Age (years)
0–<1
1–<2
2–<3
3–<4
4–<5
5–<6

344
330
340
315
263
0

21.6
20.7
21.4
19.8
16.5
0.0

1016
984
991
947
775
22

21.5
20.8
20.9
20.0
16.4
0.5

Diagnostic groupsa
Leukaemia
Central nervous system tumours
Embryonal tumours
Other

593
242
486
271

37.3
15.2
30.6
17.0

1766
720
1447
802

37.3
15.2
30.5
16.9

First half of power plant operation period
Second half of power plant operation period

698
894

43.8
56.2

2073
2662

43.8
56.2

Eligible for interview (1993–2003, selected diagnoses)

471

29.6

1402

29.6

Distance from nearest nuclear power plant (km)<5
5–<10
10–<20
20–<3030–<4040–<50
P50

77
158
523
403
225
137
69

4.8
9.9
32.9
25.3
14.1
8.6
4.3

148
464
1589
1181
726
371
256

3.1
9.8
33.6
24.9
15.3
7.8
5.4

–

03245
3.1

–

Mean proximity measureb in the inner 5-km zone
Corresponding harmonic mean distance (km)0.3133
3.2

a Controls matched to cases with respective diagnosis.
b Proximity measure = 1/distance in km (kilometres).

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E U R O P E A N J O U R N A L O F CA N C E R x x x ( 2 0 0 7 ) x x x –x x x

Table 3 – Estimated parameters from the conditional continuous logistic regression model for all cancers, diagnostic
groups and some relevant time periods
Estimated
regression
coefficient

Lower one-sided 95%
confidence limit

N cases

N controls

All malignancies 1980–2003
Diagnostic groups 1980–2003
Leukaemia
Central nervous system tumours
Embryonal tumours
All malignancies except leukaemia

1.18

0.46

1592

4735

1.75
)1.02
0.52
0.76

0.65
)3.40
)0.84
)0.20

593
242
486
999

1766
720
1447
2969

First half of power plant operation period
Second half of power plant operation period

1.89
0.54a

0.85
)0.47

698
894

2073
2662

Eligible for interview: diagnosed 1993–2003
with leukaemia, lymphoma,
or a central nervous system tumour

1.05

)0.30

471

1402

a The difference between the first and the second half was not statistically significant.

er one-sided 95% CL 1.51). Living in the inner 10-km zone had
a far smaller effect (OR = 1.18; lower one-sided 95% CL 1.03).
The fitted curve for all malignancies predicted similar OR’sfor the inner 5-km and 10-km zone as obtained by the categorical analysis (Table 4, Fig. 2).
Based on the categorical analysis, our result indicates that
29 out of the total observed 77 cases (38%; 95% CI [24%;61%])diagnosed in the inner 5-km zone in 1980–2003 may be attributed to the fact that they were living in this 5-km zone. These
were 1.2 cases per year, representing 0.2% (95% CI [0.1%; 0.4%])of all 13,373 cases of cancer in children under 5 years in Germany in those years.

3.3

Odds Ratio (log scale)

2.7

2.2

1.8

1.5

1.2

1.0
0

5

10

15

20

25

30

35

40

45

4.

Discussion

4.1.

Principal findings

50

Distance of place of residence from nuclear power station (km)Fig. 2 – Graphical representation of the main regression
analyses. Estimated regression curve for all malignancies
versus distance from nearest power plant, based on 1592
cases and 4735 matched controls based on conditional
logistic regression modelling. Distance axis cut off at 50 km.
Black line: continuous fitted regression curve. Dotted curved
line: lower one-sided 95%-confidence limit of continuous
fitted regression curve. Dotted straight lines: categoricalanalysis for <5 km and <10 km respectively.

1.05, which again does not differ much from the full data(compare Table 3).
Leaving the nuclear power stations out of the data set one
by one yielded statistically significant regression coefficientsclose to the overall estimate.
Fractional polynomial modelling and the Box–Tidwellmodel both suggested that an alternative measure of proximpﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ity of the form 1= ðdistanceÞ would fit slightly better than 1/(distance), but not significantly so.
The categorical analyses showed a statistically significant
effect for children living in the inner 5-km zone OR = 1.61
(lower one-sided 95% CL 1.26). Comparing diagnostic groups,
the effect was again found only for leukaemia (OR = 2.19; low-

Our results show an increased risk for cancer amongst children under 5 years of age living in the proximity of nuclear
power plants in Germany. The continuous model, in agreement with the categorical analyses, identified the inner 5km zone as the zone of increased risk (about 1.5-fold higher).
The observed effect was largely restricted to leukaemia(Tables 3, 4).
Expression of the categorical estimate for living in the inner 5-km zone as an attributable risk fraction would attribute
29 out of 77 observed cases (38%; 95% CI [24%;61%]) in 1980–
2003 to having lived in that zone representing 0.2% (95% CI[0.1%;0.4%]) of all 13,373 childhood cancer cases under 5 years
in 1980–2003 in Germany.

4.2.

Previous studies

The associations found in our study were strongest for leukaemia in children under 5 years of age living within a 5km zone of a nuclear power plant. This group had yielded
the most notable exploratory result in the first of the previous
ecological studies.7,8 It has to be pointed out that the cases ofthis study diagnosed in the study years 1980–1995 had alreadybeen included in the previous studies and that the results pre-Please cite this article in press as: Spix C et al., Case–control study on childhood cancer in the vicinity ..., Eur J Cancer (2007),
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E U RO P E A N J O U R NA L O F CA N C E R x x x ( 2 0 0 7 ) x x x –x x x

Table 4 – Estimated odds ratios from the conditional categorical and continuous logistic regression models for all cancers
and for diagnostic groups
OR for inner 5 km derived
from continuous modela

All malignancies
Diagnostic groups
Leukaemia
Central nervous
system tumours
Embryonal tumours

Modelling 5-km
distance categorically

OR for inner 10 km derived
from continuous modelb

Modelling
10-km distancecategoricallyOR

Lower onesided 95%
confidence
limit

OR

Lower onesided 95%
confidence
limit

OR

Lower onesided 95%
confidence
limit

OR

Lower onesided 95%
confidence
limit

1.47

1.16

1.61

1.26

1.23

1.09

1.18

1.03

1.76
0.72

1.24
0.33

2.19
0.81

1.51
0.37

1.37
0.83

1.12
0.54

1.33
1.03

1.06
0.71

1.19

0.76

1.20

0.75

1.10

0.86

1.05

0.81

Cases diagnosed/controls resident in the study area in 1980–2003.
OR: odds ratio.
a Using the mean proximity measure of the controls in the inner 5-km zone: 1/(distance in km) = 0.3245.
b Using the mean proximity measure of the controls in the inner 10-km zone: 1/(distance in km) = 0.1786.

Table 5 – Results of studies on all malignancies under the age of 5 years in the vicinity of nuclear power plants performedat the German Childhood Cancer Registry: previous studies 1 and 2 compared to recent study (categorical estimates)
Study periods

Relative risk
estimate/Odds ratio

95%-confidence
interval/lower
one sided 95%
confidence limit

Cases 5-km zone

Previous studies
45
1980–1990 Study 1
1.43[0.89; 2.43]a22
1991–1995 Study 2
0.97[0.50; 1.89]a
1980–1995 Study 1+2
1.24[0.84; 1.85]a67
Recent study: Results shown for previous studies’ study periods, for the period following the previous studies and for the total study period31
1980–1990 (period of study 1)
1.99
1.33b
1991–1995 (period of study 2)
1.41
0.90b
20
51
1980–1995 (period of previous studies 1 + 2)
1.70
1.26b
26
1996–2003 (period following previous studies)
1.45
0.96b
1980–2003 (total recent study period)

1.26b

1.61

77

Relative risks and odds ratios by different study periods in the inner 5-km zone (periods shown analogous to periods of former studies).
a Relative risk resulting from ecological study, two-sided 95% confidence interval.
b Odds ratio resulting from case–control study, lower one-sided 95% confidence limit.

sented here are consequently not entirely independent. Table
5 summarises the findings from the previous studies for all
malignancies, cases under the age of five in the inner 5-km
zone. It compares them with the results of this case–control
study split up by the previous study periods (1980–1990,
1991–1995) and separating the new study years (1996–2003).
The observed effect estimate is larger in the earliest study
period (Table 5). This corresponds to the observation, thatthe regression parameter is larger in the first half of the nuclear power plant operation periods, though not significantly
so (Table 3). While the ecological effect estimates are smaller,
they are generally in the same order of magnitude (Table 4). It
is thus unlikely, that the previous findings were affected byecological bias in a major way.
This issue will be discussed more thoroughly for leukaemia in a separate paper.14

4.3.

Strengths and weaknesses

The GCCR, founded in 1980, is a nationwide childhood cancer
registry cooperating with all paediatric oncology units andtherapy optimisation studies in Germany. Registration forcases under the age of 15 is 95% complete since the mid1980ies.23 Almost all cases are registered with their full address at the date of diagnosis. Given this data base, this is
one of the largest studies with this objective world wide
(1592 cases, including 593 leukaemia cases).
Distance to the nearest nuclear power plant at the date ofdiagnosis is a crude surrogate for potential exposure to radiation, however, it does not account for topography, weather,
vegetation, differences in background radiation, othersources of individual exposure to radiation or the time
actually spent by the individual in the home. Information

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on previous residences of the child from the questionnaire
could not be used in the analysis due to poor and selective
participation in the questionnaire part of the study (see below). The extremely low number of parents reporting occupation in a nuclear installation (0 cases, 4 controls) did not allow
evaluating an effect of parental radiation exposure.
The former studies investigated only the inner 15-km
zone. In the case control study, the study areas around thenuclear power plants were very large and included casesand controls from up to about 100-km distance from the nuclear power plants, which increases the statistical powerslightly. Adding unexposed cases and controls does not, however, cause bias.
German nuclear energy providers are required to maintainthe exposure of the population below 0.3 mSv/year.24 Compared to this, the annual background radiation exposure estimated for the German population is 1.4 mSv/year. The
average annual dose of persons of any age from medical procedures is 1.8 mSv, though this is lower for children (no specific figures given).25 The actual emissions from nuclear
power plants are far lower; e.g. for a 50-year-old person in1991 living 5 km from one of the German nuclear power stations included in the study, the expected cumulative exposure
to atmospheric discharges would have ranged from
0.0000019 mSv (Obrigheim) to 0.0003200 mSv (Gundremmingen).26 At these levels of radiation, no detectable effects are
expected from the usual models.12,13
The sensitivity analyses for the various expected andunexpected problems in control recruitment yielded statistically significant regression parameters of a similar magnitude
to that reported in Table 2. We conclude that the biases due tothese problems were small and the results cannot be explained by the biased control recruitment. The specificity ofthe effect for leukaemia makes it unlikely that biased control
recruitment is the explanation for the effects seen in thisstudy. The analysis excluding the nuclear power station areas
one by one showed that the result is not caused by a specificnuclear power plant.
With regard to uncontrolled confounding, there may beother risk factors close to nuclear power stations, although
no risk factors of the necessary strength for this effect are
known for childhood cancer and specifically childhood leukaemia. We saw considerable self-selection by the persons
who were to be interviewed, so that those who were interviewed were not representative of the study population as awhole, particularly with respect to their distance distribution
from nuclear power plants. Assessing the change in the(biased) estimate by confounders as planned nevertheless,showed that none of them changed the distance parameter
estimate by more than one standard deviation. This is true
for all diagnoses investigated in the survey subset of the study
as well as for diagnosis subgroups.

4.4.

International context

The best-known quantitative summaries of current knowledge on the effects of environmental low-dose radiation effects are based mostly on adult data. Children are included,
but their small number makes a negligible impact. Thesemodels deal mainly with solid tumours and adult leukaemia,

applying them to children or to acute leukaemia should bedone with caution.12,13 The BEIR Committee has refused to assess studies of residents living near nuclear facilities, many ofwhich had childhood cancer as the main objective, because oflack of actual data on exposure. They are reviewed, but notsummarised or discussed beyond this.12 Many other studies
have addressed the health risks of children of parents exposed (occupationally or to radiation from the atomic bombs
dropped in Hiroshima and Nagasaki) and these are therefore
not comparable. If we had nevertheless applied the models
proposed for adults, no detectable effect would have been
predicted.
A French study of a design similar to that of the earlier
incidence studies in Germany, in which SIR were computed
for communities by distance, found no elevated SIR for leukaemia amongst children under five living in the inner 5-km
zone of French nuclear installations (670 cases, SIR 0.97;
95% CI [0.69;1.33]).27 When this study was repeated, with distance replaced by estimated gaseous discharges, neither thehighest exposure category (P0.001 mSv/year; 750 cases, SIR
0.93; 95% CI [0.30;2.17]), nor any other exposure category
was associated with an elevated SIR for leukaemia.28 A recent
study addressed the risk for leukaemia of children under six
years of age in countries near the Chernobyl site (421 cases),on the basis of estimated cumulative doses from gaseous discharges and from food, derived from individual residence histories. This study estimated an OR of 1.46 (95% CI [1.00;2.12])
for doses between 1 and 5 mGy compared with <1 mGy.29
1 mGy is a far higher exposure than from a nuclear power
plant under regular conditions in Germany.26
For some of the nuclear power plants in relatively isolated
communities in northern Britain, Kinlen suggested population mixing as a potential cause of elevated leukaemia risks.30
We inspected migration figures,31 but there are no indications
that any of the nuclear sites investigated here were particularly isolated and all have average migration at any time during the study period. This is not to say that infective causes
may not in principle be an alternative explanation for the patterns we see in this study.

5.

Conclusion

The design of this study aimed to clarify issues raised by previous ecological studies in Germany by using the same dataplus more recent cases in a case–control study assigning individual distance estimates (as compared to community basedzones). In Germany 1980–2003 we see an increased risk forcancer in children under 5 years of age, particularly leukaemia, when living in proximity (<5 km) to a nuclear power station. This observation is not consistent with mostinternational studies, unexpected given the observed levelsof radiation, and remains unexplained. We cannot exclude
the possibility that this effect is the result of uncontrolled
confounding or pure chance.

Funding
This study was funded by the Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit (Federal Ministry
for the Environment, Nature Conservation and Nuclear

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Safety) via the Bundesamt für Strahlenschutz (Federal Officefor Radiation Protection), St.Sch.-Nr. 4334.
The funding agency was not involved in the data collection, the analysis, the interpretation, in writing the report,
or the decision to submit the article for publication. The sponsor’s expert committee approved the design of the study andthe statistical analysis plan before the start of the datacollection.
The research group is independent from the funding
agency. The authors are responsible for the content.

Conflict of interest statement
All authors declare that they have no conflict of interest andno organisational, personal or financial connection with otherpeople or organisations that could inappropriately influence
this work.

Acknowledgements
The authors thank Jörg Michaelis for the constructive criticism of the manuscript. The authors thank Carsten Hornbach
for the preparation of the map and geographical expertise.
Further preparatory work and technical assistance were provided by Eva Münster, Irene Jung, Melanie Kaiser, Sabine
Kleinefeld, Andreas Mergenthaler, Claudia Trübenbach andSteffen Weinand.
The authors thank the Robert-Koch-Insitut, Berlin, forperforming an audit of the data and the Koordinerungszentrum für klinische Studien (KKS), University Mainz, forperforming an independent recalculation of the regression
results.

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