The German KiKK study
In 2002 the German Radiation Protection
Agency (Bundesamt für Strahlenschutz, BfS) commissioned a
casecontrol study to investigate childhood cancers near nuclear power
plants. This so called KiKK study (Kinderkrebs um Kernkraftwerke)
was conducted by the German Childhood Registry (Deutsches
Kinderkrebsregister, DKKR) in Mainz.
The results of the KiKK study were presented in a technical report in
December 2007. At the same time, the results were published in two
scientific journals. The articles can be accessed in the web (see below).
The
polar diagram below (Fig.3.3 in KiKK study part 1) shows cancer cases (red) und
controls (green) within 50 km distance from the nearest NPP site.
The KiKK report shows the following table (Table
3.14. part 1) containing the numbers of cases (Fälle, F) and
controls (Kontrollen, K) in 7 distance categories.
r 
F 
K 
05
km 
77 
148 
510
km 
158 
464 
1020
km 
523 
1589 
2030
km 
403 
1181 
3040
km 
225 
726 
4050
km 
137 
371 
>
50 km 
69 
256 
Summe 
1592 
4735 
The 5km range in Figure 1 can be
studied in more detail. Using a graphics program it was possible
to determine the number of cases (N=21, Fig. bottom, black dots) and
controls (N=49, white dots) within the 3km range. The number of cases
and controls in the 35 km range are then obtained by substraction
from the published numbers for the whole 5km area.
A
logistic regression with model
ln(F/K)=beta0+beta1/r
yields
the following results for the parameters beta0. beta1:
parameter 
estimate 
SE 
zvalue 
pvalue 
beta0 
1,1581 
0,0406 
28,534 
<0,0001 
beta1 
1,0477 
0,4309 
2,431 
0,0150 
SE:
Standard error of estimate
zvalue: estimate divided by SE
pvalue: twosided pvalue
The result for beta1 agrees with the result in Table 3.15 of the KiKK
study (1,18
+ 0,44; p=0,0034).
A
better fit to the data is obtained with the following categorial
regression model ln(F/K)=beta0+beta1*d5km, where
d5km is a dummy variable for the 5km region (d5km=1 for
r<5km und d5km=0 for r>5km).
Die following Figure shows the relative risks (= odds ratios) in the
individual distance zones and the regression result.
Leukemia KiKK
part 1 also contains results for leukemia cases. The numbers of cases (F) and
controls (K), together with the harmonic means of the distances (r), are found in
the article by Kaatsch et al. for 6 distance zones.
r 
F 
K 
3.09 
37 
54 
7.62 
58 
173 
17.79 
332 
1048 
37.45 
135 
387 
56.98 
27 
92 
73.59 
4 
12 
Summe 
593 
1766 
Regressions with the following 5 models are conducted:
Model
1: ln(F/K) = beta0 + beta1/r
Model 2: ln(F/K) = beta0 + beta1/r + beta2/r^2
Model 3: ln(F/K) = beta0 + beta1/r^2
Model 4: ln(F/K) = beta0 + beta1/r + beta2*d5km
Model 5: ln(F/K) = beta0 + beta1*d5km
dummyvariable
d5km is 1 for r < 5 km and 0 for r > 5 km
Model
information:

DF 
SSE 
AIC 
model
1 
4 
3.3148 
39.538 
model
2 
3 
0.7197 
38.943 
model
3 
4 
1.2925 
37.516 
model
4 
3 
0.9244 
39.148 
model
5 
4 
0.9260 
37.149 
DF
= degrees of freedom
SSE = sum of squares (deviance)
AIC = AIC criterion of the goodness of fit
The following table shows the parameter estimates and standard errors (SE)
resulting from the 5 regressions:
Parameter 
estimate 
SE 
z
value 
P
value 
Model
1 
beta0 
1.2393 
0.0695 
17.84 
0.0000

beta1 
2.2113 
0.7387 
2.994 
0.0028 
Model
2 
beta0 
1.0600 
0.1310 
8.092 
0.0000 
beta1 
2.1110 
2.7950 
0.755 
0.4501 
beta2 
13.061 
8.1320 
1.606 
0.1082 
Model
3 
beta0 
1.1511 
0.0512 
22.469 
0.0000 
beta1 
7.1316 
2.1303 
3.348 
0.0008 
Model
4 
beta0 
1.1210 
0.1032 
10.858 
0.0000 
beta1 
0.0663 
1.6631 
0.040 
0.9682 
beta2 
0.7644 
0.4980 
1.535 
0.1248 
Model
5 
beta0 
1.1247 
0.0488 
23.041 
0.0000 
beta1 
0.7466 
0.2189 
3.410 
0.0006 
Results:

Model
1 yields a significant regression coefficient beta1 (p=0.0028)

Model
2 (linearquadratic model) yields a better fit than model 1. The sum
of squares (SSE) is 0.720 which compares to 3.315 for the linear model,
the corresponding values of AIC are 38.94 and 39.54.
The estimate for the linear term is negative.

Model
3 (quadratic distance trend) fits the data better than the linear model
(SSE=1.293, AIC=37.52).

Model
4 which allows for excess in the 5km zone yields a negative
regression coefficient beta1. This means that for r > 5 km the risk
does not decrease with increasing distance from the NPP.

Model
5 which only allows for an excess in the 5km zone yields the best fit
to the data by the AIC criterion (AIC=37.15)

The
RRs obtained with the linear model translate to 81 excess cases in the
study zone
(90% CI: 39118), and to 34 excess cases (90%
VB: 19 bis 47) with the quadratic model.
The following figure shows the relative risk (odds ratio) as a function of
distance from the NPP and the regression line with the categorial model (model
5).
References
Spix C. Schmiedel S. Kaatsch
P.
SchulzeRath R. Blettner M. Casecontrol study on childhood cancer in the
vicinity of nuclear power plants in Germany 19802003. Eur J Cancer. 2008
Jan;44(2):27584.
Abstract
download full article: PDF (569
kB)
Kaatsch P, Spix C, SchulzeRath R, Schmiedel S, Blettner M. Leukaemia in young
children living in the vicinity of German nuclear power plants. Int J
Cancer. 2008 Feb 15;122(4):7216. Abstract
download full article: PDF (309
kB)
