Medicine®

Observational Study

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Increases in perinatal mortality in prefecturescontaminated by the Fukushima nuclear power
plant accident in JapanA spatially stratiﬁed longitudinal study∗

Hagen Heinrich Scherb, Dr rer nat Dipl-Matha, , Kuniyoshi Mori, MDb, Keiji Hayashi, MDc
Abstract

Descriptive observational studies showed upward jumps in secular European perinatal mortality trends after Chernobyl. The question
arises whether the Fukushima nuclear power plant accident entailed similar phenomena in Japan. For 47 prefectures representing
15.2 million births from 2001 to 2014, the Japanese government provides monthly statistics on 69,171 cases of perinatal death of the
fetus or the newborn after 22 weeks of pregnancy to 7 days after birth. Employing change-point methodology for detecting
alterations in longitudinal data, we analyzed time trends in perinatal mortality in the Japanese prefectures stratiﬁed by exposure toestimate and test potential increases in perinatal death proportions after Fukushima possibly associated with the earthquake, thetsunami, or the estimated radiation exposure. Areas with moderate to high levels of radiation were compared with less exposed andunaffected areas, as were highly contaminated areas hit versus untroubled by the earthquake and the tsunami. Ten months after theearthquake and tsunami and the subsequent nuclear accident, perinatal mortality in 6 severely contaminated prefectures jumped up
from January 2012 onward: jump odds ratio 1.156; 95% conﬁdence interval (1.061, 1.259), P-value 0.0009. There were slightincreases in areas with moderate levels of contamination and no increases in the rest of Japan. In severely contaminated areas, theincreases of perinatal mortality 10 months after Fukushima were essentially independent of the numbers of dead and missing due tothe earthquake and the tsunami. Perinatal mortality in areas contaminated with radioactive substances started to increase 10 months
after the nuclear accident relative to the prevailing and stable secular downward trend. These results are consistent with ﬁndings in
Europe after Chernobyl. Since observational studies as the one presented here may suggest but cannot prove causality because of
unknown and uncontrolled factors or confounders, intensiﬁed research in various scientiﬁc disciplines is urgently needed to better
qualify and quantify the association of natural and artiﬁcial environmental radiation with detrimental genetic health effects at the
population level.
Abbreviations: CP = change-point, O = odds, OR = odds ratio, PD = perinatal death, SAS = Statistical Analysis System,
software produced by SAS Institute Inc., TEPCO = Tokyo Electric Power Company.
Keywords: change-point analysis, detrimental pregnancy outcome, ionizing radiation, nuclear accident, radiation induced genetic
effect, stillbirth(TEPCO) Fukushima No.1 nuclear power plant released
radioactive elements with an estimated total activity of 900
PBq (Peta-Becquerel) into the atmosphere.[1] The World HealthOrganization estimates that the rates of all solid cancers in maleand female infants who were exposed to radiation at the age of
one in Namie-Machi in the Fukushima prefecture, a highly
contaminated area, would increase by 14% and 24% in 15 years,
and that the rates of leukemia would increase by 83% and 57%,
respectively.[2] Moreover, the World Health Organization
estimates that the rates of thyroid cancer among those males
and females would increase 7.4 and 9 times, respectively, andsuggests increases in breast cancer as well.[2]Disorders that may occur in irradiated fetuses include cancer andprincipal radiation injuries.[3] Due to the vulnerability of the embryoand the fetus, concerns have been raised that even the low doseenergy transfer to developing tissue by the frequency magnetic ﬁeld
exposure during ultrasound examinations might entail some healthrisk.[4] As early as in 1958, UNSCEAR acknowledged that anincrease in the frequency of radiation induced dominant mutations
associated with visible effects would manifest itself to some
unknown extent as an increase in the frequency of malformations
and stillbirths.[5] In 10% of the miscarriages, a genetic defect may
cause a baby to be stillborn or to die shortly after birth (perinatal

1. Introduction
After the Great East Japan Earthquake and Tsunami on March
11, 2011, the destroyed Tokyo Electric Power Company
Editor: Roman Leischik.
The authors have no funding and conﬂicts of interest to disclose.
aHelmholtz Zentrum München, German Research Center for Environmental
Health, Institute of Computational Biology, Neuherberg, Germany, b Higashiosaka
Health Center 4-3-22 Iwatachou, Higashiosakacity, c Hayashi Children’s Clinic,
Osaka, Japan.
∗

Correspondence: Hagen Heinrich Scherb, Helmholtz Zentrum München,
German Research Center for Environmental Health, Institute of Computational
Biology, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany(e-mail: scherb@helmholtz-muenchen.de).

Copyright © 2016 the Author(s). Published by Wolters Kluwer Health, Inc. All
rights reserved.
This is an open access article distributed under the terms of the Creative
Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NCND), where it is permissible to download and share the work provided it isproperly cited. The work cannot be changed in any way or used commercially.
Medicine (2016) 95:38(e4958)
Received: 29 June 2016 / Received in ﬁnal form: 22 August 2016 / Accepted: 2
September 2016
http://dx.doi.org/10.1097/MD.0000000000004958

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in Japan, even though they have been reported in the case of theChernobyl nuclear accident.[23,27]
Based on more comprehensive and more recent data from 2001
to 2014, we aim to examine whether or not there were increases
in perinatal mortality in areas contaminated with radioactive
substances in Fukushima prefecture following the nuclear
accident. The Fukushima accident differs from the one at
Chernobyl since people in Japan were additionally affected by theearthquake and the tsunami. Therefore, we also examine whether
there are associations between the earthquake, the tsunami,
radiation, and perinatal mortality overall and in moderately andhighly contaminated prefectures.

death [PD]), possibly since a vital organ has not developed properly.
UNSCEAR emphasizes that increases in stillbirth and changes in thesex ratio are easily observable criteria (http://www.unscear.org/
unscear/en/publications/1958.html).
Lethalmutationsinhumans[5,6] may, therefore, be observed in several ways: increasein frequencies of miscarriages, perinatal mortality, stillbirths,
reduction in fertility, sterility, and disturbance in the ratio of thesexes at birth. A variety of such detrimental reproductive effects,
including early childhood cancers, after occupational, diagnostic,
therapeutic, and environmental exposures, have been investigatedand reported in the scientiﬁc literature: Atomic bombing of Japan[7–9] Windscale/Sellaﬁeld nuclear processing plant ﬁre[10,11]
 Occupational exposure[12–16]
 Diagnostic and therapeutic exposure[17–20]
 Chernobyl nuclear power plant accident[21–33]
 Fukushima nuclear power plant accident[34,35]
 Background radiation[36–38]
 Living near nuclear facilities[9,39–42]

2. Methods
The Japanese Statistics Bureau publishes demographic statistics
including the monthly numbers of live births and PDs created by
the Ministry of Health, Labour, and Welfare. Six prefectures –Iwate, Miyagi, Fukushima, Ibaraki, Tochigi, and Gunma – wereclassed as severely contaminated; they include wide areas inwhich the radiation dose in the air was higher than 0.25 mSv/h(micro-Sieverts per hour), according to a map documenting
estimated radiation doses as of December 2011.[45] Chiba,
Tokyo, and Saitama prefectures were designated as moderately
contaminated areas that involve only few areas where theradiation dose in the air was higher than 0.25 mSv/h. The rest ofJapan (38 prefectures) excluding severely and moderately
contaminated areas was considered not or only slightly affected.
Figure 1 is a map of Japan with the selected prefectures grouped

Tsuda et al[43] reported that there was an increase in thyroid
cancer morbidity after the Fukushima No.1 nuclear power plant
accident, which suggests an inﬂuence of radiation exposure.
However, the ﬁndings by Tsuda et al have been criticized forpresumably being artifacts of a screening effect.[44] Following thenuclear accident in Fukushima and based on data from 2002 to
2013, increases in the numbers of spontaneous fetal deaths,[34] aswell as an increase in the number of infant deaths[35] werereported. However, to date there are no reports of increased PDs

Figure 1. Map of Japan with prefectures classed according to wide areas in which the radiation dose in the air was lower or higher than 0.25 mSv/h (micro-Sieverts
per hour) as of December 2011.

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Table 1
LB, PD, and PDp in Japanese prefectures by contamination level after Fukushima.
Highly contaminatedprefectures (n = 6)

Moderately contaminatedprefectures (n = 3)

Rest of Japan(n = 38)

Total (n = 47)

Year

PD

LB

PDp_6

PD

LB

PDp_3

PD

LB

PDp_38

PD

LB

PDp_47

2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Total

723
697
670
653
505
522
533
486
455
452
398
438
418
389
7339

119,830
117,278
114,011
111,437
106,150
107,639
106,134
105,870
102,741
101,482
97,213
95,536
95,404
93,226
1,473,951

0.00600
0.005910.00584
0.005830.00473
0.004830.00500
0.00457
0.004410.004430.004080.00456
0.004360.004160.00495

1239
1333
1147
1074
995
1033
973
932
921
889
871
865
782
811
13,865

218,349
219,487
214,547
214,201
206,861
214,637
216,476
218,841
218,177
219,205
214,465
213,225
215,799
213,143
3,017,413

0.005640.00604
0.005320.004990.00479
0.00479
0.004470.004240.004200.004040.004040.00404
0.00361
0.00379
0.00457

4505
4295
4105
3811
3645
3539
3395
3294
3139
3167
3041
2825
2659
2547
47,967

832,285
816,895
794,882784,907749,349
770,222
767,048766,272
749,018
750,492
739,006
728,403
718,559
697,105
10,664,443

0.00538
0.005230.005140.004830.004840.00457
0.004410.004280.004170.004200.004100.00386
0.00369
0.003640.00448

6467
6325
5922
5538
5145
5094
4901
4712
4515
4508
4310
4128
3859
3747
69,171

1,170,464
1,153,660
1,123,440
1,110,545
1,062,360
1,092,498
1,089,658
1,090,983
1,069,936
1,071,179
1,050,684
1,037,164
1,029,762
1,003,474
15,155,807

0.00549
0.005450.005240.004960.004820.004640.004480.00430
0.004200.004190.00409
0.003960.00373
0.00372
0.00454

LB = live births, PD = perinatal death, PDp = perinatal death proportions.

contamination. To this end, we applied linear logistic regression
allowing for jumps at certain time lags (in months) after thetsunami to model perinatal mortality trends and to test fortrend changes possibly associated with contamination. We
denote the number of PDs by n, the number of total birth byN, and the estimated PD proportion as p = n/N. Two central
parameters in this context are the odds O = n/(N  n), and theodds ratio OR = O1/O0 = n1/(N1  n1)/(n0/(N0  n0)), which is
the ratio of the odds for 2 populations with O1 and O0 to becompared, for example, in exposed versus nonexposed strata.
The dummy variable for the time window from T onward, forexample, T = January 2012, is deﬁned as dT (t) = 0 for t < T anddT (t) = 1 for t ≥ T. The distributional assumption and theparsimonious logistic jump model for a trend in t and a jumpin T are:according to the estimated radiation exposure. Table 1 lists the live
births and the PD cases stratiﬁed by year and prefecture groupaccording to the deﬁned contamination levels. Note, the smaller
moderately exposed area represents approximately twice thepopulation of the much larger highly contaminated area.
To examine the potential inﬂuences of the earthquake and thetsunami as well as the possible effects of radiation contamination,
the 6 prefectures designated as severely contaminated weredivided into group 1: Iwate (the number of deaths and missing
due to the tsunami as of March 11, 2016: n = 5797) and Miyagi(n = 10,777), in which the numbers of the dead and missing werehigh; and group 2: Fukushima (n = 1810), Ibaraki (n = 25),
Tochigi (n = 4), and Gunma (n = 1), in which the numbers of thedead and the missing were more than 20 times lower comparedto the areas heavily hit, see Table 2.[46] Monthly perinatal
mortalities for those 2 groups were calculated based on theavailable monthly numbers of births and PDs to analyze apossible association between tsunami intensity and PD.
Temporality and biologic gradient are 2 indispensable
requisites for inferring causality. Temporality means that thepresumable cause precedes the observed effect in time, andbiologic gradient refers to the presence of an exposure–response
association.[47,48] Consequently, we investigate whether there
are changes in perinatal mortality trends after Fukushima andwhether those changes, if any, are associated with the level ofnt e Binomial ðN t ; pt Þ; log oddsðpt Þ¼ intercept þ a t þ bdT ðtÞ

ð1Þ

In formula (1), t denotes time (year and month), nt the numberof PD in t, Nt the number of total births in t, and pt is theprobability of PD in t. If time t is signiﬁcant in a certain setting it
may be considered a confounder as it is associated with theoutcome variable “perinatal death” as well as with the exposure
classed “before versus after Fukushima.” Note that theTable 2The numbers of dead and missing due to the earthquake and the tsunami in 6 highly contaminated prefectures[46] in relation to population
size of the prefectures (https://en.wikipedia.org/wiki/List_of_Japanese_prefectures_by_population).
Hit by tsunami
StrongWeak

Prefecture
Iwate
Miyagi
total
Fukushima
Ibaragi
Tochigi
Gunma
Total

Population (2010)
in 1000

Death

Missing

Death + missing

Death + missing
per 100,000

1330
2348
3678
2029
2970
2008
2008
9015

4673
9541
14,214
1613
24
4
1
1642

1124
1236
2360
197
1
0
0
198

5797
10,777
16,574
1810
25
4
1
1840

435.86458.99450.63
89.21
0.84
0.20
0.05
20.41

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assumption of a binomial distribution implies that the variancesof the random variables are determined by the binomial
parameter p. In practice, the estimated sample variances are
smaller or larger than theory predicts (heterogeneity) for
randomness or for unknown reasons, for example, unspeciﬁed
or unknown covariables. Underdispersion may be due tocorrelated variables, over-ﬁtting, or over-adjustment. Overdispersion may be a consequence of relevant variables missingin the corresponding model. Therefore, statistical models can begeneralized by introducing a heterogeneity parameter, and, to beconservative in this respect, we will allow for this extension in thecase of overdispersion but never in the case of underdispersion.[48] For example, the data and models in Figs. 3–5 involve
only minor heterogeneity: deviance/(degree of freedom) 0.951,
1.124, and 0.998, respectively. In Statistical Analysis System,
software produced by SAS Institute Inc. (SAS), for example,
correction for oversdispersion may be invoked by the option“ . . . /scale = d” in the model statement of the “procedurelogistic.” The basic model in formula (1) will be complemented
by further variables, for example, confounders, including
appropriate interactions to estimate or to account for the effects
of interesting periods or seasonality, that is, the month-to-monthvariation, or the immediate or possible late effects of the tsunami.
For the Japanese monthly PD trend functions, a change-point(CP) analysis[49,50] based on logistic regression and the minimum
deviance criterion (goodness of ﬁt) is carried out. The deviancefor a logistic model is deﬁned in equation (2).
deviance ¼ 2Sobservedlogðobserved=expectedÞ

Figure 2. Change-point analysis based on the minimum deviance criterion forthe perinatal mortality trend in the 6 severely contaminated prefectures
Fukushima, Gunma, Ibaraki, Iwate, Miyagi, and Tochigi; optimum jump in
January 2012, minimum deviance 156.89 with 165° of freedom: solid curve;and corresponding analysis for the 3 moderately contaminated prefectures
Chiba, Saitama, and Tokyo: broken curve.

Fukushima accident followed by an upward jump in perinatal
mortality.
Figure 3 presents monthly perinatal mortality in the 6 severely
contaminated prefectures: Fukushima, Gunma, Ibaraki, Iwate,
Miyagi, and Tochigi. The annual perinatal mortality proportions
are subject to a rather uniform decrease from 2001 to 2014.
Perinatal mortality proportions decline with an OR per year of
0.960; (0.952, 0.968). However, estimating a jump 10 months
after the Fukushima accident (see Fig. 2) yields the upward
jump OR 1.156; (1.061, 1.259). To concretize this effect, thejump in the perinatal mortality proportion from January 2012 to
December 2014 may be translated into 165 (66, 278) excess PD
cases. Adjusting the parsimonious basic model (1) for seasonality
by including independent dummy variables for the months
February to December yields practically the same estimates: trend
ORadjusted = 0.961; (0.953, 0.968); jump ORadjusted 1.150;(1.055, 1.253). To estimate the presumable immediate effect of
the earthquake and the tsunami in the 6 contaminated prefecturesin March and April 2011 we include in model (1) the independent

ð2Þ

The purpose of the CP method is to estimate an optimum point
in time, if any exists, when the occurrence of PDs after Fukushimachanges its trend. The data in this study were processed with
Microsoft Excel 2010. For statistical analyses, we used
MATHEMATICA 10.4 and SAS 9.4 (SAS Institute Inc.: SAS/
STAT User’s Guide, Version 9.4, Cary NC: SAS Institute Inc.,
2012). Ethical approval for this study was not necessary as only
publicly available documents and data are being used.

3. Results
For the monthly PD trend function in the 6 severely
contaminated prefectures, a CP analysis based on logistic
regression and the minimum deviance criterion (goodness of ﬁt)
is carried out in Fig. 2.[49,50] The deviance (2) of a model of type(1) with parallel trends and a possible downward or upward
jump in a given month between January 2008 and December
2014 is plotted against that month in Fig. 2. This CP analysis
discloses a unique and signiﬁcant upward jump of the perinatal
mortality in January 2012 with minimum deviance among allother months from the beginning of 2008 to the end of 2014.
For comparison, we included in Fig. 2 the deviance curve for the
3 moderately contaminated prefectures. No single distinct CP
can be identiﬁed for those less affected parts of Japan. The
deviance analyses are most informative for approximatelybalanced time periods before or after the assumed “event.”
Therefore, the analysis of the period prior to 2008 is of minor
interest in the context of our paper that puts emphasis on theyear 2011 of the Fukushima accident. The CP analysis in Fig. 2
is so to speak a comprehensive sensitivity analysis as it allows
for a wide range of potential jump dates and emphasizes the
optimum one, which is, here, the optimum time lag of 10
months between the effect and the foregoing event, that is, theFigure 3. Monthly perinatal mortality in 6 severely contaminated prefectures
Fukushima, Gunma, Ibaraki, Iwate, Miyagi, and Tochigi; jump in January 2012,
jump odds ratio 1.156 (1.061, 1.259).

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Figure 4. Monthly perinatal mortality in 3 moderately contaminated prefectures Chiba, Saitama, and Tokyo; jump in January 2012, jump odds ratio
1.068 (1.001, 1.139).

Figure 6. Monthly perinatal mortality in the 4 less tsunami-impacted but
severely radioactively exposed prefectures Fukushima, Gunma, Ibaraki, andTochigi; jump in January 2012, jump odds ratio 1.175 (1.062, 1.301).

dummies for February to December and the interaction of thosedummies for March and April (for any year) with the dummy
variable for the year 2011. It appears that the perinatal mortality
in the 6 contaminated prefectures in March and April 2011 isincreased by approximately 20% compared to March and Aprilof the remaining years: OR 1.202; (0.959, 1.506). This may be
anticipated from Fig. 3 by inspection of the somewhat elevatedperinatal mortality in March 2011.
Figure 4 presents monthly perinatal mortality in the 3
moderately contaminated prefectures: Chiba, Saitama, andTokyo. The overall trend is similar to the one in Fig. 3. In
Fig. 4, perinatal mortality proportions decline with an OR per
year of 0.960 (0.954, 0.966). The estimated jump 10 months after
the Fukushima accident yields an OR of 1.068 (1.001, 1.139).
This effect translates into 153 (10, 309) excess PD cases. The
effect estimate is nearly half the value in the highly contaminatedprefectures. Combining the data from Figs. 3 and 4 yields an
overall and more accurate estimate of the presumable total excess
PDs in Japan from January 2012 to December 2014 of 318 caseswith 95%-conﬁdence limits of (136, 519). Interestingly, thedownward trends in perinatal mortality in the contaminatedprefectures continue after Fukushima, however, at somewhat
elevated levels.
The monthly perinatal mortality in the rest of Japan, that is,Japan excluding the 6 severely and 3 moderately affected

prefectures dealt with above, is shown in Fig. 5. The overall trend
is again similar to the ones in Figs. 3 and 4. In the unaffected
Japan, the perinatal mortality declines with an OR per year of
0.971 (968, 0.974), and in contrast to the 9 contaminatedprefectures, there is no discernible jump 10 months after
Fukushima: OR 0.991 (0.958, 1.024).
Figures 6 and 7 present the perinatal mortality trends in the 6
severely contaminated prefectures stratiﬁed by the impact of theearthquake and the tsunami according to Table 2. In the 4 lesstsunami-impacted prefectures Fukushima, Gunma, Ibaraki, andTochigi, the secular downward trend is consistent with theoverall trends in the 6 severely and 3 moderately contaminatedprefectures with an OR of 0.961 (0.952, 0.970). The jump OR
1.175 (1.062, 1.301) in Fig. 6 is also consistent with the one basedon the 6 severely contaminated prefectures in Fig. 3. In Fig. 7,
representing the perinatal mortality in the 2 severely contaminated prefectures strongly impacted by the tsunami, we can see asimilar downward trend and jump from January 2012 onward
compared to the situation in Fig. 6. Additionally, in Fig. 7 weobserve an uptick of the perinatal mortality already in March andApril 2011 that can be attributed to the immediate impact of theearthquake and the tsunami. The OR of this peak in theseasonally adjusted trend model is 1.479 (1.013, 2.161) andthe jump OR is 1.132 (0.960, 1.3334), which is consistent with

Figure 7. Monthly perinatal mortality in the 2 severely tsunami and Fukushima
impacted prefectures Iwate and Myagi; jump in January 2012, jump odds ratio
1.151 (0.977, 1.355); March/April 2011 peak odds ratio 1.709 (1.186, 2.463);
trend model not adjusted for seasonality.

Figure 5. Monthly perinatal mortality in Japan excluding the 6 severely and 3
moderately affected prefectures (see Figs. 3 and 4); insigniﬁcant jump in
January 2012, jump odds ratio 0.991 (0.958, 1.024).

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the jumps in January 2012 in Figs. 3 and 6. It is interesting to notethat the perinatal mortality decreased after this uptick in March/
April 2011 and increased again 10 months after those natural andtechnical disasters in January 2012. In conclusion, the tsunami
does not confound the association of PD with the presumed
radiation exposure.

A major limitation of this study is the highly aggregated natureof the data considered impeding causal inference in principle.
The only potential confounding variables controlled for weretime (secular trend), seasonality (month-to-month variation), andthe tsunami itself. Otherwise, we are not aware of any monthly
statistics on a prefecture-by-prefecture basis that reﬂects possible
confounding variables like stresses to pregnant women and anyother risk factors for PD that could be linked to the PD
occurrence in Japan before and after Fukushima. Ideally,
population based data on perinatal risk factors will be generated
to complement future investigations. Another problem is whether
the displacement of the population during the Tsunami and thenuclear accident might have confounded our results andconclusions. We are again not aware of any data concerning
this issue. However, if young parents have been exposed and put
at a higher risk for untoward pregnancy outcome prior todisplacement, this could have biased our effect estimates
downward, as corresponding PDs would have been counted in
the “unexposed” prefectures (exposure misclassiﬁcation).
In view of the detrimental reproductive effects in Europe after
Chernobyl, and acknowledging the observed tentative spatiotemporal ecological dose–response association between radiation
exposure and perinatal mortality 10 months after Fukushima,
we conjecture that the increases of PDs in the radioactively
contaminated prefectures in Japan may possibly be due toradioactive releases by the Fukushima nuclear power plant
accident. It will be interesting to more precisely monitor the
future temporal development of the various reproductive
outcome measures in Japan stratiﬁed by radiological exposure
to weaken or to corroborate our ﬁndings and conclusions.
As the Japanese Government plans to let inhabitants return toareas prospectively exposed to radiation by less than 20 mSv/a(milli-Sieverts per year), our ﬁndings are relevant for the
resettlement of people formerly evacuated from the highly
contaminated zone, see http://www.pref.fukushima.lg.jp/site/
portal-english/rev-plan-3.html. The “Ottawa Charter of Health
Promotion”[52] emphasizes that political responsibility is needed
and that global and environmental factors play an important rolein the care for public health. To take political responsibility
requires full and continuous access to information, learning
opportunities in the various environmental health researchdisciplines, as well as adequate funding support for ecological,
environmental, and medical investigations.[53]4. Discussion
We investigated monthly perinatal mortality in Japan for theyears 2001 to 2014 with emphasis on detrimental pregnancy
outcome possibly caused by the earthquake, the tsunami, or thesubsequent Fukushima nuclear disaster in March 2011. In the 2
severely contaminated prefectures Iwate and Miyagi that werealso heavily hit by the tsunami, there was a more than 50%
increase in perinatal mortality in March and April 2011, andthere was essentially no increase in the remainder of the year
2011. By contrast, looking at the 4 other severely radioactively
affected prefectures (Fukushima, Ibaraki, Tochigi, and Gunma),
which remained nearly untroubled after the natural disasters, we
see essentially no increase in the occurrence of PDs in March andApril 2011, and this applies to the rest of that year as well. In
addition to the short-term effects in March and April 2011 in all
those 6 severely radioactively contaminated prefectures, however, we observed distinct long-term increases in perinatal mortalityof approximately 15% from January 2012 onward. Furthermore, in the 3 moderately exposed prefectures Chiba, Saitama,
and Tokyo there is a long-term relative 6.8% increase in perinatal
mortality after January 2012, and there is apparently no impact
on perinatal mortality in Japan excluding the severely andmoderately affected prefectures, neither by the earthquake andthe tsunami nor by the Fukushima accident.
Although the present study is of an ecological type based onhighly aggregated data that cannot prove causality in principle, it
nevertheless provides some evidence of causality according to thewell-known Bradford-Hill criteria: temporality and biologic
gradient. The observed effects occur 10 months after the possible
cause (ionizing radiation). This suggests impact primarily on
ovum and sperm and less on the embryo or the fetus. Moreover,the PD increases show a certain dose–response association withthe presumable exposure: unaffected as well as moderately andseverely impacted prefectures are associated with no, medium,and maximum effects, respectively. The observed optimum
overall time-lag of 10 months between the radiological event andthe jump in the PD proportion may be explained by thesuperposition of the periods necessary for the dispersal of theradioactivity (several weeks) and the pregnancy length. Note thatthe duration of pregnancies at elevated risk of adverse perinatal
outcome may be considerably shorter than the usual 9 months.
Similar spatiotemporal associations between a nuclear
accident and subsequent detrimental reproductive effects havebeen previously found in Europe after Chernobyl. A time trend
analysis of German perinatal mortality (1980–1993) disclosed a
4.8% increase in 1987, that may be linked to the Chernobyl
accident in 1986.[23] Higher contaminated parts of Germany,
Bavaria and the former German Democratic Republic, showedperinatal mortality increases of 8.5% and 8.2%, respectively.[27] Signiﬁcant ecological relative risks in the range of 1.005
to 1.020 per kBq/m2. 137Cs (kilo-Becquerel per square meter[kBq/m], Cesium [Cs]) for stillbirths and congenital malformations in Germany and Finland as well as relatively increased
stillbirth proportions across Europe were also seen after
Chernobyl.[26,31,51]

Acknowledgments
The authors thank the Editors of ‘Epidemiology’ and ‘Medicine’for general support and 6 reviewers for detailed critical andconstructive suggestions on earlier drafts. The authors also thankShinobu Katsuragi for her valuable moderation and translationthat supported and greatly facilitated the communication
between the Japanese authors and the German author.

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