Skip to main content

Response to the “Letter to the Editor” by Alfred Körblein, “Short term increase in low birthweight babies after Fukushima”

Peer Review reports

Dear Editors,

We wish to thank Alfred Körblein for raising methodological and practical issues as to how to adequately assess possible changes in the trend/s of low birth weight proportions in Japan before and after the Fukushima Daiichi Nuclear Power Plant (FDNPP) accidents [1]. Alfred Körblein’s letter provides an opportunity to explaining in detail several technical and crucial aspects of our approach to data analysis [2].

In his Fig. 1 (upper panel), Körblein fits a 5th degree polynomial logistic regression model to the combined low birth weight data of the five moderately and five highly contaminated prefectures Chiba, Fukushima, Ibaraki, Iwate, Kanagawa, Miyagi, Saitama, Tochigi, Tokyo, and Yamagata. Körblein reports a jump in this trend in 2012 with an odds ratio (OR) of 1.019, 95%-confidence interval (0.994, 1.044), p-value 0.152, which we confirm in principle. However, whereas Körblein employs the t-distribution for computing p-values in this example with 24 data points, 7 parameters (intercept, jump2012, t = time, t2, t3, t4, t5), and 17 degrees of freedom, we consider the Wald-Chi2 a more appropriate and less conservative choice. The Wald-Chi2 (with optional adjustment for overdispersion) is the default distribution of logistic regression in SAS.

Fig. 1

Low birth weight (LBW) proportion in 10 moderately or highly contaminated prefectures Chiba, Fukushima, Ibaraki, Iwate, Kanagawa, Miyagi, Saitama, Tochigi, Tokyo, and Yamagata 1995 to 2018; 4th degree polynomial logistic regression trends allowing for jumps from 2012 onward; thick gray line: jump 2012 to 2018 OR 1.027, (1.004, 1.051), p-value 0.0203; thin black line: jump 2012 to 2013 OR 1.027, (1.003, 1.052), p-value 0.0244 and jump 2014 to 2018 OR 1.024, (0.991, 1.059), p-value 0.1547

Körblein’s approach is motivated by the truism that a 5th degree polynomial fits the data better than a 4th degree polynomial. However, fit in terms of deviance is not the only important component in this context. If the polynomial degree is increased, variance inflation, over-fitting, and over-adjustment may become problematic. In order to illustrate this, consider the extreme case of a 23rd degree polynomial for the 24 data points in our examples. Such a polynomial would theoretically pass through all given data points, but it would not be possible to compare or mutually test segments of the regression line [3]. Principles that should guide the selection of an appropriate degree of the polynomial include parsimony and the precision of the regression coefficients. The Wald Chi2 p-values for t4 and t5 of the 5th degree polynomial are 0.0538 and 0.1331, respectively. By contrast, p for t4 in a 4th degree polynomial is only 0.0003, i.e. a more parsimonious polynomial yields a more precise estimate of t4. These considerations apply in principle to all four scenarios in Fig. 4 of our paper [2] as per Table 1. Since 4th degree polynomials are more parsimonious and yield more precise estimates (due to lesser variance inflation) of the regression coefficients when compared to 5th degree polynomials, we recommend the use of 4th degree polynomials in this context.

Table 1 P-values for t4 versus t4 and t5 of the 4th and the 5th degree polynomial logistic trend models, respectively; A: Japan; B: Japan excluding 10 exposed prefectures; C: 5 moderately exposed prefectures (Yamagata, Saitama, Tokyo, Kanagawa, Chiba); D: 5 highly exposed prefectures (Fukushima, Miyagi, Ibaraki, Tochigi, Iwate)

Körblein’s Fig. 1 (lower panel) is scientifically unsound. Since the 5th degree polynomial provides a superior fit and the estimated jump in 2012 is ‘insignificant’ (p-value > 0.05), Körblein tests a jump restricted to the years 2012 and 2013. This approach assumes that the environmental exposure situation after 2013 is the same as before the FDNPP accidents. We consider Körblein’s amalgamating the periods 1985 to 2011 and 2014 to 2018 in order to obtain a baseline trend illogical since the FDNPP accidents released long-lived radioactive elements. This approach also ignores that radiological accidents have been followed by long-term radiation-induced genetic effects [4,5,6,7,8,9,10,11,12,13,14]. Using a 4th degree polynomial in place of a 5th degree polynomial for modeling the low birth weight proportion in the 10 moderately or highly contaminated prefectures reveals a significant jump in 2012 with OR 1.027, (1.004, 1.051), p-value 0.0203, see Fig. 1. The division of the period 2012 to 2018 into two periods, 2012 to 2013 and 2014 to 2018 yields a somewhat weaker and less precisely estimated effect in the second period compared to the former: OR 1.024, (0.991, 1.059), p-value 0.1547. This reduced effect in a later period is compatible with the decrease in exposure due to radioactive decay and decontamination [13].

While Körblein’s statement ‘the significant result for the shift in LBW proportion obtained with model 1 is driven by the peak in 2012-2013’ is true in several selected scenarios within his framework, the p-value of > 0.05 for the jump from 2014 onward in Fig. 1 is certainly not evidence of absence of long-term genetic effects. This type of erroneous interpretation of p-values has frequently raised criticism in the past. A more recent critique has been published in Nature:Let’s be clear about what must stop: we should never conclude there is ‘no difference’ or ‘no association’ just because a P value is larger than a threshold such as 0.05. Neither should we conclude that two studies conflict because one had a statistically significant result and the other did not. These errors waste research efforts and misinform policy decisions’ [15].

In summary, Körblein’s conclusions hereunder evolve from misinterpreted analysis:

  • An analysis of low birth weight (LBW) births in ten contaminated prefectures of Japan, 1995-2018, finds a statistically significant increase in the LBW proportion in 2012-2013, but no increase after 2013.

  • The claim by Scherb that their result is evidence of a genetic radiation effect is challenged by the present analysis.


Hagen Scherb and Keiji Hayashi

Availability of data and materials

The employed data has exclusively been published previously and/or it is contained in the Tables and in the Figures included in this paper.


95%-CI or (.,.):

95%-confidence interval


Fukushima Daiichi Nuclear Power Plant


Low birth weight


Odds ratio




Statistical Analysis System, software produced by SAS Institute Inc


  1. 1.

    Körblein A. Letter to the Editor: Short term increase in low birthweight babies after Fukushima. Environ Health. 2020; ENHE-D-20-00316.

  2. 2.

    Scherb H, Hayashi K. Spatiotemporal association of low birth weight with Cs-137 deposition at the prefecture level in Japan after the Fukushima nuclear power plant accidents: an analytical-ecologic epidemiological study. Environ Health. 2020;19(1):82.

    CAS  Article  Google Scholar 

  3. 3.

    Turner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, Cheng AC, Bero L, McKenzie JE. Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: a review. J Clin Epidemiol. 2020;122:1–11.

    Article  Google Scholar 

  4. 4.

    Scherb H, Weigelt E, Brüske-Hohlfeld I. European stillbirth proportions before and after the Chernobyl accident. Int J Epidemiol. 1999;28(5):932–40.

    CAS  Article  Google Scholar 

  5. 5.

    Scherb H, Weigelt E. Congenital malformation and stillbirth in Germany and Europe before and after the Chernobyl nuclear power plant accident. Environ Sci Pollut Res Spec Issue. 2003;1:117–25.

    Google Scholar 

  6. 6.

    Sperling K, Neitzel H, Scherb H. Evidence for an increase in trisomy 21 (Down syndrome) in Europe after the Chernobyl reactor accident. Genet Epidemiol. 2012;36(1):48–55.

    Article  Google Scholar 

  7. 7.

    Scherb H, Voigt K. The human sex odds at birth after the atmospheric atomic bomb tests, after Chernobyl, and in the vicinity of nuclear facilities. Environ Sci Pollut Res Int. 2011;18(5):697–707.

    CAS  Article  Google Scholar 

  8. 8.

    Grech V. The Chernobyl accident, the male to female ratio at birth and birth rates. Acta Med (Hradec Kralove). 2014;57(2):62–7.

    CAS  Article  Google Scholar 

  9. 9.

    Grech V. Births and male:female birth ratio in Scandinavia and the United Kingdom after the Windscale fire of October 1957. Int J Risk Saf Med. 2014;26(1):45–53.

    Article  Google Scholar 

  10. 10.

    Scherb H, Kusmierz R, Voigt K. Increased sex ratio in Russia and Cuba after Chernobyl: a radiological hypothesis. Environ Health. 2013;12:63.

    Article  Google Scholar 

  11. 11.

    Scherb H, Kusmierz R, Voigt K. Secondary sex ratio and trends in the associated gender-specific births near nuclear facilities in France and Germany: update of birth counts. Reprod Toxicol. 2019;89:159–67.

    CAS  Article  Google Scholar 

  12. 12.

    Scherb H, Mori K, Hayashi K. Comment on ‘Perinatal mortality after the Fukushima accident’. J Radiol Prot. 2019;39(2):647–9.

    Article  Google Scholar 

  13. 13.

    Hayama S-I, Tsuchiya M, Ochiai K, Nakiri S, Nakanishi S, Ishii N, Kato T, Tanaka A, Konno F, Kawamoto Y, et al. Small head size and delayed body weight growth in wild Japanese monkey fetuses after the Fukushima Daiichi nuclear disaster. Sci Rep. 2017;7(1):3528.

    Article  Google Scholar 

  14. 14.

    Korsakov AV, Geger EV, Lagerev DG, Pugach LI, Mousseau TA. De novo congenital malformation frequencies in children from the Bryansk region following the Chernobyl disaster (2000–2017). Heliyon. 2020;6(8):e04616.

    Article  Google Scholar 

  15. 15.

    Amrhein V, Greenland S, McShane B. Scientists rise up against statistical significance. Nature. 2019;567(7748):305–7.

    CAS  Article  Google Scholar 

Download references


We are most grateful to Victor Grech for detailed suggestions improving our initial draft.

Ethical approval and consent to participate

Not applicable. Ethics approval and consent to participate are not required and not necessary, since only publicly available data and previously published information is being used.


The authors declare that they have no funding for this study.

Author information




Both authors contributed equally to the Letter to the Editor. Both authors approved the final manuscript.

Corresponding author

Correspondence to Hagen Scherb.

Ethics declarations

Consent for publication

Not applicable. Only anonymous data is being used.

Competing interests

The authors declare that they have no conflicts of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Scherb, H., Hayashi, K. Response to the “Letter to the Editor” by Alfred Körblein, “Short term increase in low birthweight babies after Fukushima”. Environ Health 19, 125 (2020).

Download citation


  • Radiation-induced genetic effects
  • Scientific logic
  • Statistical modeling
  • Statistical inference