Radiation and Environmental Biophysics

, Volume 50, Issue 1, pp 21–35 | Cite as

Multi-model inference of adult and childhood leukaemia excess relative risks based on the Japanese A-bomb survivors mortality data (1950–2000)

Original Paper

Abstract

Some relatively new issues that augment the usual practice of ignoring model uncertainty, when making inference about parameters of a specific model, are brought to the attention of the radiation protection community here. Nine recently published leukaemia risk models, developed with the Japanese A-bomb epidemiological mortality data, have been included in a model-averaging procedure so that the main conclusions do not depend on just one type of model or statistical test. The models have been centred here at various adult and young ages at exposure, for some short times since exposure, in order to obtain specially computed childhood Excess Relative Risks (ERR) with uncertainties that account for correlations in the fitted parameters associated with the ERR dose–response. The model-averaged ERR at 1 Sv was not found to be statistically significant for attained ages of 7 and 12 years but was statistically significant for attained ages of 17, 22 and 55 years. Consequently, such risks when applied to other situations, such as children in the vicinity of nuclear installations or in estimates of the proportion of childhood leukaemia incidence attributable to background radiation (i.e. low doses for young ages and short times since exposure), are only of very limited value, with uncertainty ranges that include zero risk. For example, assuming a total radiation dose to a 5-year-old child of 10 mSv and applying the model-averaged risk at 10 mSv for a 7-year-old exposed at 2 years of age would result in an ERR = 0.33, 95% CI: −0.51 to 1.22. One model (United Nations scientific committee on the effects of atomic radiation report. Volume 1. Annex A: epidemiological studies of radiation and cancer, United Nations, New York, 2006) weighted model-averaged risks of leukaemia most strongly by half of the total unity weighting and is recommended for application in future leukaemia risk assessments that continue to ignore model uncertainty. However, on the basis of the analysis presented here, it is generally recommended to take model uncertainty into account in future risk analyses.

References

  1. Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Caski F (eds) Proceedings of the second international symposium on information theory. Budapest, Hungary, Akademiai Kiado, pp 267–281Google Scholar
  2. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723MATHCrossRefMathSciNetADSGoogle Scholar
  3. Burnham KP, Anderson DR (1998) Model selection and inference: a practical information-theoretic approach. Springer, New YorkMATHGoogle Scholar
  4. Burnham KP, Anderson DR (2002) Model selection and multimodel inference, 2nd edn. Springer, New YorkMATHGoogle Scholar
  5. Burnham KP, Anderson DR (2004) Multimodel inference. Understanding AIC and BIC in model selection. Sociol Meth Res 33(2): 261–304, see also Workshop on Model Selection, Amsterdam http://www2.fmg.uva.nl/modelselection/. Accessed March 2010
  6. Cardis E, Vrijheid M, Blettner M, Gilbert E, Hakama M, Hill C, Howe G, Kaldor J, Muirhead CR, Schubauer-Berigan M, Yoshimura T, Bermann F, Cowper G, Fix J, Hacker C, Heinmiller B, Marshall M, Thierry-Chef I, Utterback D, Ahn Y-O, Amoros E, Ashmore P, Auvinen A, Bae J-M, Bernar Solano J, Biau A, Combalot E, Deboodt P, Diez Sacristan A, Eklof M, Engels H, Engholm G, Gulis G, Habib R, Holan K, Hyvonen H, Kerekes A, Kurtinaitis J, Malker H, Martuzzi M, Mastauskas A, Monnet A, Moser M, Pearce MS, Richardson DB, Rodriguez-Artalejo F, Rogel A, Tardy H, Telle-Lamberton M, Turai I, Usel M, Veress K (2005) Risk of cancer after low doses of ionising radiation: retrospective cohort study in 15 countries. BMJ 331:77–82CrossRefGoogle Scholar
  7. Chatfield C (1995) Model uncertainty, data mining and statistical inference (with discussion). J R Stat Soc Ser A 158:419–466CrossRefGoogle Scholar
  8. Claeskens G, Hjort NL (2008) Model selection and model averaging. Cambridge University Press, CambridgeMATHGoogle Scholar
  9. Daniels RD, Schubauer-Berigan MK (2010) A meta-analysis of leukaemia risk from protracted exposures to low-dose gamma radiation. Accepted for publication in OEMGoogle Scholar
  10. Harrell FE Jr (2001) Regression modeling strategies: with applications to linear models, logistic regression and survival analysis. Springer Series in StatisticsGoogle Scholar
  11. Hoeting JA, Madigan D, Raftery AE, Volinsky CT (1999) Bayesian model averaging: a tutorial. Stat Sci 14(4):382–417MATHCrossRefMathSciNetGoogle Scholar
  12. Kaatsch P, Spix C, Schulze-Rath R, Schmiedel S, Blettner M (2008) Leukaemia in young children living in the vicinity of German nuclear power plants. Int J Cancer 122:721–726CrossRefGoogle Scholar
  13. Laurier D, Jacob S, Bernier MO, Leuraud K, Metz C, Samson E, Laloi P (2008) Epidemiological studies of leukaemia in children and young adults around nuclear facilities: A critical review. Radiat Prot Dosim 132:182–190CrossRefGoogle Scholar
  14. Little MP, Hoel DG, Molitor J Boice JD Jr, Wakeford R, Muirhead CR (2008) New models for evaluation of radiation-induced lifetime cancer risk and its uncertainty employed in the UNSCEAR 2006 report. Radiat Res 169:660–676Google Scholar
  15. Little MP, Wakeford R, Kendall GM (2009) Updated estimates of the proportion of childhood leukaemia incidence in Great Britain that may be caused by natural background ionizing radiation. J Radiol Prot 29:467–482CrossRefGoogle Scholar
  16. Pierce DA, Stram DO, Vaeth M (1990) Allowing for random errors in radiation dose estimates for the atomic bomb survivor data. Radiat Res 123:275–284CrossRefGoogle Scholar
  17. Pierce DA, Vaeth M, Cologne J (2008) Allowance for random dose estimation errors in atomic bomb survivor studies: a revision. Radiat Res 170:118–126CrossRefGoogle Scholar
  18. Posada D, Buckley TR (2004) Model selection and model averaging in phylogenetics: advantages of Akaike information criterion and baysian approaches over likelihood ration tests. Syst Biol 53(5):793–808CrossRefGoogle Scholar
  19. Preston DL, Lubin JH, Pierce DA (1993) Epicure user`s guide. HiroSoft International Corp, SeattleGoogle Scholar
  20. Preston DL, Shimizu Y, Pierce DA, Suyama A, Mabuchi K (2003) Studies of the mortality of atomic bomb survivors. Report 13: solid cancer and noncancer disease mortality: 1950–1997. Radiat Res 160: 381–407Google Scholar
  21. Preston DL, Pierce DA, Shimizu Y, Cullings HM, Fujita S, Funamoto S, Kodama K (2004) Effects of recent changes in atomic bomb survivors dosimetry on cancer mortality risk estimates. Radiat Res 162:377–389CrossRefGoogle Scholar
  22. Preston DL, Ron E, Tokuoka S, Funamoto S, Nishi N, Soda M, Mabuchi K, Kodama K (2007) Solid cancer incidence in atomic bomb survivors: 1958–1998. Radiat Res 168:1–64CrossRefGoogle Scholar
  23. Richardson D, Sugiyama H, Nishi N, Sakata R, Shimizu Y, Grant EJ, Soda M, Hsu WL, Suyama A, Kodamae K, Kasagi F (2009) Ionizing radiation and leukemia mortality among Japanese atomic bomb survivors, 1950–2000. Radiat Res 172:368–382CrossRefGoogle Scholar
  24. Rühm W, Walsh L (2007) Current risk estimates based on the A-bomb survivors data inconsistent with ICRP recommendations on the neutron weighting factor. Radiat Prot Dosim 126:423–431CrossRefGoogle Scholar
  25. Schneider U, Walsh L (2009) Cancer risk above 1 Gy and the impact for space radiation protection. Adv Space Res 44:202–209CrossRefADSGoogle Scholar
  26. United Nations Effects of ionizing radiation (2006) United Nations scientific committee on the effects of atomic radiation UNSCEAR 2006 report. Volume 1. Annex A: epidemiological studies of radiation and cancer. United Nations, New York (2008)Google Scholar
  27. United States National Research Council, Committee to Assess Health Risks from Exposure to Low Levels of Ionizing Radiation (2006) Health risks from exposure to low levels of ionizing radiation: BEIR VII –phase 2. United States national academy of sciences. National Academy Press, WashingtonGoogle Scholar
  28. Wakeford R, Kendall GM, Little MP (2009) The proportion of childhood leukaemia incidence in Great Britain that may be caused by natural background ionizing radiation. Leukemia 23:770–776CrossRefGoogle Scholar
  29. Walsh L (2007) A short review of model selection techniques for radiation epidemiology. Radiat Environ Biophys 46:205–213CrossRefGoogle Scholar
  30. Walsh L (2010) Radiation protection in occupational and environmental settings. Accepted for publication in OEMGoogle Scholar
  31. Walsh L, Jacob P, Kaiser JC (2009) Radiation risk modeling of thyroid cancer with special emphasis on the chernobyl epidemiological data. Radiat Res 172:509–518CrossRefGoogle Scholar
  32. Young R, Kerr GD (eds) (2005) DS02: Reassessment of the atomic bomb radiation dosimetry for Hiroshima and Nagasaki, dosimetry system 2002, DS02, vol 1, 2. Radiation Effects Research Foundation, HiroshimaGoogle Scholar
  33. Zhang Z, Townsend JP (2009) Maximum-likelihood model averaging to profile clustering of site types across discrete linear sequences. PLOS Comp Biol 5(6):e1000421CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Department “Radiation Protection and Health”Federal Office for Radiation ProtectionOberschleissheimGermany
  2. 2.Institute of Radiation Protection, Helmholtz Zentrum MünchenOberschleissheimGermany

Personalised recommendations