Skip to main content

Advertisement

SpringerLink
Log in

Radio-biologically motivated modeling of radiation risks of mortality from ischemic heart diseases in the Canadian fluoroscopy cohort study

  • Original Article
  • Published:
Radiation and Environmental Biophysics Aims and scope Submit manuscript

Abstract

Recent analyses of the Canadian fluoroscopy cohort study reported significantly increased radiation risks of mortality from ischemic heart diseases (IHD) with a linear dose–response adjusted for dose fractionation. This cohort includes 63,707 tuberculosis patients from Canada who were exposed to low-to-moderate dose fractionated X-rays in 1930s–1950s and were followed-up for death from non-cancer causes during 1950–1987. In the current analysis, we scrutinized the assumption of linearity by analyzing a series of radio-biologically motivated nonlinear dose–response models to get a better understanding of the impact of radiation damage on IHD. The models were weighted according to their quality of fit and were then mathematically superposed applying the multi-model inference (MMI) technique. Our results indicated an essentially linear dose–response relationship for IHD mortality at low and medium doses and a supra-linear relationship at higher doses (> 1.5 Gy). At 5 Gy, the estimated radiation risks were fivefold higher compared to the linear no-threshold (LNT) model. This is the largest study of patients exposed to fractionated low-to-moderate doses of radiation. Our analyses confirm previously reported significantly increased radiation risks of IHD from doses similar to those from diagnostic radiation procedures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. In this study, mortality follow-up was conducted through record linkage with the Canadian Mortality Database using probabilistic linkage. The term "record linkage" refers to the process of comparing two or more records which contain identifying information to determine whether those records refer to the same individual enrolled in a cohort study. In the absence of personal identifying numbers which would allow definitive linkage to mortality outcomes (social insurance numbers were not introduced in Canada until 1964 while the study is based on the medical records for patients first admitted for treatment during 1930–1952), study investigators used a combination of identifying items such as surname; given name; day, month, and year of birth to conduct a linkage. Each pair of linked records was assigned a probabilistic weight which depends on the likelihood of the link being true (Howe 1998). A cutoff value was then used to separate possibly true links with higher linkage weights from those less likely to be true. A higher cutoff point for the internal dose–response analysis was used to avoid dilution of any association due to the presence of false positives (i.e., false linkages); the change in cutoff would not be expected to bias estimates of relative risk. Under quite general conditions, potentially substantial bias could be introduced by using absolute risk models. Therefore, analyses with EAR models should not be performed with the CFCS data because the linkage of the cohort with the mortality registry is probabilistic which could affect absolute mortality but not relative mortality models (Zablotska et al. 2014).

  2. Co-factor(s) Z, such as sex, age at first exposure or dose fractionation are often referred to in radiation epidemiology as risk effect modifiers because they are factors that modulate the main central risk per unit dose estimate.

  3. Duration of fluoroscopic procedures, respectively, fluoroscopy screenings refers to the timespan over which fluoroscopic examinations were provided.

  4. It is noted that the two-line spline model is nested with the LNT model. This can be seen in Figure S1 of the Online Resource: The two-line spline model is nested with the LTH model and the latter is nested with the LNT model (in general, Model A is nested in Model B if the parameters in Model A are a subset of the parameters in Model B). The reason why the two-line spline model was nonetheless included into Occam’s group is explained on pages 15 and 16 of the Online Resource.

References

  • Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Caski F (eds). In: Proceedings of the second international symposium on information theory. Akademiai Kiado, Budapest, pp 267–281

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723

    ADS  MathSciNet  MATH  Google Scholar 

  • Azizova TV, Grigoryeva ES, Haylock RG, Pikulina MV, Moseeva MB (2015a) Ischaemic heart disease incidence and mortality in an extended cohort of Mayak workers first employed in 1948–1982. Br J Radiol 88:20150169

    Google Scholar 

  • Azizova TV, Grigorieva ES, Hunter N, Pikulina MV, Moseeva MB (2015b) Risk of mortality from circulatory diseases in Mayak workers cohort following occupational radiation exposure. J Radiol Prot 35:517–538

    Google Scholar 

  • Brain P, Cousens R (1989) An equation to describe dose responses where there is stimulation of growth at low doses. Weed Res 29:93–96

    Google Scholar 

  • Burnham KP, Anderson DR (2002) Model selection and multimodel inference, 2nd edn. Springer, New York

    MATH  Google Scholar 

  • Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM (2006) Measurement error in nonlinear models: a modern perspective. CRC monographs on statistics and applied probability, 2nd edn. Chapman and Hall, Boca Raton

    Google Scholar 

  • Cedergreen N, Ritz C, Streibig JC (2005) Improved empirical models describing hormesis. Environ Toxicol Chem 24:3166–3172

    Google Scholar 

  • Claeskens G, Hjort NL (2008) Model selection and model averaging. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  • Darby SC, Cutter DJ, Boerma M, Constine LS, Fajardo LF, Kodama K, Mabuchi K, Marks LB, Mettler FA, Pierce LJ, Trott KR, Yeh ET, Shore RE (2010) Radiation-related heart disease: current knowledge and future prospects. Int J Radiat Oncol Biol Phys 76:656–665

    Google Scholar 

  • Darby SC, Ewertz M, McGale P, Bennet AM, Blom-Goldman U, Brønnum D, Correa C, Cutter D, Gagliardi G, Gigante B, Jensen MB, Nisbet A, Peto R, Rahimi K, Taylor C, Hall P (2013) Risk of ischemic heart disease in women after radiotherapy for breast cancer. N Engl J Med 368:987–998

    Google Scholar 

  • Ebrahimian TG, Beugnies L, Surette J, Priest N, Gueguen Y, Gloaguen C, Benderitter M, Jourdain JR, Tack K (2018) Chronic exposure to external low-dose gamma radiation induces an increase in anti-inflammatory and anti-oxidative parameters resulting in atherosclerotic plaque size reduction in ApoE–/– mice. Radiat Res 189:187–196

    ADS  Google Scholar 

  • Frey B, Hehlgans S, Rödel F, Gaipl US (2015) Modulation of inflammation by low and high doses of ionizing radiation: implications for benign and malign diseases. Cancer Lett 368:230–237

    Google Scholar 

  • Ghobadi G, van der Veen S, Bartelds B, de Boer RA, Dickinson MG, de Jong JR, Faber H, Niemantsverdriet M, Brandenburg S, Berger RM, Langendijk JA, Coppes RP, van Luijk P (2012) Physiological interaction of heart and lung in thoracic irradiation. Int J Radiat Oncol Biol Phys 84(5):e639–e646

    Google Scholar 

  • Gillies M, Richardson DB, Cardis E, Daniels RD, O’Hagan JA, Haylock R, Laurier D, Leuraud K, Moissonnier M, Schubauer-Berigan MK, Thierry-Chef I, Kesminiene A (2017) Mortality from circulatory diseases and other non-cancer outcomes among nuclear workers in France, the United Kingdom and the United States (INWORKS). Radiat Res 188:276–290

    ADS  Google Scholar 

  • Grant EJ, Brenner A, Sugiyama H, Sakata R, Sadakane A, Utada M, Cahoon EK, Milder CM, Soda M, Cullings HM, Preston DL, Mabuchi K, Ozasa K (2017) Solid cancer incidence among the life span study of atomic bomb survivors: 1958–2009. Radiat Res 187:513–537

    ADS  Google Scholar 

  • Hoeting JA, Madigan D, Raftery AE, Volinsky CT (1999) Bayesian model averaging: a tutorial. Stat Sci 14:382–417

    MathSciNet  MATH  Google Scholar 

  • Hoffmann S, Rage E, Laurier D, Laroche P, Guihenneuc C, Ancelet S (2017) Accounting for Berkson and classical measurement error in radon exposure using a Bayesian structural approach in the analysis of lung cancer mortality in the French Cohort of Uranium Miners. Radiat Res 187(2):196–209

    ADS  Google Scholar 

  • Hoving S, Heeneman S, Gijbels MJ, te Poele JA, Russell NS, Daemen MJ, Stewart FA (2008) Single-dose and fractionated irradiation promote initiation and progression of atherosclerosis and induce an inflammatory plaque phenotype in ApoE–/– mice. Int J Radiat Oncol Biol Phys 71(3):848–857

    Google Scholar 

  • Howe GR (1998) Use of computerized record linkage in cohort studies. Epidemiol Rev 20:112–121

    Google Scholar 

  • Howe GR, McLaughlin J (1996) Breast cancer mortality between 1950 and 1987 after exposure to fractionated moderate-dose-rate ionizing radiation in the Canadian Fluoroscopy Cohort Study and a comparison with breast cancer mortality in the Atomic Bomb Survivors Study. Radiat Res 145:694–707

    ADS  Google Scholar 

  • HPA (2010) Circulatory disease risk. Report of the Independent Advisory Group on Ionising Radiation. Health Protection Agency 2010; ISBN 978-0-85951-676-1

  • ICRP (2005) Low-dose extrapolation of radiation-related cancer risk. Ann ICRP 35:1–140

    Google Scholar 

  • Kaiser JC, Walsh L (2013) Independent analysis of the radiation risk for leukaemia in children and adults with mortality data (1950–2003) of Japanese A-bomb survivors. Radiat Environ Biophys 52:17–27

    Google Scholar 

  • Kreuzer M, Auvinen A, Cardis E, Hall J, Jourdain JR, Laurier D, Little MP, Peters A, Raj K, Russell NS, Tapio S, Zhang W, Gomolka M (2015) Low-dose ionising radiation and cardiovascular diseases–strategies for molecular epidemiological studies in Europe. Mutat Res Rev Mutat Res 764:90–100

    Google Scholar 

  • Kreuzer M, Sobotzki C, Schnelzer M, Fenske N (2018) Factors modifying the radon-related lung cancer risk at low exposures and exposure rates among German uranium miners. Radiat Res 189:165–176

    ADS  Google Scholar 

  • Land CE, Kwon D, Hoffman FO, Moroz B, Drozdovitch V, Bouville A, Beck H, Luckyanov N, Weinstock RM, Simon SL (2015) Accounting for shared and unshared dosimetric uncertainties in the dose response for ultrasound-detected thyroid nodules after exposure to radioactive fallout. Radiat Res 183(2):159–173

    ADS  Google Scholar 

  • Le Gallic C, Phalente Y, Manens L, Dublineau I, Benderitter M, Gueguen Y, Lehoux S, Ebrahimian TG (2015) Chronic internal exposure to low dose 137Cs induces positive impact on the stability of atherosclerotic plaques by reducing inflammation in ApoE–/– mice. PLoS One 10:e0128539

    Google Scholar 

  • Little MP (2016) Radiation and circulatory disease. Mutat Res 770(Pt B):299–318

    Google Scholar 

  • Little MP, Azizova TV, Bazyka D, Bouffler SD, Cardis E, Chekin S, Chumak VV, Cucinotta FA, de Vathaire F, Hall P, Harrison JD, Hildebrandt G, Ivanov V, Kashcheev VV, Klymenko SV, Kreuzer M, Laurent O, Ozasa K, Schneider T, Tapio S, Taylor AM, Tzoulaki I, Vandoolaeghe WL, Wakeford R, Zablotska LB, Zhang W, Lipshultz SE (2012) Systematic review and metaanalysis of circulatory disease from exposure to low-level ionizing radiation and estimates of potential population mortality risks. Environ Health Perspect 120:1503–1511

    Google Scholar 

  • Little MP, Kukush AG, Masiuk SV, Shklyar S, Carroll RJ, Lubin JH, Kwon D, Brenner AV, Tronko MD, Mabuchi K, Bogdanova TI, Hatch M, Zablotska LB, Tereshchenko VP, Ostroumova E, Bouville AC, Drozdovitch V, Chepurny MI, Kovgan LN, Simon SL, Shpak VM, Likhtarev IA (2014) Impact of uncertainties in exposure assessment on estimates of thyroid cancer risk among Ukrainian children and adolescents exposed from the Chernobyl accident. PLoS One 9(1):e85723

    ADS  Google Scholar 

  • Little MP, Kwon D, Zablotska LB, Brenner AV, Cahoon EK, Rozhko AV, Polyanskaya ON, Minenko VF, Golovanov I, Bouville A, Drozdovitch V (2015) Impact of uncertainties in exposure assessment on thyroid cancer risk among persons in Belarus exposed as children or adolescents due to the Chernobyl accident. PLoS One 10(10):e0139826

    Google Scholar 

  • Madigan D, Raftery AE (1994) Model selection and accounting for model uncertainty in graphical models using Occam’s window. J Am Statist Assoc 89:1535–1546

    MATH  Google Scholar 

  • Mancuso M, Pasquali E, Braga-Tanaka I 3rd, Tanaka S, Pannicelli A, Giardullo P, Pazzaglia S, Tapio S, Atkinson MJ, Saran A (2015) Acceleration of atherogenesis in ApoE–/– mice exposed to acute or low-dose-rate ionizing radiation. Oncotarget 6:31263–31271

    Google Scholar 

  • Mathias D, Mitchel RE, Barclay M, Wyatt H, Bugden M, Priest ND, Whitman SC, Scholz M, Hildebrandt G, Kamprad M, Glasow A (2015) Low-dose irradiation affects expression of inflammatory markers in the heart of ApoE–/– mice. PLoS One 10(3):e0119661

    Google Scholar 

  • Mitchel RE, Burchart P, Wyatt H (2007) Fractionated, low-dose-rate ionizing radiation exposure and chronic ulcerative dermatitis in normal and Trp53 heterozygous C57BL/6 mice. Radiat Res 168(6):716–724

    ADS  Google Scholar 

  • Mitchel RE, Hasu M, Bugden M, Wyatt H, Little MP, Gola A, Hildebrandt G, Priest ND, Whitman SC (2011) Low-dose radiation exposure and atherosclerosis in ApoE–/– mice. Radiat Res 175:665–676

    ADS  Google Scholar 

  • Mitchel RE, Hasu M, Bugden M, Wyatt H, Hildebrandt G, Chen YX, Priest ND, Whitman SC (2013) Low-dose radiation exposure and protection against atherosclerosis in ApoE–/– mice: the influence of P53 heterozygosity. Radiat Res 179:190–199

    ADS  Google Scholar 

  • Moseeva MB, Azizova TV, Grigoryeva ES, Haylock R (2014) Risks of circulatory diseases among Mayak PA workers with radiation doses estimated using the improved Mayak Worker Dosimetry System 2008. Radiat Environ Biophys 53:469–477

    Google Scholar 

  • NCRP (2018) Implications of recent epidemiologic studies for the linear nonthreshold model and radiation protection. Commentary No. 27. National Council on Radiation Protection and Measurements, Bethesda

    Google Scholar 

  • Noble RB, Bailer AJ, Park R (2009) Model-averaged benchmark concentration estimates for continuous response data arising from epidemiological studies. Risk Anal 29:558–564

    Google Scholar 

  • Ozasa K, Shimizu Y, Suyama A, Kasagi F, Soda M, Grant EJ, Sakata R, Sugiyama H, Kodama K (2012) Studies of the mortality of atomic bomb survivors, Report 14, 1950–2003: an overview of cancer and noncancer diseases. Radiat Res 177:229–243

    ADS  Google Scholar 

  • Ozasa K, Takahashi I, Grant EJ, Kodama K (2017) Cardiovascular disease among atomic bomb survivors. Int J Radiat Biol 93(10):1145–1150

    Google Scholar 

  • Rödel F, Frey B, Gaipl U, Keilholz L, Fournier C, Manda K, Schöllnberger H, Hildebrandt G, Rödel C (2012a) Modulation of inflammatory immune reactions by low-dose ionizing radiation: molecular mechanisms and clinical application. Curr Med Chem 19:1741–1750

    Google Scholar 

  • Rödel F, Frey B, Manda K, Hildebrandt G, Hehlgans S, Keilholz L, Seegenschmiedt MH, Gaipl US, Rödel C (2012b) Immunomodulatory properties and molecular effects in inflammatory diseases of low-dose X-irradiation. Front Oncol 2:1–9

    Google Scholar 

  • Schneider U, Ernst M, Hartmann M (2017) The dose-response relationship for cardiovascular disease is not necessarily linear. Radiat Oncol 12:74

    Google Scholar 

  • Schöllnberger H, Kaiser JC, Jacob P, Walsh L (2012) Dose-responses from multi-model inference for the non-cancer disease mortality of atomic bomb survivors. Radiat Environ Biophys 51:165–178

    Google Scholar 

  • Schöllnberger H, Eidemüller M, Cullings HM, Simonetto C, Neff F, Kaiser JC (2018) Dose-responses for mortality from cerebrovascular and heart diseases in atomic bomb survivors: 1950–2003. Radiat Environ Biophys 57(1):17–29

    Google Scholar 

  • Shimizu Y, Kodama K, Nishi N, Kasagi F, Suyama A, Soda M, Grant EJ, Sugiyama H, Sakata R, Moriwaki H, Hayashi M, Konda M, Shore RE (2010) Radiation exposure and circulatory disease risk: Hiroshima and Nagasaki atomic bomb survivor data, 1950–2003. Brit Med J 340:b5349

    Google Scholar 

  • Shore R, Walsh L, Azizova T, Rühm W (2017) Risk of solid cancer in low dose-rate radiation epidemiological studies and the dose-rate effectiveness factor. Int J Radiat Biol 93:1064–1078

    Google Scholar 

  • Shore RE, Beck HL, Boice JD Jr, Caffrey EA, Davis S, Grogan HA, Mettler FA Jr, Preston RJ, Till JE, Wakeford R, Walsh L, Dauer LT (2018) Implications of recent epidemiologic studies for the linear nonthreshold model and radiation protection. J Radiol Prot 38:1217–1233

    Google Scholar 

  • Shore RE, Beck HL, Boice JD Jr, Caffrey EA, Davis S, Grogan HA, Mettler FA Jr, Preston RJ, Till JE, Wakeford R, Walsh L, Dauer LT (2019) Recent epidemiologic studies and the linear no-threshold model for radiation protection—considerations regarding NCRP commentary 27. Health Phys 116:235–246

    Google Scholar 

  • Simonetto C, Azizova TV, Grigoryeva ES, Kaiser JC, Schöllnberger H, Eidemüller M (2014) Ischemic heart disease in workers at Mayak PA: latency of incidence risk after radiation exposure. PLoS One 9:e96309

    ADS  Google Scholar 

  • Simonetto C, Schöllnberger H, Azizova TV, Grigoryeva ES, Pikulina MV, Eidemüller M (2015) Cerebrovascular diseases in workers at Mayak PA: The difference in radiation risk between incidence and mortality. PLoS One 10:e0125904

    Google Scholar 

  • Stewart FA, Heeneman S, Te Poele J, Kruse J, Russell NS, Gijbels M, Daemen M (2006) Ionizing radiation accelerates the development of atherosclerotic lesions in ApoE–/– mice and predisposes to an inflammatory plaque phenotype prone to hemorrhage. Am J Pathol 168:649–658

    Google Scholar 

  • Takahashi I, Shimizu Y, Grant EJ, Cologne J, Ozasa K, Kodama K (2017) Heart disease mortality in the Life Span Study, 1950–2008. Radiat Res 187:319–332

    ADS  Google Scholar 

  • Tran V, Zablotska LB, Brenner AV, Little MP (2017) Radiation-associated circulatory disease mortality in a pooled analysis of 77,275 patients from the Massachusetts and Canadian tuberculosis fluoroscopy cohorts. Sci Rep 7:44147

    ADS  Google Scholar 

  • UNSCEAR (2000) Sources and Effects of Ionizing Radiation. United Nations Scientific Committee on the Effects of Atomic Radiation. UNSCEAR 2000 Report to the General Assembly, with scientific annexes. Volume II: Effects http://www.unscear.org/docs/publications/2000/UNSCEAR_2000_Report_Vol.II.pdf

  • Walsh L (2007) A short review of model selection techniques for radiation epidemiology. Radiat Environ Biophys 46:205–213

    Google Scholar 

  • Walsh L, Kaiser JC (2011) Multi-model inference of adult and childhood leukaemia excess relative risks based on the Japanese A-bomb survivors mortality data (1950–2000). Radiat Environ Biophys 50:21–35

    Google Scholar 

  • Williams LK, Csaki LS, Cantor RM, Reue K, Lawson GW (2012) Ulcerative dermatitis in C57BL/6 mice exhibits an oxidative stress response consistent with normal wound healing. Comp Med 62(3):166–171

    Google Scholar 

  • World Health Organization (2013) The top 10 causes of death. http://who.int/mediacentre/factsheets/fs310/en/index.html. Accessed 24 Nov 2014

  • Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, McQueen M, Budaj A, Pais P, Varigos J, Lisheng L, INTERHEART Study Investigators (2004) Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 364:937–952

    Google Scholar 

  • Zablotska LB, Little MP, Cornett RJ (2014) Potential increased risk of ischemic heart disease mortality with significant dose fractionation in the Canadian Fluoroscopy Cohort Study. Am J Epidemiol 179(1):120–131

    Google Scholar 

Download references

Acknowledgements

This work was supported by a project from the Federal Office for Radiation Protection (BfS) (contract no. 3615S42221). The project has also received funding from the Euratom research and training program 2014-2018 under grant agreement No 755523 (MEDIRAD). Dr. Zablotska’s work was supported by the National Cancer Institute of the National Institutes of Health (award numbers R03CA188614 and R01CA197422). We thank Dr. Michaela Kreuzer (BfS) for valuable comments related to a project report for BfS and for her support of the BfS project mentioned above. We would also like to thank Dr. Peter Jacob (RADRISK, D-83727 Schliersee) and Dr. Axel Böttger at the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) and the BMU for enabling the BfS project. We are very grateful to the reviewers for taking the time to perform the reviews and for their valuable comments.

Author information

Authors and Affiliations

  1. Department of Radiation Sciences, Institute of Radiation Medicine, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany

    Helmut Schöllnberger, Jan Christian Kaiser & Markus Eidemüller

  2. Division UR-Environmental Radioactivity, Federal Office for Radiation Protection, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany

    Helmut Schöllnberger

  3. Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, San Francisco, CA, 94158, USA

    Lydia B. Zablotska

Authors
  1. Helmut Schöllnberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jan Christian Kaiser
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Markus Eidemüller
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lydia B. Zablotska
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Helmut Schöllnberger.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

411_2019_819_MOESM1_ESM.pdf

The Online Resource provides in Table S1 characteristics of the Canadian Fluoroscopy Cohort Study Data. Subsequently, the baseline model from Simonetto et al. (2014) that had been developed for the Mayak workers cohort is presented. This is followed by the baseline model applied in the present study. Page 7 gives the mathematical form of all dose–response models that were tested in the present study. The next section provides a detailed explanation how the AIC-weights are calculated for both, the sparse and rich model approaches. It supplies an equation that was used to calculate the normalized AIC-weights given in Table 3 (main text). Page 10 contains the section “Software” and gives a brief introduction to the software package used for the analyses. Figure S1 provides the number of model parameters for the applied dose–response models and relation between the models regarding their nestedness. Table S2 supplies model parameters (baseline and radiation-associated), maximum likelihood estimates and Wald-type standard errors for the four final non-nested models that were used for MMI (sparse model approach). The next section gives a detailed description of how the model selection was performed according to the sparse model approach. This is followed by Table S3 on page 17. This table is an extension of Table 3 (main text) and provides the results of fitting the dose–response models from Fig. 1 as ERR models to the CFCS data. Among other information, the final deviance values are provided together with the AIC-values, normalized and bilateral AIC-weights. All of this information is given for the sparse and the rich model approaches. Figure S2 shows the baseline cases as predicted by the ERR-LNT model versus attained age with the secular trend together with crude rates. The references are provided on page 21. (DOC 235 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schöllnberger, H., Kaiser, J.C., Eidemüller, M. et al. Radio-biologically motivated modeling of radiation risks of mortality from ischemic heart diseases in the Canadian fluoroscopy cohort study. Radiat Environ Biophys 59, 63–78 (2020). https://doi.org/10.1007/s00411-019-00819-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00411-019-00819-9

Keywords

Search

Navigation