Title: Methodological problems in epidemiological data: the case of correlation between radon level and lung cancer

Authors: Joanna Reszczyńska; Maciej Pylak; Krzysztof W. Fornalski; S.J. Mortazavi; L. Dobrzyński

Addresses: National Centre for Nuclear Research (NCBJ), A. Sołtana 7, 05-400 Otwock-Świerk, Poland; Department of Biophysics and Human Physiology, Medical University of Warsaw, Chałubińskiego 5, 02-004 Warsaw, Poland ' National Centre for Nuclear Research (NCBJ), A. Sołtana 7, 05-400 Otwock-Świerk, Poland; Institute of Physics, Polish Academy of Sciences (IF PAN), Lotników 32/46, Waraw, Poland ' National Centre for Nuclear Research (NCBJ), A. Sołtana 7, 05-400 Otwock-Świerk, Poland; Ex-Polon Laboratory, Podleśna 81a, 05-552 Łazy, Poland ' Fox Chase Cancer Center, 333 Cottman Ave, Philadelphia, PA 19111, USA ' National Centre for Nuclear Research (NCBJ), A. Sołtana 7, 05-400 Otwock-Świerk, Poland

Abstract: This paper focuses on the relationship between radon concentration and lung cancer morbidity from the methodological point of view. Geographically aggregated data on cancer risk factors, collected for 3142 US counties and county-equivalents are discussed. Apart from the Least-Squares and Bayesian linear regression analysis, for the first time the Maximum Entropy Method (MEM) is used to investigate this type of correlation, where major confounding factors under considerations are altitude and ultraviolet (type B) radiation. First two methods of analysis show statistically significant decrease in the group with the high smoking prevalence. This trend did not depend on the sex of the subjects or their prevalence of smoking. The use of MEM provides a much richer picture of a clear trend of decreasing morbidity of lung cancer with increasing radon concentration level. Last but not least, it is shown that the data binning has to be made carefully as otherwise the conclusions based on the data can be dubious.

Keywords: lung cancer risk; radon; linear no-threshold theory; data analysis; maximum entropy method; Bayesian analysis; ecological studies.

DOI: 10.1504/IJLR.2020.113541

International Journal of Low Radiation, 2020 Vol.11 No.3/4, pp.207 - 226

Received: 06 Feb 2020
Accepted: 26 May 2020
Published online: 10 Mar 2021 *

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