Literature DB >> 2253600

A test of the linear-no threshold theory of radiation carcinogenesis.

B L Cohen1.   

Abstract

It has been pointed out that, while an ecological study cannot determine whether radon causes lung cancer, it can test the validity of a linear-no threshold relationship between them. The linear-no threshold theory predicts a substantial positive correlation between the average radon exposure in various counties and their lung cancer mortality rates. Data on living areas of houses in 411 counties from all parts of the United States exhibit, rather, a substantial negative correlation with the slopes of the lines of regression differing from zero by 10 and 7 standard deviations for males and females, respectively, and from the positive slope predicted by the theory by at least 16 and 12 standard deviations. When the data are segmented into 23 groups of states or into 7 regions of the country, the predominantly negative slopes and correlations persist, applying to 18 of the 23 state groups and 6 of the 7 regions. Five state-sponsored studies are analyzed, and four of these give a strong negative slope (the other gives a weak positive slope, in agreement with our data for that state). A strong negative slope is also obtained in our data on basements in 253 counties. A random selection-no charge study of 39 high and low lung cancer counties (+4 low population states) gives a much stronger negative correlation. When nine potential confounding factors are included in a multiple linear regression analysis, the discrepancy with theory is reduced only to 12 and 8.5 standard deviations for males and females, respectively. When the data are segmented into four groups by population, the multiple regression vs radon level gives a strong negative slope for each of the four groups. Other considerations are introduced to reduce the discrepancy, but it remains very substantial. Since cigarette sales data are available only on a statewide basis, mean radon data for states are analyzed. The linear regression for lung cancer rates vs radon levels is also negative and has a much steeper slope than that for the county data. When cigarette sales per capita is introduced into the regression, the negative slope for dependence on radon level is essentially unchanged.

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Year:  1990        PMID: 2253600     DOI: 10.1016/s0013-9351(05)80119-7

Source DB:  PubMed          Journal:  Environ Res        ISSN: 0013-9351            Impact factor:   6.498


  13 in total

1.  Migration bias in ecologic studies.

Authors:  S Tong
Journal:  Eur J Epidemiol       Date:  2000-04       Impact factor: 8.082

Review 2.  Hormesis, an update of the present position.

Authors:  Lennart Johansson
Journal:  Eur J Nucl Med Mol Imaging       Date:  2003-04-26       Impact factor: 9.236

3.  Deadly radon in montana?

Authors:  Jerome S Puskin
Journal:  Dose Response       Date:  2011-07-12       Impact factor: 2.658

Review 4.  Indoor radon and lung cancer. Estimating the risks.

Authors:  J M Samet
Journal:  West J Med       Date:  1992-01

5.  Indoor radon--what is to be done?

Authors:  M Roach; K A Weaver
Journal:  West J Med       Date:  1992-01

6.  Smoking and hormesis as confounding factors in radiation pulmonary carcinogenesis.

Authors:  Charles L Sanders; Bobby R Scott
Journal:  Dose Response       Date:  2006-12-06       Impact factor: 2.658

7.  State-level relationships cannot tell us anything about individuals.

Authors:  Alex H S Harris; Keith Humphreys; John W Finney
Journal:  Am J Public Health       Date:  2015-02-25       Impact factor: 9.308

8.  A preliminary study for conducting a rational assessment of radon exposure levels.

Authors:  Hyung-Jin Jeon; Dae-Ryoung Kang; Sang-Baek Go; Tae-Hyun Park; Si-Hyun Park; Jung-Eun Kwak; Cheol-Min Lee
Journal:  Environ Sci Pollut Res Int       Date:  2017-04-26       Impact factor: 4.223

9.  Epidemiology Without Biology: False Paradigms, Unfounded Assumptions, and Specious Statistics in Radiation Science (with Commentaries by Inge Schmitz-Feuerhake and Christopher Busby and a Reply by the Authors).

Authors:  Bill Sacks; Gregory Meyerson; Jeffry A Siegel
Journal:  Biol Theory       Date:  2016-06-17

10.  Why health care process performance measures can have different relationships to outcomes for patients and hospitals: understanding the ecological fallacy.

Authors:  John W Finney; Keith Humphreys; Daniel R Kivlahan; Alex H S Harris
Journal:  Am J Public Health       Date:  2011-07-21       Impact factor: 9.308

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