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.2010 Jan 6;8(2):125-47.
doi: 10.2203/dose-response.09-019.Fornalski.

The healthy worker effect and nuclear industry workers

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The healthy worker effect and nuclear industry workers

Krzysztof W Fornalski et al. Dose Response..

Abstract

The linear no-threshold (LNT) dose-effect relationship has been consistently used by most radiation epidemiologists to estimate cancer mortality risk. The large scattering of data by International Agency for Research on Cancer, IARC (Vrijheid et al. 2007; Therry-Chef et al. 2007; Cardis et al. 2007), interpreted in accordance with LNT, has been previously demonstrated (Fornalski and Dobrzyński 2009). Using conventional and Bayesian methods the present paper demonstrates that the standard mortality ratios (SMRs), lower in the IARC cohort of exposed nuclear workers than in the non exposed group, should be considered as a hormetic effect, rather than a healthy worker effect (HWE) as claimed by the IARC group.

Keywords: Bayesian analysis; dose-response; healthy worker effect; hormesis; low radiation; nuclear industry workers.

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Figures

FIGURE 1.
FIGURE 1.
Weighted averages of all causes SMRs as a function of the duration of employment (on the left) and the age at death (on the right).
FIGURE 2.
FIGURE 2.
The SMR for all cancers (two black bars) and all causes (two grey bars) in two cases: control group of the general population and nuclear industry workers from 15 countries.
FIGURE 3.
FIGURE 3.
SMR [%] for a) deaths from all causes, b) cancer deaths vs average dose per year [mSv/y]; the grey horizontal line is an average obtained following Bayesian analysis, the dashed line is a Bayesian fit (“Bayes2”) and the dotted line is a standard least squares (χ2) fit. All uncertainties are taken from IARC (Vrijheidet al. 2007; Therry-Chefet al. 2007; Cardiset al. 2007).
FIGURE 4.
FIGURE 4.
Examples of Bayesian analysis fitting (solid lines) to simulated points containing intentional outliers. The dotted lines are obtained from minimizing χ2 function (least squares) – poor fit is seen. The functionf(x) shows the fitting with only 3 outliers. Theg(x) contains many more outliers, but Bayesian analysis (solid line) still works well. In the example ofh(x), where the number of outliers is higher than correct data, the Bayesian approach cannot help: there are more points showing another trend than the intended one. However, when number of outliers is high and they lie above and below the main trend, the Bayesian analysis still works (seej(x)).
FIGURE 5.
FIGURE 5.
Relative Risk (RR) vs cumulative dose [mSv] from all cancers deaths. Solid horizontal line corresponds to RR=1, the highest dashed line is a fit as given by IARC (Vrijheidet al. 2007; Therry-Chefet al. 2007; Cardiset al. 2007), grey solid line corresponds to the least-squares solution, and dotted line is a Bayesian fit, when the point for 175 mSv is not taken into account.
FIGURE 6.
FIGURE 6.
The Relative Risk (RR) connected with the cumulative dose for a) the leukemias without CLL, and b) all cancers without leukemias. The local maximum for the 7.5 mSv is clearly seen in both cases.
See this image and copyright information in PMC

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