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对近期核工业工人癌症死亡率流行病学研究的批判

A Critique of Recent Epidemiologic Studies of Cancer Mortality Among Nuclear Workers.

作者信息

Scott Bobby R

机构信息

Lovelace Respiratory Research Institute, Albuquerque, NM, USA.

出版信息

Dose Response. 2018 May 28;16(2):1559325818778702. doi: 10.1177/1559325818778702. eCollection 2018 Apr-Jun.

Abstract

Current justification by linear no-threshold (LNT) cancer risk model advocates for its use in low-dose radiation risk assessment is now mainly based on results from flawed and unreliable epidemiologic studies that manufacture small risk increases (ie, phantom risks). Four such studies of nuclear workers, essentially carried out by the same group of epidemiologists, are critiqued in this article. Three of the studies that forcibly applied the LNT model (inappropriate null hypothesis) to cancer mortality data and implicated increased mortality risk from any radiation exposure, no matter how small the dose, are demonstrated to manufacture risk increases for doses up to 100 mSv (or 100 mGy). In a study where risk reduction (hormetic effect/adaptive response) was implicated for nuclear workers, it was assumed by the researchers to relate to a "strong healthy worker effect" with no consideration of the possibility that low radiation doses may help prevent cancer mortality (which is consistent with findings from basic radiobiological research). It was found with basic research that while large radiation doses suppress our multiple natural defenses (barriers) against cancer, these barriers are enhanced by low radiation doses, thereby decreasing cancer risk, essentially rendering the LNT model to be inconsistent with the data.

摘要

目前,线性无阈(LNT)癌症风险模型在低剂量辐射风险评估中的应用依据,主要基于存在缺陷且不可靠的流行病学研究结果,这些研究制造出了微小的风险增加(即虚幻风险)。本文对四项主要由同一组流行病学家开展的核工业工人相关研究进行了批判。其中三项研究强行将LNT模型(不恰当的零假设)应用于癌症死亡率数据,并暗示任何辐射暴露,无论剂量多小,都会增加死亡风险,结果表明这些研究在高达100毫希沃特(或100毫戈瑞)的剂量下制造出了风险增加。在一项涉及核工业工人风险降低(兴奋效应/适应性反应)的研究中,研究人员认为这与“强烈的健康工人效应”有关,而没有考虑低辐射剂量可能有助于预防癌症死亡的可能性(这与基础放射生物学研究结果一致)。基础研究发现,大剂量辐射会抑制我们对癌症的多种天然防御(屏障),而低辐射剂量会增强这些屏障,从而降低癌症风险,这使得LNT模型与数据不一致。

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