Ulanowski Alexander, Kaiser Jan Christian, Schneider Uwe, Walsh Linda
Institute of Radiation Medicine, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
International Atomic Energy Agency, IAEA Environmental Laboratories, 2444, Seibersdorf, Austria.
Radiat Environ Biophys. 2019 Aug;58(3):305-319. doi: 10.1007/s00411-019-00794-1. Epub 2019 Apr 20.
The problem of expressing cumulative detrimental effect of radiation exposure is revisited. All conventionally used and computationally complex lifetime or time-integrated risks are based on current population and health statistical data, with unknown future secular trends, that are projected far into the future. It is shown that application of conventionally used lifetime or time-integrated attributable risks (LAR, AR) should be limited to exposures under 1 Gy. More general quantities, such as excess lifetime risk (ELR) and, to a lesser extent, risk of exposure-induced death (REID), are free of dose constraints, but are even more computationally complex than LAR and AR and rely on the unknown total radiation effect on demographic and health statistical data. Appropriate assessment of time-integrated risk of a specific outcome following high-dose (more than 1 Gy) exposure requires consideration of competing risks for other radiation-attributed outcomes and the resulting ELR estimate has an essentially non-linear dose response. Limitations caused by basing conventionally applied time-integrated risks on current population and health statistical data are that they are: (a) not well suited for risk estimates for atypical groups of exposed persons not readily represented by the general population; and (b) not optimal for risk projections decades into the future due to large uncertainties in developments of the future secular trends in the population-specific disease rates. Alternative disease-specific quantities, baseline and attributable survival fractions, based on reduction of survival chances are considered here and are shown to be very useful in circumventing most aspects of these limitations. Another main quantity, named as radiation-attributed decrease of survival (RADS), is recommended here to represent cumulative radiation risk conditional on survival until a certain age. RADS, historically known in statistical literature as "cumulative risk", is only based on the radiation-attributed hazard and is insensitive to competing risks. Therefore, RADS is eminently suitable for risk projections in emergency situations and for estimating radiation risks for persons exposed after therapeutic or interventional medical applications of radiation or in other highly atypical groups of exposed persons, such as astronauts.
本文重新审视了辐射暴露累积有害效应的表达问题。所有传统使用的、计算复杂的终身或时间积分风险均基于当前人口和健康统计数据，这些数据的未来长期趋势未知，却被外推至遥远的未来。研究表明，传统使用的终身或时间积分归因风险（LAR、AR）的应用应限于1 Gy以下的暴露。更通用的量，如超额终身风险（ELR）以及在较小程度上的暴露诱发死亡风险（REID），不受剂量限制，但计算比LAR和AR更复杂，且依赖于辐射对人口和健康统计数据的未知总体效应。对高剂量（超过1 Gy）暴露后特定结果的时间积分风险进行适当评估，需要考虑其他辐射归因结果的竞争风险，并且由此得出的ELR估计具有本质上的非线性剂量反应。基于当前人口和健康统计数据应用传统时间积分风险所导致的局限性在于：（a）不太适合对一般人群难以代表的非典型暴露人群进行风险估计；（b）由于特定人群疾病率未来长期趋势发展存在很大不确定性，因此对未来几十年的风险预测并非最优。本文考虑了基于生存机会减少的替代疾病特定量、基线和归因生存分数，并表明它们在规避这些局限性的大多数方面非常有用。本文推荐另一个主要量，称为辐射归因生存减少（RADS），以表示达到一定年龄生存条件下的累积辐射风险。RADS在统计文献中历史上称为“累积风险”，仅基于辐射归因风险，且对竞争风险不敏感。因此，RADS非常适合在紧急情况下进行风险预测，以及估计在放射治疗或介入性医疗应用辐射后暴露的人员或其他高度非典型暴露人群（如宇航员）的辐射风险。
