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.

中文翻译：

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关于药物和放射防护中放射风险的预后估计。

再次提出了表达辐射暴露的累积有害作用的问题。所有传统上使用且计算复杂的生命周期或时间综合风险都是基于当前人口和健康统计数据，而未来的长期趋势未知，这些趋势预计将持续很长时间。结果表明，应将常规使用的寿命或时间综合归因风险（LAR，AR）的应用范围限制在1 Gy以下。更一般的数量，例如超生风险（ELR），以及较小程度的暴露诱发死亡的风险（REID），没有剂量限制，但与LAR和AR相比，计算复杂度更高，并且依赖未知量总辐射对人口和健康统计数据的影响。对高剂量（大于1 Gy）暴露后特定结局的时间综合风险进行适当的评估，需要考虑其他辐射引起的结局的竞争风险，并且得出的ELR估计值基本上具有非线性剂量反应。将常规应用的时间综合风险基于当前人口和健康统计数据所造成的局限性在于：（a）不太适合一般人群不易代表的非典型暴露人群的风险估计；（b）由于特定人群疾病率未来世俗趋势的发展存在很大的不确定性，因此对于未来数十年的风险预测而言并非最佳。特定疾病的替代量，基线和归因生存率，在本文中考虑了基于减少生存机会的基础，并被证明在规避这些限制的大多数方面非常有用。在此建议使用另一个主要量，称为辐射引起的生存期降低（RADS），以代表在一定年龄之前生存的累积辐射风险。RADS在统计文献中历史上被称为“累积风险”，仅基于辐射引起的危害，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，并适合在放射线的治疗或介入性医学应用后暴露的人，或在其他极不典型的暴露人群（如宇航员）中评估辐射风险。