Swartout J C, Price P S, Dourson M L, Carlson-Lynch H L, Keenan R E
National Center for Environmental Assessment, U.S. EPA, Cincinnati, Ohio 45268, USA.
Risk Anal. 1998 Jun;18(3):271-82. doi: 10.1111/j.1539-6924.1998.tb01294.x.
Determining the probabilistic limits for the uncertainty factors used in the derivation of the Reference Dose (RfD) is an important step toward the goal of characterizing the risk of noncarcinogenic effects from exposure to environmental pollutants. If uncertainty factors are seen, individually, as "upper bounds" on the dose-scaling factor for sources of uncertainty, then determining comparable upper bounds for combinations of uncertainty factors can be accomplished by treating uncertainty factors as distributions, which can be combined by probabilistic techniques. This paper presents a conceptual approach to probabilistic uncertainty factors based on the definition and use of RfDs by the U.S. EPA. The approach does not attempt to distinguish one uncertainty factor from another based on empirical data or biological mechanisms but rather uses a simple displaced lognormal distribution as a generic representation of all uncertainty factors. Monte Carlo analyses show that the upper bounds for combinations of this distribution can vary by factors of two to four when compared to the fixed-value uncertainty factor approach. The probabilistic approach is demonstrated in the comparison of Hazard Quotients based on RfDs with differing number of uncertainty factors.
确定参考剂量（RfD）推导过程中使用的不确定性因素的概率极限，是朝着表征接触环境污染物产生非致癌效应风险这一目标迈出的重要一步。如果将不确定性因素单独视为不确定性来源的剂量缩放因子的“上限”，那么通过将不确定性因素视为分布，并采用概率技术进行组合，就可以确定不确定性因素组合的可比上限。本文基于美国环境保护局（EPA）对参考剂量的定义和使用，提出了一种关于概率性不确定性因素的概念方法。该方法并非试图根据经验数据或生物学机制区分一个不确定性因素与另一个不确定性因素，而是使用简单的移位对数正态分布作为所有不确定性因素的通用表示。蒙特卡罗分析表明，与固定值不确定性因素方法相比，这种分布组合的上限可能会相差两到四倍。基于具有不同数量不确定性因素的参考剂量的危险商比较中展示了概率方法。
