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美国选定砷热点地区感知死亡率风险的经验模型。

An empirical model of perceived mortality risks for selected U.S. Arsenic hot spots.

机构信息

School of Engineering, University of Guelph, Ontario, Canada.

出版信息

Risk Anal. 2010 Oct;30(10):1550-62. doi: 10.1111/j.1539-6924.2010.01450.x.

Abstract

Researchers have long recognized that subjective perceptions of risk are better predictors of choices over risky outcomes than science-based or experts' assessments of risk. More recent work suggests that uncertainty about risks also plays a role in predicting choices and behavior. In this article, we develop and estimate a formal model for an individual's perceived health risks associated with arsenic contamination of his or her drinking water. The modeling approach treats risk as a random variable, with an estimable probability distribution whose variance reflects uncertainty. The model we estimate uses data collected from a survey given to a sample of people living in arsenic-prone areas in the United States. The findings from this article support the fact that scientific information is essential to explaining the mortality rate perceived by the individuals, but uncertainty about the probability remains significant.

摘要

研究人员早就认识到,与基于科学或专家对风险的评估相比,对风险的主观感知是对风险结果做出选择的更好预测因素。最近的研究表明,对风险的不确定性也会在预测选择和行为方面发挥作用。在本文中,我们开发并估计了一个与个人饮用水砷污染相关的健康风险感知的个体模型。该建模方法将风险视为随机变量,具有可估计的概率分布,其方差反映了不确定性。我们估计的模型使用从在美国砷高发地区抽取的样本中进行的调查收集的数据。本文的研究结果支持这样一个事实,即科学信息对于解释个人感知的死亡率至关重要,但对概率的不确定性仍然很重要。

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