Pleil Joachim D, Sobus Jon R
a Human Exposure and Atmospheric Sciences Division, NERL/ORD, U.S. Environmental Protection Agency , Research Triangle Park , North Carolina , USA.
J Toxicol Environ Health A. 2016;79(18):837-47. doi: 10.1080/15287394.2016.1193108.
Exposure-based risk assessment employs large cross-sectional data sets of environmental and biomarker measurements to predict population statistics for adverse health outcomes. The underlying assumption is that long-term (many years) latency health problems including cancer, autoimmune and cardiovascular disease, diabetes, and asthma are triggered by lifetime exposures to environmental stressors that interact with the genome. The aim of this study was to develop a specific predictive method that provides the statistical parameters for chronic exposure at the individual level based upon a single spot measurement and knowledge of global summary statistics as derived from large data sets. This is a profound shift in exposure and health statistics in that it begins to answer the question "How large is my personal risk?" rather than just providing an overall population-based estimate. This approach also holds value for interpreting exposure-based risks for small groups of individuals within a community in comparison to random individuals from the general population.
基于暴露的风险评估利用环境和生物标志物测量的大型横断面数据集来预测不良健康结果的人群统计数据。其基本假设是，包括癌症、自身免疫性和心血管疾病、糖尿病和哮喘在内的长期（多年）潜伏性健康问题是由与基因组相互作用的环境压力源的终身暴露引发的。本研究的目的是开发一种特定的预测方法，该方法基于单点测量以及从大型数据集中得出的全球汇总统计知识，提供个体层面慢性暴露的统计参数。这是暴露和健康统计方面的一个深刻转变，因为它开始回答“我的个人风险有多大？”这个问题，而不仅仅是提供基于总体人群的估计。与来自一般人群的随机个体相比，这种方法对于解释社区内小群体个体基于暴露的风险也具有价值。
