Kennedy Marc, Hart Andy
Food and Environment Research Agency, Sand Hutton, York, North Yorkshire, UK.
Risk Anal. 2009 Oct;29(10):1427-42. doi: 10.1111/j.1539-6924.2009.01265.x. Epub 2009 Jul 23.
We propose new models for dealing with various sources of variability and uncertainty that influence risk assessments for dietary exposure. The uncertain or random variables involved can interact in complex ways, and the focus is on methodology for integrating their effects and on assessing the relative importance of including different uncertainty model components in the calculation of dietary exposures to contaminants, such as pesticide residues. The combined effect is reflected in the final inferences about the population of residues and subsequent exposure assessments. In particular, we show how measurement uncertainty can have a significant impact on results and discuss novel statistical options for modeling this uncertainty. The effect of measurement error is often ignored, perhaps due to the laboratory process conforming to the relevant international standards, for example, or is treated in an ad hoc way. These issues are common to many dietary risk analysis problems, and the methods could be applied to any food and chemical of interest. An example is presented using data on carbendazim in apples and consumption surveys of toddlers.
我们提出了新的模型，用于处理影响膳食暴露风险评估的各种变异性和不确定性来源。所涉及的不确定或随机变量可能以复杂的方式相互作用，重点在于整合其影响的方法，以及评估在计算污染物（如农药残留）膳食暴露量时纳入不同不确定性模型组件的相对重要性。综合效应反映在关于残留量总体的最终推断以及后续的暴露评估中。特别是，我们展示了测量不确定性如何对结果产生重大影响，并讨论了用于对这种不确定性进行建模的新颖统计选项。测量误差的影响通常被忽略，这可能是由于实验室过程符合相关国际标准，或者是以临时方式处理。这些问题在许多膳食风险分析问题中都很常见，并且这些方法可应用于任何感兴趣的食品和化学品。使用苹果中多菌灵的数据以及幼儿消费调查给出了一个示例。
