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Rethinking the Meaning of Concentration-Response Functions and the Estimated Burden of Adverse Health Effects Attributed to Exposure Concentrations

机译：重新思考浓度-响应函数的含义以及暴露浓度对健康不良影响的估计负担

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摘要

Four articles by Anenberg et al., Fann et al., Shin et al., and Smith contribute valuable perspectives and syntheses to a large and growing literature that estimates the burden of mortality risks attributed to fine particulate matter (PM2.5) based on estimated epidemiological associations, summarized as concentration-response (C-R) relations. This comment questions the use of C-R relations to predict or estimate how changing exposure concentrations would change responses in a population. C-R associations typically reflect modeling choices, and equally good choices can commonly lead to conflicting conclusions about the signs, significance, and magnitudes of C-R relations and regression coefficients. This indicates that C-R relations do not necessarily reflect underlying stable causal laws useful for making risk predictions, but only choices about how to describe past data, with no uniquely correct choice being determined by the data. Similarly, currently available C-R data typically do not suffice to make valid predictions about how future changes in concentrations will affect responses. These difficulties can be substantially overcome by model ensemble and causal graph modeling and time series methods, but these require different data and knowledge-for example, knowledge of how multiple variables depend on each other, rather than only of how one dependent variable is associated with multiple explanatory variables-than that captured by traditional C-R models or expressible by any single C-R coefficient or curve.
机译：Anenberg等人，Fann等人，Shin等人和Smith的四篇文章为大量不断增长的文献提供了有价值的观点和综合信息，这些文献基于对细颗粒物（PM2.5）的估计来估计死亡风险。估计的流行病学关联，概括为集中反应（CR）关系。该评论质疑使用C-R关系来预测或估计暴露浓度的变化将如何改变人群的反应。 C-R关联通常反映建模选择，同样好的选择通常会导致关于C-R关系的符号，重要性和大小以及回归系数的结论相互矛盾。这表明C-R关系不一定反映可用于进行风险预测的基本稳定因果规律，而只是关于如何描述过去数据的选择，而没有由数据确定唯一正确的选择。同样，当前可用的C-R数据通常不足以对未来浓度的变化将如何影响响​​应做出有效的预测。通过模型集成和因果图建模以及时间序列方法可以基本上克服这些困难，但是这些需要不同的数据和知识，例如，有关多个变量如何相互依赖的知识，而不仅仅是与一个因变量如何相关的知识多个解释变量-比传统CR模型捕获的变量或任何单个CR系数或曲线均可表达。

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