Dominici Francesca, Zigler Corwin
Department of Biostatistics, Harvard H.T. Chan School of Public Health, Boston, Massachusetts.
Am J Epidemiol. 2017 Dec 15;186(12):1303-1309. doi: 10.1093/aje/kwx307.
The contentious political climate surrounding air pollution regulations has brought some researchers and policy-makers to argue that evidence of causality is necessary before implementing more stringent regulations. Recently, investigators in an increasing number of air pollution studies have purported to have used "causal analysis," generating the impression that studies not explicitly labeled as such are merely "associational" and therefore less rigorous. Using 3 prominent air pollution studies as examples, we review good practices for how to critically evaluate the extent to which an air pollution study provides evidence of causality. We argue that evidence of causality should be gauged by a critical evaluation of design decisions such as 1) what actions or exposure levels are being compared, 2) whether an adequate comparison group was constructed, and 3) how closely these design decisions approximate an idealized randomized study. We argue that air pollution studies that are more scientifically rigorous in terms of the decisions made to approximate a randomized experiment are more likely to provide evidence of causality and should be prioritized among the body of evidence for regulatory review accordingly. Our considerations, although presented in the context of air pollution epidemiology, can be broadly applied to other fields of epidemiology.
围绕空气污染法规的政治气候充满争议，这使得一些研究人员和政策制定者认为，在实施更严格的法规之前，因果关系的证据是必要的。最近，越来越多的空气污染研究中的调查人员声称使用了“因果分析”，给人一种印象，即没有明确标明使用因果分析的研究仅仅是“关联性的”，因此不够严谨。我们以3项著名的空气污染研究为例，回顾如何批判性地评估空气污染研究在多大程度上提供了因果关系证据的良好做法。我们认为，因果关系的证据应该通过对设计决策的批判性评估来衡量，例如：1）正在比较哪些行动或暴露水平；2）是否构建了一个足够的对照组；3）这些设计决策与理想化的随机研究的接近程度。我们认为，在为近似随机实验所做的决策方面，更具科学严谨性的空气污染研究更有可能提供因果关系的证据，因此在监管审查的证据体系中应被优先考虑。我们的考量虽然是在空气污染流行病学的背景下提出的，但可以广泛应用于流行病学的其他领域。
