Cox L A, Ricci P F
Cox and Associates, Denver and University of Colorado, USA.
Environ Int. 2005 Apr;31(3):377-97. doi: 10.1016/j.envint.2004.08.010.
Causal inference of exposure-response relations from data is a challenging aspect of risk assessment with important implications for public and private risk management. Such inference, which is fundamentally empirical and based on exposure (or dose)-response models, seldom arises from a single set of data; rather, it requires integrating heterogeneous information from diverse sources and disciplines including epidemiology, toxicology, and cell and molecular biology. The causal aspects we discuss focus on these three aspects: drawing sound inferences about causal relations from one or more observational studies; addressing and resolving biases that can affect a single multivariate empirical exposure-response study; and applying the results from these considerations to the microbiological risk management of human health risks and benefits of a ban on antibiotic use in animals, in the context of banning enrofloxacin or macrolides, antibiotics used against bacterial illnesses in poultry, and the effects of such bans on changing the risk of human food-borne campylobacteriosis infections. The purposes of this paper are to describe novel causal methods for assessing empirical causation and inference; exemplify how to deal with biases that routinely arise in multivariate exposure- or dose-response modeling; and provide a simplified discussion of a case study of causal inference using microbial risk analysis as an example. The case study supports the conclusion that the human health benefits from a ban are unlikely to be greater than the excess human health risks that it could create, even when accounting for uncertainty. We conclude that quantitative causal analysis of risks is a preferable to qualitative assessments because it does not involve unjustified loss of information and is sound under the inferential use of risk results by management.
从数据中推断暴露-反应关系的因果关系是风险评估中一个具有挑战性的方面，对公共和私人风险管理具有重要意义。这种推断本质上是经验性的，基于暴露（或剂量）-反应模型，很少源于单一数据集；相反，它需要整合来自不同来源和学科的异质信息，包括流行病学、毒理学以及细胞和分子生物学。我们讨论的因果关系方面集中在以下三个方面：从一项或多项观察性研究中对因果关系得出合理推断；解决和消除可能影响单一多变量经验性暴露-反应研究的偏差；在禁止恩诺沙星或大环内酯类药物（用于家禽细菌性疾病的抗生素）以及此类禁令对改变人类食源性弯曲杆菌感染风险的影响的背景下，将这些考虑的结果应用于人类健康风险和动物抗生素使用禁令的微生物风险管理。本文的目的是描述评估经验性因果关系和推断的新因果方法；举例说明如何处理多变量暴露或剂量反应建模中经常出现的偏差；并以微生物风险分析为例，对因果推断的案例研究进行简化讨论。案例研究支持这样的结论：即使考虑到不确定性，禁令给人类健康带来的益处也不太可能大于其可能造成的额外人类健康风险。我们得出结论，风险的定量因果分析优于定性评估，因为它不会导致不合理的信息损失，并且在管理层对风险结果进行推断性使用时是合理的。
