Kaufman J S, Cooper R S
Department of Research Planning and Evaluation, Carolinas Medical Center, Charlotte, NC 28232-2861, USA.
Am J Epidemiol. 1999 Jul 15;150(2):113-20. doi: 10.1093/oxfordjournals.aje.a009969.
Social factors are associated with a wide variety of health outcomes. Social epidemiology has successfully used the traditional methods of surveillance and description to establish consistent relations between social factors and health status. Epidemiology as an etiologic science, however, has been largely ineffective in moving toward causal explanations for these observed patterns. Using the counterfactual approach to causal inference, the authors describe several fundamental problems that often arise when researchers seek to infer explanatory mechanisms from data on social factors. Contrasts that form standard causal effect estimates require implicit unobserved (counterfactual) quantities, because observational data provide only one exposure state for each individual. Although application of counterfactual arguments has successfully advanced etiologic understanding in other observational settings, the particular nature of social factors often leads to logical contradictions or misleading inferences when investigators fail to clearly articulate the counterfactual contrasts that are implied. For example, because social factors are often attributes of individuals and are components of structured social relations, random assignment is not plausible even as a hypothetical experiment, making typical epidemiologic contrasts inappropriate and the inference equivocal at best. Accordingly, more deliberate and creative approaches to causal inference in social epidemiology are required. Infectious disease epidemiology and systems analysis provide examples of approaches to causal inference that can be used when statistical mimicry of simple experimental designs is not tenable. In an era of increasing social inequality, valid approaches for the study of social factors and health are needed more urgently than ever.
社会因素与各种各样的健康结果相关。社会流行病学已成功运用传统的监测和描述方法，确立了社会因素与健康状况之间的一致关系。然而，作为一门病因学科学，流行病学在为这些观察到的模式寻求因果解释方面，很大程度上是无效的。作者运用因果推断的反事实方法，描述了研究人员试图从社会因素数据中推断解释机制时经常出现的几个基本问题。构成标准因果效应估计的对比需要隐含的未观察到的（反事实的）量，因为观察数据为每个个体仅提供一种暴露状态。尽管反事实论证的应用已在其他观察环境中成功推进了病因学理解，但当研究人员未能清晰阐明所隐含的反事实对比时，社会因素的特殊性质往往会导致逻辑矛盾或误导性推断。例如，由于社会因素通常是个体的属性且是结构化社会关系的组成部分，即使作为一种假设实验，随机分配也不合理，这使得典型的流行病学对比不恰当，且推断充其量是模棱两可的。因此，社会流行病学中需要更审慎和创造性的因果推断方法。传染病流行病学和系统分析提供了一些因果推断方法的例子，当简单实验设计的统计模拟不可行时，可以使用这些方法。在社会不平等加剧的时代，对社会因素与健康研究的有效方法比以往任何时候都更迫切需要。
