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社会流行病学中随机试验的因果推断。

Causal inference from randomized trials in social epidemiology.

作者信息

Kaufman Jay S, Kaufman Sol, Poole Charles

机构信息

Department of Epidemiology (CB#7435), School of Public Health, University of North Carolina, Pittsboro Road McGavran-Greenberg Hall, Chapel Hill, NC 27599-7435, USA.

出版信息

Soc Sci Med. 2003 Dec;57(12):2397-409. doi: 10.1016/s0277-9536(03)00135-7.

Abstract

Social epidemiology is the study of relations between social factors and health status in populations. Although recent decades have witnessed a rapid development of this research program in scope and sophistication, causal inference has proven to be a persistent dilemma due to the natural assignment of exposure level based on unmeasured attributes of individuals, which may lead to substantial confounding. Some optimism has been expressed about randomized social interventions as a solution to this long-standing inferential problem. We review the causal inference problem in social epidemiology, and the potential for causal inference in randomized social interventions. Using the example of a currently on-going intervention that randomly assigns families to non-poverty housing, we review the limitations to causal inference even under experimental conditions and explain which causal effects become identifiable. We note the benefit of using the randomized trial as a conceptual model, even for design and interpretation of observational studies in social epidemiology.

摘要

社会流行病学是研究人群中社会因素与健康状况之间的关系。尽管近几十年来这一研究项目在范围和复杂性方面迅速发展,但由于基于个体未测量属性的暴露水平的自然分配,因果推断已被证明是一个长期存在的难题,这可能导致严重的混杂。对于随机社会干预作为解决这一长期推理问题的方法,人们表达了一些乐观态度。我们回顾了社会流行病学中的因果推断问题,以及随机社会干预中因果推断的潜力。以目前正在进行的一项将家庭随机分配到非贫困住房的干预为例,我们回顾了即使在实验条件下因果推断的局限性,并解释了哪些因果效应是可识别的。我们指出,即使对于社会流行病学观察性研究的设计和解释,将随机试验用作概念模型也是有益的。

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