Bringing context back into epidemiology: Variables and fallacies in multilevel analysis
Bringing context back into epidemiology: Variables and fallacies in multilevel analysis
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DOI：
10.2105/ajph.88.2.216
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发表时间：
1998-02-01
影响因子：
12.7
通讯作者：
Diez-Roux, AV
中科院分区：
文献类型：
--
作者：
Diez-Roux, AV
当前流行病学研究的很大一部分是基于方法论的个人主义：即健康和疾病在人群中的分布可以完全根据个人的特征来解释的概念。本文讨论了在流行病学研究中纳入群体或宏观水平变量的必要性，从而在健康结果的研究中纳入多层次的确定。这些类型的分析，被称为背景分析或多层次分析，挑战流行病学家开发跨层次的疾病因果关系的理论模型，并解释群体水平和个人水平如何水平变量在形成健康和疾病方面相互作用。他们还提出了一系列的方法论问题，包括需要选择适当的上下文单元和上下文变量，正确地指定个人层面的模型，并在某些情况下，在上下文中的个人之间的剩余相关性。尽管其复杂性，多层次的分析有可能重新强调宏观层面的变量在塑造健康和疾病的人群中的作用。
A large portion of current epidemiologic research is based on methodologic individualism: the notion that the distribution of health and disease in populations can be explained exclusively in terms of the characteristics of individuals. The present paper discusses the need to include group-or macro-level variables in epidemiologic studies, thus incorporating multiple levels of determination in the study of health outcomes.These types of analyses, which have been called contextual or multilevel analyses, challenge epidemiologists to develop theoretical models of disease causation that extend across levels and explain how group-level and individual-level variables interact in shaping health and disease. They also raise a series of methodological issues, including the need to select the appropriate contextual unit and contextual variables, to correctly specify the individual-level model, and, in some cases, to account for residual correlation between individuals within contexts. Despite its complexities, multilevel analysis holds potential for reemphasizing the role of macro-level variables in shaping health and disease in populations.