Oakes J Michael
Division of Epidemiology and Population Research Center, University of Minnesota, 1300 South 2nd Street, Minneapolis, MN 55454, USA.
Soc Sci Med. 2004 May;58(10):1929-52. doi: 10.1016/j.socscimed.2003.08.004.
The resurgence of interest in the effect of neighborhood contexts on health outcomes, motivated by advances in social epidemiology, multilevel theories and sophisticated statistical models, too often fails to confront the enormous methodological problems associated with causal inference. This paper employs the counterfactual causal framework to illuminate fundamental obstacles in the identification, explanation, and usefulness of multilevel neighborhood effect studies. We show that identifying useful independent neighborhood effect parameters, as currently conceptualized with observational data, to be impossible. Along with the development of a dependency-based methodology and theories of social interaction, randomized community trials are advocated as a superior research strategy, one that may help social epidemiology answer the causal questions necessary for remediating disparities and otherwise improving the public's health.
受社会流行病学、多层次理论和复杂统计模型发展的推动，对邻里环境对健康结果影响的兴趣再度兴起，但往往未能直面与因果推断相关的巨大方法学问题。本文采用反事实因果框架，以阐明多层次邻里效应研究在识别、解释及实用性方面的基本障碍。我们表明，按照目前对观测数据的概念化方式，识别有用的独立邻里效应参数是不可能的。随着基于依赖性的方法和社会互动理论的发展，随机社区试验被倡导为一种更优的研究策略，它可能有助于社会流行病学回答解决差异及改善公众健康所需的因果问题。
