Jackson Christopher, Best Nicky, Richardson Sylvia
Department of Epidemiology and Public Health, Imperial College School of Medicine, London, U.K.
Stat Med. 2006 Jun 30;25(12):2136-59. doi: 10.1002/sim.2370.
In typical small-area studies of health and environment we wish to make inference on the relationship between individual-level quantities using aggregate, or ecological, data. Such ecological inference is often subject to bias and imprecision, due to the lack of individual-level information in the data. Conversely, individual-level survey data often have insufficient power to study small-area variations in health. Such problems can be reduced by supplementing the aggregate-level data with small samples of data from individuals within the areas, which directly link exposures and outcomes. We outline a hierarchical model framework for estimating individual-level associations using a combination of aggregate and individual data. We perform a comprehensive simulation study, under a variety of realistic conditions, to determine when aggregate data are sufficient for accurate inference, and when we also require individual-level information. Finally, we illustrate the methods in a case study investigating the relationship between limiting long-term illness, ethnicity and income in London.
在典型的健康与环境小区域研究中，我们希望利用汇总数据或生态数据来推断个体层面变量之间的关系。由于数据中缺乏个体层面的信息，这种生态推断往往存在偏差且不够精确。相反，个体层面的调查数据通常没有足够的能力来研究健康状况在小区域内的差异。通过用区域内个体的小样本数据补充汇总层面的数据，可以减少这些问题，这些个体数据能直接将暴露因素和结果联系起来。我们概述了一个层次模型框架，用于结合汇总数据和个体数据来估计个体层面的关联。我们在各种现实条件下进行了全面的模拟研究，以确定何时汇总数据足以进行准确推断，以及何时我们还需要个体层面的信息。最后，我们在一个案例研究中展示了这些方法，该案例研究调查了伦敦地区长期慢性病、种族和收入之间的关系。
