Department of Epidemiology and Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32611, USA.
Stat Med. 2010 Aug 15;29(18):1890-9. doi: 10.1002/sim.3946.
In social epidemiology, one often considers neighborhood or contextual effects on health outcomes, in addition to effects of individual exposures. This paper is concerned with the estimation of an individual exposure effect in the presence of confounding by neighborhood effects, motivated by an analysis of National Health Interview Survey (NHIS) data. In the analysis, we operationalize neighborhood as the secondary sampling unit of the survey, which consists of small groups of neighboring census blocks. Thus the neighborhoods are sampled with unequal probabilities, as are individuals within neighborhoods. We develop and compare several approaches for the analysis of the effect of dichotomized individual-level education on the receipt of adequate mammography screening. In the analysis, neighborhood effects are likely to confound the individual effects, due to such factors as differential availability of health services and differential neighborhood culture. The approaches can be grouped into three broad classes: ordinary logistic regression for survey data, with either no effect or a fixed effect for each cluster; conditional logistic regression extended for survey data; and generalized linear mixed model (GLMM) regression for survey data. Standard use of GLMMs with small clusters fails to adjust for confounding by cluster (e.g. neighborhood); this motivated us to develop an adaptation. We use theory, simulation, and analyses of the NHIS data to compare and contrast all of these methods. One conclusion is that all of the methods perform poorly when the sampling bias is strong; more research and new methods are clearly needed.
在社会流行病学中，人们除了考虑个体暴露对健康结果的影响外，还常常考虑邻里或环境效应对健康结果的影响。本文关注的是在存在邻里效应混杂的情况下，对个体暴露效应的估计，这是对国家健康访谈调查（NHIS）数据的分析。在分析中，我们将邻里定义为调查的二级抽样单位，由相邻的小块街区组成。因此，邻里是不等概率抽样的，邻里内的个体也是如此。我们开发并比较了几种方法来分析个体层面的二分制教育对接受充分的乳房 X 光筛查的影响。在分析中，由于卫生服务的可及性和邻里文化的差异等因素，邻里效应可能会混淆个体效应。这些方法可以分为三大类：用于调查数据的普通逻辑回归，每个簇都有或没有固定效应；扩展用于调查数据的条件逻辑回归；以及用于调查数据的广义线性混合模型（GLMM）回归。标准的小簇 GLMM 应用未能调整簇（如邻里）引起的混杂；这促使我们开发了一种适应方法。我们使用理论、模拟和 NHIS 数据的分析来比较和对比所有这些方法。一个结论是，当抽样偏差较强时，所有方法的性能都很差；显然需要更多的研究和新方法。
