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Efforts to Adjust for Confounding by Neighborhood Using Complex Survey Data by Babette A. Brumback; Amy B. Dailey; Zhulin He; Lyndia C. Brumback; Melvin D. Livingston is a Mathematics article available to read on EtoBox.

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## Abstract 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;

Who reads Efforts to Adjust for Confounding by Neighborhood Using Complex Survey Data?

Author

Babette A. Brumback; Amy B. Dailey; Zhulin He; Lyndia C. Brumback; Melvin D. Livingston

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0277-6715)

Published

2010

Language

EN

Field

Mathematics (Physical Sciences)