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Activity Number: 597
Type: Topic Contributed
Date/Time: Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
Sponsor: Social Statistics Section
Abstract #315016 View Presentation
Title: Methods of Estimating or Accounting for Neighborhood Associations with Health Using Complex Survey Data
Author(s): Babette Brumback* and Amy Dailey and Zhuangyu Cai
Companies: University of Florida and Gettysburg College and University of Florida
Keywords: health disparities ; conditional logistic regression ; generalized linear mixed model ; composite likelihood ; complex survey data
Abstract:

Reasons for health disparities may include neighborhood-level factors, such as availability of health services, social norms, and environmental determinants, as well as individual-level factors. Investigating health inequalities using nationally or locally representative data often requires an approach that can accommodate a complex sampling design, in which individuals have unequal probabilities of selection into the study. We review and compare methods of estimating or accounting for neighborhood influences with complex survey data. We considered 3 types of methods, each generalized for use with complex survey data: ordinary regression, conditional likelihood regression, and generalized linear mixed-model regression. The relative strengths and weaknesses of each method differ from one study to another; we provide an overview of the advantages and disadvantages of each method theoretically, in terms of the nature of the estimable associations and the plausibility of the assumptions required for validity, and also practically, via a simulation study and analyses of health disparities in repeat mammography screening and oral preventive health using NHIS and BRFSS data.


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