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Activity Number: 615
Type: Contributed
Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #312247
Title: Semiparametric Generalized Linear Mixed Model for Localized Health Estimates
Author(s): Yueyan Wang*+ and Hongjian Yu and Pan Wang and Jean Opsomer and David Grant and Ninez Ponce
Companies: University of California, Los Angeles and University of California, Los Angeles and University of California, Los Angeles and Colorado State University and University of California, Los Angeles Center for Health Policy Research and University of California, Los Angeles
Keywords: semi-parametric ; generalized linear mixed model ; geo-additive ; small area estimation ; California Health Interview Survey
Abstract:

With the growing need for health data at an increasingly granular and customized geographical level, new challenges are posed to the established small area estimation approach. Data in fine geographic areas are sparse, and it is cost-prohibitive to increase the sample for the areas of interest in the survey design. In addition, spatial dependence is possible and may not be appropriately modeled with linear relationships. In this study, we propose an approach to address these specific issues by combining a generalized linear mixed model and a non-parametric smooth trend on socio-demographic contextual variables and geo-coordinates. Conceptually, we model the socio-demographic-adjacency as well as the geo-adjacency of the health indicators. The model could be fitted using existing procedures for generalized linear mixed model. We provide a method for computing small-area prediction mean squared error through replication methods which takes survey design into account. An example of the application is demonstrated using data from California Health Interview Survey, the largest state health survey in the U.S.


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