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Health Place. 2015 Nov;36:35-46. doi: 10.1016/j.healthplace.2015.08.009. Epub 2015 Sep 12.

A common spatial factor analysis model for measured neighborhood-level characteristics: The Multi-Ethnic Study of Atherosclerosis.

Author information

1
University of North Carolina at Chapel Hill, Department of Biostatistics, Gillings School of Global Public Health, Hall, CB # 7420, Chapel USA. Electronic address: nethery@live.unc.edu.
2
Yale University, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
3
University of North Carolina at Chapel Hill, Department of Biostatistics, Gillings School of Global Public Health, Hall, CB # 7420, Chapel USA.
4
Drexel University, Department of Epidemiology and Biostatistics, Philadelphia, PA, USA.
5
University of North Carolina at Chapel Hill, Department of Epidemiology, Gillings School of Global Public Health, Chapel Hill, NC, USA.
6
Drexel University, School of Public Health, Philadelphia, PA, USA.

Abstract

The purpose of this study was to reduce the dimensionality of a set of neighborhood-level variables collected on participants in the Multi-Ethnic Study of Atherosclerosis (MESA) while appropriately accounting for the spatial structure of the data. A common spatial factor analysis model in the Bayesian setting was utilized in order to properly characterize dependencies in the data. Results suggest that use of the spatial factor model can result in more precise estimation of factor scores, improved insight into the spatial patterns in the data, and the ability to more accurately assess associations between the neighborhood environment and health outcomes.

KEYWORDS:

Bayesian analysis; Body mass index; Factor analysis; Spatial statistics

PMID:
26372887
PMCID:
PMC4679666
[Indexed for MEDLINE]
Free PMC Article
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