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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Distributed Lag Models: Examining Associations Between the Built Environment and Health
Epidemiology (Cambridge, Mass.), v 27(1), pp 116-124
01 Jan 2016
PMID: 26414942
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Built environment factors constrain individual level behaviors and choices, and thus are receiving increasing attention to assess their influence on health. Traditional regression methods have been widely used to examine associations between built environment measures and health outcomes, where a fixed, prespecified spatial scale (e.g., 1 mile buffer) is used to construct environment measures. However, the spatial scale for these associations remains largely unknown and misspecifying it introduces bias. We propose the use of distributed lag models (DLMs) to describe the association between built environment features and health as a function of distance from the locations of interest and circumvent a-priori selection of a spatial scale. Based on simulation studies, we demonstrate that traditional regression models produce associations biased away from the null when there is spatial correlation among the built environment features. Inference based on DLMs is robust under a range of scenarios of the built environment. We use this innovative application of DLMs to examine the association between the availability of convenience stores near California public schools, which may affect children’s dietary choices both through direct access to junk food and exposure to advertisement, and children’s body mass index z scores.
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Details
- Title
- Distributed Lag Models
- Creators
- Jonggyu Baek - University of MichiganBrisa N. Sánchez - San Francisco State UniversityVeronica J. Berrocal - University of California, San FranciscoEmma V. Sanchez-Vaznaugh - San Francisco State University
- Publication Details
- Epidemiology (Cambridge, Mass.), v 27(1), pp 116-124
- Publisher
- Wolters Kluwer Health, Inc
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000366318100017
- Scopus ID
- 2-s2.0-84949626903
- Other Identifier
- 991020100209304721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Public, Environmental & Occupational Health