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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Integrating multiple built environment data sources
The annals of applied statistics, v 17(2), pp 1722-1739
01 Jun 2023
PMID: 39605798
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Studies examining the contribution of the built environment to health often rely on commercial data sources to derive exposure measures, such as the number of specific food outlets in study participants' neighborhoods. Data on the location of community amenities (e.g., food outlets) can be col-lected from multiple sources. However, these commercial listings are known to have ascertainment errors and thus provide conflicting claims about the number and location of amenities. We propose a method that integrates expo-sure measures from different databases, while accounting for ascertainment errors, and obtains unbiased health effects of latent exposure. We frame the problem of conflicting exposure measures as a problem of two contingency tables with partially known margins, with the entries of the tables modeled using a multinomial distribution. Available estimates of source quality were embedded in a joint model for observed exposure counts, latent exposures, and health outcomes. Simulations show that our modeling framework yields substantially improved inferences regarding the health effects. We used the proposed method to estimate the association between children's body mass index (BMI) and the concentration of food outlets near their schools when both the NETS and Reference USA databases are available.
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Details
- Title
- Integrating multiple built environment data sources
- Creators
- Jung Yeon Won - University of MichiganMichael R. Elliott - University of MichiganEmma V. Sanchez-Vaznaugh - San Francisco State UniversityBrisa N. Sanchez - Drexel University, Urban Health Collaborative
- Publication Details
- The annals of applied statistics, v 17(2), pp 1722-1739
- Publisher
- INST MATHEMATICAL STATISTICS-IMS
- Number of pages
- 18
- Grant note
- R01-HL131610; R01-HL136718 / NIH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000985804300036
- Scopus ID
- 2-s2.0-85159837988
- Other Identifier
- 991021861207904721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Statistics & Probability