INTEGRATING MULTIPLE BUILT ENVIRONMENT DATA SOURCES

• 成果类型: Article
• 署名作者: Won, Jung Yeon; Elliott, Michael R.; Sanchez-Vaznaugh, Emma V.; Sanchez, Brisa N.
• 署名单位: University of Michigan System; University of Michigan; California State University System; San Francisco State University; Drexel University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1692
• 发表日期: 2023
• 页码: 1722-1739
• 关键词: body-mass index; measurement error; bayesian-approach; population-size; food; models; mixtures; CHILDREN; Poisson; number

摘要

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.