Background: Transport walking has drawn growing interest due to its potential to increase levels of physical activities and reduce reliance on vehicles. While existing studies have compared built environment-health associations between Euclidean buffers and network buffers, no studies have systematically quantified the extent of bias in health effect estimates when exposures are measured in different buffers. Further, prior studies have done the comparisons focusing on only one or two geographic regions, limiting generalizability and restricting ability to test whether direction or magnitude of bias are different by context. This study aimed to quantify the degree of bias in associations between built environment exposures and transport walking when exposures were operationalized using Euclidean buffers rather than network buffers in diverse contexts.
Methods: We performed a simulations study to systematically evaluate the degree of bias in associations between built environment exposures in Euclidean buffers and network buffers and transport walking, assuming network buffers more accurately captured true exposures. Additionally, we used empirical data from a multi-ethnic, multi-site cohort to compare associations between built environment amenities and walking for transport where built environment exposures were derived using Euclidean buffers versus network buffers.
Results: Simulation results found that the bias induced by using Euclidean buffer models was consistently negative across the six study sites (ranging from -80% to -20%), suggesting built environment exposures measured using Euclidean buffers underestimate health effects on transport walking. Percent bias was uniformly smaller for the larger 5 km scale than the 1 km and 0.25 km spatial scales, independent of site or built environment categories. Empirical findings aligned with the simulation results: built environment-health associations were stronger for built environment exposures operationalized using network buffers than using Euclidean buffers.
Conclusion: This study is the first to quantify the extent of bias in the magnitude of the associations between built environment exposures and transport walking when the former are measured in Euclidean buffers vs. network buffers, informing future research to carefully conceptualize appropriate distance-based buffer metrics in order to better approximate real geographic contexts. It also helps contextualize existing research in the field that used Euclidean buffers when that were the only option. Further, this study provides an example of the uncertain geographic context problem.
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http://dx.doi.org/10.1186/s12942-022-00310-7 | DOI Listing |
Pharmacoeconomics
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Belgian Health Care Knowledge Centre, Brussels, Belgium.
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January 2025
Nanotechnology and Bio-Engineering Research Group, Atlantic Technological University, ATU Sligo, Ash Lane, Sligo, F91 YW50, Ireland.
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January 2025
Mechatronics Engineering Department, Yıldız Technical University, Besiktas, Istanbul 34349, Türkiye.
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December 2024
Department of Architectural and Construction Design, Faculty of Architecture, Wrocław University of Science and Technology, Politechnika Wrocławska 27, 50-370 Wrocław, Poland.
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View Article and Find Full Text PDFAnimals (Basel)
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School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK.
The chicken is the world's most farmed animal. In this work, we introduce the Chicks4FreeID dataset, the first publicly available dataset focused on the reidentification of individual chickens. We begin by providing a comprehensive overview of the existing animal reidentification datasets.
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