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A pan-Canadian dataset of neighbourhood retail food environment measures using Statistics Canada's Business Register
- Publication Year :
- 2022
- Publisher :
- Apollo - University of Cambridge Repository, 2022.
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Abstract
- BACKGROUND: The objective of this study was to create the Canadian Food Environment Dataset (Can-FED) and to demonstrate its validity. DATA AND METHODS: Food outlet data were extracted from Statistics Canada's Business Register (BR) in 2018. Retail food environment access measures (both absolute and relative measures) were calculated using network buffers around the centroid of 56,589 dissemination areas in Canada. A k-medians clustering approach was used to create categorical food environment variables that were easy to use and amenable to dissemination. Validity of the measures was assessed by comparing the food environment measures from Can-FED with measures created using Enhanced Points of Interest data by DMTI Spatial Inc. and data from a municipal health inspection list. Validity was also assessed by calculating the geographic variability in food environments across census metropolitan areas (CMAs) and assessing associations between CMA-level food environments and CMA-level health indicators. RESULTS: Two versions of Can-FED were created: a researcher file that must be accessed within a secure Statistics Canada environment and a general-use file available online. Agreement between Can-FED food environment measures and those derived from a proprietary dataset and a municipal health inspection list ranged from rs=0.28 for convenience store density and rs=0.53 for restaurant density. At the CMA level, there is wide geographic variation in the food environment with evidence of patterning by health indicators. INTERPRETATION: Can-FED is a valid and accessible dataset of pan-Canadian food environment measures that was created from the BR, a data source that has not been explored fully for health research.
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....c1ff2507f4ab624a796d870523cfb12b
- Full Text :
- https://doi.org/10.17863/cam.83271