摘要 : Objective: Commercially available business (CAB) datasets for food environments have been investigated for error in large urban contexts and some rural areas, but there is a relative dearth of literature that reports error across ... 展开 Objective: Commercially available business (CAB) datasets for food environments have been investigated for error in large urban contexts and some rural areas, but there is a relative dearth of literature that reports error across regions of variable rurality. The objective of the current study was to assess the validity of a CAB dataset using a government dataset at the provincial scale. Design: A ground-truthed dataset provided by the government of Newfoundland and Labrador (NL) was used to assess a popular commercial dataset. Concordance, sensitivity, positive-predictive value (PPV) and geocoding errors were calculated. Measures were stratified by store types and rurality to investigate any association between these variables and database accuracy. Setting: NL, Canada. Participants: The current analysis used store-level (ecological) data. Results: Of 1125 stores, there were 380 stores that existed in both datasets and were considered true-positive stores. The mean positional error between a ground-truthed and test point was 17 center dot 72 km. When compared with the provincial dataset of businesses, grocery stores had the greatest agreement, sensitivity = 0 center dot 64, PPV = 0 center dot 60 and concordance = 0 center dot 45. Gas stations had the least agreement, sensitivity = 0 center dot 26, PPV = 0 center dot 32 and concordance = 0 center dot 17. Only 4 % of commercial data points in rural areas matched every criterion examined. Conclusions: The commercial dataset exhibits a low level of agreement with the ground-truthed provincial data. Particularly retailers in rural areas or belonging to the gas station category suffered from misclassification and/or geocoding errors. Taken together, the commercial dataset is differentially representative of the ground-truthed reality based on store-type and rurality/urbanity. 收起