Faculty of Health, School of Health Administration, Dalhousie University, 5850 College Street, PO Box 15000, Halifax, NS B3H 4R2, Canada.
Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON M5T 3M7, Canada.
Public Health Nutr. 2020 Aug;23(11):1889-1895. doi: 10.1017/S1368980019004506. Epub 2020 Apr 16.
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.
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.
NL, Canada.
The current analysis used store-level (ecological) data.
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 17·72 km. When compared with the provincial dataset of businesses, grocery stores had the greatest agreement, sensitivity = 0·64, PPV = 0·60 and concordance = 0·45. Gas stations had the least agreement, sensitivity = 0·26, PPV = 0·32 and concordance = 0·17. Only 4 % of commercial data points in rural areas matched every criterion examined.
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.
已针对大型城市和部分农村地区的食品环境商业（CAB）数据集的错误进行了调查，但关于不同农村程度地区的错误报告相对较少。本研究的目的是在省级规模上使用政府数据集评估 CAB 数据集的有效性。
使用纽芬兰和拉布拉多省政府提供的经过实地核实的数据集来评估流行的商业数据集。计算了一致性、灵敏度、阳性预测值（PPV）和地理编码错误。根据商店类型和农村程度对措施进行分层，以调查这些变量与数据库准确性之间的任何关联。
加拿大纽芬兰和拉布拉多省。
目前的分析使用了商店层面（生态）数据。
在 1125 家商店中，有 380 家商店同时存在于两个数据集中，被认为是真正的阳性商店。实地核实点和测试点之间的平均位置误差为 17.72 公里。与省级商业数据集相比，杂货店的一致性最高，灵敏度=0.64、PPV=0.60 和一致性=0.45。加油站的一致性最低，灵敏度=0.26、PPV=0.32 和一致性=0.17。农村地区只有 4%的商业数据点符合检查的所有标准。
商业数据集与经过实地核实的省级数据显示出低水平的一致性。特别是农村地区或属于加油站类别的零售商受到了分类错误和/或地理编码错误的影响。总体而言，商业数据集在基于商店类型和农村/城市程度的基础上，对实地核实的现实具有不同的代表性。
