Abstract
BackgroundFood environment characterization in health studies often requires data on the location of food stores and restaurants. While commercial business lists are commonly used as data sources for such studies, current literature provides little guidance on how to use validation study results to make decisions on which commercial business list to use and how to maximize the accuracy of those lists. Using data from a retrospective cohort study [Weight And Veterans’ Environments Study (WAVES)], we (a) explain how validity and bias information from existing validation studies (count accuracy, classification accuracy, locational accuracy, as well as potential bias by neighborhood racial/ethnic composition, economic characteristics, and urbanicity) were used to determine which commercial business listing to purchase for retail food outlet data and (b) describe the methods used to maximize the quality of the data and results of this approach.MethodsWe developed data improvement methods based on existing validation studies. These methods included purchasing records from commercial business lists (InfoUSA and Dun and Bradstreet) based on store/restaurant names as well as standard industrial classification (SIC) codes, reclassifying records by store type, improving geographic accuracy of records, and deduplicating records. We examined the impact of these procedures on food outlet counts in US census tracts.ResultsAfter cleaning and deduplicating, our strategy resulted in a 17.5% reduction in the count of food stores that were valid from those purchased from InfoUSA and 5.6% reduction in valid counts of restaurants purchased from Dun and Bradstreet. Locational accuracy was improved for 7.5% of records by applying street addresses of subsequent years to records with post-office (PO) box addresses. In total, up to 83% of US census tracts annually experienced a change (either positive or negative) in the count of retail food outlets between the initial purchase and the final dataset.DiscussionOur study provides a step-by-step approach to purchase and process business list data obtained from commercial vendors. The approach can be followed by studies of any size, including those with datasets too large to process each record by hand and will promote consistency in characterization of the retail food environment across studies.
Highlights
Food environment characterization in health studies often requires data on the location of food stores and restaurants
Column 1 in Table 6 shows the total number of records that were purchased from InfoUSA and Dun and Bradstreet; Column 2 is the number of records purchased by standard industrial classification (SIC) code; and Column 3 shows the additional number of records purchased because of the chain name list
This paper responds to the need for both strategies to improve retail food environment measurement and transparency in environmental characterization that will facilitate comparability between studies [27]
Summary
Food environment characterization in health studies often requires data on the location of food stores and restaurants. Because there are so many unique sources for government data, validation studies have shown results ranging from fair to almost perfect [6]. Another secondary source for retail food outlet data is phone books/yellow pages [6, 11]. Compiling and entering data from phone books/yellow pages across multiple administrative areas can be costly and challenging depending on the study scope, and validation studies have shown wide variability in data quality, with Fleischhacker et al [6] reporting fair to almost perfect validity
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