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Background: Food 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. Methods: We 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. Results: After 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. Discussion: Our 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. [ABSTRACT FROM AUTHOR]

Background: Low fruit and vegetable consumption is a risk factor for poor health. Studies have shown consumption varies across neighbourhoods, with lower intakes in disadvantaged neighbourhoods. However, findings are inconsistent, suggesting that socio-spatial inequities in diet could be context-specific, highlighting a need for international comparisons across contexts. This study examined variations in fruit and vegetable consumption among adults from neighbourhoods of varying socioeconomic status (SES) across seven countries (Australia, Canada, Netherlands, New Zealand, Portugal, Scotland, US). Methods: Data from seven existing studies, identified through literature searches and knowledge of co-authors, which collected measures of both neighbourhood-level SES and fruit and vegetable consumption were used. Logistic regression was used to examine associations between neighbourhood-level SES and binary fruit and vegetable consumption separately, adjusting for neighbourhood clustering and age, gender and education. As much as possible, variables were treated in a consistent manner in the analysis for each study to allow the identification of patterns of association within study and to examine differences in the associations across studies. Results: Adjusted analyses showed evidence of an association between neighbourhood-level SES and fruit consumption in Canada, New Zealand and Scotland, with increased odds of greater fruit intake in higher SES neighbourhoods. In Australia, Canada, New Zealand and Portugal, those residing in higher SES neighbourhoods had increased odds of greater vegetable intake. The other studies showed no evidence of a difference by neighbourhood-level SES. Conclusions: Acknowledging discrepancies across studies in terms of sampling, measures, and definitions of neighbourhoods, this opportunistic study, which treated data in a consistent manner, suggests that associations between diet and neighbourhood-level socioeconomic status vary across countries. Neighbourhood socioeconomic disadvantage may differentially impact on access to resources in which produce is available in different countries. Neighbourhood environments have the potential to influence behaviour and further research is required to examine the context in which these associations arise. [ABSTRACT FROM AUTHOR]

Background: Aspects of the food environment such as the availability of different types of food stores have recently emerged as key modifiable factors that may contribute to the increased prevalence of obesity. Given that many of these studies have derived their results based on secondary datasets and the relationship of food stores with individual weight outcomes has been reported to vary by store type, it is important to understand the extent to which often-used secondary data correctly classify food stores. We evaluated the classification bias of food stores in Dun & Bradstreet (D&B) and InfoUSA commercial business lists. Methods: We performed a full census in 274 randomly selected census tracts in the Chicago metropolitan area and collected detailed store attributes inside stores for classification. Store attributes were compared by classification match status and store type. Systematic classification bias by census tract characteristics was assessed in multivariate regression. Results: D&B had a higher classification match rate than InfoUSA for supermarkets and grocery stores, while InfoUSA was higher for convenience stores. Both lists were more likely to correctly classify large supermarkets, grocery stores, and convenience stores with more cash registers and different types of service counters (supermarkets and grocery stores only). The likelihood of a correct classification match for supermarkets and grocery stores did not vary systemically by tract characteristics whereas convenience stores were more likely to be misclassified in predominately Black tracts. Conclusion: Researches can rely on classification of food stores in commercial datasets for supermarkets and grocery stores whereas classifications for convenience and specialty food stores are subject to some systematic bias by neighborhood racial/ethnic composition. [ABSTRACT FROM AUTHOR]