Open Access Research

Classification bias in commercial business lists for retail food stores in the U.S.

Euna Han1*, Lisa M Powell2, Shannon N Zenk3, Leah Rimkus2, Punam Ohri-Vachaspati4 and Frank J Chaloupka5

Author Affiliations

1 College of Pharmacy, Gachon University, 534-2 Yeonsu3-dong, Yeonsu-gu, Incheon, 406-799, Korea

2 Institute for Health Research and Policy & Department of Economics, University of Illinois at Chicago, 1747 West Roosevelt Road, Chicago, IL, 60608, USA

3 College of Nursing, University of Illinois at Chicago, 845 South Damen Avenue MC 802, Chicago, IL, 60612, USA

4 School of Nutrition and Health Promotion, Arizona State University, 500 N. Third Street, Phoenix, AZ, 85004, USA

5 Department of Economics & Institute for Health Research and Policy, University of Illinois at Chicago, 1747 West Roosevelt Road, Chicago, IL, 60608, USA

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International Journal of Behavioral Nutrition and Physical Activity 2012, 9:46  doi:10.1186/1479-5868-9-46

Published: 18 April 2012

Abstract

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.

Keywords:
Commercial business lists; Food stores; Classification error