title: Classification bias in commercial business lists for retail food stores in the U.S.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: College of Pharmacy, Gachon University, Yeonsu-gu, Incheon, Korea. eahan@gachon.ac.kr
authors.forename: Euna
authors.initials: E
authors.lastname: Han
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Powell
authors.forename: Shannon N
authors.initials: SN
authors.lastname: Zenk
authors.forename: Leah
authors.initials: L
authors.lastname: Rimkus
authors.forename: Punam
authors.initials: P
authors.lastname: Ohri-Vachaspati
authors.forename: Frank J
authors.initials: FJ
authors.lastname: Chaloupka
mesh_terms: D019352:Censuses; D003132:Commerce / Q000145:classification*; D016208:Databases, Factual; D005523:Food Supply / Q000145:classification* / Q000706:statistics & numerical data*; D006801:Humans; D040541:Marketing / Q000145:classification; D015999:Multivariate Analysis; D015995:Prevalence; D012044:Regression Analysis; D012959:Socioeconomic Factors; D014481:United States