Angela D Liese1, Timothy L Barnes, Archana P Lamichhane, James D Hibbert, Natalie Colabianchi, Andrew B Lawson. 1. Center for Research in Nutrition and Health Disparities, Arnold School of Public Health, University of South Carolina, Columbia, SC; Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC. Electronic address: liese@sc.edu.
Abstract
OBJECTIVE: Commercial listings of food retail outlets are increasingly used by community members and food policy councils and in multilevel intervention research to identify areas with limited access to healthier food. This study quantified the amount of count, type, and geospatial error in 2 commercial data sources. METHODS: InfoUSA and Dun and Bradstreet were compared with a validated field census and validity statistics were calculated. RESULTS: Considering only completeness, Dun and Bradstreet data undercounted 24% of existing supermarkets and grocery stores, and InfoUSA, 29%. In addition, considering accuracy of outlet type assignment increased the undercount error to 42% and 39%, respectively. Marked overcount existed as well, and only 43% of existing supermarkets were correctly identified with respect to presence, outlet type, and location. CONCLUSIONS AND IMPLICATIONS: Relying exclusively on secondary data to characterize the food environment will result in substantial error. Whereas extensive data cleaning can offset some error, verification of outlets with a field census is still the method of choice.
OBJECTIVE: Commercial listings of food retail outlets are increasingly used by community members and food policy councils and in multilevel intervention research to identify areas with limited access to healthier food. This study quantified the amount of count, type, and geospatial error in 2 commercial data sources. METHODS: InfoUSA and Dun and Bradstreet were compared with a validated field census and validity statistics were calculated. RESULTS: Considering only completeness, Dun and Bradstreet data undercounted 24% of existing supermarkets and grocery stores, and InfoUSA, 29%. In addition, considering accuracy of outlet type assignment increased the undercount error to 42% and 39%, respectively. Marked overcount existed as well, and only 43% of existing supermarkets were correctly identified with respect to presence, outlet type, and location. CONCLUSIONS AND IMPLICATIONS: Relying exclusively on secondary data to characterize the food environment will result in substantial error. Whereas extensive data cleaning can offset some error, verification of outlets with a field census is still the method of choice.
Authors: Lisa M Powell; Euna Han; Shannon N Zenk; Tamkeen Khan; Christopher M Quinn; Kevin P Gibbs; Oksana Pugach; Dianne C Barker; Elissa A Resnick; Jaana Myllyluoma; Frank J Chaloupka Journal: Health Place Date: 2011-06-02 Impact factor: 4.078
Authors: Angela D Liese; Natalie Colabianchi; Archana P Lamichhane; Timothy L Barnes; James D Hibbert; Dwayne E Porter; Michele D Nichols; Andrew B Lawson Journal: Am J Epidemiol Date: 2010-10-20 Impact factor: 4.897
Authors: Peter James; Jaime E Hart; J Aaron Hipp; Jonathan A Mitchell; Jacqueline Kerr; Philip M Hurvitz; Karen Glanz; Francine Laden Journal: Cancer Epidemiol Biomarkers Prev Date: 2017-02-14 Impact factor: 4.254
Authors: Iris N Gomez-Lopez; Philippa Clarke; Alex B Hill; Daniel M Romero; Robert Goodspeed; Veronica J Berrocal; V G Vinod Vydiswaran; Tiffany C Veinot Journal: J Urban Health Date: 2017-06 Impact factor: 3.671
Authors: Michael P Burke; Lauren H Martini; Christine E Blake; Nicholas A Younginer; Carrie L Draper; Bethany A Bell; Angela D Liese; Sonya J Jones Journal: J Nutr Educ Behav Date: 2017-01-07 Impact factor: 3.045
Authors: Peter James; Marta Jankowska; Christine Marx; Jaime E Hart; David Berrigan; Jacqueline Kerr; Philip M Hurvitz; J Aaron Hipp; Francine Laden Journal: Am J Prev Med Date: 2016-08-12 Impact factor: 5.043