259925 Business-list data vs. ground observations - there's just no substitute for shoe leather in food-environment research

Monday, October 29, 2012

Sean C. Lucan, MD, MPH, MS , Department of Family and Social Medicine, Albert EInstein College of Medicine / Montefiore Medical Center, Bronx, NY
Andrew Maroko, PhD , Department of Health Sciences, Lehman College, City University of New York, Bronx, NY
Joel Bumol, BS , Albert Einstein College of Medicine, Yeshiva University, Bronx, NY
Luis Torrens, BA , School of Public Health at Hunter College, City University of New York, New York, NY
Heather Carlos, MS , -, Norris Cotton Cancer Center, Lebanon, NH
Ethan Berke, MD, MPH, MS , The Dartmouth Institute for Health Policy and Clinical Practice, Prevention Research Center at Dartmouth, Lebanon, NH
INTRODUCTION: Measuring the food environment through direct observation is resource-intensive and logistically challenging. A common solution is to use commercially-available business-list data. We sought to determine how a comprehensive dataset, by an industry leader in business lists, performs on the ground compared to direct observation. METHODS: Using a random sample of 150 street segments across the Bronx, researchers compared business-list data to direct ground observations. The main outcome measures were sensitivity, specificity, and positive and negative predictive value (PPV and NPV) of the business-list data for various types of food retail. To categorize food retail, researchers used Standard Industrial Classification (SIC) codes. RESULTS: The comprehensive business list had an overall sensitivity of 56.2%, specificity of 52.8%, PPV of 58.5% and NPV of 50.5%. Sub-groupings of food retail by SIC codes (e.g. “variety stores”, “grocery stores”, “specialty food stores”, and “restaurants”), showed better specificities and NPVs, with ranges from 83% and 80% respectively (for restaurants) to 94% and 97% respectively (specialty food stores). Sensitivities varied from 35% (grocery stores) to 68% (specialty food store). PPVs varied from 45% (grocery stores) to 68% (restaurants). CONCLUSIONS: Despite advertised completeness and accuracy of the business-list data, overall the data poorly represent the actual food environment on the ground, with especially poor sensitivity and PPV for grocery stores. Such data is wholly inadequate to substitute for direct ground observations, raising serious concerns about the validity of food-environment research using these kinds of datasets.

Learning Areas:
Biostatistics, economics
Environmental health sciences
Public health or related research

Learning Objectives:
Explain the problems of relying on business-list data to measure food environments. Demonstrate why even data from an industry leader in business lists is inadequate for food-environment studies (and may lead to incorrect conclusions).

Keywords: Food and Nutrition, Environment

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a grant-funded public-health researcher, focusing on how the food environment influences people's dietary behaviors. I am also a practicing family physician in the Bronx, treating patients afflicted by obesity and diet-related diseases.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.