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Comparing the accuracy of food outlet datasets in an urban environment

Michelle S. Wong, Jennifer M. Peyton, Timothy M. Shields, Frank C. Curriero, Kimberly A. Gudzune
  • Jennifer M. Peyton
    Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University School of Medicine, United States
  • Timothy M. Shields
    Department of Epidemiology, Johns Hopkins School of Public Health, United States
  • Frank C. Curriero
    Department of Epidemiology, Johns Hopkins School of Public Health, United States
  • Kimberly A. Gudzune
    Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University School of Medicine; Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Abstract

Studies that investigate the relationship between the retail food environment and health outcomes often use geospatial datasets. Prior studies have identified challenges of using the most common data sources. Retail food environment datasets created through academic-government partnership present an alternative, but their validity (retail existence, type, location) has not been assessed yet. In our study, we used ground-truth data to compare the validity of two datasets, a 2015 commercial dataset (InfoUSA) and data collected from 2012 to 2014 through the Maryland Food Systems Mapping Project (MFSMP), an academic-government partnership, on the retail food environment in two low-income, inner city neighbourhoods in Baltimore City. We compared sensitivity and positive predictive value (PPV) of the commercial and academic-government partnership data to ground-truth data for two broad categories of unhealthy food retailers: small food retailers and quick-service restaurants. Ground-truth data was collected in 2015 and analysed in 2016. Compared to the ground-truth data, MFSMP and InfoUSA generally had similar sensitivity that was greater than 85%. MFSMP had higher PPV compared to InfoUSA for both small food retailers (MFSMP: 56.3% vs InfoUSA: 40.7%) and quick-service restaurants (MFSMP: 58.6% vs InfoUSA: 36.4%). We conclude that data from academic-government partnerships like MFSMP might be an attractive alternative option and improvement to relying only on commercial data. Other research institutes or cities might consider efforts to create and maintain such an environmental dataset. Even if these datasets cannot be updated on an annual basis, they are likely more accurate than commercial data.

Keywords

Retail food environment; Urban food environment; Validity; Geographic Information Systems

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Submitted: 2017-01-16 21:07:48
Published: 2017-05-11 12:42:44
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Copyright (c) 2017 Michelle Sandra Wong

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