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Comparing the accuracy of food outlet datasets in an urban environment

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...

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Autores principales: Wong, Michelle S., Peyton, Jennifer M., Shields, Timothy M., Curriero, Frank C., Gudzune, Kimberly A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6411042/
https://www.ncbi.nlm.nih.gov/pubmed/28555478
http://dx.doi.org/10.4081/gh.2017.546
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author Wong, Michelle S.
Peyton, Jennifer M.
Shields, Timothy M.
Curriero, Frank C.
Gudzune, Kimberly A.
author_facet Wong, Michelle S.
Peyton, Jennifer M.
Shields, Timothy M.
Curriero, Frank C.
Gudzune, Kimberly A.
author_sort Wong, Michelle S.
collection PubMed
description 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 prodictive 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 g eater 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.
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spelling pubmed-64110422019-03-11 Comparing the accuracy of food outlet datasets in an urban environment Wong, Michelle S. Peyton, Jennifer M. Shields, Timothy M. Curriero, Frank C. Gudzune, Kimberly A. Geospat Health Article 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 prodictive 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 g eater 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. 2017-05-11 /pmc/articles/PMC6411042/ /pubmed/28555478 http://dx.doi.org/10.4081/gh.2017.546 Text en http://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (CC BY-NC 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
spellingShingle Article
Wong, Michelle S.
Peyton, Jennifer M.
Shields, Timothy M.
Curriero, Frank C.
Gudzune, Kimberly A.
Comparing the accuracy of food outlet datasets in an urban environment
title Comparing the accuracy of food outlet datasets in an urban environment
title_full Comparing the accuracy of food outlet datasets in an urban environment
title_fullStr Comparing the accuracy of food outlet datasets in an urban environment
title_full_unstemmed Comparing the accuracy of food outlet datasets in an urban environment
title_short Comparing the accuracy of food outlet datasets in an urban environment
title_sort comparing the accuracy of food outlet datasets in an urban environment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6411042/
https://www.ncbi.nlm.nih.gov/pubmed/28555478
http://dx.doi.org/10.4081/gh.2017.546
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