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Examining the validity and utility of two secondary sources of food environment data against street audits in England

BACKGROUND: Secondary data containing the locations of food outlets is increasingly used in nutrition and obesity research and policy. However, evidence evaluating these data is limited. This study validates two sources of secondary food environment data: Ordnance Survey Points of Interest data (POI...

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Autores principales: Wilkins, Emma L., Radley, Duncan, Morris, Michelle A., Griffiths, Claire
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738834/
https://www.ncbi.nlm.nih.gov/pubmed/29262827
http://dx.doi.org/10.1186/s12937-017-0302-1
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author Wilkins, Emma L.
Radley, Duncan
Morris, Michelle A.
Griffiths, Claire
author_facet Wilkins, Emma L.
Radley, Duncan
Morris, Michelle A.
Griffiths, Claire
author_sort Wilkins, Emma L.
collection PubMed
description BACKGROUND: Secondary data containing the locations of food outlets is increasingly used in nutrition and obesity research and policy. However, evidence evaluating these data is limited. This study validates two sources of secondary food environment data: Ordnance Survey Points of Interest data (POI) and food hygiene data from the Food Standards Agency (FSA), against street audits in England and appraises the utility of these data. METHODS: Audits were conducted across 52 Lower Super Output Areas in England. All streets within each Lower Super Output Area were covered to identify the name and street address of all food outlets therein. Audit-identified outlets were matched to outlets in the POI and FSA data to identify true positives (TP: outlets in both the audits and the POI/FSA data), false positives (FP: outlets in the POI/FSA data only) and false negatives (FN: outlets in the audits only). Agreement was assessed using positive predictive values (PPV: TP/(TP + FP)) and sensitivities (TP/(TP + FN)). Variations in sensitivities and PPVs across environment and outlet types were assessed using multi-level logistic regression. Proprietary classifications within the POI data were additionally used to classify outlets, and agreement between audit-derived and POI-derived classifications was assessed. RESULTS: Street audits identified 1172 outlets, compared to 1100 and 1082 for POI and FSA respectively. PPVs were statistically significantly higher for FSA (0.91, CI: 0.89–0.93) than for POI (0.86, CI: 0.84–0.88). However, sensitivity values were not different between the two datasets. Sensitivity and PPVs varied across outlet types for both datasets. Without accounting for this, POI had statistically significantly better PPVs in rural and affluent areas. After accounting for variability across outlet types, FSA had statistically significantly better sensitivity in rural areas and worse sensitivity in rural middle affluence areas (relative to deprived). Audit-derived and POI-derived classifications exhibited substantial agreement (p < 0.001; Kappa = 0.66, CI: 0.63–0.70). CONCLUSIONS: POI and FSA data have good agreement with street audits; although both datasets had geographic biases which may need to be accounted for in analyses. Use of POI proprietary classifications is an accurate method for classifying outlets, providing time savings compared to manual classification of outlets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi: 10.1186/s12937-017-0302-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-57388342018-01-02 Examining the validity and utility of two secondary sources of food environment data against street audits in England Wilkins, Emma L. Radley, Duncan Morris, Michelle A. Griffiths, Claire Nutr J Research BACKGROUND: Secondary data containing the locations of food outlets is increasingly used in nutrition and obesity research and policy. However, evidence evaluating these data is limited. This study validates two sources of secondary food environment data: Ordnance Survey Points of Interest data (POI) and food hygiene data from the Food Standards Agency (FSA), against street audits in England and appraises the utility of these data. METHODS: Audits were conducted across 52 Lower Super Output Areas in England. All streets within each Lower Super Output Area were covered to identify the name and street address of all food outlets therein. Audit-identified outlets were matched to outlets in the POI and FSA data to identify true positives (TP: outlets in both the audits and the POI/FSA data), false positives (FP: outlets in the POI/FSA data only) and false negatives (FN: outlets in the audits only). Agreement was assessed using positive predictive values (PPV: TP/(TP + FP)) and sensitivities (TP/(TP + FN)). Variations in sensitivities and PPVs across environment and outlet types were assessed using multi-level logistic regression. Proprietary classifications within the POI data were additionally used to classify outlets, and agreement between audit-derived and POI-derived classifications was assessed. RESULTS: Street audits identified 1172 outlets, compared to 1100 and 1082 for POI and FSA respectively. PPVs were statistically significantly higher for FSA (0.91, CI: 0.89–0.93) than for POI (0.86, CI: 0.84–0.88). However, sensitivity values were not different between the two datasets. Sensitivity and PPVs varied across outlet types for both datasets. Without accounting for this, POI had statistically significantly better PPVs in rural and affluent areas. After accounting for variability across outlet types, FSA had statistically significantly better sensitivity in rural areas and worse sensitivity in rural middle affluence areas (relative to deprived). Audit-derived and POI-derived classifications exhibited substantial agreement (p < 0.001; Kappa = 0.66, CI: 0.63–0.70). CONCLUSIONS: POI and FSA data have good agreement with street audits; although both datasets had geographic biases which may need to be accounted for in analyses. Use of POI proprietary classifications is an accurate method for classifying outlets, providing time savings compared to manual classification of outlets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi: 10.1186/s12937-017-0302-1) contains supplementary material, which is available to authorized users. BioMed Central 2017-12-20 /pmc/articles/PMC5738834/ /pubmed/29262827 http://dx.doi.org/10.1186/s12937-017-0302-1 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Wilkins, Emma L.
Radley, Duncan
Morris, Michelle A.
Griffiths, Claire
Examining the validity and utility of two secondary sources of food environment data against street audits in England
title Examining the validity and utility of two secondary sources of food environment data against street audits in England
title_full Examining the validity and utility of two secondary sources of food environment data against street audits in England
title_fullStr Examining the validity and utility of two secondary sources of food environment data against street audits in England
title_full_unstemmed Examining the validity and utility of two secondary sources of food environment data against street audits in England
title_short Examining the validity and utility of two secondary sources of food environment data against street audits in England
title_sort examining the validity and utility of two secondary sources of food environment data against street audits in england
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738834/
https://www.ncbi.nlm.nih.gov/pubmed/29262827
http://dx.doi.org/10.1186/s12937-017-0302-1
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