Monitoring the Nutrient Composition of Food Prepared Out-of-Home in the United Kingdom: Database Development and Case Study
BackgroundHand transcribing nutrient composition data from websites requires extensive human resources and is prone to error. As a result, there are limited nutrient composition data on food prepared out of the home in the United Kingdom. Such data are crucial for understandi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2022-09-01
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| Series: | JMIR Public Health and Surveillance |
| Online Access: | https://publichealth.jmir.org/2022/9/e39033 |
| _version_ | 1827857996821037056 |
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| author | Yuru Huang Thomas Burgoine Michael Essman Dolly R Z Theis Tom R P Bishop Jean Adams |
| author_facet | Yuru Huang Thomas Burgoine Michael Essman Dolly R Z Theis Tom R P Bishop Jean Adams |
| author_sort | Yuru Huang |
| collection | DOAJ |
| description |
BackgroundHand transcribing nutrient composition data from websites requires extensive human resources and is prone to error. As a result, there are limited nutrient composition data on food prepared out of the home in the United Kingdom. Such data are crucial for understanding and monitoring the out-of-home food environment, which aids policy making. Automated data collection from publicly available sources offers a potential low-resource solution to address this gap.
ObjectiveIn this paper, we describe the first UK longitudinal nutritional database of food prepared out of the home, MenuTracker. As large chains will be required to display calorie information on their UK menus from April 2022, we also aimed to identify which chains reported their nutritional information online in November 2021. In a case study to demonstrate the utility of MenuTracker, we estimated the proportions of menu items exceeding recommended energy and nutrient intake (eg, >600 kcal per meal).
MethodsWe have collated nutrient composition data of menu items sold by large chain restaurants quarterly since March 2021. Large chains were defined as those with 250 employees or more (those covered by the new calorie labeling policy) or belonging to the top 100 restaurants based on sales volume. We developed scripts in Python to automate the data collection process from business websites. Various techniques were used to harvest web data and extract data from nutritional tables in PDF format.
ResultsAutomated Python programs reduced approximately 85% of manual work, totaling 500 hours saved for each wave of data collection. As of January 2022, MenuTracker has 76,405 records from 88 large out-of-home food chains at 4 different time points (ie, March, June, September, and December) in 2021. In constructing the database, we found that one-quarter (24.5%, 256/1043) of large chains, which are likely to be subject to the United Kingdom’s calorie menu labeling regulations, provided their nutritional information online in November 2021. Across these chains, 24.7% (16,391/66,295) of menu items exceeded the UK government’s recommendation of a maximum of 600 kcal for a single meal. Comparable figures were 46.4% (29,411/63,416) for saturated fat, 34.7% (21,964/63,388) for total fat, 17.6% (11,260/64,051) for carbohydrates, 17.8% (11,434/64,059) for sugar, and 35.2% (22,588/64,086) for salt. Furthermore, 0.7% to 7.1% of the menu items exceeded the maximum daily recommended intake for these nutrients.
ConclusionsMenuTracker is a valuable resource that harnesses the power of data science techniques to use publicly available data online. Researchers, policy makers, and consumers can use MenuTracker to understand and assess foods available from out-of-home food outlets. The methods used in development are available online and can be used to establish similar databases elsewhere. |
| first_indexed | 2024-03-12T12:48:24Z |
| format | Article |
| id | doaj.art-67cb5b845b5440869d2f528c874ce935 |
| institution | Directory Open Access Journal |
| issn | 2369-2960 |
| language | English |
| last_indexed | 2024-03-12T12:48:24Z |
| publishDate | 2022-09-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Public Health and Surveillance |
| spelling | doaj.art-67cb5b845b5440869d2f528c874ce9352023-08-28T23:02:36ZengJMIR PublicationsJMIR Public Health and Surveillance2369-29602022-09-0189e3903310.2196/39033Monitoring the Nutrient Composition of Food Prepared Out-of-Home in the United Kingdom: Database Development and Case StudyYuru Huanghttps://orcid.org/0000-0002-6800-0272Thomas Burgoinehttps://orcid.org/0000-0001-6936-3801Michael Essmanhttps://orcid.org/0000-0001-5017-3880Dolly R Z Theishttps://orcid.org/0000-0001-8309-8565Tom R P Bishophttps://orcid.org/0000-0002-3407-2526Jean Adamshttps://orcid.org/0000-0002-5733-7830 BackgroundHand transcribing nutrient composition data from websites requires extensive human resources and is prone to error. As a result, there are limited nutrient composition data on food prepared out of the home in the United Kingdom. Such data are crucial for understanding and monitoring the out-of-home food environment, which aids policy making. Automated data collection from publicly available sources offers a potential low-resource solution to address this gap. ObjectiveIn this paper, we describe the first UK longitudinal nutritional database of food prepared out of the home, MenuTracker. As large chains will be required to display calorie information on their UK menus from April 2022, we also aimed to identify which chains reported their nutritional information online in November 2021. In a case study to demonstrate the utility of MenuTracker, we estimated the proportions of menu items exceeding recommended energy and nutrient intake (eg, >600 kcal per meal). MethodsWe have collated nutrient composition data of menu items sold by large chain restaurants quarterly since March 2021. Large chains were defined as those with 250 employees or more (those covered by the new calorie labeling policy) or belonging to the top 100 restaurants based on sales volume. We developed scripts in Python to automate the data collection process from business websites. Various techniques were used to harvest web data and extract data from nutritional tables in PDF format. ResultsAutomated Python programs reduced approximately 85% of manual work, totaling 500 hours saved for each wave of data collection. As of January 2022, MenuTracker has 76,405 records from 88 large out-of-home food chains at 4 different time points (ie, March, June, September, and December) in 2021. In constructing the database, we found that one-quarter (24.5%, 256/1043) of large chains, which are likely to be subject to the United Kingdom’s calorie menu labeling regulations, provided their nutritional information online in November 2021. Across these chains, 24.7% (16,391/66,295) of menu items exceeded the UK government’s recommendation of a maximum of 600 kcal for a single meal. Comparable figures were 46.4% (29,411/63,416) for saturated fat, 34.7% (21,964/63,388) for total fat, 17.6% (11,260/64,051) for carbohydrates, 17.8% (11,434/64,059) for sugar, and 35.2% (22,588/64,086) for salt. Furthermore, 0.7% to 7.1% of the menu items exceeded the maximum daily recommended intake for these nutrients. ConclusionsMenuTracker is a valuable resource that harnesses the power of data science techniques to use publicly available data online. Researchers, policy makers, and consumers can use MenuTracker to understand and assess foods available from out-of-home food outlets. The methods used in development are available online and can be used to establish similar databases elsewhere.https://publichealth.jmir.org/2022/9/e39033 |
| spellingShingle | Yuru Huang Thomas Burgoine Michael Essman Dolly R Z Theis Tom R P Bishop Jean Adams Monitoring the Nutrient Composition of Food Prepared Out-of-Home in the United Kingdom: Database Development and Case Study JMIR Public Health and Surveillance |
| title | Monitoring the Nutrient Composition of Food Prepared Out-of-Home in the United Kingdom: Database Development and Case Study |
| title_full | Monitoring the Nutrient Composition of Food Prepared Out-of-Home in the United Kingdom: Database Development and Case Study |
| title_fullStr | Monitoring the Nutrient Composition of Food Prepared Out-of-Home in the United Kingdom: Database Development and Case Study |
| title_full_unstemmed | Monitoring the Nutrient Composition of Food Prepared Out-of-Home in the United Kingdom: Database Development and Case Study |
| title_short | Monitoring the Nutrient Composition of Food Prepared Out-of-Home in the United Kingdom: Database Development and Case Study |
| title_sort | monitoring the nutrient composition of food prepared out of home in the united kingdom database development and case study |
| url | https://publichealth.jmir.org/2022/9/e39033 |
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