Evidence map›Paper›PMID 36074559›Full record

ArticleJMIR public health and surveillance2022

Monitoring the Nutrient Composition of Food Prepared Out-of-Home in the United Kingdom: Database Development and Case Study.

Yuru Huang, Thomas Burgoine, Michael Essman, Dolly R Z Theis, Tom R P Bishop, Jean Adams

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yuru HuangMedical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0002-6800-0272
Thomas BurgoineMedical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0001-6936-3801
Michael EssmanMedical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0001-5017-3880
Dolly R Z TheisMedical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0001-8309-8565
Tom R P BishopMedical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0002-3407-2526
Jean AdamsMedical Research Council (MRC) Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0002-5733-7830

Funding

Medical Research Council MC_UU_00006/7
6 · The paper itself

Abstract

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.

Indexed as

Food LabelingNutrition PolicyHumansMealsNutrientsNutritive Valuechainsdata sciencefood prepared out of the homenutritional databaseout-of-homeweb scraping

Identifiers

PMID36074559
PMCPMC9501650

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.