Evidence map›Paper›PMID 34589209›Full record

ArticleJournal of nutritional science2021

Categorising ultra-processed foods in large-scale cohort studies: evidence from the Nurses' Health Studies, the Health Professionals Follow-up Study, and the Growing Up Today Study.

Neha Khandpur, Sinara Rossato, Jean-Philippe Drouin-Chartier, Mengxi Du, Euridice M Steele, Laura Sampson, Carlos Monteiro, Fang F Zhang, Walter Willett, Teresa T Fung and 1 more

Abstract read
In one paragraph

Article in Journal of nutritional science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 1 pooled it
–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

73 citing papers in PubMed, 1 synthesis or guideline pooled it.

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13 more citing papers are in PubMed but not listed here.

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

11 authors.

Neha KhandpurDepartment of Nutrition, School of Public Health, University of São Paulo, Av. Dr. Arnaldo, 715, São Paulo, Brazil.
Sinara RossatoDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Jean-Philippe Drouin-ChartierDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID 0000-0002-6733-4801
Mengxi DuDepartment of Public Health & Community Medicine, Friedman School of Nutrition, Tufts University, Medford, MA, USA.ORCID 0000-0002-6406-7250
Euridice M SteeleDepartment of Nutrition, School of Public Health, University of São Paulo, Av. Dr. Arnaldo, 715, São Paulo, Brazil.
Laura SampsonDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Carlos MonteiroDepartment of Nutrition, School of Public Health, University of São Paulo, Av. Dr. Arnaldo, 715, São Paulo, Brazil.
Fang F ZhangDepartment of Public Health & Community Medicine, Friedman School of Nutrition, Tufts University, Medford, MA, USA.
Walter WillettDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Teresa T FungDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID 0000-0002-0159-482X
Qi SunDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This manuscript details the strategy employed for categorising food items based on their processing levels into the four NOVA groups. Semi-quantitative food frequency questionnaires (FFQs) from the Nurses' Health Studies (NHS) I and II, the Health Professionals Follow-up Study (HPFS) and the Growing Up Today Studies (GUTS) I and II cohorts were used. The four-stage approach included: (i) the creation of a complete food list from the FFQs; (ii) assignment of food items to a NOVA group by three researchers; (iii) checking for consensus in categorisation and shortlisting discordant food items; (iv) discussions with experts and use of additional resources (research dieticians, cohort-specific documents, online grocery store scans) to guide the final categorisation of the short-listed items. At stage 1, 205 and 315 food items were compiled from the NHS and HPFS, and the GUTS FFQs, respectively. Over 70 % of food items from all cohorts were assigned to a NOVA group after stage 2. The remainder were shortlisted for further discussion (stage 3). After two rounds of reviews at stage 4, 95⋅6 % of food items (NHS + HPFS) and 90⋅7 % items (GUTS) were categorised. The remaining products were assigned to a non-ultra-processed food group (primary categorisation) and flagged for sensitivity analyses at which point they would be categorised as ultra-processed. Of all items in the food lists, 36⋅1 % in the NHS and HPFS cohorts and 43⋅5 % in the GUTS cohorts were identified as ultra-processed. Future work is needed to validate this approach. Documentation and discussions of alternative approaches for categorisation are encouraged.

Indexed as

Fast FoodsDietFollow-Up StudiesHumansCohort studiesDiet categorisationExpert discussionFFQ, food frequency questionnaireFood frequency questionnairesGUTS, Growing Up Today StudyNHS, Nurses’ Health StudyNova groupsUltra-processed foods

Identifiers

PMID34589209
PMCPMC8453454

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Registered trials

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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.