Evidence map›Paper›PMID 36913457›Full record

ArticleThe Journal of nutrition2023

Identifying and Estimating Ultraprocessed Food Intake in the US NHANES According to the Nova Classification System of Food Processing.

Eurídice Martínez Steele, Lauren E O'Connor, Filippa Juul, Neha Khandpur, Larissa Galastri Baraldi, Carlos A Monteiro, Niyati Parekh, Kirsten A Herrick

Open access · greenAbstract read
In one paragraph

Article in The Journal of nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.

0numbers the graph read from it
0cells of the map it votes in
96citing papers in PubMed
21.0field-weighted citation impact, top 1% of its field
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

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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

96 citing papers in PubMed, 118 citations in OpenAlex.

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 3 countries.

Eurídice Martínez SteeleDepartment of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil; Center for Epidemiological Studies in Health and Nutrition, University of São Paulo, São Paulo, Brazil. Electronic address: emar_steele@hotmail.com.
Lauren E O'ConnorRisk Factor Assessment Branch, Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
Filippa JuulDepartment of Public Health Policy and Management, School of Global Public Health, New York University, New York, NY, USA.
Neha KhandpurDepartment of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil; Center for Epidemiological Studies in Health and Nutrition, University of São Paulo, São Paulo, Brazil; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Larissa Galastri BaraldiCenter for Epidemiological Studies in Health and Nutrition, University of São Paulo, São Paulo, Brazil; Center for Food Studies and Research (NEPA), University of Campinas, Campinas, Brazil.
Carlos A MonteiroDepartment of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil; Center for Epidemiological Studies in Health and Nutrition, University of São Paulo, São Paulo, Brazil.
Niyati ParekhPublic Health Nutrition Program, School of Global Public health, New York University, New York, NY, USA; Department of Population Health Sciences, NYU Grossman School of Medicine, New York University, New York, NY, USA; Rory Meyers School of Nursing, New York University, New York, NY, USA.
Kirsten A HerrickRisk Factor Assessment Branch, Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
Universidade de São Paulo · BRNational Institutes of Health · USNew York University · USHarvard University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe degree of food processing may be an important dimension of diet in how it relates to health outcomes. A major challenge is standardizing food processing classification systems for commonly used datasets.

objectivesTo standardize and increase transparency in its application, we describe the approach used to classify foods and beverages according to the Nova food processing classification in the 24-h dietary recalls from the 2001-2018 cycles of What We Eat in America (WWEIA), NHANES, and investigate variability and potential for Nova misclassification within WWEIA, NHANES 2017-2018 data via various sensitivity analyses.

methodsFirst, we described how the Nova classification system was applied to the 2001-2018 WWEIA, NHANES data using the reference approach. Second, we calculated the percentage energy from Nova groups [1: unprocessed or minimally processed foods, 2: processed culinary ingredients, 3: processed foods, and 4: ultraprocessed foods (UPFs)] for the reference approach using day 1 dietary recall data from non-breastfed participants aged ≥1 y from the 2017-2018 WWEIA, NHANES. We then conducted 4 sensitivity analyses comparing potential alternative approaches (e.g., opting for more vs. less degree of processing for ambiguous items) to the reference approach, to assess how estimates differed.

resultsThe energy contribution of UPFs using the reference approach was 58.2% ± 0.9% of the total energy; unprocessed or minimally processed foods contributed 27.6% ± 0.7%, processed culinary ingredients contributed 5.2% ± 0.1%, and processed foods contributed 9.0% ± 0.3%. In sensitivity analyses, the dietary energy contribution of UPFs ranged from 53.4% ± 0.8% to 60.1% ± 0.8% across alternative approaches.

conclusionsWe present a reference approach for applying the Nova classification system to WWEIA, NHANES 2001-2018 data to promote standardization and comparability of future research. Alternative approaches are also described, with total energy from UPFs differing by ∼6% between approaches for 2017-2018 WWEIA, NHANES.

Indexed as

DietFast FoodsEatingEnergy IntakeFood HandlingHumansNutrition Surveys24-hour recallsdegree of processingdietary intakeNHANESNovaultraprocessedWWEIA

Identifiers

PMID36913457
PMCPMC10127522
OpenAlexW4311544523

What OpenQuestion holds

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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.