Evidence map›Paper›PMID 40465876›Full record

ArticleRevista brasileira de epidemiologia = Brazilian journal of epidemiology2025

Nova food classification system: a contribution from Brazilian epidemiology.

Maria Laura da Costa Louzada, Kamila Tiemann Gabe

Abstract read
In one paragraph

Article in Revista brasileira de epidemiologia = Brazilian journal of epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
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

2 authors.

Maria Laura da Costa LouzadaUniversidade de São Paulo, Center for Epidemiological Research in Nutrition and Health - São Paulo (SP), Brazil.ORCID http://orcid.org/0000-0002-3756-2301
Kamila Tiemann GabeUniversidade de São Paulo, Center for Epidemiological Research in Nutrition and Health - São Paulo (SP), Brazil.ORCID http://orcid.org/0000-0001-5138-9552

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This essay presents the Nova food classification, a conceptual innovation of Brazilian epidemiology, describing its genesis, the scientific evidence derived from its application, and its implications for public health. Created in 2010, Nova classifies foods into four groups based on the degree of processing: unprocessed or minimally processed foods, processed culinary ingredients, processed foods, and ultra-processed foods. Since its development, several epidemiological studies have demonstrated the negative impacts of high consumption of ultra-processed foods on health, such as associations with various non-communicable diseases, including obesity, diabetes, cardiovascular diseases and mental health outcomes. In Brazil, the consumption of these foods has increased significantly in recent decades, with the caloric share of ultra-processed foods rising from 12.6 to 18.4% between 2002-2003 and 2017-2018, with this increase being more pronounced among more vulnerable socioeconomic groups. Nova underpins the recommendations of the Brazilian Dietary Guidelines and has played a crucial role in informing public policies, such as the update of the National basic food basket and the guidelines of the National School Feeding Program, which aim to limit the access to ultra-processed foods. Finally, the essay addresses the political and scientific challenges, including the need for more experimental studies to strengthen the evidence and the potential of fiscal and marketing regulation strategies that take into account the impact of food processing on health.

Indexed as

Fast FoodsFoodFood HandlingBrazilHumansNutrition PolicyPublic Health

Identifiers

PMID40465876
PMCPMC12129239

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.