Evidence map›Paper›PMID 42757202›Full record

ArticleEnvironmental research letters : ERL [Web site]2026

Health and environmental co-benefits of reducing ultra-processed food consumption in Argentina: a modelling study.

Christian García-Witulski, Mariano Rabassa, Oscar Melo, Juliana Helo Sarmiento, Valentina Strappa

Abstract read
In one paragraph

Article in Environmental research letters : ERL [Web site], 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Christian García-WitulskiCentro de Desarrollo Humano Sostenible, Pontificia Universidad Católica Argentina, Buenos Aires, Argentina.ORCID https://orcid.org/0000-0001-6444-0521
Mariano RabassaCentro de Desarrollo Humano Sostenible, Pontificia Universidad Católica Argentina, Buenos Aires, Argentina.ORCID https://orcid.org/0000-0002-7638-400X
Oscar MeloCentro Interdisciplinario de Cambio Global, Pontificia Universidad Católica de Chile, Santiago, Chile.
Juliana Helo SarmientoFacultad de Economía, Universidad de los Andes, Bogotá, Colombia.
Valentina StrappaCentro Latinoamericano de Excelencia en Cambio Climático y Salud, Universidad Peruana Cayetano Heredia, Lima, Perú.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Reducing ultra-processed food (UPF) consumption is widely proposed to improve population health, but whether it also generates environmental co-benefits depends on the foods that replace UPFs in the diet. We developed a comparative risk assessment macrosimulation using nationally representative dietary data from Argentina to estimate the mortality and economic burden attributable to UPF consumption and to evaluate policy-relevant reduction scenarios. The scenarios reduced UPF exposure without replacement or replaced the displaced dietary energy or food mass using alternative baskets, including one aligned with the National Dietary Guidelines. We also decomposed the environmental effects of the guideline-concordant basket by food group. In 2019, UPF consumption was associated with 9823 premature deaths, 349 686 years of life lost, and 0.84 billion international dollars in indirect costs. Under the most ambitious reduction target, alternative replacement baskets averted the same 8747 deaths but produced markedly different environmental outcomes, ranging from improvements in all four environmental indicators to increases in all four. The guideline-concordant basket increased water use and eutrophication while producing little change in greenhouse gas emissions and land use. The food-group decomposition showed that these aggregate outcomes reflected opposing contributions from a small number of foods. Reducing UPF consumption can generate substantial health and economic benefits, but environmental co-benefits are not automatic and depend on the dietary patterns that replace UPFs.

Indexed as

Argentinacomparative risk assessmenteconomic costsenvironmental impactsmortalityultra-processed foods

Identifiers

PMID42757202
PMCPMC13584633

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.