Premature deaths attributable to the consumption of ultra-processed foods: a comparative assessment modelling study in eight countries
Bibliographic record
Abstract
ABSTRACT Background Ultra-processed foods (UPFs) are becoming dominant in the global food and supply. Prospective cohort studies have found an association between UPF dietary pattern and increased risk of several non-communicable diseases and all-cause mortality. In this study, we (1) estimated the risk of all-cause mortality associated for each 10% increase in the share of UPF consumption in the total energy intake; (2) estimated the population attributable fractions (PAF) and the total number of premature deaths attributable to the consumption of UPF in adults (30-69 years) from 8 selected countries. Methods First, we performed a dose-response meta-analysis of observational cohort studies assessing the association between UPFs dietary pattern and all-cause mortality. As we found evidence of linearity, we estimated the pooled RR (and its 95% CI) for all-cause mortality per each 10% increment in the % UPF. Then, we estimated the population attributable fraction (PAF) of premature all-cause mortality attributable to UPF in 8 selected countries with relatively low (Colombia and Brazil), intermediate (Chile and Mexico), and high (Australia, Canada, UK, and US) UPF consumption. Results We found a linear dose-response association between UPF intake and all-cause mortality, with a 2.7% increased risk of all-cause mortality per 10% increase in the % UPF. Considering the magnitude of the association between UPFs intake and all-cause mortality, and the dietary share of UPF in each of the 8 selected countries, we estimated that 4% (Colombia) to 14% (United Kingdom and United States) of premature deaths were attributable to UPF intake. Conclusions Our findings support that UPF intake contributes significantly to the overall burden of disease in many countries and its reduction should be included in national dietary guideline recommendations and addressed in public policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.024 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".