Global trends in ultraprocessed food and drink product sales and their association with adult body mass index trajectories
Bibliographic record
Abstract
Summary This study evaluated global trends in ultraprocessed food and drink (UPFD) volume sales/capita and associations with adult body mass index (BMI) trajectories. Total food/drink volume sales/capita from Euromonitor for 80 countries (2002‐2016) were matched to mean adult BMI from the NCD Risk Factor Collaboration (2002‐2014). Products were classified as UPFD/non‐UPFD according to the NOVA classification system. Mixed models for repeated measures were used to analyse associations between UPFD volume sales/capita and adult BMI trajectories, controlling for confounding factors. The increase in UPF volume sales was highest for South and Southeast Asia (67.3%) and North Africa and the Middle East (57.6%), while for UPD, the increase was highest for South and Southeast Asia (120.0%) and Africa (70.7%). In 2016, baked goods were the biggest contributor to UPF volume sales (13.1%‐44.5%), while carbonated drinks were the biggest contributor to UPD volume sales (40.2%‐86.0%). For every standard deviation increase (51 kg/capita, 2002) in UPD volume sales, mean BMI increased by 0.195 kg/m 2 for men ( P < .001) and 0.072 kg/m 2 for women ( P = .003). For every standard deviation (40 kg/capita, 2002) increase in UPF volume sales, mean BMI increased by 0.316 kg/m 2 for men ( P < .001), while the association was not significant for women. Increases in UPFD volume sales/capita were positively associated with population‐level BMI trajectories.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".