Nutrition transition's latest stage: Are ultra‐processed food increases in low‐ and middle‐income countries dooming our preschoolers' diets and future health?
Bibliographic record
Abstract
INTRODUCTION: Rapid shifts in dietary patterns, marked by increased consumption of ultra-processed foods (UPFs), are increasingly impacting the health and wellbeing of infants and toddlers in low- and middle-income countries. METHODS: Utilizing data from the Demographic and Health Surveys, other national surveys, NCD-RisC data and Euromonitor sales data, we examine changes in stunting and overweight/obesity prevalence alongside the latest data on UPF consumption trends. RESULTS: The prevalence of overweight/obesity among children and mothers is increasing rapidly while stunting rates decline slowly. Simultaneously, there is a significant increase in consumption of UPFs, especially among preschool-aged children. Increasingly, poorer households are experiencing faster rates of increase in overweight and obesity prevalence compared to wealthier households. Results highlight the early socialization of infants and toddlers to unhealthy discretionary foods including UPFs, potentially setting the stage for long-term dietary preferences that favour food with high sugar or excess sodium. CONCLUSION: There is an urgent need to address the rapid increases in UPF consumption among infants and toddlers. Options include expanding the WHO Code on marketing to protect 0-3-year-olds; creating front-of-package warning labels focusing on products for children ages 0-3 years to remove all added sugar and limit sodium in foods and beverages they consume.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".