Nutrient Profiling Models in Low- and Middle-Income Countries Considering Local Nutritional Challenges: A Systematic Review
Bibliographic record
Abstract
Micronutrient deficiencies, undernutrition, and overweight/obesity are prevalent in low- and middle-income countries (LMICs). Nutrient profiling models (NPMs), initially developed to help reduce the prevalence of diet-related chronic diseases in Western countries, could be one solution to promote nutrient-dense foods in LMICs. This study reviewed government-endorsed NPMs implemented in LMICs and assessed their key components in relation to country-specific nutritional challenges. The peer-reviewed and grey literature were systematically reviewed to identify government-endorsed NPMs implemented in LMICs to promote healthier choices among adults. Their key metrics, including scope, components, units, and validation method, were extracted. The prevalence of undernutrition; overweight/obesity; and iron, vitamin A, and iodine deficiencies were extracted from the Global Health Observatory and the Global Burden of Disease study. NPMs have been implemented in 16 LMICs to encourage healthier choices, mostly through front-of-pack labeling schemes. Warning Label schemes are used to strongly discourage the consumption of energy-dense products in countries where overnutrition affects most of the population, such as Latin American LMICs. A "Keyhole" front-of-pack labeling scheme was implemented only in North Macedonia. It limits sugar, fat, and salt while promoting fibers, fruits, vegetables, nuts, and legumes to prevent overnutrition and diet-related chronic diseases. "Choices" schemes that focus on positive messages have been implemented in Southeast Asia and Zambia where over- and undernutrition coexist. "Choices" criteria encourage the consumption of category-specific vitamins and minerals, in addition to advocating limiting certain nutrients. In LMICs, NPMs focus on discouraging the consumption of sugar, fat, and salt. Additionally, NPMs promote category-specific micronutrients in countries where undernutrition remains prevalent or food components associated with a reduced risk of diet-related chronic diseases, including whole grains and fibers, in countries where overnutrition is the main nutrition-related public health issue. This study was registered at PROSPERO as CRD42023468807.
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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 | 0.016 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".