Nutrition situation in Latin America and the Caribbean: current scenario, past trends, and data gaps.
Bibliographic record
Abstract
OBJECTIVE: To determine the current nutritional status in Latin America and the Caribbean (LAC) and identify data gaps and trends in nutrition surveillance. METHODS: A systematic Internet search was conducted to identify official sources that allowed for monitoring of LAC countries' nutritional status, including progress toward World Health Organization Global Nutrition Targets 2025. Reports from national nutrition surveillance systems and reports on nationally representative surveys were collected and collated to 1) analyze nutritional status, based on life-course anthropometric indicators and biomarkers, and 2) identify gaps in data availability and trends in nutritional deficiencies. Information on iron, vitamin A, iodine, folate, and vitamin B12 deficiency was also collected and collated. RESULTS: Twenty-two of the 46 LAC countries/territories (48%) had information on undernutrition (stunting, underweight, and wasting) in children under 5 years old and women of reproductive age (WRA). Seventeen countries (38%) had information on anemia in children under 5 years old and WRA, and 12 (27%) had information on anemia in pregnant women. Although overall nutritional status has improved in the past few decades in all countries in the region, some LAC countries still had a high prevalence of stunting and anemia in children and WRA. Overweight affected at least 50% of WRA in nine countries with available data, and was increasing in children. Data for school-age children, adolescents, adult males, and older adults were scarce in the region. CONCLUSIONS: Overall nutritional status has improved in the LAC countries with available information, but more efforts are needed to scale up nutrition-sensitive and nutrition-specific interventions to tackle malnutrition in all its forms, as stunting, anemia, and vitamin A deficiency are still a public health problem in many countries, and overweight is an epidemic. Nutrition information systems are weak in the region, and countries need to strengthen their capacity to monitor nutritional status indicators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".