Infant nutrition and growth: trends and inequalities in four population-based birth cohorts in Pelotas, Brazil, 1982–2015
Bibliographic record
Abstract
BACKGROUND: Levels of child undernutrition have declined in many middle-income countries, whereas overweight and obesity have increased. We describe time trends in nutritional indicators at age 1 year in the 1982, 1993, 2004 and 2015 Pelotas (Brazil) Birth Cohorts. METHODS: Each study included all children born in the urban area of the city, with over 4 200 births in each cohort. Children were measured at approximately 12 months of age. Anthropometric indicators were calculated according to World Health Organization Growth Standards. Stunting and wasting were defined as <-2 Z scores for length for age and weight for length, and overweight as >2 Z scores for weight for length. Prevalence was stratified by sex, maternal skin colour and family income. RESULTS: The prevalence of stunting declined by 53% (from 8.3% to 3.9%) from 1982 to 2015. Wasting prevalence remained stable at low levels (1.8% in 1982 and 1.7% in 2015), whereas overweight increased by 88% (6.5% to 12.2%). Undernutrition was more common among boys, those born to mothers with brown or black skin colour and in the poorest quintile of families. Socioeconomic inequalities in undernutrition decreased markedly over time. Overweight was markedly more common among the rich in 1982, but fast increase among the poor eliminated socioeconomic differences by 2015, when all groups showed similar prevalence. CONCLUSIONS: Our results confirm the rapid nutrition transition in Brazil, with marked reduction in levels and inequalities in undernutrition in parallel with a rapid increase in overweight, which became the main nutritional problem for children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".