Ethnoracial disparities in childhood growth trajectories in Brazil: a longitudinal nationwide study of four million children
Bibliographic record
Abstract
Growth is an important marker of child health and development. It is related to aspects such as social-economic conditions, being affected by social inequalities. Race is a social construct that functions as an essential tool of racism. It creates social hierarchy, resulting in segregation, different quality and access to health care, and unequal distributions of social determinants on health. Understanding the effects of ethno-racial inequalities on growth trajectories is essential to improve children development and well-being. In the study we investigated child growth according to maternal ethno-racial group using a nationwide Brazilian database. Data were obtained via linkage of the CIDACS Birth Cohort and the Brazilian Food the Nutrition Surveillance System. It included 4,090,271 children under 5 years old, born at term, followed-up between 2008 and 2017. We used mixed-effects model to estimate both original and standardized forms of length/height and weight trajectories. Results pointed out to growth differences among the racial groups, concerning both markers. Children born to Indigenous mothers presented the most critical situation, followed by those born to Parda, Asian-descent, and Black mothers. The strengthening of policies aimed at protecting Indigenous children should be urgently undertaken to address systematic ethno-racial health inequalities. Additionally, these findings may help policymakers to achieve the United Nation’s 2030 Sustainable Development Goals (eradication of hunger and all forms of malnutrition).
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".