Tracking maternal, infant, and young child nutrition in Brazil after a decade without evidence
Bibliographic record
Abstract
Monitoring and tracking the health and nutrition of populations are essential parts of the global commitment to improve overall well-being 1,2 .The United Nations 2030 Agenda for Sustainable Development Goals (SDG) acknowledges the importance of solid and robust data availability at national and regional levels for monitoring the progress and policy across nations 3 .Nationally-representative household surveys have been conducted worldwide, especially in low-resource settings, and helped to understand the achievements and challenges that countries must overcome to reduce the burden of illnesses according to each context.Considering that health inequalities persist worldwide, this type of data also allows for the investigation of disaggregate estimates by socioeconomic level, education, geography, among others.The path towards narrowing such inequalities depends on the production of evidence to track the status of minorities, within and between countries, and, also, to support practices, programs, and policies to reduce the gaps and to trace the impact of intervention 2 .Such evidence could guide health and well-being policies in a better cost-effective way 4 .In Brazil, a large country with the largest economy in Latin America, a lack of evidence on maternal-child nutrition indicators remained for 13 years until the completion of the 2019 Brazilian National Survey and Child Nutrition (ENANI-2019).In this sense, most public policies and pragmatic activities targeting mothers and child nutrition relied upon the results of the 2006 Brazilian National Survey on Demography and Health of Women and Children (PNDS 2006) 5 .Castro et al. 6 reported the findings of descriptive trend analysis of child nutrition indicators of international relevance comparing both surveys on domains such as anthropometry, feeding practices, and micronutrient deficiencies.Despite improvements being noted on some indicators during the period, others did not change, and some even worsened.Over decades, Brazil had been classified as a country where anemia and vitamin A deficiency were moderate public health problems, especially affecting the most disadvantaged in the poorest areas.The ENANI-2019 showed that important progress in the reduction of both micronutrient deficiencies was accomplished since they are now classified as mild public health problems with striking reductions in the inequalities by region, maternal education, and race/skin color 7,8 .The ENANI-2019 included recommended and reliable methods of collection, transportation, storage, and analysis in the micronutrient assessment, which increased the reliability of results, whereas the PNDS 2006 presented methodological concerns that could have negatively affected their results (for example, ENANI-2019 used capillary blood sample for the retinol essays, whereas the PNDS 2006
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".