Tracking maternal, infant, and young child nutrition in Brazil after a decade without evidence
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».