MétaCan
Menu
Retour à la cohorte
Enregistrement W4406084510 · doi:10.1002/ped4.12461

Challenges and inequity in neonatal and child health: Tip of the iceberg from Global Burden of Disease indicators

2025· letter· en· W4406084510 sur OpenAlexaboutno aff
Yuan Zhang, Shu Wang, Mingyan Hei

Notice bibliographique

RevuePediatric Investigation · 2025
Typeletter
Langueen
DomaineHealth Professions
ThématiqueGlobal Health Care Issues
Établissements canadiensnon disponible
Organismes subventionnairesNational Natural Science Foundation of ChinaInstitute for Health Metrics and EvaluationUniversity of Washington
Mots-clésMedicineBurden of diseaseDisease burdenInfant mortalityChild mortalityFertilityGlobal healthDiseaseDemographyEnvironmental healthCause of deathPediatricsPopulationPublic health

Résumé

récupéré en direct d'OpenAlex

To the Editor: Despite positive trends in many important indicators of the newly released Global Burden of Disease (GBD) 2021 studies,1-3 there remains a challenging burden of disease and death on neonatal and child health.2, 3 A reduction in age-standardized deaths caused by neonatal disorders was observed from 46.0 deaths (95% uncertainty interval [UI] 43.5–48.9) per 100 000 people in 1990 and 30.7 deaths (95% UI 26.8–35.3) per 100 000 people in 2019, globally.2 The latest GBD 2021 study suggested a further reduction to 29.6 deaths (95% UI 25.3–34.4) per 100 000 people,2 suggesting that global efforts to improve neonatal and child health are encouraged. However, from 1990 to 2021, neonatal disorders remained the leading cause of age-standardized mortality and years of life lost.2 Additionally, neonatal disorders and other conditions during childhood were the leading cause of disability-adjusted life-years globally in 2021, as in every year of the previous decades.3 These estimates indicate that health crises and burdens are still faced by neonates and children (Figure 1). Another potential unexposed challenge could be current and future fertility. From 1950 to 2021, fertility declined globally and is predicted to continue to decline.4 This trend causes future worries about decreasing investment and recognition of the importance of neonatal and child health. Reductions in fertility rates are closely linked to national development. Research indicates that increased levels of female education and greater access to contraceptives contribute to lower fertility rates. Projections suggest that the global population will experience a significant decline by 2100.5 Countries with low fertility rates but high incomes, such as the United States, Australia, and Canada, are likely to sustain their working-age populations through net migration. However, this trend may have adverse effects on the labor force, economic growth, and social support systems in areas experiencing the most pronounced fertility declines.6-8 Concurrently, the geographic distribution of live births is changing, with a higher proportion now occurring in the lowest-income countries.4 In these settings, the risk of mortality and disease burden among neonates and children is even more pronounced.2 For low-income countries, an increase in fertility rates, when coupled with insufficient medical resources, presents substantial challenges to ensuring the health and well-being of newborns and children. This could further exacerbate the regional inequity in neonatal and child health and become a threat to global health. Thus, positive indicators from GBD 2021 studies can also mask the tip of the iceberg. Every nation confronts unique challenges in striving for the health and well-being of newborns and children. Policymakers and public health officials worldwide endeavor to achieve sustainable development by formulating child health policies that are customized to their specific contexts, capacities, and stages of development. The urgent task is to reinforce the focus on neonatal and children as a vulnerable group, addressing key areas such as nutrition, education, and health through empowerment and economic support. To address the regional inequity in neonatal and child health, it is essential to increase investment in healthcare infrastructure and services in underserved areas. Additionally, training and deploying more healthcare workers to these regions can significantly improve access to quality care. Implementing community-based health programs that focus on maternal and child health education can also play a crucial role in narrowing the gap. We call for continuous initiatives and actions in facing challenges and inequity in neonatal and child health to achieve the 2030 Sustainable Development Goals (Goal 3: Good Health and Well-being, Goal 10: Reduced Inequalities).9 We would like to thank the Institute for Health Metrics and Evaluation (IHME), University of Washington, for their GBD studies, data, and visualization tools, which are freely publicly available. Dr. Shu Wang is a GBD Senior Collaborator and contributed to the GBD studies. This study was supported by the National Natural Science Foundation of China (82371712). The authors declare no conflict of interest.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,396
Score d'incertitude au seuil0,996

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,041
Tête enseignante GPT0,355
Écart entre enseignants0,314 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2025
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revuePediatric InvestigationMême sujetGlobal Health Care IssuesTravaux en français237 207