Challenges and inequity in neonatal and child health: Tip of the iceberg from Global Burden of Disease indicators
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".