Ending Preventable Neonatal Deaths: Multicountry Evidence to Inform Accelerated Progress to the Sustainable Development Goal by 2030
Bibliographic record
Abstract
INTRODUCTION: The Sustainable Development Goal (SDG) 3.2 aims for every country to reach a neonatal mortality rate (NMR) of ≤12/1,000 live births by 2030. More than 60 countries are off track, and 2.3 million newborns still die each year. Urgent action is needed, but varies by context, notably mortality level. METHODS: We applied a five-phase NMR transition model based on national analyses for 195 UN member states: I (NMR >45), II (30-<45), III (15-<30), IV (5-<15), and V (<5). We analyzed data over the last century from selected countries to inform strategies to reach SDG3.2. We also undertook impact analyses for packages of care using the Lives Saved Tool software. RESULTS: An NMR of <15/1,000 requires firstly wide-scale access to maternity care and hospital care for small and sick newborns, including skilled nurses and doctors, safe oxygen use, and respiratory support, such as CPAP. Neonatal mortality could be reduced to the SDG target of ≤12/1,000 with further scale-up of small and sick newborn care. To reduce neonatal mortality further, more investment is required in infrastructure, device bundles (e.g., phototherapy, ventilation), and careful attention to infection prevention. To reach phase V (NMR <5), which is closer to ending preventable newborn deaths, additional technologies and therapies such as mechanical ventilation and surfactant replacement therapy are needed, as well as higher staffing ratios. CONCLUSIONS: Learning from high-income country is important, including what not to do. Introduction of new technologies should be according to the country's phase. Early focus on disability-free survival and family involvement is also crucial.
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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 | 0.099 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".