State of neonatal health care in eight countries of the SAARC region, South Asia: how can we make a difference?
Bibliographic record
Abstract
The South Asian Association for Regional Cooperation (SAARC) is an organization of eight countries--Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, Sri Lanka and Afghanistan. The major objectives of this review are to examine trends and progress in newborn and neonatal health care in the region. A landscape analysis of the current state of neonatal mortality, stillbirths and trends over the years for each country and the effective interventions to reduce neonatal mortality and stillbirths was undertaken. A modelling exercise using the Lives Saved Tool (LiST) was also undertaken to determine the impact of scaling up a set of essential interventions on neonatal mortality and stillbirths. The findings demonstrate that there is an unacceptably high and uneven burden of neonatal mortality and stillbirths in the region which together account for 39% of global neonatal deaths and 41% of global stillbirths. Progress is uneven across countries in the region, with five of the eight SAARC countries having reduced their neonatal mortality rate by more than 50% since 1990, while India (43%), Afghanistan (29%) and Pakistan (25%) have made slower progress and will not reach their MDG4 targets. The major causes of neonatal mortality are intrapartum-related deaths, preterm birth complications and sepsis which account for nearly 80% of all deaths. The LiST analysis shows that a gradual increase in coverage of proven available interventions until 2020 followed by a uniform scale-up to 90% of all interventions until 2030 could avert 52% of neonatal deaths (0.71 million), 29% of stillbirths (0.31 million) and achieve a 31% reduction in maternal deaths (0.25 million). The analysis demonstrates that the Maldives and Sri Lanka have done remarkably well while other countries need greater attention and specific focus on strategies to improve neonatal health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".