Trends and determinants of newborn mortality in Kyrgyzstan: a Countdown country case study
Bibliographic record
Abstract
BACKGROUND: Kyrgyzstan has made considerable progress in reducing child mortality compared with other countries in the region, despite a comparatively low economic standing. However, maternal mortality is still high. Given the availability of an established birth registration system, we aimed to comprehensively assess the trends and determinants of reproductive, maternal, newborn, and child health in Kyrgyzstan. METHODS: For this Countdown to 2030 country case study, we used publicly available data repositories and the national birth registry of Kyrgyzstan to examine trends and inequalities of reproductive, maternal, and newborn health and mortality between 1990 and 2018, at a national and subnational level. Coverage of newborn and maternal health interventions was assessed and disaggregated by equity dimensions. We did Oaxaca-Blinder decomposition to determine the contextual factors associated with the observed decline in newborn mortality rates. We also undertook a comprehensive review of national policies and programmes, as well as a prospective Lives Saved Tool analysis, to highlight interventions that have the potential to avert the most maternal, neonatal, and child deaths. FINDINGS: Over the past two decades, Kyrgyzstan reduced newborn mortality rates by 46% and mortality rates of children younger than 5 years by 69%, whereas maternal mortality rates were reduced by 7% and stillbirth rates by 29%. The leading causes of neonatal deaths were prematurity and asphyxia or hypoxia, and preterm small-for-gestational-age infants were more than 80 times more likely to die in their first month of life compared with those born appropriate-for-gestational age at term. Except for contraceptive use, coverage of essential interventions has increased and is generally high, with limited sociodemographic inequities. With scale-up of a few essential neonatal and maternal interventions, 39% of neonatal deaths, 11% of stillbirths, and 19% of maternal deaths could be prevented by 2030. INTERPRETATION: Kyrgyzstan has reduced newborn mortality rates considerably, with the potential for further reduction. To achieve and exceed the Sustainable Development Goal 3 targets for newborn survival and reducing stillbirths, Kyrgyzstan needs to scale up packages of interventions for the care of small and sick babies, assure quality of care in all health-care facilities with regionalised perinatal care, and create a linked national registry for mothers and neonates with rapid feedback and accountability. FUNDING: US Fund for UNICEF under the Countdown to 2015, UNICEF Kyrgyzstan Office.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".