Rate, Risk Factors, and Causes of Neonatal Deaths in Jordan: Analysis of Data From Jordan Stillbirth and Neonatal Surveillance System (JSANDS)
Bibliographic record
Abstract
Background: It has been estimated that 27.8 million neonates will die worldwide between 2018 and 2030 if no improvements in neonatal and maternal care take place. The aim of this study was to determine the rate, risk factors, and causes of neonatal mortality in Jordan. Methods: In August 2019, an electronic stillbirths and neonatal deaths surveillance system (JSANDS) was established in in three large cities through five hospitals. Data on all births, neonatal mortality and their causes, and other characteristics in the period between August 2019 – January 2020 were exported from the JSANDS and analysed. Results: A total of 10328 births (10226 live births (LB) and 102 stillbirths) were registered in the study period, with a rate of 14.1 deaths per 1000 LBs; 76% were early neonatal deaths and 24% were late deaths. The odds of deaths in the Ministry of Health hospitals were almost 21 times (OR = 20.8, 95% CI: 2.8, 153.1) higher than that in private hospitals. Low birthweight and preterm babies were significantly more likely to die during the neonatal period compared to full-term babies. The odds of neonatal mortality were significantly higher among babies born to housewives compared to those who were born to employed women (OR = 2.7; 95% CI: 1.2, 6.0). Main causes of neonatal deaths that occurred pre-discharge were respiratory and cardiovascular disorders (43%) and low birthweight and preterm (33%). The main maternal conditions that attributed to these deaths were complications of the placenta and cord, complications of pregnancy, and medical and surgical conditions. The main cause of neonatal deaths that occurred post-discharge were low birthweight and preterm (42%). Conclusions: The rate of neonatal mortality have not decreased since 2012 and the majority of neonatal deaths occurred could have been prevented. Regular antenatal visits, in which any possible diseases or complications of pregnant women or foetal anomalies, need to be fully documented and monitored with appropriate and timely medical intervention to minimize such deaths.
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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 | 0.001 |
| 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".