Newborn Mortality And Its Associated Factors Among Neonates Admitted At Public Hospitals In Afar Region; A Health Facility Based Study
Bibliographic record
Abstract
Abstract Background: Neonatal mortality is a public health issue in developing countries, such as Ethiopia. Unfortunately, the issue is noticeably under-reported and underestimated, so the true gravity of the situation cannot be acknowledged. Subsequently, Afar in Ethiopia contributes the largest burden of under-five mortality when compared to other regions in the country. Regrettably, there is no current information to the rates and predictors of neonatal mortality for the region even for the health facilities. Thus, this study aims to assess neonatal mortality and associated factors in pastoral region, Afar region. Methods: A health facility-based retrospective cross-sectional study was conducted on 403 neonates admitted to the neonatal intensive care unit (NICU) from May 1st 2015 - May 2nd 2019. Medical records were reviewed and audited for both mothers and neonates to collect data using a standardized data extraction checklist. The medical records were selected using a systematic sampling technique. Binary logistic regression with odds ratio and 95% Confidence interval was calculated to assess the association between neonatal mortality and associated factors. Finally, the statistical significance level was declared at a p-value <0.05. Results: In this study, 391 medical records of neonates were included with the data complete rate of 97.02%. The prevalence of neonatal mortality was 14.6% (95% CI 11.0%-18.4%) with mortality rate of 35.5 per 1000 live births. A multivariable logistic regression showed that the lack of antenatal care (ANC) follow up (AOR = 5.92; 95%CI 2.34, 14.97: P<0.001), giving birth through cesarean section (AOR=3.52; 95%CI 1.22, 10.12: P<0.05), giving birth through assisted delivery (AOR=3.28 (1.14, 9.46): P<0.05), having a temperature less than 36.5oC within the first hour of admission (AOR= 5.89; 95%CI 2.32, 14.94: P<0.001), and perinatal asphyxia (AOR= 6.67; 95%CI 2.35, 18.89: P<0.001) were significantly associated with neonatal mortality. Conclusion: This study revealed that the rate of neonatal mortality is still too high compared to the studies conducted in non-pastoral regions of the nation. Thus, the health facilities should give due attention to improve antenatal care, neonatal resuscitation and follow the standard of care protocol for admitted neonates. Additional community based studies supported with qualitative methods are recommended.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".