The Magnitude of Neonatal Mortality and Its Predictors in Ethiopia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Although neonatal death is a global burden, it is the highest in sub-Saharan African countries such as Ethiopia. Moreover, there is disparity in the prevalence and associated factors of studies. Therefore, this study was aimed at providing pooled national prevalence and predictors of neonatal mortality in Ethiopia. METHODS: The following databases were systematically explored to search for articles: Boolean operator, Cochrane Library, PubMed, EMBASE, Hinari, and Google Scholar. Selection, screening, reviewing, and data extraction were done by two reviewers independently using Microsoft Excel spreadsheet. The modified Newcastle-Ottawa Scale (NOS) and the Joanna Briggs Institute Prevalence Critical Appraisal tools were used to assess the quality of evidence. All studies conducted in Ethiopia and reporting the prevalence and predictors of neonatal mortality were included. Data were extracted using Microsoft Excel spreadsheet software and imported into Stata version 14s for further analysis. Publication bias was checked using funnel plots and Egger's and Begg's tests. Heterogeneity was also checked by Higgins's method. A random effects meta-analysis model with 95% confidence interval was computed to estimate the pooled effect size (i.e., prevalence and odds ratio). Moreover, subgroup analysis based on region, sample size, and study design was done. RESULTS: = 98.8%). The subgroup analysis indicated that the highest prevalence was observed in the Amhara region, 20.3% (95% CI: 9.6, 31.1), followed by Oromia, 18.8% (95% CI: 11.9, 49.4). Gestational age [AOR: 1.32 (95% CI: 1.07, 1.58)], neonatal sepsis [AOR: 1.23 (95% CI: 1.05, 1.4)], respiratory distress syndromes (RDS) [AOR: 1.18 (95% CI: 0.87, 1.49)], and place of residency [AOR: 1.93 (95% CI: 1.13, 2.73)] were the most important predictors. CONCLUSIONS: Neonatal mortality in Ethiopia was significantly decreased. There was evidence that neonatal sepsis, gestational age, and place of residency were the significant predictors. RDS were also a main predictor of mortality even if not statistically significant. We strongly recommended that health care workers should give a priority for preterm neonates with diagnosis with sepsis and RDS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".