The magnitude of neonatal mortality and its predictors in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstracts Background Although neonatal death is a global burden, it is the highest in Sub Saharan Africa countries such as Ethiopia. This study was aimed to provide pooled national prevalence and predictors of neonatal mortality in Ethiopia. Objective To assess the pooled prevalence and predictors of neonatal mortality in Ethiopia. Search Strategy global databases were systematically explored. Systematically searched using the following databases: Boolean operator, Cochrane library, PubMed, EMBASE, HINARI, and Google Scholar. Selection, screening, reviewing and data extraction was done by two reviewers independently using Microsoft excel spread sheet. The modified Newcastle–Ottawa Scale (NOS) and the Joanna Briggs Institute Prevalence Critical Appraisal tools were used to assess the quality of evidence Selection criteria All studies conducted in Ethiopia and reporting the prevalence and predictors of neonatal mortality were included Data Collection and Analysis Data were extracted using a Microsoft Excel spreadsheet software and imported into STATA Version 14 s for further analysis. The pooled effect size with 95% confidence interval of neonatal mortality rate was determined using a weighted inverse variance random-effects model. Publication bias was checked using funnel plots, Egger’s and bagger’s regression test. Heterogeneity also checked by Higgins’s method. A random effects meta-analysis model 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 were done. Results After reviewing 88 studies, 12 studies fulfilled the inclusion criteria and were included in the meta-analysis. The pooled national prevalence of neonatal mortality in Ethiopia was 16.3% (95% CI: 11.9, 20.7, I 2 =88.6%). The subgroup analysis indicated that the highest prevalence was observed in Amhara region with a prevalence of 20.3% (95% CI: 9.6, 31.1, I 2 =98.8) followed by Oromia, 18.8% (95%CI: 11.9,49.4, I 2 =99.5). Gestational age AOR,1.14 (95% CI: 0.94, 1.3), neonatal sepsis (OR:1.2(95% CI: 0.8, 1.5), respiratory distros (OR: 1.2(95% CI: 0.8, 1.5) and place of residency (OR:1.93 (95% CI:1.1,2.7) were the most important predictor. Conclusions neonatal mortality in Ethiopia was significantly decreased than the national report. There was evidence that neonatal sepsis, gestational age, respiratory distress were the significant predictors. We strongly recommended that health care workers should give a priority for the identified predictors.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.013 | 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) | 0.004 | 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".