Maternal mortality in Ethiopia (2015–2025): a systematic review of recent evidence and determinants
Bibliographic record
Abstract
BACKGROUND: Despite major policy reforms and improvements in healthcare coverage, maternal mortality remains a critical public health burden in Ethiopia. While progress has been made since the Millennium Development Goals era, the maternal mortality ratio (MMR) still exceeds national and global targets. This systematic review synthesizes evidence from the past decade (2015-2025) to describe the magnitude, determinants, and regional disparities of maternal mortality in Ethiopia, highlighting persistent challenges and future priorities. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (registered in International Prospective Register of Systematic Reviews (PROSPERO)), we systematically searched PubMed, Scopus, Web of Science, Embase, Cochrane, and African Journal Online(AJOL), supplemented with grey literature from World Health Organization (WHO), United Nations International Children's Emergency Fund (UNICEF), and the Ethiopian Ministry of Health. Studies published in English between January 2015 and September 2025 was included. Data extraction followed standardized templates, and study quality was appraised using Joanna Briggs Institute (JBI) and Newcastle-Ottawa Scale (NOS) tools. Given methodological heterogeneity, a narrative synthesis approach was applied. RESULTS: A total of 61 studies met inclusion criteria, encompassing all Ethiopian regions. The pooled MMR was estimated at 366.6 maternal deaths per 100,000 live births, showing only modest progress from previous decades. The leading causes of maternal death were obstetric hemorrhage (29.6%), hypertensive disorders (22.1%), sepsis (14.8%), obstructed labor (11.3%), and unsafe abortion (8.5%). Determinants aligned with the three-delay model: (1) delayed decision-making from low awareness and sociocultural barriers; (2) delayed access due to distance, transport, and cost; and (3) delayed care from health-system shortages and weak referral mechanisms. Socioeconomic inequality, inadequate antenatal care (< 4 visits), rural residence, and low maternal education consistently increased risk. CONCLUSIONS: Maternal mortality in Ethiopia remains unacceptably high yet preventable. Persistent inequities, poor service quality, and health-system gaps continue to drive maternal deaths. Despite national initiatives such as the Health Sector Transformation Plan II (2015-2025) and the Maternal and Child Health Roadmap, progress is uneven. Achieving the SDG 3.1 target of < 70 deaths per 100,000 live births by 2030 demands stronger referral systems, equitable resource distribution, and quality-focused maternal health interventions. Targeted regional interventions and stronger Emergency Obstetric and Newborn Care (EmONC) readiness are essential for achieving the Sustainable Development Goals (SDG) 3.1 target. Future research should integrate longitudinal, spatial, machine learning and Bayesian models to pinpoint high-risk areas and evaluate the impact of health-system reforms.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".