Magnitude of Cardiovascular Disease Admissions and Their Outcomes in Ethiopia: A Systematic Review and Meta-Analysis (2025)
Bibliographic record
Abstract
Introduction: CVDs pose a significant burden, particularly in developing countries, affecting individuals, families, healthcare systems, and economies. Cardiovascular diseases are the leading cause of death worldwide, with a disproportionate burden observed in low- and middle-income countries where access to preventive and acute care is limited. So this review is aimed to estimate pooled magnitude of cardiovascular disease admissions and its outcome in Ethiopia. Methods and materials: A systematic search of published studies from PubMed, Scopus, web of science, google scholar, and reference lists of identified studies were conducted. This meta-analysis follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The quality of studies was assessed by the modified Newcastle-Ottawa Scale (NOS). Meta-analysis was carried out using a random-effects method using the STATA™ Version 14 software. Result: Ten studies involving 38,440 participants were included in this meta-analysis. The magnitude of cardiovascular admission were ranged from 17.5 - 53.9 %; with pooled magnitude of cardiovascular admission with a random-effects model in Ethiopia was 36.3% (95% CI: 27.6- 45.0). Whereas the magnitude of cardiovascular admission in Addis Ababa region was 40.19 %; 95% CI: (28.3- 51.9) and 45.2%; 95% CI :( 28.1- 62.2) in Tigray region. The magnitude of cardiovascular disease admission outcome ranges from 13.2- 24.3%. Conclusion: According to this study, there is a significant burden of cardiovascular disease admissions in the region. Notably, the Addis Ababa region reports an admission rate of 40.19%, while the Tigray region has an even higher rate of 45.2%. Therefore, urgent targeted interventions and healthcare strategies are needed.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.050 |
| Bibliometrics | 0.010 | 0.008 |
| 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".