The burden of stroke and modifiable risk factors in Ethiopia: A systemic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The burden and contribution of modifiable risk factors of stroke in Ethiopia are unclear. Knowledge about this burden and modifying risk factors is pivotal for establishing stroke prevention strategies. In recent decades, the issue of lifestyle and behavioral modification is a key to improve the quality of life. The modifiable risk factors are an importance as intervention strategies aimed at reducing these factors can subsequently reduce the risk of stroke. So far, many primary studies were conducted to estimate the burden of stroke and modifiable risk factors in Ethiopia. However, the lack of a nationwide study that determines the overall pooled estimation of burden and modifiable risk factors of stroke is a research gap. METHODS: To conduct this systemic review and meta-analysis, we are following the PRISMA checklist. Three authors searched and extracted the data from the CINAHL (EBSCO), MEDLINE (via Ovid), PubMed, EMcare, African Journals Online (AJOL), and Google scholar. The quality of the primary study was assessed using the Newcastle-Ottawa Scale (NOS) by two independent reviewers. The primary studies with low and moderate risk of bias were included in the final analysis. The authors presented the pooled estimated burden of stroke and its modifiable risk factors. The registered protocol number in PROSPERO was CRD42020221906. RESULTS: In this study, the pooled burden of hemorrhagic and ischemic stroke were 46.42% (95%CI: 41.82-51.53; I2 = 91.6%) and 51.40% (95%CI: 46.97-55.82; I2 = 85.5%) respectively. The overall magnitude of modifiable risk factor of hypertension, alcohol consumption and dyslipidemia among stroke patients were 49% (95%CI: 43.59, 54.41), 24.96% (95CI%:15.01, 34.90), and 20.99% (95%CI: 11.10, 30.88), respectively. The least proportion of stroke recovery was in the Oromia region (67.38 (95%CI: 41.60-93.17; I2 = 98.1%). Farther more, the proportion of stroke recovery was decreased after 2017 (70.50 (56.80-84.20). CONCLUSIONS: In our study, more than 90% of stroke patients had one or more modifiable risk factors. All identified modifiable stroke risk factors are major public health issues in Ethiopia. Therefore, strategy is designed for stroke prevention to decrease stroke burden through targeted modification of a single risk factor, or a cluster of multiple risk factors, used on a population, community, or individual level.
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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 | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.012 | 0.011 |
| 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".