Prevalence of and risk factors for hypertension in Ethiopia: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: A number of epidemiological studies were performed to know the prevalence of and the risk factors for hypertension. However, these studies reported inconsistent results. As a result, this systematic review and meta-analysis were planned to generate representative data on the prevalence of and risk factors for hypertension among the Ethiopian adult population. METHODS: Five electronic databases, namely, PubMed, Science Direct, Google Scholar, Hinari, and African Journals Online, were searched for studies published in English from 1 January 2010 to 31 August 2020. Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument and Newcastle-Ottawa scale were used for data extraction and quality assessment for this review. Stata version 14 statistical software was used for the analysis, and due to high heterogeneity a random effects model was used for meta-analysis at 95% confidence interval (CI). RESULTS: value of 96.1%. Older age (≥40 years) (adjusted odds ratio [AOR]: 3.46 [95% CI: 2.67, 4.49]), urban residence (AOR: 1.47 [95% CI: 1.28, 1.70]), educational status less than grade 12 (AOR: 1.67 [95% CI: 1.38, 2.01]), family history of hypertension (AOR: 4.33 [95% CI: 2.95, 6.34]), diabetes mellitus (DM) (AOR: 5.18 [95% CI: 3.01, 8.88]), body mass index (BMI) ≥25 (AOR: 3.79 [95% CI: 2.61, 5.50]), central obesity (AOR: 1.91 [95% CI: 1.09, 3.36]), and alcohol consumption (AOR: 1.72 [95% CI: 1.26, 2.34]) were the identified risk factors for hypertension. CONCLUSION: The pooled prevalence of hypertension is relatively higher as compared to the previous reports in Ethiopia. Older age, urban residence, lower educational coverage, family history of hypertension, DM, BMI ≥25, alcohol consumption, and central obesity were the risk factors for hypertension. The governments and stakeholders should design an appropriate strategy to prevent and control the disease in the Ethiopian population.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".