Prevalence of Chronic Kidney Disease and Associated Factors Among Hypertensive Patients in Ethiopia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background: - Hypertension is most common global cause of cardiovascular disease and death with rising worldwide prevalence, particularly in low-income countries. Patients with target organ damage, particularly chronic kidney disease, are more common as a result of hypertension. Despite the fact that many primary studies have reported the prevalence of chronic renal disease among hypertension patients, their findings have shown significant diversity with regard to the disease's prevalence in Ethiopia. Thus, the purpose of this study was to determine the overall prevalence of chronic kidney disease and its associated factors among Ethiopian hypertensive patients. Method: - The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist was used to select potential studies. PubMed, scopes and web of science were searched to identify relevant studies. To examine for heterogeneity among the included studies, the I2 statistic was employed. A random-effects model was applied to estimate the pooled effect size across studies. The Egger's regression test and a funnel plot were employed to look for evidence of publication bias. The quality of included studies was assessed by utilizing the Newcastle-Ottawa Scale. The estimated pooled prevalence and related variables of chronic renal disease were determined using STATA software version 17.0. Result: - Four research totaling 1,675 study participants were included in this meta-analysis. The pooled prevalence of CKD among hypertensive patients was found to be 21% (95% CI: 18–24, I2 = 59.45%. Age ≥ 60 years (OR = 1.73, 95%, CI: 1.02, 2.44), uncontrolled hypertension (OR = 4.64, 95%, CI: 1.83, 7.44), duration of hypertension >=10 years (OR = 6.05; 95% CI 3.40–8.70) and having co-morbid diseases (OR = 4.24; 95% CI: 1.46–7.03) were significantly associated with CKD. Conclusion: - This study showed that the prevalence of chronic kidney disease remains high among hypertensive patients in Ethiopia. This study found that Age ≥ 60 years, uncontrolled hypertension, duration of hypertension >=10 years and having co-morbid diseases were associated with chronic kidney disease among hypertensive patients. Therefore, situation-based interventions and context-specific preventive strategies should be developed to reduce the prevalence and risk factors of chronic kidney disease among hypertensive patients.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".