Prevalence of chronic kidney disease and associated factors among patients with chronic illness in Ethiopia: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective: The main aim of this systematic review and meta-analysis is to provide summarized evidence on the prevalence of chronic kidney disease and associated factors among patients with chronic illness in Ethiopia. Method: Databases of MEDLINE/PubMed, Embase, Google Scholar, CINAHL, Cochrane library, and ScienceDirect were searched. In addition, gray literatures were searched manually from university repositories. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist was used to select potential studies. Microsoft Excel 2013 sheet template was used to extract data. The quality of included studies was assessed by utilizing the Newcastle-Ottawa Scale. STATA software version 14.0 is used to compute the estimated pooled prevalence and associated factors of chronic kidney disease. Result: Twelve articles that fulfilled the inclusion criteria were included. The pooled estimate of chronic kidney disease among patients with chronic illnesses in Ethiopia is 21.71% (95% confidence interval: 17.67, 25.74). The highest prevalence of chronic kidney disease among patients with chronic illnesses is from Oromia (32.55% (confidence interval: 19.91, 45.19)). Glomerular filtration rate showed a comparable pooled prevalence from Cockroft-Gault and MDRD methods; 22.38% (confidence interval: 15.83, 28.92), 22.18 (confidence interval: 18.01, 26.34), respectively. Hypertensives become more likely to have chronic kidney disease compared with normotensive patients, (odds ratio = 3.01, 95% confidence interval: 1.33, 6.81). Conclusion: Prevalence of chronic kidney disease among chronic illness patients was significantly high. Hypertension is significantly associated with chronic kidney disease. Hence, we recommend that continuous screening of possible risk factors and proper follow-up and management strategies should be designed.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.010 | 0.009 |
| 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".