Prevalence of Chronic Kidney Disease as a Marker of Hypertension Target Organ Damage in Africa: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction . Hypertension is a major global cause of cardiovascular disease and death with rising worldwide prevalence, particularly in low‐income countries. With low awareness, poor treatment, and low control of hypertension in Africans, there is an increased number of patients with target organ damage (TOD), especially chronic kidney disease (CKD), as a consequence of hypertension. The aim of our study is to assess the prevalence of CKD from studies in Africa reporting TOD related to hypertension. Methods . We performed a search of PubMed/MEDLINE, Web of Science, EBSCOhost, and African Journals Online (AJOL) for studies reporting on CKD as TOD in patients with hypertension. The pooled estimate of CKD was then presented by subregions, age group, eGFR equations, and urban or rural location. Results . We identified 1,334 articles from which 12 studies were included for quantitative analysis. The studies included 5297 participants from 6 countries (Ghana, Nigeria, Uganda, Tanzania, Democratic Republic of Congo, and South Africa). The pooled prevalence of CKD was 17.8% (95% CI 13.0–23.3%), and CKD was significantly more prevalent in West Africa (21.3% (95% CI: 16.1–27.0); p < 0.0001) and in studies conducted in urban settings ( p < 0.001). CKD prevalence was not significantly different by type of GFR equation or age. Conclusion . This study reports a high prevalence of CKD related to hypertension with a higher prevalence in urban than rural areas. This emphasizes the role of hypertension in causing kidney damage, and the need for strategies to improve awareness, treatment, and control of hypertension in Africans. This study is registered with PROSPERO registration number CRD42018089263 .
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".