Prevalence and distribution of primary glomerular diseases in Africa: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
Glomerulonephritis (GN) is a predominant cause of kidney failure in Africa. The prevalence of primary GNs varies widely across Africa depending on the relative proportion of secondary GNs and genetic predispositions. We assessed the overall and sub-regional prevalence of primary GN and its histologic subtypes in Africa. We searched PubMed, EMBASE and African Journals Online for studies of biopsy-proven primary GNs across all age groups in Africa published between 2010 and 2022. Data for primary GNs [minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), mesangioproliferative GN (MesPGN), membranoproliferative GN (MPGN), post-infectious GN (PIGN), IgA Nephropathy (IgAN), and crescentic GN (CresGN)] were extracted. Pooled prevalence was determined using the random effects model. Seventeen eligible articles (n = 6,494 individuals) from 8 African countries met the inclusion criteria. The overall pooled prevalence of FSGS, MCD, MN, MPGN, MesPGN, PIGN, IgAN and CresGN was 26.10%, 22.40%, 8.40%, 6.40%, 6.40%, 2.60%, 2.60%, 1.40%, respectively. Only 4 studies (23.5%) used light microscopy (LM), immunofluorescence (IF), and electron microscopy (EM) for diagnosis. There were significant differences in the distribution of histologic subtypes in the paediatric compared to the adult population and across geographic sub-regions, with West Africa having a higher prevalence of FSGS. Overall, the dominance of FSGS across most regions and age groups has implications for disease diagnosis and ongoing care. Research efforts to understand the impact of this trend on kidney disease outcomes and efforts to improve kidney biopsy practice as a means of early disease detection are needed in Africa.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".