Prevalence and risk factors of metabolic-associated fatty liver disease in sub-Saharan Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Sub-Saharan Africa (SSA) is undergoing an epidemiological transition with a steady rise in non-communicable diseases. Among these diseases, metabolic dysfunction-associated fatty liver disease (MAFLD) has emerged as a rapidly increasing public health burden, but is inaccurately documented. We characterized the MAFLD prevalence and identified associated risk factors among adults in SSA. Methods: We searched PubMed/Medline, Cochrane, Embase, Web of Science, Google Scholar, and African Journals Online for studies looking into the prevalence of and the risk factors for MAFLD in SSA. Studies from 1990 in the English language were included, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used for reporting. The quality of the studies was assessed using the Newcastle-Ottawa Scale. A random-effects model was used to estimate the prevalence and the risk factors with 95% confidence intervals (CIs). Meta-regression was used for the subgroup analyses to account for heterogeneity. Stata version 17 software was used for the analysis. This study was registered with PROSPERO (registration no. CRD42024506067). Results: . 12.17%). Interpretation: MAFLD is highly prevalent in SSA, with a steady increasing magnitude. The significant risk factors included diabetes, hypertension, obesity, and female sex. This study underscores the emerging need of clinicians in SSA to screen MAFLD among patients at high risk and to instigate tailored care. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42024506067.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.011 | 0.011 |
| 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".