Prevalence of cardiometabolic diseases in Sub-Saharan Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Cardiometabolic diseases (CMDs) are increasingly prevalent in Sub-Saharan Africa (SSA), underscoring the need to understand the existing public health burden. This would guide future policy strategies and interventions to mitigate the challenges posed. The aim of this systematic review was to provide a comprehensive overview of CMDs prevalence in SSA. A PRISMA 2020 compliant systematic literature search was conducted using MEDLINE and The Cochrane Library up to December 2024, including population-based studies with ≥100 participants, aged ≥15 years, and reporting CMDs prevalence in SSA. Random effects meta-analyses were conducted for prevalence, and meta-regression, for temporal trends, evaluated using the median data collection year. Overall, 266 unique studies of 846,511 participants were included; Ethiopia (n = 53), Nigeria (n = 36) and Ghana (n = 20) represented the most studies. Prevalences for the most widely studied condition included type 2 diabetes (T2D) (6.1%; 95% CI = 5.3–7.0), hypertension (27.1%; 95% CI = 25.5–28.8), stroke (1.4%; 95% CI = 1.0–2.0), hypercholesterolemia (11.3%; 95% CI = 7.4–17.0) and cardiovascular diseases (4.8%; 95% CI = 2.5–8.9). The temporal prevalence of hypertension and T2D between 2006 and 2014 showed no statistical significance (β = –0.0289 per year; p = 0.11) and (β = 0.0131 per year; p = 0.49), respectively. For stroke, a statistically significant temporal decline was observed beginning 2010 (β = b–0.1244 per year; p < 0.001). This systematic review reveals a substantial public health burden of CMDs in SSA. The high prevalence emphasises the need for targeted CMDs preventative care strategies in SSA. Notably, most studies were from Ethiopia and Nigeria, indicating the need for more research in other SSA countries for a comprehensive understanding of CMDs in the region.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.009 | 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) | 0.004 | 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".