Stroke in Africa: A systematic review and meta-analysis of the incidence and case-fatality rates
Bibliographic record
Abstract
Background: The burden of stroke (a leading cause of disability and mortality) in Africa appears to be increasing, but a systematic review of the best available data to support or refute this observation is lacking. Aim: To determine the incidence and 1-month case-fatality rates from high-quality studies of stroke epidemiology among Africans. Summary of review: We searched and retrieved eligible articles on stroke epidemiology among indigenous Africans in bibliographic databases (MEDLINE, ScienceDirect, Google Scholar, and Cochrane library) using predefined search terms from the earliest records through January 2022. Methodological assessment of eligible studies was conducted using the Newcastle–Ottawa scale. Pooling of incidence and case-fatality rates was performed via generalized linear models (Poisson-Normal random-effects model). Of the 922 articles retrieved, 14 studies were eligible for inclusion. The total number of stroke cases was 2568, with a population denominator (total sample size included in population-based registries or those who agreed to participate in door-to-door community studies) of 3,384,102. The pooled crude incidence rate of stroke per 100,000 persons in Africa was 106.49 (95% confidence interval (CI) = 58.59–193.55), I 2 = 99.6%. The point estimate of the crude incidence rate was higher among males, 111.33 (95% CI = 56.31–220.12), I 2 = 99.2%, than females, 91.14 (95% CI = 47.09–176.37), I 2 = 98.9%. One-month case-fatality rate was 24.45 (95% CI = 16.84–35.50), I 2 = 96.8%, with lower estimates among males, 22.68 (95% CI = 18.62–27.63), I 2 = 12.9%, than females, 27.57 (95% CI = 21.47–35.40), I 2 = 51.6%. Conclusion: The burden of stroke in Africa remains very high. However, little is known about the dynamics of stroke epidemiology among Africans due to the dearth of high-quality evidence. Further continent-wide rigorous epidemiological studies and surveillance programs using the World Health Organization STEPwise approach to Surveillance (WHO STEPS) framework are needed.
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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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.044 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".