Stroke Epidemiology, Care, and Outcomes in Kenya: A Scoping Review
Bibliographic record
Abstract
Background: Stroke is a leading cause of death and disability in sub-Saharan Africa with increasing incidence. In Kenya, it is a neglected condition with a paucity of evidence despite its need for urgent care and hefty economic burden. Therefore, we reviewed studies on stroke epidemiology, care, and outcomes in Kenya to highlight existing evidence and gaps on stroke in Kenya. Methods: We reviewed all published studies on epidemiology, care, and outcomes of stroke in Kenya between 1 January 1990 to 31 December 2020 from PubMed, Web of Science, EBSCOhost, Scopus, and African journal online. We excluded case reports, reviews, and commentaries. We used the Newcastle-Ottawa scale adapted for cross-sectional studies to assess the quality of included studies. Results: Twelve articles were reviewed after excluding 111 duplicates and 94 articles that did not meet the inclusion criteria. Five studies were of low quality, two of medium quality, and five of high quality. All studies were hospital-based and conducted between 2003 and 2017. Of the included studies, six were prospective and five were single-center. Stroke patients in the studies were predominantly female, in their seventh decade with systemic hypertension. The mortality rate ranged from 5 to 27% in-hospital and 23.4 to 26.7% in 1 month. Conclusions: Our study highlights that stroke is a significant problem in Kenya, but current evidence is of low quality and limited in guiding policy development and improving stroke care. There is thus a need for increased investment in hospital- and community-based stroke care and research.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".