Exploring the occurence and risk factors of post-stroke depression among stroke survivors in Africa: a comprehensive systematic review and meta-analysis
Bibliographic record
Abstract
Post-stroke depression is a significant health concern, especially in developing countries. The high prevalence, incidence, and complexity of depression among stroke survivors pose a substantial occurence on vulnerable individuals. This study aimed to investigate the prevalence, incidence, and risk factors of post-stroke depression among stroke survivors in Africa. PubMed, WHO Global Index Medicus, Web of Science, Cochrane Library, Google Scholar, ScienceDirect, HINARI, and Google were the sources of data searching. Literature reporting the prevalence, incidence and risk factors of post-stroke depression in Africa was included. The quality of each study was evaluated using the Newcastle-Ottawa Quality Assessment Scale (NOS). Data were extracted using a Microsoft Excel spreadsheet, and data analysis was performed using STATA version 11. Heterogeneity between studies was checked using the I2 statistical test. Publication bias was checked using Egger’s statistical test and funnel plot. A total of twenty-two relevant studies with 3175 stroke patients were included in this systematic review and meta-analysis. The overall estimated pooled prevalence and incidence of depression among stroke survivors in Africa were found to be 42.5% (95% CI = 26.9, 58.1) and 33.2% (95% CI = 23.3, 43.0), respectively. The subgroup analysis further revealed that Nigeria had the highest prevalence of depression at 47.6% (95% CI: 15.1, 80.1), followed by Ethiopia at 44.4% (95% CI: 28.2, 60.6). This study did not identify any factors that were positively associated with post-stroke depression. The prevalence and incidence of depression among stroke survivors are notably high. Despite the high occurence, this study did not identify specific risk factors positively associated with post-stroke depression. Consequently, addressing post-stroke depression through integrated care models, routine screening, and targeted interventions is crucial for enhancing the quality of life and rehabilitation outcomes for stroke survivors 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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.008 | 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) | 0.003 | 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".