Prevalence of Anxiety and Depression Among the General Population in Africa During the COVID-19 Pandemic: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Medical and socio-economic uncertainties surrounding the COVID-19 pandemic have had a substantial impact on mental health. This study aimed to systematically review the existing literature reporting the prevalence of anxiety and depression among the general populace in Africa during the COVID-19 pandemic and examine associated risk factors. Methods: A systematic search of the following databases African Journal Online, CINAHL, PubMed, Scopus, and Web of Science was conducted from database inception until 30th September 2021. Studies reporting the prevalence of anxiety and/or depression among the general populace in African settings were considered for inclusion. The methodological quality of included studies was assessed using the Agency for Healthcare Research and Quality (AHRQ). Meta-analyses on prevalence rates were conducted using Comprehensive Meta-analysis software. Results: = 99.45%) were reported across Africa during the COVID-19 pandemic. Sex (female) and history of existing medical/chronic conditions were identified as major risk factors for anxiety and depression. Conclusions: The evidence put forth in this synthesis demonstrates the substantial impact of the pandemic on the pervasiveness of these psychological symptoms among the general population. Governments and stakeholders across continental Africa should therefore prioritize the allocation of available resources to institute educational programs and other intervention strategies for preventing and ameliorating universal distress and promoting psychological wellbeing. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021228023, PROSPERO CRD42021228023.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".