Assessing anxiety, depression and insomnia symptoms among Ebola survivors in Africa: A meta-analysis
Bibliographic record
Abstract
Background During health disaster events such as the current devastating havoc being inflicted on countries globally by the SARS-CoV-19 pandemic, mental health problems among survivors and frontline workers are likely concerns. However, during such health disaster events, stakeholders tend to give more precedence to the socio-economic and biomedical health consequences at the expense of mental health. Meanwhile, studies show that regardless of the kind of disaster/antecedent, all traumatic events trigger similar post-traumatic stress symptoms among survivors, families, and frontline workers. Thus, our study investigated the prevalence of anxiety, depression and insomnia symptoms among survivors of the 2014–2016 Ebola virus disease that plagued the West African sub-region. Methods We systematically retrieved peer-reviewed articles published between 1970 and 2019 from seven electronic databases, including Google Scholar, MEDLINE, PsychInfo, PubMed, Scopus, Springer Link, Web of Science on Ebola and post-traumatic stress disorder symptoms. A comprehensive hand search complemented this literature search. Of the 87 articles retrieved, only 13 met the inclusion criteria for this meta-analysis. Results After heterogeneity, influence, and publication bias analysis, our meta-analysis pooled proportion effects estimates showed a moderate to a high prevalence of anxiety (14%; 99% CI: 0.05–0.30), depression (15%; 99% CI: 0.11–0.21), and insomnia (22%; 99% CI: 0.13–0.36). Effect estimates ranging from (0.13; 99% CI: 0.05, 0.28) through to (0.11; 99% CI: 0.05–0.22), (0.15; 99% CI: 0.09–0.25) through to (0.13; 99% CI: 0.08–0.21) and (0.23; 99% CI: 0.11–0.41) to (0.23; 99% CI: 0.11–0.41) were respectively reported for anxiety, depression and insomnia symptoms. These findings suggest a significant amount of EVD survivors are struggling with anxiety, depression and insomnia symptoms. Conclusion Our study provided the first-ever meta-analysis evidence of anxiety, depression, and insomnia symptoms among EVD survivors, and suggest that the predominant biomedical health response to regional and global health disasters should be complemented with trauma-related mental health services.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.054 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".