Prevalence of post‐traumatic stress disorder and depressive symptoms among civilians residing in armed conflict‐affected regions: a systematic review and meta‐analysis
Bibliographic record
Abstract
Background: Globally, populations afflicted by armed conflict are known to have high rates of mental health disorders. Aims: This meta-analysis aims to estimate the prevalence of post-traumatic stress disorder (PTSD) and depressive symptoms among civilians residing in armed conflict-affected regions. Methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. A literature search employing MEDLINE(R), Embase Classic+Embase, APA PsycINFO, Ovid Healthstar, Journal@Ovid Full Text, Cochrane, PTSDpubs and CINAHL was conducted from inception until 19 March 2024 to identify relevant studies. Quality assessment was performed using the Joanna Briggs Institute Critical Appraisal Checklist for Prevalence Studies, and a Comprehensive Meta-Analysis was used to conduct the statistical analysis. Results: The search yielded 38 595 articles, of which 57 were considered eligible for inclusion. The included studies comprised data from 64 596 participants. We estimated a prevalence of 23.70% (95% CI 19.50% to 28.40%) for PTSD symptoms and 25.60% (95% CI 20.70% to 31.10%) for depressive features among war-afflicted civilians. The subgroup analysis based on time since the war and the country's economic status revealed the highest prevalence for both PTSD and depressive symptoms was present during the years of war and in low/middle-income countries. Conclusions: The results of this study provide conclusive evidence of the detrimental impacts of armed conflict on mental health outcomes. Hence, it is crucial to emphasise the significance of both physical and mental health in the aftermath of war and take appropriate humanistic measures to overcome challenges in the management of psychiatric illnesses. PROSPERO registration number: CRD42023416096.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.047 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".