Prevalence of Depression, Anxiety, and Post-Traumatic Stress Disorder and Their Associated Factors Among Internally Displaced Persons in Burao, Somaliland: Community-Based Cross-Sectional Study
Bibliographic record
Abstract
Objective: This study assessed the prevalence of depression, anxiety, and post-traumatic stress disorder (PTSD) and examined their associated factors among internally displaced persons (IDPs) in Burao, Somaliland. Methods: A community-based cross-sectional study was conducted from April 15 to May 30, 2025, among 372 IDPs aged ≥18 years selected through systematic random sampling from two settlements. Data were collected using structured interviewer-administered questionnaires incorporating internationally validated tools: The Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Primary Care PTSD Screen for DSM-5 (PC-PTSD-5). Descriptive statistics were computed, followed by univariable and multivariable logistic regression analyses to identify factors associated with each disorder. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported, with p < 0.05 considered statistically significant. Results: The prevalence of depression, anxiety, and PTSD was 63.4%, 53.0%, and 38.2%, respectively. For depression, participants displaced for 1-5 years were less likely to report depressive symptoms (AOR = 0.14, 95% CI: 0.04-0.47). For anxiety, married individuals had higher odds (AOR = 3.38, 95% CI: 1.38-8.28), larger household size increased risk (AOR = 1.89, 95% CI: 1.11-3.22), while secondary education was protective (AOR = 0.06, 95% CI: 0.01-0.47). For PTSD, older age was a strong predictor (26-35 years: AOR = 2.41, 95% CI: 1.10-5.30; 36-60 years: AOR = 3.58, 95% CI: 1.62-7.89), and being single increased odds (AOR = 4.41, 95% CI: 1.24-15.75). Conclusion: Mental disorders are highly prevalent among IDPs in Burao, with depression being the most common. Each disorder demonstrated distinct risk profiles: demographic, educational, and displacement-related factors influenced the likelihood of depression, anxiety, and PTSD differently. Tailored psychosocial and community-based mental health interventions are urgently needed to address these specific risk factors and reduce the mental health burden among displaced populations.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".