Diagnostic test accuracy of screening tools for post-traumatic stress disorder among refugees and asylum seekers: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Refugees and asylum seekers often experience traumatic events resulting in a high prevalence of post-traumatic stress disorder (PTSD). Undiagnosed PTSD can have detrimental effects on resettlement outcomes. Immigration medical exams provide an opportunity to screen for mental health conditions in refugee and asylum seeker populations and provide links to timely mental health care. Objective: To assess the diagnostic accuracy of screening tools for PTSD in refugee and asylum seeker populations. Methods: We systematically searched Medline, Embase, PsycINFO, CENTRAL and CINAHL up to 29 September 2022. We included cohort-selection or cross-sectional study designs that assessed PTSD screening tools in refugee or asylum seeker populations of all ages. All reference standards were eligible for inclusion, with a clinical interview considered the gold standard. We selected studies and extracted diagnostic test accuracy data in duplicate. Risk of bias and applicability concerns were addressed using QUADAS-2. We meta-analyzed findings using a bivariate random-effects model. We partnered with a patient representative and a clinical psychiatrist to inform review development and conduct. Results: Our review includes 28 studies (4,373 participants) capturing 16 different screening tools. Nine of the 16 tools were developed specifically for refugee populations. Most studies assessed PTSD in adult populations, but three included studies focused on detecting PTSD in children. Nine studies looked at the Harvard Trauma Questionnaire (HTQ) with diagnostic cut-off points ranging from 1.17 to 2.5. Meta-analyses revealed a summary point sensitivity of 86.6% (95%CI 0.791; 0.917) and specificity of 78.9% (95%CI 0.639; 0.888) for these studies. After evaluation, we found it appropriate to pool other screening tools (Posttraumatic Stress Disorder Checklist, the Impact of Event Scale, and the Posttraumatic Diagnostic Scale) with the HTQ. The area under the curve for this model was 79.4%, with a pooled sensitivity of 86.2% (95%CI 0.759; 0.925) and a specificity of 72.2% (95%CI 0.616; 0.808). Conclusions: Our review identified several screening tools that perform well among refugees and asylum seekers, but no single tool was identified as being superior. The Refugee Health Screener holds promise as a practical instrument for use in immigration medical examinations because it supports the identification of PTSD, depression, and anxiety across diverse populations. Future research should consider tool characteristics beyond sensitivity and specificity to facilitate implementation in immigration medical exams. Registration: Open Science Framework: 10.17605/OSF.IO/PHNJV.
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 | 0.027 | 0.107 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.036 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 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".