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Record W4296061600 · doi:10.3389/fpsyt.2022.978703

Prevalence of depression, anxiety and post-traumatic stress in war- and conflict-afflicted areas: A meta-analysis

2022· review· en· W4296061600 on OpenAlexaff
Isis Claire Z. Y. Lim, Wilson Tam, Agata Chudzicka‐Czupała, Roger S. McIntyre, Kayla M. Teopiz, Roger Ho, Cyrus S. H. Ho

Bibliographic record

VenueFrontiers in Psychiatry · 2022
Typereview
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversity of TorontoBrain and Cognition Discovery FoundationUniversity Health Network
FundersInstitute for Health Innovation and Technology, National University of Singapore
KeywordsAnxietyDepression (economics)Traumatic stressPsycINFOMeta-analysisMedicinePopulationPsychiatryClinical psychologyMental healthMEDLINEInternal medicineEnvironmental health

Abstract

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Background With the rise of fragility, conflict and violence (FCV), understanding the prevalence and risk factors associated with mental disorders is beneficial to direct aid to vulnerable groups. To better understand mental disorders depending on the population and the timeframe, we performed a systematic review to investigate the aggregate prevalence of depression, anxiety and post-traumatic stress symptoms among both civilian and military population exposed to war. Methods We used MEDLINE (PubMed), Web of Science, PsycINFO, and Embase to identify studies published from inception or 1–Jan, 1945 (whichever earlier), to 31–May, 2022, to reporting on the prevalence of depression, anxiety and post-traumatic stress symptoms using structured clinical interviews and validated questionnaires as well as variables known to be associated with prevalence to perform meta-regression. We then used random-effects bivariate meta-analysis models to estimate the aggregate prevalence rate. Results The aggregate prevalence of depression, anxiety and post-traumatic stress during times of conflict or war were 28.9, 30.7, and 23.5%, respectively. Our results indicate a significant difference in the levels of depression and anxiety, but not post-traumatic stress, between the civilian group and the military group respectively (depression 34.7 vs 21.1%, p < 0.001; anxiety 38.6 vs 16.2%, p < 0.001; post-traumatic stress: 25.7 vs 21.3%, p = 0.256). The aggregate prevalence of depression during the wars was 38.7% (95% CI: 30.0–48.3, I 2 = 98.1%), while the aggregate prevalence of depression post-wars was 29.1% (95% CI: 24.7–33.9, I 2 = 99.2%). The aggregate prevalence of anxiety during the wars was 43.4% (95% CI: 27.5–60.7, I 2 = 98.6%), while the aggregate prevalence of anxiety post-wars was 30.3% (95% CI: 24.5–36.9, I 2 = 99.2%). The subgroup analysis showed significant difference in prevalence of depression, and anxiety between the civilians and military group ( p < 0.001). Conclusion The aggregate prevalence of depression, anxiety and post-traumatic stress in populations experiencing FCV are 28.9, 30.7, and 23.5%, respectively. There is a significant difference in prevalence of depression and anxiety between civilians and the military personnels. Our results show that there is a significant difference in the prevalence of depression and anxiety among individuals in areas affected by FCV during the wars compared to after the wars. Overall, these results highlight that mental health in times of conflict is a public health issue that cannot be ignored, and that appropriate aid made available to at risk populations can reduce the prevalence of psychiatric symptoms during time of FCV. Systematic Review Registration https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=337486 , Identifier 337486.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.027
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0210.054
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.090
GPT teacher head0.388
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations176
Published2022
Admission routes1
Has abstractyes

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