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Record W3047493740 · doi:10.1371/journal.pmed.1003337

The prevalence of mental illness in refugees and asylum seekers: A systematic review and meta-analysis

2020· review· en· W3047493740 on OpenAlexaboutno aff
Rebecca Blackmore, Jacqueline Boyle, Mina Fazel, Sanjeeva Ranasinha, Kylie M. Gray, Grace Fitzgerald, Marie Misso, Melanie Gibson‐Helm

Bibliographic record

VenuePLoS Medicine · 2020
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilEdith Cowan UniversityVictoria UniversityMonash UniversityMedical Research CouncilAustralian Rotary HealthStanford Maternal and Child Health Research InstituteWindermere Foundation
KeywordsRefugeePsycINFOMedicineCINAHLMental healthObservational studyMEDLINEMental illnessSystematic reviewAsylum seekerFamily medicinePsychiatryPsychological interventionDemographyGeographyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Globally, the number of refugees and asylum seekers has reached record highs. Past research in refugee mental health has reported wide variation in mental illness prevalence data, partially attributable to methodological limitations. This systematic review aims to summarise the current body of evidence for the prevalence of mental illness in global refugee populations and overcome methodological limitations of individual studies. METHODS AND FINDINGS: A comprehensive search of electronic databases was undertaken from 1 January 2003 to 4 February 2020 (MEDLINE, MEDLINE In-Process, EBM Reviews, Embase, PsycINFO, CINAHL, PILOTS, Web of Science). Quantitative studies were included if diagnosis of mental illness involved a clinical interview and use of a validated assessment measure and reported at least 50 participants. Study quality was assessed using a descriptive approach based on a template according to study design (modified Newcastle-Ottawa Scale). Random-effects models, based on inverse variance weights, were conducted. Subgroup analyses were performed for sex, sample size, displacement duration, visa status, country of origin, current residence, type of interview (interpreter-assisted or native language), and diagnostic measure. The systematic review was registered with PROSPERO (CRD) 42016046349. The search yielded a result of 21,842 records. Twenty-six studies, which included one randomised controlled trial and 25 observational studies, provided results for 5,143 adult refugees and asylum seekers. Studies were undertaken across 15 countries: Australia (652 refugees), Austria (150), China (65), Germany (1,104), Italy (297), Lebanon (646), Nepal (574), Norway (64), South Korea (200), Sweden (86), Switzerland (164), Turkey (238), Uganda (77), United Kingdom (420), and the United States of America (406). The prevalence of posttraumatic stress disorder (PTSD) was 31.46% (95% CI 24.43-38.5), the prevalence of depression was 31.5% (95% CI 22.64-40.38), the prevalence of anxiety disorders was 11% (95% CI 6.75-15.43), and the prevalence of psychosis was 1.51% (95% CI 0.63-2.40). A limitation of the study is that substantial heterogeneity was present in the prevalence estimates of PTSD, depression, and anxiety, and limited covariates were reported in the included studies. CONCLUSIONS: This comprehensive review generates current prevalence estimates for not only PTSD but also depression, anxiety, and psychosis. Refugees and asylum seekers have high and persistent rates of PTSD and depression, and the results of this review highlight the need for ongoing, long-term mental health care beyond the initial period of resettlement.

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)0.020
metaresearch head score (Gemma)0.049
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.025
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.049
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0250.034
Bibliometrics0.0110.011
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.089
GPT teacher head0.409
Teacher spread0.320 · 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

Citations909
Published2020
Admission routes1
Has abstractyes

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