Prevalence of Mental Health Disorders Among Individuals Experiencing Homelessness
Bibliographic record
Abstract
Importance: Several factors may place people with mental health disorders, including substance use disorders, at increased risk of experiencing homelessness and experiencing homelessness may also increase the risk of developing mental health disorders. Meta-analyses examining the prevalence of mental health disorders among people experiencing homelessness globally are lacking. Objective: To determine the current and lifetime prevalence of mental health disorders among people experiencing homelessness and identify associated factors. Data Sources: A systematic search of electronic databases (PubMed, MEDLINE, PsycInfo, Embase, Cochrane, CINAHL, and AMED) was conducted from inception to May 1, 2021. Study Selection: Studies investigating the prevalence of mental health disorders among people experiencing homelessness aged 18 years and older were included. Data Extraction and Synthesis: Data extraction was completed using standardized forms in Covidence. All extracted data were reviewed for accuracy by consensus between 2 independent reviewers. Random-effects meta-analysis was used to estimate the prevalence (with 95% CIs) of mental health disorders in people experiencing homelessness. Subgroup analyses were performed by sex, study year, age group, region, risk of bias, and measurement method. Meta-regression was conducted to examine the association between mental health disorders and age, risk of bias, and study year. Main Outcomes and Measures: Current and lifetime prevalence of mental health disorders among people experiencing homelessness. Results: A total of 7729 citations were retrieved, with 291 undergoing full-text review and 85 included in the final review (N = 48 414 participants, 11 154 [23%] female and 37 260 [77%] male). The current prevalence of mental health disorders among people experiencing homelessness was 67% (95% CI, 55-77), and the lifetime prevalence was 77% (95% CI, 61-88). Male individuals exhibited a significantly higher lifetime prevalence of mental health disorders (86%; 95% CI, 74-92) compared to female individuals (69%; 95% CI, 48-84). The prevalence of several specific disorders were estimated, including any substance use disorder (44%), antisocial personality disorder (26%), major depression (19%), schizophrenia (7%), and bipolar disorder (8%). Conclusions and Relevance: The findings demonstrate that most people experiencing homelessness have mental health disorders, with higher prevalences than those observed in general community samples. Specific interventions are needed to support the mental health needs of this population, including close coordination of mental health, social, and housing services and policies to support people experiencing homelessness with mental disorders.
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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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".