Prevalence of suicidal ideation and suicide attempts in individuals with psychosis and bipolar disorder in South Asia: systematic review and meta-analysis
Bibliographic record
Abstract
Background Suicidal ideation and attempts are growing public health concerns globally. Evidence from high-income countries suggests that individuals with psychosis and bipolar disorder are at increased risk of suicidal ideation and attempts, but there is a scarcity of evidence from South Asia. Aims To estimate the prevalence of suicidal ideation and attempts in individuals with psychosis and bipolar disorder in South Asia. Method In this systematic review and meta-analysis, four databases (PsycINFO, Web of Science, EMBASE and Medline) were searched until December 2022. Pooled prevalence was estimated with random-effects models. Heterogeneity was quantified with the I 2 -statistic. Results The pooled sample size across the 21 studies was 3745 participants, 1941 (51.8%) of which were male. The pooled prevalence of suicide attempts in South Asian people with either psychosis or bipolar disorder was 22% (95% CI 17–27; n = 15). The pooled prevalence of suicidal ideation with psychosis or bipolar disorder combined was 38% (95% CI 27–51; n = 10). Meta-regression, subgroup and sensitivity analysis showed that the pooled prevalence estimates for both suicide attempt and ideation remained unaffected by variations in critical appraisal ratings and study designs. Only one study reported data on suicide-related deaths. Conclusions One in four individuals diagnosed with psychosis or bipolar disorder have reported suicide attempts, whereas up to one in three have experienced suicidal ideation. These findings underscore the urgent need for clinicians to regularly assess and monitor suicidal ideation and attempts among individuals with these disorders in South Asia.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".