Psychiatric diagnoses in 3275 suicides: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: It is well known that most suicide cases meet criteria for a psychiatric disorder. However, rates of specific disorders vary considerably between studies and little information is known about gender and geographic differences. This study provides overall rates of total and specific psychiatric disorders in suicide completers and presents evidence supporting gender and geographic differences in their relative proportion. METHODS: We carried out a review of studies in which psychological autopsy studies of suicide completers were performed. Studies were identified by means of MEDLINE database searches and by scanning the reference list of relevant publications. Twenty-three variables were defined, 16 of which evaluating psychiatric disorders. Mantel-Haenszel Weighted Odds Ratios were estimated for these 16 outcome variables. RESULTS: Twenty-seven studies comprising 3275 suicides were included, of which, 87.3% (SD 10.0%) had been diagnosed with a mental disorder prior to their death. There were major gender differences. Diagnoses of substance-related problems (OR = 3.58; 95% CI: 2.78-4.61), personality disorders (OR = 2.01; 95% CI: 1.38-2.95) and childhood disorders (OR = 4.95; 95% CI: 2.69-9.31) were more common among male suicides, whereas affective disorders (OR = 0.66; 95% CI: 0.53-0.83), including depressive disorders (OR = 0.53; 95% CI: 0.42-0.68) were less common among males. Geographical differences are also likely to be present in the relative proportion of psychiatric diagnoses among suicides. CONCLUSIONS: Although psychopathology clearly mediates suicide risk, gender and geographical differences seem to exist in the relative proportion of the specific psychiatric disorders found among suicide completers.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.048 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.004 | 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".