Suicide and suicide attempts in people with severe mental disorders in Butajira, Ethiopia: 10 year follow-up of a population-based cohort
Bibliographic record
Abstract
BACKGROUND: People with severe mental disorders (SMD) are at higher risk of suicide. However, research into suicide attempts and completed suicide in people with SMD in low- and middle-income countries is mostly limited to patients attending psychiatric facilities where selection bias is likely to be high. METHODS: A population-based cohort of 919 people with SMD from rural Ethiopia (who received standardized clinician diagnoses of schizophrenia (n = 358) major depressive disorder (n = 216) and bipolar I disorder (n = 345)) were followed up annually for an average of 10 years. The Longitudinal Interval Follow-up Evaluation chart was administered by psychiatrists and used to evaluate systematically suicidal behavior and risk factors, which may be amenable to intervention. RESULTS: Over the follow-up period, the cumulative risk of suicide attempt was 26.3% for major depression, 23.8% for bipolar I disorder and 13.1% for schizophrenia, (p < 0.001). The overall incidence of completed suicide was 200.2/100,000 person-years (CI = 120.6, 312.5). Hanging was the most frequent method used (71.5%) for both attempters and completers. Most people who completed suicide were successful on the first attempt (84.2%), but the case-fatality rate for suicide attempt was 9.7%. In the adjusted logistic regression model, being currently married (Adjusted OR) =2.17, 95% CI = 1.21, 3.91), and having a diagnosis of bipolar I disorder (Adjusted OR = 2.59, 95% CI = 1.57, 4.26) or major depression (Adjusted OR = 2.71, 95% CI = 1.60, 4.58) were associated significantly with increased risk of suicide attempts. CONCLUSION: In this sample of people with SMD from a rural setting, the rate of suicide was high. Initiatives to integrate mental health service into primary care need to focus on limiting access to suicide methods in people with SMD in addition to expanding access to mental health care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".