Acute Risk of Suicide and Suicide Attempts Associated with Recent Diagnosis of Mental Disorders: A Population-Based, Propensity Score—Matched Analysis
Bibliographic record
Abstract
OBJECTIVE: To determine the degree of risk during the first year after diagnosis with a mental illness. METHODS: We used propensity scoring to create a matched sample for all identified suicide attempts and suicide deaths in the province of Manitoba from 1996 to 2009. This study identified 2100 suicide deaths and 8641 attempted suicides. Three control subjects were identified for every case and matched on age, sex, income decile, region of residence, and marital status. Five categories of physician-diagnosed mental disorders were tested: schizophrenia, anxiety, depression, dementia, and substance abuse. Logistic regression was used to determine the risk for suicide attempts and suicide deaths overall, and within 3 time periods since initial diagnosis: 1 to 90 days, 91 to 364 days, and 365 or more days. RESULTS: All disorders, except dementia, were independently related to death. All disorders were related to suicide attempts. The risk of dying by suicide was particularly high within the first 90 days after initial diagnosis for many disorders, including depression (adjusted odds ratio [AOR] 7.33; 95% CI 4.76 to 11.3), substance use disorders (AOR 4.07; 95% CI 2.43 to 6.82), and schizophrenia (AOR 20.91; 95% CI 2.55 to 172). Depression and anxiety disorders had elevated risk in the first year for suicide attempts. CONCLUSIONS: These data suggest that several mental disorders independently increase the risk of suicide attempts and death by suicide after controlling for all mental disorders and demographic risk factors. Clinicians should be aware of the heightened risk of suicide and suicidal behaviour within the first 3 months after initial diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".