Medical Illness and the Risk of Suicide in the Elderly
Bibliographic record
Abstract
BACKGROUND: Suicide is a leading cause of death, and rates are especially high among the elderly. Medical illnesses may predispose to suicide, but few controlled studies have examined the association between specific diseases and suicide. We explored the relationship between treatment for several illnesses and the risk of suicide in elderly patients using a population-based approach. METHODS: All Ontario residents 66 years or older who committed suicide between January 1, 1992, and December 31, 2000, were identified from provincial coroners' records. Their prescription records during the preceding 6 months were compared with those of living matched controls (1:4) to determine the presence or absence of 17 illnesses potentially associated with suicide. RESULTS: During the 9-year study period, we identified 1354 elderly patients who died of suicide. The most common mechanisms involved firearms (28%), hanging (24%), and self-poisoning (21%). Specific illnesses associated with suicide included congestive heart failure (odds ratio [OR], 1.73; 95% confidence interval [CI], 1.33-2.24), chronic obstructive lung disease (OR, 1.62; 95% CI, 1.37-1.92), seizure disorder (OR, 2.95; 95% CI, 1.89-4.61), urinary incontinence (OR, 2.02; 95% CI, 1.29-3.17), anxiety disorders (OR, 4.65; 95% CI, 4.07-5.32), depression (OR, 6.44; 95% CI, 5.45-7.61), psychotic disorders (OR, 5.09; 95% CI, 3.94-6.59), bipolar disorder (OR, 9.20, 95% CI, 4.38-19.33), moderate pain (OR, 1.91; 95% CI, 1.66-2.20), and severe pain (OR, 7.52; 95% CI, 4.93-11.46). Treatment for multiple illnesses was strongly related to a higher risk of suicide. Almost half the patients who committed suicide had visited a physician in the preceding week. CONCLUSIONS: Many common illnesses are independently associated with an increased risk of suicide in the elderly. The risk is greatly increased among patients with multiple illnesses. These data may help clinicians to identify elderly patients at risk of suicide and open avenues for prevention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".