Risk of suicide and suicide attempts associated with physical disorders: a population-based, balancing score-matched analysis
Bibliographic record
Abstract
BACKGROUND: The association between physical disorders and suicide remains unclear. The aim of this study was to examine the relationship between physical disorders and suicide after accounting for the effects of mental disorders. METHOD: Individuals who died by suicide (n = 2100) between 1996 and 2009 were matched 3:1 by balancing score to general population controls (n = 6300). Multivariate conditional logistic regression compared the two groups across physician-diagnosed physical disorders [asthma, chronic obstructive pulmonary disease (COPD), ischemic heart disease, hypertension, diabetes, cancer, multiple sclerosis and inflammatory bowel disease], adjusting for mental disorders and co-morbidity. Secondary analyses examined the risk of suicide according to time since first diagnosis of each physical disorder (1-90, 91-364, ⩾ 365 days). Similar analyses also compared individuals with suicide attempts (n = 8641) to matched controls (n = 25 923). RESULTS: Cancer was associated with increased risk of suicide [adjusted odds ratio (AOR) 1.40, 95% confidence interval (CI) 1.03-1.91, p < 0.05] even after adjusting for all mental disorders. The risk of suicide with cancer was particularly high in the first 90 days after initial diagnosis (AOR 4.10, 95% CI 1.71-9.82, p < 0.01) and decreased to non-significance after 1 year. Women with respiratory diseases had elevated risk of suicide whereas men did not. COPD, hypertension and diabetes were each associated with increased odds of suicide attempts in adjusted models (AORs ranged from 1.20 to 1.73). CONCLUSIONS: People diagnosed with cancer are at increased risk of suicide, especially in the 3 months following initial diagnosis. Increased support and psychiatric involvement should be considered for the first year after cancer diagnosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".