Lifetime experience of multiple common mental disorders and 19-year mortality: results from a Canadian population-based cohort
Bibliographic record
Abstract
AIMS: To examine the impact of multiple psychiatric disorders over the lifetime on risk of mortality in the general population. METHODS: Data came from a random community-based sample of 1397 adults in Atlantic Canada, recruited in 1992. Major depression, dysthymia, panic disorder, generalised anxiety disorder and alcohol use disorders were assessed using the Diagnostic Interview Schedule (DIS). Vital status of participants through 2011 was determined using probabilistic linkages to the Canadian Mortality Database. Cox proportional hazard models with age at study entry as the time scale were used to investigate the relationship between DIS diagnoses and mortality, adjusted for participant education, smoking and obesity at baseline. RESULTS: Results suggested that mood and anxiety disorders rarely presented in isolation - the majority of participants experienced multiple psychiatric disorders over the lifetime. Elevated risk of death was found among men with both major depression and dysthymia (HR 2.56; 95% CI 1.12-5.89), depression and alcohol use disorders (HR 2.45; 95% CI 1.18-5.10) and among men and women who experienced both panic disorder and alcohol use disorders (HR 3.80; 95% CI 1.19-12.16). CONCLUSION: The experience of multiple mental disorders over the lifetime is extremely common, and associated with increased risk of mortality, most notably among men. Clinicians should be aware of the importance of considering contemporaneous symptoms of multiple psychiatric conditions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".