Mortality Associated with Major Depression in a Canadian Community Cohort
Bibliographic record
Abstract
OBJECTIVE: Prior studies have reported that major depressive episodes (MDEs) are associated with elevated mortality. However, the association has not always persisted after adjustment for other mortality risk factors. In our study, we examine this issue using data from a longitudinal Canadian study (the National Population Health Survey [NPHS]). The NPHS included a more comprehensive set of mortality determinants than prior studies, allowing a more comprehensive assessment of the effect of MDEs on mortality. METHODS: The NPHS began data collection in 1994 and follow-up data were available to 2006 at the time of this analysis. The NPHS assessed depression using a short-form version of the Composite International Diagnostic Interview. Mortality was assessed as part of the cohort's follow-up, including linkage to vital statistics data. RESULTS: During follow-up, 2019 deaths occurred in the eligible part of the NPHS cohort. Consistent with prior studies, MDEs were strongly predictive of mortality when basic adjustments were made for age and sex (hazard ratio [HR] 2.0, 95% CI 1.4 to 2.9). The association disappeared with adjustment for other variables that predict mortality risk (HR 1.1, 95% CI 0.7 to 1.7). CONCLUSIONS: MDE is a strong predictor of mortality in the general population. This analysis failed to identify an independent effect of MDE when adjustments were made for other risk factors. However, the lack of a strong independent effect on mortality does not preclude an etiologic impact of MDE. MDE itself is intertwined with health-related changes that predict mortality and its impact may be mediated by these variables.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".