All‐cause and cause‐specific mortality in people with depression: a large‐scale systematic review and meta‐analysis of relative risk and aggravating or attenuating factors, including antidepressant treatment
Bibliographic record
Abstract
Depression has been reported to be associated with premature mortality. However, no meta‐analysis has comprehensively examined all‐cause and cause‐specific mortality risk in people with this condition, focusing also on possible aggravating and attenuating factors, including antidepressant treatment. We conducted a systematic review and meta‐analysis of cohort studies to synthesize mortality risk estimates associated with depression (major depressive disorder and dysthymia) due to any and specific causes, and when depression is accompanied by comorbid conditions. Effects of antidepressant medication and electroconvulsive therapy (ECT), and other potential moderators of mortality risk, were evaluated. We searched EMBASE, Medline and PsycINFO databases up to January 26, 2025, pooling mortality estimates using random‐effect models. Publication bias, subgroup and meta‐regression analyses, and quality assessment (Newcastle‐Ottawa Scale) were performed. Across 268 studies, 10,842,094 individuals with depression and 2,837,933,536 control subjects were included. All‐cause mortality was doubled in people with depression versus no depression/general population controls (relative risk, RR=2.10, 95% CI: 1.87‐2.35, I 2 =99.9%), being especially high for suicide (RR=9.89, 95% CI: 7.59‐12.88, I 2 =99.6%), but also elevated for natural causes (RR=1.63, 95% CI: 1.51‐1.75, I 2 =99.6%). Among individuals with versus without depression matched for comorbid conditions, the depression‐associated mortality risk was also significantly elevated (RR=1.29, 95% CI: 1.21‐1.37, I 2 =99.9%). Depression with versus without psychotic symptoms (RR=1.61, 95% CI: 1.45‐1.78, I 2 =6.3%), and treatment‐resistant versus non‐treatment‐resistant depression (RR=1.27, 95% CI: 1.16‐1.39, I 2 =85.3%), conferred an incremental mortality risk. Antidepressant use (versus no antidepressant use) was associated with significantly lower all‐cause mortality in people with depression (RR=0.79, 95% CI: 0.68‐0.93, I 2 =99.2%). ECT use (versus no ECT use) was associated with reduced all‐cause mortality (RR=0.73, 95% CI: 0.66‐0.82, I 2 =0%), natural‐cause mortality (RR=0.76, 95% CI: 0.59‐0.97, I 2 =12.0%), and suicide (RR=0.67, 95% CI: 0.53‐0.85, I 2 =32.3%). Our results affirm heightened mortality risk in depression, identify clinically relevant patient subgroups with increased mortality risk, and highlight mortality‐reducing effects of antidepressant treatment and ECT. Multipronged intervention approaches targeting physical health improvement and suicide risk alleviation, optimizing antidepressant treatment, and pursuing early identification and effective interventions for psychotic and treatment‐resistant depression, could help reduce this mortality gap, which is still growing.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".