The Epidemiology of Excess Mortality in People with Mental Illness
Bibliographic record
Abstract
OBJECTIVE: to investigate the burden of excess mortality among people with mental illness in developed countries, how it is distributed, and whether it has changed over time. METHOD: we conducted a systematic search of MEDLINE, restricting our attention to peer-reviewed studies and reviews published in English relating to mortality and mental illness. Because of the large number of studies that have been undertaken during the last 30 years, we have selected a representative cross-section of studies for inclusion in our review. RESULTS: there is substantial excess mortality in people with mental illness for almost all psychiatric disorders and all main causes of death. Consistently elevated rates have been observed across settings and over time. The highest numbers of excess deaths are due to cardiovascular and respiratory diseases. With life expectancy increasing in the general population, the disparity in mortality outcomes for people with mental illness is increasing. CONCLUSIONS: without the development of alternative approaches to promoting and treating the physical health of people with mental illness, it is possible that the disparity in mortality outcomes will persist.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".