The impact of psychiatric and medical comorbidity on the risk of mortality: a population-based analysis
Bibliographic record
Abstract
BACKGROUND: As life expectancy increases, more people have chronic psychiatric and medical health disorders. Comorbidity may increase the risk of premature mortality, an important challenge for health service delivery. METHODS: Population-based cohort study in Ontario, Canada of all 11 246 910 residents aged ⩾16 and <105 on 1 April 2012 and alive on 31 March 2014. Secondary analyses included subjects having common medical disorders in 10 separate cohorts. Exposures were psychiatric morbidity categories identified using aggregated diagnosis groups (ADGs) from Johns Hopkins Adjusted Clinical Groups software® (v10.0); ADG 25: Persistent/Recurrent unstable conditions; e.g. acute schizophrenic episode, major depressive disorder (recurrent episode), ADG 24: Persistent/Recurrent stable conditions; e.g. depressive disorder, paranoid personality disorder, ADG 23: Time-limited/minor conditions; e.g. adjustment reaction with brief depressive reaction. The outcome was all-cause mortality (April 2014-March 2016). RESULTS: Over 2 years' follow-up, there were 188 014 deaths (1.7%). ADG 25 conferred an almost threefold excess mortality after adjustment compared to having no psychiatric morbidity [adjusted hazard ratio 2.94 (95% CI 2.91-2.98, p < 0.0001)]. Adjusted hazard ratios for ADG 24 and ADG 23 were 1.12 (95% CI 1.11-1.14, p < 0.0001) and 1.31 (95% CI 1.26-1.36, p < 0.0001). In all 10 medical disorder cohorts, ADG 25 carried significantly greater mortality risk compared to no psychiatric comorbidity. CONCLUSIONS: Psychiatric disorders, particularly those graded persistent/recurrent and unstable, were associated with excess mortality in the whole population, and in the medical disorder cohorts examined. Future research should examine whether service design accounting for psychiatric disorder comorbidity improves outcomes across the spectrum of medical disorders.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".