Mortality in people with psychotic disorders in Finland: A population-based 13-year follow-up study
Bibliographic record
Abstract
Introduction People with psychotic disorders have increased mortality compared to the general population. The mortality is mostly due to natural causes and it is disproportionately high compared to the somatic morbidity of people with psychotic disorders. Objectives We aimed to find predictors of mortality in psychotic disorders and to evaluate the extent to which sociodemographic and health-related factors explain the excess mortality. Methods In a nationally representative sample of Finns aged 30–70 years (n = 5642), psychotic disorders were diagnosed in 2000–2001. Information on mortality and causes of death was obtained of those who died by the end of year 2013. Cox proportional hazards models were used to investigate the mortality risk. Results Adjusting for age and sex, diagnosis of nonaffective psychotic disorder (NAP) (n = 106) was statistically significantly associated with all-cause mortality (HR 2.99, 95% CI 2.03–4.41) and natural-cause mortality (HR 2.81, 95% CI 1.85–4.28). After adjusting for sociodemographic factors, health status, inflammation and smoking, the HR dropped to 2.11 (95% CI 1.10–4.05) for all-cause and to 1.98 (95% CI 0.94–4.16) for natural-cause mortality. Within the NAP group, antipsychotic use at baseline was associated with reduced HR for natural-cause mortality (HR 0.25, 95% CI 0.07–0.96), and smoking with increased HR (HR 3.54, 95% CI 1.07–11.69). Conclusions The elevated mortality risk associated with NAP is only partly explained by socioeconomic factors, lifestyle, cardiometabolic comorbidities and inflammation. Smoking cessation should be prioritized in treatment of psychotic disorders. More research is needed on the quality of treatment of somatic conditions in people with psychotic disorders. Disclosure of interest Jaakko Keinänen owns shares in pharmaceutical company Orion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".