Cause and Rate of Death in People With Schizophrenia Across the Lifespan
Bibliographic record
Abstract
OBJECTIVE: To compare the causes and rates of death for people with and without schizophrenia in Manitoba, Canada. METHOD: Using de-identified administrative databases at the Manitoba Centre for Health Policy, a population-based analysis was performed to compare age- and sex-adjusted 10-year (1999-2008) mortality rates, overall and by specific cause, of decedents aged 10 years or older who had 1 diagnosis of schizophrenia (ICD-9-CM code 295, ICD-10-CA codes F20, F21, F23.2, F25) over a 12-year period (N = 9,038) to the rest of the population (N = 969,090). RESULTS: The mortality rate for those with schizophrenia was double that of the rest of the population (20.00% vs. 9.37%). The all-cause mortality rate was higher for people with schizophrenia compared to all others (168.9 vs. 99.1 per thousand; relative risk [RR] = 1.70, P < .0001); rates of death due to suicide (RR = 8.67, P < .0001), injury (RR = 2.35, P < .0001), respiratory illness (RR = 2.00, P < .0001), and circulatory illness (RR = 1.64, P < .0001) were also significantly higher in people with schizophrenia. Overall cancer deaths were similar (28.6 vs. 27.3 per thousand, P = .42, NS) except in the middle-aged group (40-59), in which cancer death rates were significantly higher for those with schizophrenia (28.7 vs. 11.6 per thousand; RR = 2.48, P < .01). Mortality rates due to lung cancer were significantly higher in people with schizophrenia (9.4 vs. 6.4 per thousand, RR = 1.45, P < .001). CONCLUSIONS: People with schizophrenia are at increased risk of death compared to the general population, and the majority of these deaths are occurring in older age from physical disease processes. Risk of cancer mortality is significantly higher in middle-aged but not younger or older patients with schizophrenia. Understanding these patients' vulnerabilities to physical illness has important public health implications for prevention, screening, and treatment as the population ages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".