Trends in standardized mortality among individuals with schizophrenia, 1993–2012: a population-based, repeated cross-sectional study
Bibliographic record
Abstract
BACKGROUND: We examined mortality time trends and premature mortality among individuals with and without schizophrenia over a 20-year period. METHODS: In this population-based, repeated cross-sectional study, we identified all individual deaths that occurred in Ontario between 1993 and 2012 in persons aged 15 and over. We plotted overall and cause-specific age- and sex-standardized mortality rates (ASMRs), stratified all-cause ASMR trends by sociodemographic characteristics, and analyzed premature mortality using years of potential life lost. Additionally, we calculated mortality rate ratios (MRRs) using negative binomial regression with adjustment for age, sex, income, rurality and year of death. RESULTS: We identified 31 349 deaths among persons with schizophrenia, and 1 589 902 deaths among those without schizophrenia. Mortality rates among people with schizophrenia were 3 times higher than among those without schizophrenia (adjusted MRR 3.12, 95% confidence interval 3.06-3.17). All-cause ASMRs in both groups declined in parallel over the study period, by about 35%, and were higher for men, for those with low income and for rural dwellers. The absolute ASMR difference also declined throughout the study period (from 16.15 to 10.49 deaths per 1000 persons). Cause-specific ASMRs were greater among those with schizophrenia, with circulatory conditions accounting for most deaths between 1993 and 2012, whereas neoplasms became the leading cause of death for those without schizophrenia after 2005. Individuals with schizophrenia also died, on average, 8 years younger than those without schizophrenia, losing more potential years of life. INTERPRETATION: Although mortality rates among people with schizophrenia have declined over the past 2 decades, specialized approaches may be required to close the persistent 3-fold relative mortality gap with the general population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".