A Surveillance System to Monitor Excess Mortality of People with Mental Illness in Canada
Bibliographic record
Abstract
OBJECTIVE: Outcome measures are rarely available for surveillance and system performance monitoring for mental disorders and addictions. Our study aims to demonstrate the feasibility and face validity of routinely measuring the mortality gap in the Canadian context at the provincial and regional levels using the methods and data available to the Canadian Chronic Disease Surveillance System (CCDSS) of the Public Health Agency of Canada. METHODS: We used longitudinal data from the Quebec Integrated Chronic Disease Surveillance System, which also provides aggregated data to the CCDSS. This includes data from the health insurance registry physician claims and the hospital discharge abstract for all mental disorder diagnoses (International Classification of Diseases [ICD]-9 290-319 or ICD-10 F00-F99). Patients were defined as having had received a mental disorder diagnosis at least once during the year. Life expectancy was measured using Chiang's method for abridged life tables, complemented by the Hsieh method for adjustment of the last age interval. RESULTS: We found a lower life expectancy among psychiatric patients of 8 years for men and 5 years for women. For patients with schizophrenia, life expectancy was lowered by 12 years for men and 8 years for women. Cardiovascular disease and cancer were the most common causes of premature death. Findings were consistent across time and regions of the province. Lower estimates of the mortality gap, compared with literature, could be explained by the inclusion of primary care patients and methods. CONCLUSIONS: Our study demonstrates the feasibility of using administrative data to measure the impact of current and future mental health plans in Canada provided the techniques can be replicated in other Canadian provinces.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".