Temporal trends in cardiovascular disease risk factor profiles in a population-based schizophrenia sample: a repeat cross-sectional study
Bibliographic record
Abstract
BACKGROUND: People with schizophrenia have an increased burden of cardiovascular diseases (CVD); however, little is known about the cardiovascular risk factor profiles of non-institutionalised individuals with schizophrenia. This study estimated the prevalence of CVD risk factors in a population-based sample of Canadians with and without schizophrenia. METHODS: Ontario respondents of the Canadian Community Health Survey were linked to administrative health databases; using a validated algorithm, we identified 1103 non-institutionalised individuals with schizophrenia and 156 376 without schizophrenia. We examined the prevalence of eight CVD risk factors: smoking, diabetes, hypertension, obesity, physical inactivity, fruit/vegetables consumption, psychosocial stress and binge drinking. To examine temporal trends, we compared prevalence estimates from 2001-2005 to 2007-2010. RESULTS: The prevalence of most CVD risk factors was significantly higher among those with schizophrenia than the general population. Obesity and diabetes prevalence increased by 39% and 71%, respectively, in the schizophrenia group vs 11% and 24%, respectively, in the non-schizophrenia group between the two time periods. Unlike the general population, smoking rates among those with schizophrenia did not decline. Almost 90% of individuals with schizophrenia had at least one CVD risk factor and almost 40% had ≥3 co-occurring risk factors. CONCLUSION: Individuals with schizophrenia had a greater prevalence of individual and multiple CVD risk factors compared with those without schizophrenia, which persisted over time. Our findings suggest that public health efforts to reduce the burden of CVD risk factors have not been as effective in the schizophrenia population, thus highlighting the need for more targeted interventions and prevention strategies.
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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 | 0.001 | 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) | 0.001 | 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".