Cardiovascular risk and disease among people with mental health disorders in Canada
Bibliographic record
Abstract
Although research has revealed that there is excess cardiovascular morbidity among persons with mental health disorders (MHD), there is limited evidence specific to the Canadian context. It is also unclear if elevated rates of cardiovascular disease (CVD) can be attributed to MHDs themselves or to exposure to psychoactive medications. Moreover, few researchers have extended their investigations to account for psychiatric comorbidity or have applied cardiovascular risk prediction algorithms to conduct comprehensive assessments of this population’s heart health. Three studies were undertaken utilizing cross-sectional data obtained via the Canadian Community Health Survey Cycle 1.2. MHDs were assessed using the World Mental Health Composite International Diagnostic Interview. Frequency of psychoactive medication use, the presence of heart disease, and stroke and cardiovascular risk factors were also assessed. Framingham risk prediction algorithms were used to calculate cardiovascular risk. Descriptive statistics were employed to estimate the prevalence of CVD in people with a range of MHDs and psychoactive medication use. Associations between MHDs, psychoactive medication use, and cardiovascular risk and CVD were analyzed with logistic regression. Confidence intervals for the proportions and odds ratios were calculated using weighted bootstrapped estimates to take into account the complex survey design and nonresponse. Our examination of psychiatric comorbidity revealed that a small proportion of Canadians suffer from a heavy burden of MHDs and that disorders often co-occur. Respondents with any lifetime MHD were twice as likely to have a history of heart disease or stroke, and those without heart disease or stroke were more likely to be at high risk of developing CVD within 30-years compared to people without a diagnosis of a MHD. Similarly, people reporting psychoactive medication use were twice as likely to have heart disease, three times as likely to report having had a stroke, and more likely to be in the highest 30-year risk category for CVD compared to people not reporting psychoactive medication use. Negligible confounding was found between the effects of psychoactive medication use and having a MHD on heart health. These findings underscore the need for continued monitoring of CVD among Canadians with MHDs and development of effective preventative 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".