Prevalence of Mood and Anxiety Disorders in Canadians with Cardiovascular Disease: A Cross-Sectional Study
Bibliographic record
Abstract
Abstract Context: Mood and anxiety disorders appear to be overrepresented in adults with cardiovascular diseases (CVDs). These disorders have been associated with poorer biopsychosocial outcomes in this population. Mood and anxiety disorders often co-occur, but the prevalence of this comorbidity and their potential additive effects in patients with CVD remain understudied. Aims: This study aimed to estimate the prevalence and co-occurrence rates of mood and anxiety disorders in the general adult population living with CVD. Associations between mood and anxiety disorder status and sociodemographic characteristics, somatic comorbidities, perceived mental health, and health-care service use were also investigated. Methods: A total of 6,792 adults aged 25 years or older and living with CVD were selected from the 2015–2016 Canadian Community Health Survey. Mood and anxiety disorders were identified based on self-report diagnoses made by a qualified health professional. All other variables were assessed using questionnaires. Results: An estimated 17.7% of the studied population reported having been diagnosed with a mood or anxiety disorder. More precisely, 6.6% reported a mood disorder, 5.5% reported an anxiety disorder, and an additional 5.6% reported both. The presence of mood or anxiety disorders was associated with poorer perceived mental health and higher health-care service use, and these associations were stronger when mood and anxiety co-occurred. Conclusions: Approximately one in six adults with CVD reported suffering from mood or anxiety disorders, and a third of them presented both conditions. This study also suggests that co-occurring anxiety and mood disorders lead to greater vulnerability than either disorder in adults with CVD.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".