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Record W4385703169 · doi:10.31219/osf.io/m4ns7

Cardiovascular risk and disease among people with mental illness in Canada

2023· preprint· en· W4385703169 on OpenAlexaboutno aff
Catherine Goldie

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMental illnessStroke (engine)ComorbidityPopulationDiseaseBody mass indexPsychiatryMental healthInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Objective: People with mental illness have poorer physical health and to suffer from higher rates of morbidity and mortality due to cardiovascular disease (CVD) than the general population. However, this has not been confirmed in a large, unselected community-based Canadian sample. The purpose of this study is to ascertain the association between mental illness and a history of heart disease and stroke, and 10-year and 30-year cardiovascular risk. Methods: Cross-sectional data of 36,984 respondents collected via the Canadian Community Health Survey Cycle 1.2 were analyzed. Mood and anxiety disorders were assessed with the World Mental Health – Composite International Diagnostic Interview instrument. Respondents’ self-reported diagnostic histories of schizophrenia, cardiovascular disease (CVD) (including heart disease and stroke), and cardiovascular risk (including diabetes, hypertension, treatment for hypertension, tobacco use and body mass index) were also ascertained. Framingham 10-year and 30-year risk prediction algorithms were used to calculate cardiovascular risk. A stratified multistage cluster sampling design was used. Descriptive statistics were used to estimate the prevalence of CVD across a range of mental illness and accounted for psychiatric comorbidity. Bivariate associations between mental illness and cardiovascular risk, heart disease and stroke were analyzed with logistic regression models. Results: Respondents who reported having any lifetime mental illness were twice as likely to have a history of heart disease (OR=2.0 [95% CI: 1.8, 2.2]) or stroke (OR = 2.3 [95% CI: 1.7, 3.0]), after controlling for sociodemographic differences, compared to people without a mental illness. Respondents with any mood or any anxiety disorder were one and a half times (OR = 1.5 [95% CI: 1.3, 1.8], OR = 1.5 [95% CI: 1.2, 1.8], respectively) more likely to have heart disease and almost two times (OR = 1.8 [95% CI: 1.1, 2.7], OR = 1.8 [95% CI: 1.1, 2.8], respectively) more likely to have had a stroke compared with people without mood or anxiety disorders, after adjusting for sociodemographic differences. When pairings were examined between psychiatric disorders, the association with heart disease and stroke was stronger compared with having the specific disorder alone. Respondents who were identified as having any lifetime mental disorder were less likely (OR = 0.8 [95% CI: 0.8, 0.9]) to be at high risk of developing CVD within 10 years. This same group was slightly more likely (OR = 1.2 [95% CI: 1.1, 1.4]) to be at high risk of developing CVD within 30 years. Conclusion: This descriptive study underscores the association between mental illness, CVD and cardiovascular risk and highlights some novel methods to examine cardiovascular health at a population level. The findings also serve as a call to action for all Canadian health professionals to work to improve the cardiovascular health of this population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.010
GPT teacher head0.256
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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