Disparities in Screening and Treatment of Cardiovascular Diseases in Patients With Mental Disorders Across the World: Systematic Review and Meta-Analysis of 47 Observational Studies
Bibliographic record
Abstract
OBJECTIVE: This study used meta-analysis to assess disparities in cardiovascular disease (CVD) screening and treatment in people with mental disorders, a group that has elevated CVD incidence and mortality. METHODS: The authors searched PubMed and PsycInfo through July 31, 2020, and conducted a random-effect meta-analysis of observational studies comparing CVD screening and treatment in people with and without mental disorders. The primary outcome was odds ratios for CVD screening and treatment. Sensitivity analyses on screening and treatment separately and on specific procedures, subgroup analyses by country, and by controlling for confounding by indication, as well as meta-regressions, were also run, and publication bias and quality were assessed. RESULTS: Forty-seven studies (N=24,400,452 patients, of whom 1,283,602 had mental disorders) from North America (k=26), Europe (k=16), Asia (k=4), and Australia (k=1) were meta-analyzed. Lower rates of screening or treatment in patients with mental disorders emerged for any CVD (k=47, odds ratio=0.773, 95% CI=0.742, 0.804), coronary artery disease (k=34, odds ratio=0.734, 95% CI=0.690, 0.781), cerebrovascular disease (k=8, odds ratio=0.810, 95% CI=0.779, 0.842), and other mixed CVDs (k=11, odds ratio=0.839, 95% CI=0.761, 0.924). Significant disparities emerged for any screening, any intervention, catheterization or revascularization in coronary artery disease, intravenous thrombolysis for stroke, and treatment with any and with specific medications for CVD across all mental disorders (except for CVD medications in mood disorders). Disparities were largest for schizophrenia, and they differed across countries. Median study quality was high (Newcastle-Ottawa Scale score, 8); higher-quality studies found larger disparities, and publication bias did not affect results. CONCLUSIONS: People with mental disorders, and those with schizophrenia in particular, receive less screening and lower-quality treatment for CVD. It is of paramount importance to address underprescribing of CVD medications and underutilization of diagnostic and therapeutic procedures across all mental disorders.
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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 | 0.024 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.053 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".