Racial and Ethnic Disparities in Primary Prevention of Cardiovascular Disease
Bibliographic record
Abstract
Cardiovascular disease (CVD) disproportionately affects ethnic-minority groups globally. Ethnic-minority groups face particularly high CVD burden and mortality, exacerbated by disparities across modifiable risk factors, wider determinants of health, and limited access to preventative interventions. This narrative review summarizes evidence on modifiable risk factors, such as physical activity, hypertension, diet, smoking, alcohol consumption, diabetes, and the polypill for the primary prevention of CVD in ethnic minorities. Across these factors, we find inequities in risk factor prevalence. The evidence underscores that inequalities in accessibility to interventions and treatments impede progress in reducing CVD risk using primary prevention interventions for ethnic-minority people. Although culturally tailored interventions show promise, further research is required across the different risk factors. Social determinants of health and structural inequities also exacerbate CVD risk for ethnic-minority people and warrant greater attention. Additionally, we find that only limited ethnicity-specific data and guidelines are available on CVD primary prevention interventions for most risk factors. To address these gaps in research, we provide recommendations that include the following: investigating the sustainability and real-world effectiveness of culturally sensitive interventions; ensuring that ethnic-minority peoples' perspectives are considered in research; longitudinal tracking of risk factors; interventions and outcomes in ethnic-minority people; and ensuring that data collection and reporting of ethnicity data are standardized.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".