Reducing inequalities in cardiovascular disease: focus on marginalized populations considering ethnicity and race
Bibliographic record
Abstract
Cardiovascular disease (CVD) and its risk factors are more prevalent among traditionally marginalized racial, ethnic, and Indigenous groups. These populations also often face greater barriers to accessing cardiovascular health care, further contributing to the health equity gap. To address the challenge of inequalities and disparities in cardiovascular health outcomes, the Lancet Regional Health-Europe convened experts to evaluate the current state of knowledge on inequalities and disparities in cardiovascular health among marginalized populations and propose recommendations to address these disparities. This Series paper aims to review disparities in CVD referring to coronary heart disease and stroke, based on race, ethnicity, ancestry, and Indigeneity emphasizing the intersection of these factors with sex, gender, and socioeconomic status (SES) across Europe and North America. These regions were chosen as they have well established health-care systems, with persistent, and in some regions widening, disparities in cardiovascular health and outcomes. Ethnicity and race should be measured in a standardized manner in health-care administrative databases to identify high risk groups who might need focused programmes to improve health-care access and to address bias and inequities in care. Strategies that policymakers, health-care professionals, and advocacy groups can use to advance cardiovascular health equity include improving access to health-care systems and research for high-risk communities, fostering trust between these communities and public health providers, and enhancing the delivery of evidence-based therapies for the prevention and treatment of 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".