Why is ethnicity important in cardiovascular care?
Bibliographic record
Abstract
Over the past few decades advances in the prevention, diagnosis and treatment of cardiovascular diseases (CVDs) have resulted in marked reductions in age- and sex-adjusted mortality.1 Regardless, CVD continues to exact a disproportionate toll on several ethnic and racial groups, and for many these disparities have grown in magnitude over time with generally smaller improvements seen in minority groups.2,3 Numerous complex factors have contributed to what is often referred to as the ‘treatment gap’ between ethnic and non-ethnic populations,4–6 the majority of which can be categorized into three levels: patient, health-care system and environmental.4,7 At the patient level there are distinct and well-established differences in CVD risk factor profiles that place ethnic groups at higher risk. For example, for decades blood pressure has been significantly higher for African-Americans with an earlier onset of hypertension compared with their White counterparts,8 South Asians tend to have a disproportionate burden of diabetes and more harmful types of low-density lipoprotein cholesterol and less helpful types of high-density lipoprotein cholesterol compared with those with European ancestry,2 and a disproportionate prevalence of diabetes, up to six time higher, in Hispanics, non-Hispanic Blacks and non-Hispanic Asians compared with non-Hispanic Whites.2 Further compounding the issue is the significant atypical presentation of cardiac symptoms in ethnic groups such as African-American, Asian, South Asian and Hispanic.9 Also at the patient level, cultural norms, values and practices contribute to increased risk of CVD. For example, individual health behaviours such as exercise and diet (e.g. high concentration of oil and ghee used in Indian diets),10 illness beliefs that result in poor symptom recognition,11 poor health literacy which contributes to medication non-adherence,12 and social and family structure that impacts on timeliness to seek and act on professional advice.11 At the health system level cultural competency plays a key role. For example, unintentional bias stemming from inadequate knowledge and sensitivity to cultural differences of patients from ethnic backgrounds can lead to differential treatment and referral patterns.4 Not only do ethnic groups experience limited access to care in general,13 they face more barriers to CVD diagnosis,14,15 lower rates of cardiac intervention4 and overall lower quality health care.16 Additionally, ethnic groups have a higher prevalence of unrecognized CVD risk factors that remain untreated, resulting in higher morbidity and mortality.7 To ensure delivery of culturally appropriate cardiovascular care, providers should be aware of their own cultural values and beliefs, and develop a cultural repertoire that enables them to engage effectively with diversity and promote culturally sensitive care.17 Other notable variables at the health systems level include higher-quality facilities being less accessible to ethnic groups, complexities involved in navigating bureaucracy and administration, for example, access to insurance and pharmaceutical benefits, and failure to address language and health literacy barriers.18 In addition, more attention needs to be focused not only on the experiences and needs of patients, but of their family members, especially the partner, who often influences and provides much informal care, especially in acute presentation and during early convalescence.19 Factors at the environmental level predominantly encompass those relating to socioeconomic status. Environmental factors consist of educational attainment, subjective perceptions of social status and social class, linguistic isolation, geographical location and fiscal variables such as insurance status.20 Socioeconomic differences in health outcomes have been widely documented for most health conditions and ethnic groups. People who are poorer and less educated are more likely to suffer from diseases, to experience loss of functioning, to be cognitively and physically impaired and to experience a higher mortality rate.21 Consequently, environmental factors are often assumed to be the root cause of differences in health outcomes for ethnic groups. Also adding to the ethnicity treatment gap is a significant lack of epidemiological data on ‘at-risk’ ethnic groups. For example, a review of large-scale epidemiological data on CVDs and diabetes in migrant and ethnic minority groups in Europe concluded that high-quality, comparable, routine data on CVD health status of ethnic minority populations were not available.22 Other reasons adding to the paucity of epidemiological ethnic data include: lack of consensus on ethnicity indicators identifying ethic groups in available data sources; language and communication difficulties; limited access to interpreters; lack of cultural awareness; and unjustified exclusion of ethnic minority groups.22,23 In addition, primarily due to a lack of intervention, rates of evidence-based change in minority groups are rarely reported. It is well-evidenced that a person’s ethnicity places them at higher likelihood of experiencing CVD and reduced chance of survival compared with their White counterparts. What is less clear is the evidence-base in support of reducing the treatment gap between ethnic and non-ethnic groups. To eliminate ethnic disparities in CVD, systematic and routine collation of epidemiological data, inclusive of all ethnic groups, is essential for the generation of aetiological hypotheses to support public health policy and planning. Only then can we work toward developing a more actionable research agenda that promotes real-world solutions to reduce disparity and improve population-level cardiovascular care. This special edition of the European Journal of Cardiovascular Nursing, ‘How does patient ethnicity influence care?’, is aimed at identifying areas where ethnic disparities are most urgent to enable us, as researchers and clinicians, to better: Detect cardiovascular disease in ethnic groups; Identify the risk factors in ethnic groups; Enable focused delivery of culturally sensitive healthcare programmes to ethnic groups. A holistic approach encompassing ‘patient’, ‘health-care system’ and ‘environmental’ components is crucial to reducing ethnic disparity. Systematic and routine collation of epidemiological data is essential to support public health policy and planning. An actionable research agenda aimed at population-level cardiovascular care is necessary to provide real-world solutions to reduce ethnic disparities. The authors declare that there is no conflict of interest. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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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 | 0.014 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.033 | 0.040 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".