Ethnic variations in cardiovascular disease (CVD) risk factors and associations with prevalent CVD and CVD mortality in the United States
Bibliographic record
Abstract
OBJECTIVE: To explore the association between ethnicity and cardiovascular disease (CVD) risk factors, including physical inactivity, obesity, hypertension, type 2 diabetes (T2D), lack of health insurance and low family income in a nationally representative sample of U.S. adults. RESEARCH DESIGN AND METHODS: Adults from the National Health and Nutrition Examination Survey (NHANES 2011-2020, n = 17,355) were classified as having CVD risk factors based on both self-reported and metabolic data. Ethnic differences in how these CVD risk factors relate to prevalent CVD and CVD mortality was examined in Whites, Blacks, Asians and Hispanics. RESULTS: Compared to Whites, significant disparities were noted in several CVD risk factors in ethnic minorities, such as lower PA, lower income, and more prevalent metabolic risk factors. Blacks and Hispanics commonly had higher prevalent CVD risk as compared to Whites even after adjusting for income and metabolic risk factors. Physical inactivity was most strongly associated with prevalent CVD and CVD mortality among Whites and Blacks. There were no ethnic differences in the inverse association between income and prevalent CVD risk, but Blacks with low income were associated with the greatest elevated CVD mortality. Hypertension and T2D were similarly related with prevalent CVD across ethnic groups, but Blacks and Hispanics with hypertension or T2D were at greater CVD mortality risk as compared to Whites. CONCLUSION: Our study identified that socioeconomic and metabolic risk factors may relate differently to CVD outcomes among ethnic minority groups in the United States. Addressing these ethnic disparities in health warrants further investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".