Educational Attainment and Cardiovascular Disease Symptoms among Americans Entering Mid-Adulthood
Bibliographic record
Abstract
Although the education-health gradient is well documented, relatively little is known about its application to cardiovascular disease (CVD), the leading cause of mortality in the United States. Furthermore, critical nuances across educational levels and demographic subgroups remain underexplored. The authors examine the association between education and CVD among U.S. adults entering mid-adulthood, leveraging a novel symptom-based CVD index capturing clinical indicators such as angina or dyspnea and a detailed 13-category measure of educational attainment. Waves I and V of the nationally representative National Longitudinal Study of Adolescent to Adult Health sample ( n = 11,806) are analyzed for the full cohort and by sex/gender and race/ethnicity; models also account for adolescent confounders and adulthood social and economic mediators. The findings reveal profound disparities: adults with professional or doctoral degrees report 0.6 CVD symptoms on average, versus 1.8 among those without high school credentials, a threefold difference. However, there are also important deviations from the expected gradient. Adults with sub-baccalaureate education report no fewer symptoms than high school graduates, whereas General Educational Development diploma recipients resemble dropouts more than diploma holders. Racial/ethnic patterns differ markedly: most strikingly, Black adults derive no significant CVD returns from college completion compared with high school graduation. These patterns challenge assumptions about education’s universal benefits and underscore the importance of structural and intersectional approaches to addressing health disparities and designing equitable health policies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".