Gender disparities in delayed angina diagnosis: insights from 2001–2020 NHANES data
Bibliographic record
Abstract
BACKGROUND: Women with coronary artery disease (CAD) are more likely than men to experience a delay in diagnosis, which is attributed to differences in clinical presentation. The objective of this study is to examine any persistent disparities in timely CAD diagnosis in the United States (U.S.) among women who present with clinically similar symptoms and demographic characteristics to their male counterparts. METHODS: From the 2001 - 2020 National Health and Nutrition Examination Survey (NHANES) data, participants were categorized as having missed angina if they experienced angina and did not self-report a prior diagnosis of angina pectoris or CAD. We assessed the association between gender and missed angina using weighted multivariate logistic regression models representative of the U.S. POPULATION: Mortality follow-up data were available for participants up to December 31, 2018. RESULTS: Of 874 participants with missed angina, 551 (63%) were women and 323 (37.0%) were men. Baseline characteristics showed that women and men with missed angina were more likely than their diagnosed counterparts to be younger, of ethnic minorities, uninsured, and smokers. Women with missed angina were more likely to be in a relationship than diagnosed women, while the opposite pattern was observed in men. The odds ratio of missed angina in women compared to men was 2.61 (95% CI: 1.73, 3.94) after adjusting for age, race, education, body mass index, smoking, alcohol use, income, insurance, and comorbidities. Among participants who had a cardiac cause of death, the odds of missed angina in women compared to men was 3.02 (95% CI: 1.18, 7.75) in the adjusted model. CONCLUSION: Women with similar CAD symptoms to their male counterparts still have higher odds of going undiagnosed. This relationship extends to individuals who ultimately die of cardiac causes. Potential solutions to this disparity include addressing overgeneralized perceptions of differences in the prevalence and presentation of CAD between genders and exploring targeted screening programs for women with risk factors. Further research accounting for healthcare access and proximity to care is needed to support our findings. Timely recognition of CAD in women is essential to decreasing preventable mortality.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".