Aspirin Use among Adults with Cardiovascular Disease in the United States: Implications for an Intervention Approach
Bibliographic record
Abstract
Cardiovascular disease (CVD) is a major underlying cause of death, with high economic burden in most countries, including the United States. Lifestyle modifications and the use of antiplatelet therapy, such as aspirin, can contribute significantly to secondary prevention of CVD in adults. This study examined the prevalence and associated factors of aspirin use for the secondary prevention of angina pectoris, myocardial infarction (MI), and cerebrovascular disease (stroke) in a sample of American adults. The 2015 Behavioral Risk Factor Surveillance System (BRFSS) dataset was analyzed for this cross-sectional study. Almost 16% of the study population (N = 441,456) had angina, MI, or stroke. Weighted percentages of respondents with angina, MI, and stroke were 4%, 4.3%, and 3%, respectively. Overall, weighted prevalence of daily (or every other day) aspirin use was about 65%, 71%, and 57% among respondents with angina, MI, and stroke, respectively. Factors that were significantly associated with aspirin use included male sex, more than high school education, high blood pressure, diabetes, and less than excellent general health. There were existing differences among individuals with CVD based on diagnosis, demographic and socioeconomic status in the use of aspirin for secondary prevention. Resources for promoting aspirin use should be directed toward groups with lower utilization.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| 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".