Abstract 17306: Primary Prevention Aspirin Use Trends and Associations With Health Beliefs Among African Americans, 2015-2019
Bibliographic record
Abstract
Introduction: Cardiovascular disease (CVD) disproportionately affects African Americans. Aspirin has long been recommended as an option to reduce cardiovascular events. However, recent clinical trials involving primary prevention aspirin have prompted changes in national guidelines restricting the aspirin recommended population. Hypothesis: Primary prevention aspirin use will decline over the 5 year period 2015-2019. Methods: Using 3 cross-sectional surveys, data were collected from self-identified African Americans in 2015, 2017 and 2019, querying information on CVD risk factors, health behaviors and beliefs, and aspirin use. Poisson regression modeling was used to estimate age- and risk-factor adjusted aspirin prevalence, trends and associations. Results: A total of 1,491 African Americans adults, ages 45-79, 61% women and no prior CVD completed surveys and were included in this analysis. There was no change in age- and risk factor-adjusted aspirin use over the 3 surveys for women (37%, 34% and 35% respectively) or men (27%, 25%, 30% respectively). However, fewer participants believed aspirin was helpful in 2019--75% vs 84% in 2015 (p<0.05). In the total sample (n=1,491) aspirin discussions with a health care practitioner were highly associated with aspirin use (aRR 2.97, 95% CI 2.49-3.54), as were several health beliefs and social norms that affirm preventive behaviors and aspirin use (Figure). Conclusion: Despite major changes in national guidelines and negative perceptions of aspirin effectiveness in the media, overall primary prevention aspirin use did not significantly change in this African American sample from 2015 to 2019.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".