Abstract P242: Aspirin Awareness and Use for Cardiovascular Disease Prevention in a Community of Color
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) and CVD risk factors are high among people of color. Aspirin (ASA) can reduce CVD risk. Yet little is known about primary prevention ASA use and its determinants among people of color. This study examined associations for ASA-related health beliefs and social norms, CVD risk factors and ASA use among Hispanic/Latino (HL) and African American (AA) adults without CVD history. Methods: In-person surveys were administered to adults ages 45-79 years at health fairs and community events in the Minneapolis-St. Paul metro querying demographics, CVD history, CVD risk factors and ASA use. CVD risk perceptions and ASA-related health beliefs and social norms were evaluated using a 4-point Likert scale. Poisson regression with adjustment for age, sex and risk factors was used to examine associations with ASA use. Surveys were offered in English or Spanish. Results: Of the 583 survey participants with no CVD history, 152 identified as HL and 431 as Non-Hispanic-AA. Compared with AA, the HL sample was younger, mean (SD) age of 53.0 (8.6) years vs 59.3 (9.3) years; 54.6% vs 65.7% were female. Risk factors were common among HL and AA, including diabetes (37.5% and 29.0%), hyperlipidemia (28.3% and 36.7%), hypertension (36.8% and 61.0%) and smoking (13.8% and 21.1%). Twenty-two percent of HL and 34% of AA participants regularly used ASA. When asked questions regarding health beliefs and social norms, HL adults had a high frequency of “Don’t Know” responses compared with AA’s (Figure). Participants with more “Don’t Know” responses were less likely to use ASA—RR of 0.87 (95% CI 0.77-0.99). Conversely, ASA use was positively associated with “Agree” responses (RR 1.40, 95% CI 1.29-1.52) and CVD risk factors (RR 1.43 95% CI 1.28-1.59). Associations did not differ by race/ethnicity (interaction p >0.05). Conclusion: ASA uncertainty is more pronounced among HL adults than AA and is associated with less preventive ASA use. Efforts should be made to improve knowledge regarding the appropriate use of ASA in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".