Abstract 4371131: Trends in Angina and Heart Attack Prevalence by Sex, Region, and Social Vulnerability Among U.S. Adults (2019–2023)
Bibliographic record
Abstract
Background: Angina and myocardial infarction (MI) are major contributors to cardiovascular morbidity and mortality. We assessed recent trends in angina and MI prevalence by sex, region, and Social Vulnerability Index (SVI). Methods: Serial cross-sectional analysis using 2019–2023 National Health Interview Survey (NHIS) data. Adults aged 18–64 were included for angina; ≥18 years for MI. Outcomes were self-reported, physician-diagnosed angina or MI. Analyses were stratified by sex, region (Northeast, Midwest, South, West), and SVI level (Little/None, Low, Medium, High). Linear regression tested annual trends and interaction effects. Survey-weighted analyses were conducted using R v4.4.2. Results: National angina prevalence remained stable (1.3%–1.7%), while MI prevalence increased (+0.58%/year, p < 0.05). Sex: Males consistently had higher prevalence for both conditions. In 2023, angina was 1.9% in males vs. 1.4% in females; MI was 3.8% vs. 2.3%. Only MI showed a significant upward trend in males (p < 0.05); trend differences by sex were non-significant (p = 0.17 for angina; p = 0.975 for MI). Region: Highest prevalence for both conditions was seen in the South and Northeast (angina up to 1.9%, MI up to 3.4%), lowest in the West (angina ~1.5%, MI 2.1%). No significant regional trend differences were observed (p > 0.60). SVI: Medium vulnerability areas had the highest prevalence for both angina (~1.7%) and MI (3.5%). MI prevalence declined (−0.20%/year, p < 0.05), with significant trend differences in Low and Medium SVI groups (both p < 0.05). Angina trends by SVI were stable (interaction p = 0.17), but disparities persisted. Conclusion: From 2019–2023, angina remained stable while MI prevalence increased. Males, residents of the South, and individuals in medium SVI areas had consistently higher cardiovascular burden. Persistent disparities underscore the need for targeted public health interventions.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".