Trends in coronary artery disease and dyslipidemia-related mortality in the USA from 1999-2020
Bibliographic record
Abstract
BACKGROUND: This study examined trends and disparities in USA mortality rates associated with the co-occurrence of coronary artery disease (CAD) and dyslipidemia from 1999-2020. METHODS: Data were obtained from the multiple cause of death files using CDC WONDER, spanning 1999-2020. ICD-10 codes (I20-I25 for CAD and E78 for dyslipidemia) identified CAD and dyslipidemia-related deaths in adults aged 25 and older. Statistical analyses examined demographic and regional mortality distributions. Joinpoint regression analysis determined trends in age-adjusted mortality rates (AAMR), estimating annual percentage changes (APC). RESULTS: Between 1999 and 2020, 613,969 CAD and dyslipidemia-related deaths occurred in the USA. The AAMR per 100,000 increased from 6.2 in 1999 to 19.0 in 2020. The AAMR rose sharply from 1999-2005 (APC: 10.2; 95% CI: 9.1, 11.3), increased from 2005-2010 (APC: 3.3; 95% CI: 2.6, 5.0), stabilized through 2010-2016 (APC: 0.8; 95% CI: -0.5, 1.4), and increased again from 2016-2019 (APC: 3.0; 95% CI: 1.7, 4.7). Men accounted for 59.8% of deaths, with an AAMR of 18.2, compared to 8.7 for women. Non-Hispanic (NH) American Indian (13.4) and NH white populations (13.3) had the highest AAMRs, followed by NH black or African American (12), Hispanic or Latino (9.8), and NH Asian or Pacific Islanders (9.1). The Midwest had the highest AAMR (14.1), followed by the West (13.8), South (12.2), and Northeast (11.3). Nonmetropolitan areas had higher AAMRs (14.7) compared to metropolitan areas (12.4). CONCLUSIONS: Mortality due to concurrent CAD and dyslipidemia is increasing. Targeted interventions are needed to reduce mortality among vulnerable groups.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".