Analysis of Acute Myocardial Infarction Mortality Trends in the African American Population in the United States (1999 - 2020)
Bibliographic record
Abstract
Background: Acute myocardial infarction (AMI) remains a leading cause of mortality in the African American population, warranting an examination of regional and demographic trends to inform health policies. Methods: Utilizing the Centers for Disease Control and Prevention's WONDER death certificate database, we conducted a comprehensive analysis of AMI mortality from 1999 to 2020 in African Americans and overall adults aged 25 and older. Age-adjusted mortality rates (AAMRs) per 100,000 persons were calculated and stratified by year, sex, race, and geographic region. Joinpoint regression facilitated the assessment of mortality trends, revealing average annual percentage changes (AAPCs) with 95% confidence intervals (CIs). Results: Over the study period (1999 - 2020), there were 3,015,339 total deaths due to AMI in adults aged 25 and older. African Americans had the highest AAMR, at 71.5, followed by Whites, at 63.5, and the lowest among Asians, at 32.6. Overall, AAMR decreased in the African American population from 128.5 in 1999 to 48.5 in 2020, with an AAPC of -5.29 (95% CI: -5.69 to -4.9). AAMR decreased from 109 in 1999 to 37.6 in 2020 in African American females. African American males experienced a decline from 157.8 to 63.4 in AAMR. African American males had a higher overall AAMR (88.6) than females (59.3). Regionally, AAMR was highest in the South (77.6) and lowest in the Northeast (57.6) among African Americans. Conclusions: While AMI mortality has declined, persistent differences persist in the African American community. African American males experience a higher mortality rate as compared to females. Regional variations, notably the higher AAMR in the Southern region, emphasize the need for targeted health policies to mitigate disparities and enhance healthcare access. These measures may include expanding insurance coverage and improving access to healthcare, education, food, and employment for African Americans.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".