Abstract 15758: Association of Socioeconomic, Racial, and Regional Factors in In-Hospital Mortality Among Acute Myocardial Infarction Patients in the United States: A National Analysis of 2.8 Million Admissions
Bibliographic record
Abstract
Background: Socioeconomic, racial, and regional disparities have been associated with worse clinical outcomes among patients with coronary disease. We evaluated the association of income, race, and geographic variation and in-hospital mortality among acute myocardial infarction (AMI) admissions in the United States. Methods: We conducted a retrospective cohort study using the Nationwide Inpatient Sample from 2015 to 2019. A multi-level logistic regression model was used (with sampling weights) to investigate the association between in-hospital mortality and income quartiles by patient’s ZIP code, race, and hospital regions, while adjusting for hospital clustering, lifestyle factors, clinical history, and hospital-level factors. Results: A total of 2,798,225 hospitalizations (≥18 years) with a principal diagnosis of AMI were identified. In multivariable analysis, compared with the highest income quartile, residents in the lowest income quartile (OR=1.10 [1.08–1.13] P <0.001) or second lowest income quartile (OR=1.07 [1.05–1.09] P <0.001) had higher odds of in-hospital mortality. Compared with those identifying as White, Black (OR=0.89 [0.87–0.91] P <0.001) and Hispanic (OR=0.91 [0.88–0.93] P <0.001) groups had lower odds of mortality, while Asian or Pacific Islander (OR=1.07 [1.03–1.11] P <0.001), Native American (OR=1.11 [1.02–1.21] P <0.05), and Unspecified groups (OR=1.09 [1.05–1.13] P <0.001) had higher odds of mortality. Residents in the South had higher mortality than those in the Northeast (OR=1.06 [1.00–1.12] P <0.05). Conclusion: Our large contemporary study shows that lowest income residents, Whites, Asian or Pacific Islanders, and Native Americans and residents of South had higher in-hospital mortality compared with highest income residents, Blacks and Hispanics, and residents in the Northeast. Additional studies are needed to better understand the complex mechanisms that underpin disparities in outcomes among AMI patients.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".