Socioeconomic disparities in the management and outcomes of acute myocardial infarction
Bibliographic record
Abstract
BACKGROUND: Patients from lower socioeconomic status areas have poorer outcomes following acute myocardial infarction (AMI); however, how ethnicity modifies such socioeconomic disparities is unclear. METHODS: Using the UK Myocardial Ischaemia National Audit Project (MINAP) registry, we divided 370 064 patients with AMI into quintiles based on Index of Multiple Deprivation (IMD) score, comprising seven domains including income, health, employment and education. We compared white and 'ethnic-minority' patients, comprising Black, Asian and mixed ethnicity patients (as recorded in MINAP); further analyses compared the constituents of the ethnic-minority group. Logistic regression models examined the role of the IMD, ethnicity and their interaction on the odds of in-hospital mortality. RESULTS: More patients from the most deprived quintile (Q5) were from ethnic-minority backgrounds (Q5; 15% vs Q1; 4%). In-hospital mortality (OR 1.10, 95% CI 1.01 to 1.19, p=0.025) and major adverse cardiovascular event (MACE) (OR 1.07, 95% CI 1.00 to 1.15, p=0.048) were more likely in Q5, and MACE was more likely in ethnic-minority patients (OR 1.40, 95% CI 1.00 to 1.95, p=0.048) versus white (OR 1.05, 95% CI 0.98 to 1.13, p=0.027) in Q5. In subgroup analyses, Black patients had the highest in-hospital mortality within the most affluent quintile (Q1) (Black: 0.079, 95% CI 0.046 to 0.112, p<0.001; White: 0.062, 95% CI 0.059 to 0.066, p<0.001), but not in Q5 (Black: 0.065, 95% CI 0.054 to 0.077, p<0.001; White: 0.065, 95% CI 0.061 to 0.069, p<0.001). CONCLUSION: Patients with a higher deprivation score were more often from an ethnic-minority background, more likely to suffer in-hospital mortality or MACE when compared with the most affluent quintile, and this relationship was stronger in ethnic minorities compared with White 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".