Socioeconomic position and incidence of acute myocardial infarction: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: A negative socioeconomic gradient is established for coronary heart disease (CHD) mortality and survival, while socioeconomic patterning of disease incidence is less well investigated. To study socioeconomic inequalities in the incidence of acute myocardial infarction (AMI), the major component of CHD, a meta-analysis was undertaken to summarise existing evidence on the issue. METHODS: A systematic search was performed in PubMed and EMBASE databases for observational studies on AMI incidence and socioeconomic position (SEP), published in English to April 2009. A random-effects model was used to pool the risks estimates from the individual studies. RESULTS: Among 1181 references, 70 studies fulfilled the inclusion criteria. An overall increased risk of AMI among the lowest SEP was found for all three indicators: income (pooled RR 1.71, 95% CI 1.43 to 2.05), occupation (pooled RR 1.35, 95% CI 1.19 to 1.53) and education (pooled RR 1.34, 95% CI 1.22 to 1.47). The strongest associations were seen in high-income countries such as USA/Canada and Europe, while the results were inconsistent for middle and low-income regions. CONCLUSION: AMI incidence is associated with low SEP. The nature of social stratification at the level of economic development of a country could be involved in the differences of risk of AMI between social groups.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.029 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".