Education and risk for acute myocardial infarction in 52 high, middle and low-income countries: INTERHEART case-control study
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of education and other measures of socioeconomic status (SES) on risk of acute myocardial infarction (AMI) in patients and controls from countries with diverse economic circumstances (high, middle, and low income countries). DESIGN: Case-control study. SETTING: 52 countries from all inhabited regions of the world. PARTICIPANTS: 12242 cases and 14622 controls. MAIN OUTCOME MEASURES: First non-fatal AMI. RESULTS: SES was measured using education, family income, possessions in the household and occupation. Low levels of education (< or =8 years) were more common in cases compared to controls (45.0% and 38.1%; p<0.0001). The odds ratio (OR) for low education adjusted for age, sex and region was 1.56 (95% confidence interval 1.47 to 1.66). After further adjustment for psychosocial, lifestyle, other factors and mutually for other socioeconomic factors, the OR associated with education < or =8 years was 1.31 (1.20 to 1.44) (p<0.0001). Modifiable lifestyle factors (smoking, exercise, consumption of vegetables and fruits, alcohol and abdominal obesity) explained about half of the socioeconomic gradient. Family income, numbers of possessions and non-professional occupation were only weakly or not at all independently related to AMI. In high-income countries (World Bank Classification), the risk factor adjusted OR associated with low education was 1.61 (1.33 to 1.94), whereas it was substantially lower in low-income and middle-income countries: 1.25 (1.14 to 1.37) (p for interaction 0.045). CONCLUSION: Of the SES measures we studied, low education was the marker most consistently associated with increased risk for AMI globally, most markedly in high-income countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".