P3410Variations in socioeconomic status and cardiovascular disease: risk factors,incidence and case fatality. Rates and management in 20 countries
Bibliographic record
Abstract
Background: It is not known whether associations between low socioeconomic status (SES) and cardiovascular disease (CVD) documented in high-income countries are also observed in low- and middle-income countries. Further, the reasons for gradients in CVD with respect to SES remain inadequately understood. Purpose: To investigate associations between SES (education and household wealth) and CVD and mortality across 20 low-, middle-, and high-income countries. Methods: SES (education and household wealth) was documented in 154,169 individuals from 5 low-, 11 middle-, and 4 high-income countries, who were followed for a mean of 6.9 years with respect to mortality and CVD events (CVD death, myocardial infarction, stroke and heart failure) and whether risk factors (INTERHEART risk score), and prevention and treatment of CVD paralleled these variations Findings: Education and wealth were both inversely related to mortality with the former being the stronger predictor, whereas education but not wealth predicted CVD incidence. The hazard ratio (HR) for major CVD associated with low versus high education, after adjustment for wealth and other factors, increased from being non-significant in high-income countries (HR 1.23 (0.96–1.58), intermediate in middle-income countries (HR 1.59 (1.42–1.78) and marked in low-income countries (HR 2.23 (1.79–2.77) (p for interaction<0.0001). Patterns were similar for mortality, with corresponding HRs 1.50 (1.14–1.98), 1.80 (1.58–2.06), and 2.76 (2.29–3.31) (p for interaction<0.0001). CVD risk factors, on the other hand, were lower in those with the highest, compared to the lowest, education in high-income countries, but lowest among the least educated in low-income countries (p for interaction<0.0001). The odds ratio for the 28-day case fatality after major CVD events among those with the lowest compared to the highest education was greatest in low-income countries (OR=2.23 (1.4–3.4)), intermediate in middle-income countries (1.90 (1.4–2.7)) and non-significant in high-income countries (0.66 (0.2–1.9)). Intensities of management of hypertension and diabetes treatment and secondary prevention were lower in people with the lowest, compared to the highest education in low-income countries, whereas there were lesser differences in middle-income countries and no differences 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 | 0.002 |
| 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 | 0.000 | 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".