Socio-economic inequality in the use of secondary prevention of cardiovascular disease
Bibliographic record
Abstract
Background The WHO target of a 25% reduction in premature mortality from non-communicable diseases by 2025 can only be achieved by expanding secondary prevention of cardiovascular diseases (CVD). We investigate socio-economic inequality in the use of secondary prevention for CVD in 21 countries at varying levels of economic development. Methods Data for this study comes from the PURE study, the largest study of CVD incidence, mortality and risk factors globally. Use of secondary prevention (defined as using one or more medications for cholesterol or blood pressure lowering, or an antiplatelet drug) was analysed in 11,381 patients with CVD in 21 countries at different levels of economic development (Argentina, Bangladesh, Brazil, Canada, Chile, China, Colombia, India, Iran, Malaysia, Occupied Palestinian Territory, Pakistan, Philippines, Poland, Saudi Arabia, South Africa, Sweden, Tanzania, Turkey, United Arab Emirates, Zimbabwe). Socio-economic inequality was estimated using Wagstaff concentration indices (WCI), scaled from -1 (pro-poor) to 1 (pro-rich), standardized by age and sex. Results Use of secondary prevention among CVD patients is low, ranging from 2.1% (95% confidence interval: 0.5-8.8) in Tanzania to 85.3% (95% confidence interval: 72.8-92.7) in the United Arab Emirates. Use was pro-rich in China (WCI: 0.111; p:0.007), Colombia (WCI: 0.1857; p:0.014), India (WCI:0.568; p:<0.001), and Pakistan (WCI:0.641; p:0.005), pro-poor in Brazil (WCI: -0.127; p:0.06), and almost equal in Canada (WCI:0.035; p = 0.485), Argentina (WCI:0.012; p = 0.73) and Poland (WCI:0.002; p = 0.972). Conclusions Use of adequate secondary prevention medication for CVD is low in many countries at all levels of economic development, and unequally distributed, particularly in lower income countries. Policies that improve use among the poor in such countries as Iran and Canada may provide valuable insights to improving secondary prevention of CVD in other countries. Key messages: Use of medication for secondary prevention of CVD is alarmingly low. In many countries with the lowest use, pro-rich inequality is greatest.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".