Secondary Prevention Medications in 17 Countries Grouped by Income Level (PURE)
Bibliographic record
Abstract
BACKGROUND: It is unclear whether global use of medications for secondary cardiovascular (CVD) prevention is improving over time. OBJECTIVES: This study across 17 high-, middle- and low-income countries described variations in secondary CVD prevention medication use over a median follow-up of 12 years. METHODS: In the multinational PURE (Prospective Urban Rural Epidemiology) cohort study, we conducted a repeated cross-sectional analysis to examine temporal variations in the use of secondary prevention medications in participants with CVD. In participants with coronary artery disease, we focused on antiplatelet agents, statins, renin-angiotensin system (RAS) inhibitors, and β-blockers. In participants with stroke, we focused on antiplatelet agents, statins, RAS inhibitors, and other blood pressure-lowering drugs. Medications were collected at baseline and on 4 subsequent follow-up visits. RESULTS: The analysis included 7,409 participants with a diagnosis of CVD at the baseline visit, 8,792 at the second visit, 9,236 at the third visit, 11,082 at the fourth visit, and 11,677 at the last visit. The median age at baseline was 58.0 years, and 52.9% of the participants were female. The median follow-up was 12 years, with the median year of the baseline visit in 2007 and the fifth visit in 2019. Over this period, use of 1 or more classes of medications for secondary CVD prevention was 41.3% (95% CI: 40.2%-42.4%) at baseline, peaked at 43.1% (95% CI: 42.0%-44.1%), and then decreased to 31.3% (95% CI: 30.4%-32.1%) by the last study visit. In high-income countries, this use decreased from 88.8% (95% CI: 86.6%-91.0%) to 77.3% (95% CI: 74.9%-79.6%). In upper-middle-income countries, this use increased from 55.0% (95% CI: 52.8%-57.3%) to 61.1% (95% CI: 59.1%-63.1%). In lower-middle-income countries, use of at least 1 class of medications was 29.5% (95% CI: 28.1%-30.9%) at baseline, peaked at 31.7% (95% CI: 30.4%-33.1%), and then decreased to 13.4% (95% CI: 12.5%-14.2%) by the last visit. In low-income countries, use of at least 1 class of medications was 20.8% (95% CI: 18.1%-23.5%) at baseline, peaked at 47.3% (95% CI: 44.8%-49.9%), and then decreased to 27.5% (95% CI: 25.2%-29.9%) by the last study visit. CONCLUSIONS: Globally and in most country income-level groups, the use of medications for secondary CVD prevention has been low, with little improvement over time.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".