Secondary CV Prevention in South America in a Community Setting: The PURE Study
Bibliographic record
Abstract
BACKGROUND: Despite the availability of evidence-based therapies, there is no information on the use of medications for the secondary prevention of cardiovascular disease in urban and rural community settings in South America. OBJECTIVES: This study sought to assess the use, and its predictors, of effective secondary prevention therapies in individuals with a history of coronary heart disease (CHD) or stroke. METHODS: In the PURE (Prospective Urban Rural Epidemiological) study, we enrolled 24,713 individuals from South America ages 35 to 70 years from 97 rural and urban communities in Argentina, Brazil, Chile, and Colombia. We assessed the use of proven therapies with standardized questionnaires. We report estimates of drug use at national, community, and individual levels and the independent predictors of their utilization through a multivariable analysis model. RESULTS: Of 24,713 individuals, 910 had a self-reported CHD event (at a median of 5 years earlier) and 407 had stroke (6 years earlier). The proportions of individuals with CHD who received antiplatelet medications (30.1%), beta-blockers (34.2%), angiotensin-converting enzyme inhibitors, or angiotensin-receptor blockers (36.0%), or statins (18.0%) were low; with even lower proportions among stroke patients (antiplatelets 24.3%, angiotensin-converting enzyme inhibitors/angiotensin-receptor blockers 37.6%, statins 9.8%). A substantial proportion of patients did not receive any proven therapy (CHD 31%, stroke 54%). A minority of patients received either all 4 (4.1%) or 3 proven therapies (3.3%). Male sex, age >60 years, better education, more wealth, urban location, diabetes, and obesity were associated with higher rates of medication use. In a multivariable model, markers of wealth had the largest impact in secondary prevention. CONCLUSIONS: There are large gaps in the use of proven medications for secondary prevention of cardiovascular disease in South America. Strategies to improve the sustained use of these medications will likely reduce cardiovascular disease burden substantially.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".