Availability and affordability of medicines and cardiovascular outcomes in 21 high-income, middle-income and low-income countries
Bibliographic record
Abstract
OBJECTIVES: We aimed to examine the relationship between access to medicine for cardiovascular disease (CVD) and major adverse cardiovascular events (MACEs) among people at high risk of CVD in high-income countries (HICs), upper and lower middle-income countries (UMICs, LMICs) and low-income countries (LICs) participating in the Prospective Urban Rural Epidemiology (PURE) study. METHODS: We defined high CVD risk as the presence of any of the following: hypertension, coronary artery disease, stroke, smoker, diabetes or age >55 years. Availability and affordability of blood pressure lowering drugs, antiplatelets and statins were obtained from pharmacies. Participants were categorised: group 1-all three drug types were available and affordable, group 2-all three drugs were available but not affordable and group 3-all three drugs were not available. We used multivariable Cox proportional hazard models with nested clustering at country and community levels, adjusting for comorbidities, sociodemographic and economic factors. RESULTS: Of 163 466 participants, there were 93 200 with high CVD risk from 21 countries (mean age 54.7, 49% female). Of these, 44.9% were from group 1, 29.4% from group 2 and 25.7% from group 3. Compared with participants from group 1, the risk of MACEs was higher among participants in group 2 (HR 1.19, 95% CI 1.07 to 1.31), and among participants from group 3 (HR 1.25, 95% CI 1.08 to 1.50). CONCLUSION: Lower availability and affordability of essential CVD medicines were associated with higher risk of MACEs and mortality. Improving access to CVD medicines should be a key part of the strategy to lower CVD globally.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".