Pharmaco-Disparities in Heart Failure: A Survey of the Affordability of Guideline Recommended Therapy in 10 Countries
Bibliographic record
Abstract
AIMS: Heart failure with reduced ejection fraction (HFrEF) is treatable but guideline-directed medical therapy (GDMT) may not be affordable or accessible to people living with the disease. METHODS AND RESULTS: In this cross-sectional survey, we investigated the price, affordability, and accessibility of four pivotal classes of HFrEF GDMT: angiotensin-converting enzyme inhibitors (ACEi), angiotensin receptor blockers (ARB) or angiotensin-neprilysin inhibitors (ARNI); beta-blockers; mineralocorticoid receptor antagonists (MRA); and sodium glucose co-transporter 2 inhibitors (SGLT2i). We sampled online or community pharmacies in 10 countries across a range of World Bank income groups, assessing mean 30 day retail prescription prices, affordability relative to gross national income per capita per month, and accessibility. We reported median price ratios relative to the International Reference Standard. We performed a literature review to evaluate accessibility to GDMT classes through publicly funded drug programmes in each country. HFrEF GDMT prices, both absolute and relative to the international reference, were highest in the United States and lowest in Pakistan and Bangladesh. The most expensive drug was the ARNI, sacubitril/valsartan, with a mean (standard deviation, SD) 30 day price ranging from $11.06 (0.81) in Pakistan to $611.50 (3.54) in United States. The least expensive drug was the MRA, spironolactone, with a mean (SD) 30 day price ranging from $0.18 (0.00) in Pakistan to $12.32 (0.00) in England. Affordability (SD) of quadruple therapy-ARNI, beta-blockers, MRA, and SGLT2i-was best in high-income and worst in low-income countries, ranging from 1.49 (0.00)% of gross national income per capita per month in England to 232.47 (31.47)% in Uganda. Publicly funded drug programmes offset costs for eligible patients, but ARNI and SGLT2i were inaccessible through these programmes in low- and middle-income countries. Price, affordability, and access were substantially improved in all countries by substituting ARNI for ACEi/ARB. CONCLUSIONS: There was marked variation between countries in the retail price of HFrEF GDMT. Despite higher prices in high-income countries, GDMT was more accessible and affordable than in low- and middle-income countries. Publicly funded drug programmes in lower income countries increased affordability but limited access to newer HFrEF GDMT classes. Pharmaco-disparities must be addressed to improve HFrEF outcomes 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".