Wealth-related inequalities in the awareness, treatment and control of hypertension in 21 countries
Bibliographic record
Abstract
Background Effective policies to control hypertension require an understanding of its distribution in the population and the barriers people face along the pathway from detection through to treatment and control. This study describes the scale and patterns of wealth-related inequalities in awareness, treatment and control of hypertension in 21 countries using data from the Prospective Urban and Rural Epidemiology study. Methods 163,397 adults aged 35 to 70 years were recruited from 661 urban and rural communities in selected low-, middle- and high-income countries. Using blood pressure measurements, self-reported health and household data, the magnitude of wealth-related inequalities in levels of hypertension awareness, treatment, and control are examined, with concentration indices estimated after adjusting for age and sex. Results Overall, the magnitude of wealth-related inequalities in hypertension awareness, treatment, and control is higher in poor than rich countries. Levels of hypertension awareness and treatment tended to be higher among wealthier households in poorer countries; while a similar pro-rich distribution was observed for hypertension control in countries at all levels of economic development. In some countries, hypertension awareness is greater among the poor (Sweden, Argentina, Poland), as is treatment (Sweden, Poland) and control (Sweden). Discussion Inequality in hypertension management outcomes decreased as countries became richer but the considerable diversity of patterns of wealth-related inequality even among countries at similar levels of economic development shows the importance of specific aspects of health systems in improving hypertension management. These findings show that some, but not all, countries, including those with limited resources, have been able to achieve equitable management of hypertension but strategies must be tailored national contexts. Key messages: Wealth-related inequalities in hypertension control vary greatly among countries While inequalities are less in richer countries, some poor countries also perform well
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".