Which characteristics of frontline health systems affect the control of hypertension?
Bibliographic record
Abstract
Background Health systems in countries at all levels of development are being challenged by the growing burden of cardiovascular diseases (CVD). If health systems are to respond effectively, the barriers faced by those seeking care must be understood and overcome. This study estimated the effects of service-level barriers to the control of hypertension, a leading CVD risk factor. Methods Survey data are from hypertensive individuals in 17 countries who were already aware of their condition. The outcome is hypertension control, and the six service-level determinants include the availability in the community of at least one 1) public primary care facility, 2) private primary care facility, 3) public hospital, or 4) private hospital; 5) the number of blood pressure-lowering drug categories available at a community pharmacy; and 6) the price for a monthly supply of the cheapest blood pressure-lowering drug available. Observations were aggregated by country level of economic development (i.e. low-, lower middle-, upper middle- and high-income, respectively LIC, LMIC, UMIC, HIC). For each determinant, we estimated odds ratios (OR) using multi-level logit models adjusted for a range of controls. Results Availability of a public primary care facility or a private hospital did not affect the likelihood of controlling hypertension. However, availability of a private primary care facility did increase the likelihood of controlling hypertension in LICs only; and the availability of a public hospital was associated with increased likelihood of hypertension control in HICs only. Similarly, greater availability of blood pressure medicines increased the odds of control in LMICs only; while higher prices reduced the odds of control in LICs. Conclusions Access to private care and affordable medication was important for hypertension control in low-income countries. This may reflect weaknesses in the public system, pluralistic systems, and more convenient access to private providers. Key messages: Access to private care and affordable medication was important for hypertension control in low-income countries. This study demonstrates the gains that can be achieved by addressing barriers to hypertension care - and chronic disease control more generally - in low- and middle-income countries.
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 | 0.002 | 0.018 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".