HEARTS in the Americas: Targeting Health System Change to Improve Population Hypertension Control
Bibliographic record
Abstract
PURPOSE OF REVIEW: HEARTS in the Americas is the regional adaptation of Global Hearts, the World Health Organization initiative for cardiovascular disease (CVD) prevention and control. Its overarching goal is to drive health services to change managerial and clinical practice in primary care settings to improve hypertension control and CVD risk management. This review describes the HEARTS in the Americas initiative. First, the regional epidemiological situation of CVD mortality and population hypertension control trends are summarized; then the rationale for its main intervention components: the primary care-oriented management system and the HEARTS Clinical Pathway are described. Finally, the key factors for accelerating the expansion of HEARTS are examined: medicines, team-based care, and a system for monitoring and evaluation. RECENT FINDINGS: Thus far, 33 countries in Latin America and the Caribbean have committed to integrating this program across their primary healthcare network by 2025. The increase in hypertension coverage and control in primary health care settings compared with the traditional model is promising and confirms that the interventions under the HEARTS umbrella are feasible and acceptable to communities, patients, providers, decision-makers, and funders. This review highlights some cases of successful implementation. Scaling up effective treatment for hypertension and optimization of CVD risk management is a pragmatic way to accelerate the reduction of CVD mortality while strengthening primary healthcare systems to respond effectively, with quality, and equitably, to the challenge of non-communicable diseases, not only in low-middle income countries but in all communities 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".