Association between population hypertension control and ischemic heart disease and stroke mortality in 36 countries of the Americas, 1990-2019: an ecological study
Bibliographic record
Abstract
Objective: To quantify the association between the prevalence of population hypertension control and ischemic heart disease (IHD) and stroke mortality in 36 countries of the Americas from 1990 to 2019. Methods: This ecologic study uses the prevalence of hypertension, awareness, treatment, and control from the NCD-RisC and IHD and stroke mortality from the Global Burden of Disease Study 2019. Regression analysis was used to assess time trends and the association between population hypertension control and mortality. Results: Between 1990 and 2019, age-standardized death rates due to IHD and stroke declined annually by 2.2% (95% confidence intervals: -2.4 to -2.1) and 1.8% (-1.9 to -1.6), respectively. The annual reduction rate in IHD and stroke mortality deaccelerated to -1% (-1.2 to -0.8) during 2000-2019. From 1990 to 2019, the prevalence of hypertension controlled to a systolic/diastolic blood pressure ≤140/90 mmHg increased by 3.2% (3.1 to 3.2) annually. Population hypertension control showed an inverse association with IHD and stroke mortality, respectively, regionwide and in all but 3 out of 36 countries. Regionwide, for every 1% increase in population hypertension control, our data predicted a reduction of 2.9% (-2.94 to -2.85) in IHD deaths per 100 000 population, equivalent to an averted 25 639 deaths (2.5 deaths per 100 000 population) and 2.37% (-2.41 to -2.33) in stroke deaths per 100 000 population, equivalent to an averted 9 650 deaths (1 death per 100 000 population). Conclusion: There is a strong ecological negative association between IHD and stroke mortality and population hypertension control. Countries with the best performance in hypertension control showed better progress in reducing CVD mortality. Prediction models have implications for hypertension management in most populations in the Region of the Americas and other parts of the world.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".