The impact of changes in population blood pressure on hypertension prevalence and control in China
Bibliographic record
Abstract
In China, there are approximately 250 million adults who have hypertension with low rates of awareness, treatment and control. Changes in lifestyles at a population level have the potential to enhance or deteriorate the prevention and control of hypertension. We used data from a regional hypertension survey to examine the impact of 2/1 mm Hg decreases or increases in population blood pressure on hypertension prevalence, and rates of unawareness of the hypertension diagnosis, treatment, and control. The primary analysis was based on the average blood pressure of respondents from three visits and a diagnostic threshold of 140/90 mm Hg for hypertension. Secondary analyses examined average blood pressure from the first survey visit and also a diagnostic threshold of 130/80 mm Hg for hypertension. The baseline hypertension prevalence was 33.4%, and rates of unawareness of the hypertension diagnosis, treatment, and control were 74.2%, 25.8%, and 9.7%, respectively. Decreases or increases in blood pressure by 10/5 mm Hg resulted in changes in hypertension prevalence (22.1% vs 53.4%) and rates of unawareness of the diagnosis (60.9% vs 83.8%), treatment (39.1% vs 16.2%), and control (21.2% vs 3.6%), respectively. Similar trends were seen in the secondary analyses. Population changes in lifestyle could have a very large impact on the prevalence and control of hypertension in China. The results support implementation of programs to improve population lifestyles while implementing health services policies to enhance the clinical management of hypertension.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".