Hypertension prevalence, awareness, treatment, and control in 115 rural and urban communities involving 47 000 people from China
Bibliographic record
Abstract
BACKGROUND: Identification and treatment of hypertension in China remain suboptimal despite high prevalence of hypertension and increasing incidence of stroke and myocardial infarction. OBJECTIVE: This study reported blood pressure levels, prevalence, awareness, treatment, and control rates of hypertension, in addition to drug treatments in China. METHODS: This is a country-specific analysis of 45 108 individuals, average age 51.4 (standard deviation 9.6) (35-70) years, enrolled between 2005 and 2009, from 70 rural and 45 urban communities in 12 provinces. RESULTS: Among 18 915 (41.9% overall population) hypertensive participants, 7866 (41.6%) were aware, 6503 (34.4%) treated but only 1545 (8.2%) controlled. Prevalence of hypertension was higher, but awareness, treatment, and control were lower in rural than urban residents. Prevalence of hypertension was highest in eastern (44.3%), intermediate in central (39.3%), and lowest in western regions (37.0%). Awareness was higher in central (44.3%) and eastern (42.4%) but lower in western regions (37.0%). Similar patterns were observed in treatment rates, 37.7% central, 35.2% eastern, and 26.7% in western regions with control rates of 8.3% in eastern, 7.6% central, and 5.3% west regions. Of 4744 participants receiving documented treatments, 37.5% received traditional combination drugs alone, 55.4% western drugs alone and 7.1% combination of traditional combination drug in addition to western drugs. CONCLUSION: In China, hypertension is common, and while recent studies suggest some improvements, more than half of affected individuals were unaware that they had hypertension. Rates of control remain low. National programs effective in preventing and controlling hypertension in China are urgently needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".