Differences in prevalence of hypertension and associated risk factors in urban and rural residents of the northeastern region of the People’s Republic of China: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Hypertension is a significant global public health problem and recognized as an important risk factor for cardiovascular diseases. This study was designed to assess the current prevalence of hypertension and to explore risk factors associated with hypertension by urban and rural status to guide the prevention and control of hypertension in Jilin province. METHODS: A multi-stage stratified random cluster sampling method was used to obtain data on hypertension, which was investigated by physical examination and face-to-face questionnaire in July 2014-December 2015. Sample data were analyzed by complex weighted statistical analysis to estimate blood pressure levels and prevalence of hypertension in the province. Multivariable logistic regression analysis was used to identify factors influencing hypertension rates. RESULTS: The prevalence of hypertension was significantly higher in rural areas than urban areas (25.93% versus 22.73%, respectively). The rates of hypertension known (46.7% versus 38.1%, respectively), control (13.7% versus 5.0%, respectively), and controlled among treated subjects (38.3% versus 17.5%, respectively) were higher in urban areas than in rural areas (all p < 0.001), while the treatment rate was not statistically significantly different between urban and rural areas (35.9% versus 28.4%, respectively). After adjusting for demographic covariates, hypertension prevalence in rural areas was still significantly greater than in urban areas (adjusted OR = 1.22; 95%CI: 1.10, 1.36; p < 0.001). Common risk factors for hypertension among urban and rural residents included older age; male; married; employed; less education; overweight/obese; greater abdominal waist circumference; family history of hypertension, stroke, or coronary heart disease; current smoker; alcohol consumption; higher visceral adiposity index; and higher body fat percentage. CONCLUSIONS: This study identified an increased risk for hypertension in rural regions of Jilin province, suggesting that rural hypertension screening and treatment guidelines should receive greater attention.
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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 | 0.001 |
| 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".