Utilisation of national community-based blood pressure monitoring service among adult Chinese and its association with hypertension treatment and blood pressure control—a mediation analysis
Bibliographic record
Abstract
BACKGROUND: Community-based blood pressure (BP) monitoring plays an important role in national hypertension management in China. However, the utilisation of this service, together with its associations on hypertension treatment and BP control has not been fully investigated. METHODS: The study population was from the China Health and Retirement Longitudinal Study (CHARLS) in 2015. Cross-sectional data of 2487 hypertensive persons were included as subjects. Stratified sample households were selected from 450 villages or communities of 150 counties from 28 provinces. Finally, 21,097 individuals were interviewed successfully. The main outcome was hypertension control (having average BP under 140-90 mmHg). The main independent variable was utilisation of community-based BP monitoring service (having BP examination once a season or more). The mediators were hypertension treatment (currently taking any antihypertensive medicine) and lifestyle factors (alcohol intake, physical activity, smoke). We performed chi-square and binary logistic regression to analyse associations of BP monitoring with hypertension treatment and blood pressure control. The mediation model was examined by the Sobel test. RESULTS: Mean age of the population was 64.2 (0.24). The percentage of males was 42.8%. Prevalence of community-based BP monitoring was 32.1%. Patients who used this service had higher odds of hypertension treatment (β = 1.259, P < 0.01, OR = 3.52, CI = 2.467-5.030), and BP control (β = 0.220, P < 0.05, OR = 1.246, CI = 1.035-1.499). Medication treatment played a complete mediating role between monitoring and hypertension control in this study (t = 4.51, P < 0.001). Those who underwent BP monitoring tended to be those who did not finish primary school education (χ2 = 30.300, P < 0.001), had poorer household income (χ2 = 18.298, P < 0.05), and lived in rural areas rather than in urban areas (χ2 = 40.369, P < 0.001). CONCLUSIONS: Although the use of BP monitoring service had no direct effect on BP control, it had a positive effect on BP control through the full mediation effect of hypertension treatment. Termly BP monitoring by community-based health expertise among hypertensive persons, for instance, once a season, can be recommended to public health policymakers for BP control through instructions on medication treatment and health behaviours.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".