Prevalence and Factors Associated with Prehypertension and Hypertension Among Adults: Baseline Findings of PURE Malaysia Cohort Study
Bibliographic record
Abstract
Although pre-hypertension and hypertension can be detected at the primary healthcare level and low-cost treatments can effectively control its complications, hypertension is still the world's leading preventable risk factor. Therefore, the present study aimed to determine the prevalence and its risk factors among Malaysian adults. A cross-sectional study involving 7,585 adults was performed covering the rural and urban areas. Respondents with Systolic Blood Pressure (SBP) of 120–139 mmHg and/or Diastolic Blood Pressure (DBP) of 80–89 mmHg was categorized as pre-hypertensive, and hypertensive for respondents with an SBP of ≥ 140 mmHg and/or DBP of ≥ 90 mmHg. Respondents reported with pre-hypertension and hypertension were 40.7% and 38.0%, respectively. Those residing in a rural area, older age, male, family history of hypertension, and overweight or obese were associated with higher odds of pre-hypertension and hypertension. Unique to hypertension, the factors included low educational level (AOR: 1.349; 95% CI: 1.146, 1.588), unemployment (1.350; 1.16, 1.572), comorbidity of diabetes (1.474; 1.178, 1.844) and inadequate fruit consumption (1.253; 1.094, 1.436). As the pre-hypertensive state may affect the prevalence of hypertension, proactive strategies are needed to increase early detection of the disease among specific group of those residing in a rural area, older age, male, family history of hypertension, and overweight or obese.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".