Health-Risk Factors and the Prevalence of Hypertension: Cross-Sectional Findings from a National Cohort of 87 143 Thai Open University Students
Bibliographic record
Abstract
BACKGROUND: Thailand is undergoing a health-risk transition which increases chronic diseases, particularly hypertension, as a result of a rapid transition from a developing to a developed country. This study analyzes the effect of health-risk factors such as demography, socioeconomic status (SES) and body mass index (BMI) on the prevalence of hypertension. METHODS: This was a cross-sectional analysis using data obtained in 2005 from 87,143 Sukhothai Thammathirat Open University (STOU) students participating in the Thai Cohort Study (mean age 30.5 years, 54.7% female). Adjusted odds ratios of the association between risk factors and hypertension were analysed across two age groups by sex, after controlling for the confounding factors such as SES and BMI. RESULTS: The prevalence of hypertension in men was approximately twice as high as that in women (6.9% vs 2.6%). Hypertension was associated with ageing, a lower education attainment, a higher BMI and having underlying diseases in both sexes. In men, hypertension was associated with being single, having a high income, spending more time on screens (TV & PC), cigarette smoking and drinking alcohol. In women, it was directly correlated with instant and roasted or smoked food consumption. CONCLUSIONS: Hypertension was highly associated with obesity and having underlying disease. The Thai health-risk transition is in a later stage. Thais should now be educated about the danger of high blood pressure and the protective power of a low fat and low salt diet, and a normal BMI. Cessation of smoking and moderation in alcohol intake should be promoted.
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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 | 0.001 | 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".