Health-Risk Transition and 8-Year Hypertension Incidence in a Nationwide Thai Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Rapid economic growth is transforming Thailand into a middle-income country. Also emerging are chronic diseases particularly hypertension, diabetes mellitus and kidney disease. There are few studies of the incidence of hypertension. We analyse the effect on 8-year incidence of hypertension of transitional health-risk factors including demography, socioeconomic status (SES), body mass index (BMI), sedentariness, physical activity, underlying diseases, personal behaviours, food, fruit and vegetable consumption.DESIGN & METHODS: Health-risk factors and their effects on the incidence of hypertension were evaluated prospectively in the national Thai Cohort Study from 2005 to 2013. All data were derived from 40,548 Sukhothai Thammathirat Open University students returning mail-based questionnaire surveys in both 2005 and 2013. Adjusted relative risks of association between each risk factor and incidence of hypertension were calculated after controlling for confounding factors.RESULTS: In Thailand, the 8-year incidence of hypertension was 5.1% (men 7.1%, women 3.6%). Hypertension was associated with ageing, higher BMI, diabetes mellitus, chronic kidney disease, high lipids, SES, lower education level, lower household asset, physical inactivity, smoking, instant food intake and soft drink. Sex, having a partner, urbanization and sedentary habits had no influence on hypertension.CONCLUSION: In Thailand, hypertension is becoming a serious risk factor for chronic disease with a wide array of associations with modern life. As Thailand’s socio-economy develops the health-risk transition will further impact on population health. Thais should be encouraged by government policy to consume less instant food, maintain normal BMI, increased physical activity, stop smoking and consume less soft drink.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".