Death in Transitional Asia: 11-Year All-Cause Mortality in the Thai Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Thailand is experiencing a substantial reduction in overall mortality, an ageing society and increasing prevalence of non-communicable diseases. There is an urgent need to understand locally important risk factors for this new disease burden and their distribution. We investigated risk factors for mortality in a large cohort of Thai adults and report on key trends. PARTICIPANTS: A nationwide cohort of 87,151 Thai adults followed up since 2005 with their data records linked to the Thai civil registration system to monitor mortality up to the end of 2016. METHODS: We used logistic regression models to measure associations between a large range of socio-demographic, health behaviour and health status variables and all-cause mortality. RESULTS: 1402 cohort members died between 2005 and 2016. In fully-adjusted models higher income, female sex, and higher education had the strongest protective effects against mortality. Normal body weight also protected (AOR 0.71 [0.52-0.96] with Obese as reference). Heavy smoking (AOR 1.48 [1.29-1.70]), and regular alcohol consumption (AOR 1.37 [1.12-1.68]) were associated with the highest mortality. Experiencing injury in the year proceeding the baseline survey also associated with increased mortality, while urbanising since childhood had a protective effect. CONCLUSION: This study adds to evidence regarding risks for all-cause mortality in Thailand. Results indicate the need for Thailand to maintain successful tobacco control programs and to address the effects of increased alcohol consumption. The protective effect of higher education is particularly important in Thailand given the growing proportion of the population who are finishing high school and moving to higher education.
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 | 0.002 |
| 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".