Tobacco Use: A Major Risk Factor for Non Communicable Diseases in South-East Asia Region
Bibliographic record
Abstract
Tobacco use is a serious public health problem in the South East Asia Region where use of both smoking and smokeless form of tobacco is widely prevalent. The region has almost one quarter of the global population and about one quarter of all smokers in the world. Smoking among men is high in the Region and women usually take to chewing tobacco. The prevalence across countries varies significantly with smoking among adult men ranges from 24.3% (India) to 63.1% (Indonesia) and among adult women from 0.4% (Sri Lanka) to 15% (Myanmar and Nepal). The prevalence of smokeless tobacco use among men varies from 1.3% (Thailand) to 31.8% (Myanmar), while for women it is from 4.6% (Nepal) to 27.9% (Bangladesh). About 55% of total deaths are due to Non communicable diseases (NCDs) with 53.4% among females with highest in Maldives (79.4%) and low in Timor-Leste (34.4%). Premature mortality due to NCDs in young age is high in the region with 60.7% deaths in Timor Leste and 60.6% deaths in Bangladesh occurring below the age of 70 years. Age standardized death rate per 100,000 populations due to NCDs ranges from 793 (Bhutan) and 612 (Maldives) among males and 654 (Bhutan) and 461 (Sri Lanka) among females respectively. Out of 5.1 millions tobacco attributable deaths in the world, more than 1 million are in South East Asia Region (SEAR) countries. Reducing tobacco use is one of the best buys along with harmful use of alcohol, salt reduction and promotion of physical activity for preventing NCDs. Integrating tobacco control with broader population services in the health system framework is crucial to achieve control of NCDs and sustain development in SEAR countries.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".