Trends in bidi and cigarette smoking in India from 1998 to 2015, by age, gender and education
Bibliographic record
Abstract
OBJECTIVES: Smoking of cigarettes or bidis (small, locally manufactured smoked tobacco) in India has likely changed over the last decade. We sought to document trends in smoking prevalence among Indians aged 15-69 years between 1998 and 2015. DESIGN: Comparison of 3 nationally representative surveys representing 99% of India's population; the Special Fertility and Mortality Survey (1998), the Sample Registration System Baseline Survey (2004) and the Global Adult Tobacco Survey (2010). SETTING: India. PARTICIPANTS: About 14 million residents from 2.5 million homes, representative of India. MAIN OUTCOME MEASURES: Age-standardised smoking prevalence and projected absolute numbers of smokers in 2015. Trends were stratified by type of tobacco smoked, age, gender and education level. FINDINGS: The age-standardised prevalence of any smoking in men at ages 15-69 years fell from about 27% in 1998 to 24% in 2010, but rose at ages 15-29 years. During this period, cigarette smoking in men became about twofold more prevalent at ages 15-69 years and fourfold more prevalent at ages 15-29 years. By contrast, bidi smoking among men at ages 15-69 years fell modestly. The age-standardised prevalence of any smoking in women at these ages was 2.7% in 2010. The smoking prevalence in women born after 1960 was about half of the prevalence in women born before 1950. By contrast, the intergenerational changes in smoking prevalence in men were much smaller. The absolute numbers of men smoking any type of tobacco at ages 15-69 years rose by about 29 million or 36% in relative terms from 79 million in 1998 to 108 million in 2015. This represents an average increase of about 1.7 million male smokers every year. By 2015, there were roughly equal numbers of men smoking cigarettes or bidis. About 11 million women aged 15-69 smoked in 2015. Among illiterate men, the prevalence of smoking rose (most sharply for cigarettes) but fell modestly among men with grade 10 or more education. The ex-smoking prevalence in men at ages 45-59 years rose modestly but was low: only 5% nationally with about 4 current smokers for every former smoker. CONCLUSIONS: Despite modest decreases in smoking prevalence, the absolute numbers of male smokers aged 15-69 years has increased substantially over the last 15 years. Cigarettes are displacing bidi smoking, most notably among young adult men and illiterate men. Tobacco control policies need to adapt to these changes, most notably with higher taxation on tobacco products, so as to raise the currently low levels of adult smoking cessation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".