Tobacco use in India: prevalence and predictors of smoking and chewing in a national cross sectional household survey
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence and the socioeconomic and demographic correlates of tobacco consumption in India. DESIGN: Cross sectional, nationally representative population based household survey. SUBJECTS: 315 598 individuals 15 years or older from 91 196 households were sampled in National Family Health Survey-2 (1998-99). Data on tobacco consumption were elicited from household informants. Measures and methods: Prevalence of current smoking and current chewing of tobacco were used as outcome measures. Simple and two way cross tabulations and multivariate logistic regression analysis were the main analytical methods. RESULTS: Thirty per cent of the population 15 years or older-47% men and 14% of women-either smoked or chewed tobacco, which translates to almost 195 million people-154 million men and 41 million women in India. However, the prevalence may be underestimated by almost 11% and 1.5% for chewing tobacco among men and women, respectively, and by 5% and 0.5% for smoking among men and women, respectively, because of use of household informants. Tobacco consumption was significantly higher in poor, less educated, scheduled castes and scheduled tribe populations. The prevalence of tobacco consumption increased up to the age of 50 years and then levelled or declined. The prevalence of smoking and chewing also varied widely between different states and had a strong association with individual's sociocultural characteristics. CONCLUSION: The findings of the study highlight that an agenda to improve health outcomes among the poor in India must include effective interventions to control tobacco use. Failure to do so would most likely result in doubling the burden of diseases-both communicable and non-communicable-among India's teeming poor. There is a need for periodical surveys using more consistent definitions of tobacco use and eliciting information on different types of tobacco consumed. The study also suggests a need to adjust the prevalence estimates based on household informants.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".