Visualizing data: Trends in smoking tobacco prices and taxes in India
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background</ns4:bold> <ns4:bold>:</ns4:bold> Tobacco smoking remains a leading risk factor for disease burden globally. In India alone, about 1 million deaths are caused annually by smoking. Although increasing tobacco prices has consistently been found to be the most effective intervention to reduce tobacco use, the documentation of prices and taxes across time and space has not been an essential component of tobacco control surveillance in most jurisdictions. This study aimed to examine, using graphical methods, trends in smoking tobacco taxes and prices in India at national and state-level. </ns4:p> <ns4:p> <ns4:bold>Methods</ns4:bold> <ns4:bold>:</ns4:bold> We used retail prices, price indices, and unit values (household expenditures on a commodity divided by the quantity purchased) collected and reported by government agencies. For bidis and cigarettes, we examined current and real (inflation-adjusted) prices, affordability (cost in terms of income), and key tax changes at both national and state-level. </ns4:p> <ns4:p> <ns4:bold>Results</ns4:bold> <ns4:bold>:</ns4:bold> We show that real prices of bidis and cigarettes were relatively flat (even decreasing in the case of bidis) between 2000 and 2007, and clearly increasing from 2010. When rising income is taken into account, however, both cigarettes and bidis have become more affordable since 2000. We found that some but not all tax changes were accompanied by price changes and in particular, that tax decreases did not result in price decreases. </ns4:p> <ns4:p> <ns4:bold>Conclusion</ns4:bold> <ns4:bold>:</ns4:bold> It is feasible to evaluate tax and price policies at national and regional level using routinely collected data. </ns4:p>
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".