Referee report. For: Impact of cigarette tax increase on health and financing outcomes in four Indian states [version 1; peer review: 1 approved, 2 approved with reservations]
Bibliographic record
Abstract
Background : In India, about one million deaths occur every year due to smoking. Tobacco taxation is the most effective intervention in reducing smoking. In this paper, we examine the impact of a one-time large cigarette price increase, through an increase in excise tax, on health and financing outcomes in four Indian states. Methods : We used extended cost-effectiveness analysis to estimate, across income quintiles, the life-years gained, treatment cost averted, number of men avoiding catastrophic health expenditures and extreme poverty, additional tax revenue collected, and savings to the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) with a cigarette price increase to Indian Rupees (INR) 10 plus 10% ad valorem in four Indian states. Results : With the price increase, about 1.5 million men would quit smoking across the four states, with the bottom income group having 7.4 times as many quitters as the top income group (485,725 vs 65,762). As a result of quitting, about 665,000 deaths would be averted. This would yield about 11.9 million life-years, with the bottom income group gaining 7.3 times more than the top income group. Of the INR 1,729 crore in treatment cost averted, the bottom income group would avert 7.4 times more than the top income group. About 454,000 men would avoid catastrophic health expenditures and 75,000 men would avoid falling into extreme poverty. The treatment cost and impoverishment averted would save about INR 672 crore in AB-PMJAY. The tax increase would in turn, generate an additional tax revenue of about INR 4,385 crore. In contrast to the distribution of health benefits, the extra revenue generated from the top income group would be about 3.1 times that from the bottom income group. Conclusions : Cigarette tax increase can provide significant health and economic gains and is a pro-poor policy for India.
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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 | 0.004 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.438 | 0.126 |
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".