The association between taxation increases and changes in alcohol consumption and traffic fatalities in Thailand
Bibliographic record
Abstract
In 2005, 2007 and 2009, alcohol taxation rates changed in Thailand. It is unknown if these changes are associated with alcohol consumption and traffic fatality rates. Using monthly data from October 2004 to September 2011, we examined the relationship between alcohol taxation rate changes adjusted for inflation and changes in adult per capita consumption of alcohol (data obtained from the Excise Department) and the rate of traffic fatalities (data obtained from the National Police Institute). Only the taxation increase in 2009 was significantly associated with an immediate and sustained reduction of 0.38 (95% confidence interval (CI): 0.24–0.52) fatalities per 100 000 people per 30 days. The 2005, 2007 and 2009 taxation increases were associated with immediate and sustained reductions of 0.07 (95% CI: 0.04–0.09), 0.06 (95% CI: 0.02–0.10) and 0.05 (95% CI: 0.01–0.09) litres of pure alcohol consumption per capita per 30 days, respectively. This is the first study to examine the impact of taxation on alcohol-related harms in a low- to middle-income country. Taxation changes were associated with reductions in the rate of traffic fatalities and in alcohol consumption; however, reductions of traffic fatalities were not always significant.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".