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Record W2178503434 · doi:10.1093/pubmed/fdv163

The association between taxation increases and changes in alcohol consumption and traffic fatalities in Thailand

2015· article· en· W2178503434 on OpenAlexafffund
Bundit Sornpaisarn, Kevin D. Shield, Joanna E Cohen, Robert Schwartz, Jürgen Rehm

Bibliographic record

VenueJournal of Public Health · 2015
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health ResearchThai Health Promotion FoundationCentre for Addiction and Mental Health
KeywordsExcisePer capitaAlcohol consumptionConfidence intervalAlcoholConsumption (sociology)Environmental healthCase fatality rateMedicineDemographyPoison controlEconomicsInternal medicinePopulationBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.249
GPT teacher head0.411
Teacher spread0.162 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2015
Admission routes2
Has abstractyes

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