What is the lifetime cost of alcohol consumption? an estimation of economic burden in Thailand
Bibliographic record
Abstract
This study aimed to estimate the lifetime cost of alcohol consumption per individual drinker in Thailand to support policy formulation. Using an incidence-based cost-of-illness (COI) approach, a hybrid model combining a decision tree and a Markov model, incorporating six major alcohol-related diseases and conditions (i.e., hypertension, hemorrhagic stroke, liver cirrhosis, liver cancer, alcohol use disorders, and road injuries), was employed to analyze both direct costs (i.e., direct medical, direct nonmedical, property damage) and indirect costs (i.e., absenteeism, premature mortality). All costs were reported in Thai baht 2022 (35.06 THB = 1US$). From a societal perspective, the lifetime costs for individual male and female drinker were estimated at 721,344 THB (95% CI: 687,910-754,779) and 263,812 THB (95% CI: 249,250-278,374), respectively. Quitting earlier reduced costs significantly, with average quitting ages resulting in the cost of 568,932 THB for males and 115,167 THB for females. On average, each Thai drinker incurs a cost of 498,196 THB. These findings highlight the substantial economic burden of alcohol consumption in Thailand, underscoring the critical need for effective interventions and policies, along with more rigorous enforcement of current regulations aimed at encouraging early cessation and preventing the initiation of drinking, such as through advertising bans, sales restrictions, improving access to counseling and treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".