Lifetime costs of alcohol consumption in Thailand: protocol for an incidence-based cost-of-illness study using Markov model
Bibliographic record
Abstract
INTRODUCTION: Several prevalence-based cost-of-illness (COI) studies have been conducted to estimate the economic burden of alcohol consumption borne by a particular society in a given year. Yet there are few studies examining the economic costs incurred by an individual drinker over his/her lifetime. Thus, this study aims to estimate the costs incurred by an individual drinker's alcohol consumption over his or her lifetime in Thailand. METHODS AND ANALYSIS: An incidence-based COI approach will be employed. To project individuals' associated costs over a lifetime, a Markov modelling technique will be used. The following six alcohol-related diseases/conditions will be considered in the model: hypertension, haemorrhagic stroke, liver cirrhosis, liver cancer, alcohol use disorders and road injury. The analysis will cover both direct (ie, direct healthcare cost, costs of property damage due to road traffic accidents) and indirect costs (ie, productivity loss due to premature mortality and hospital-related absenteeism). The human capital approach will be adopted to estimate the cost of productivity loss. All costs will be presented in Thai baht, 2022. ETHICS AND DISSEMINATION: The Institutional Review Board of Mahidol University, Faculty of Dentistry/Faculty of Pharmacy has confirmed that no ethical approval is required (COE.No.MU-DT/PY-IRB 2021/010.0605). Dissemination of the study findings will be carried out through peer-reviewed publications, conferences and engagement with policy-makers and public health stakeholders.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.067 | 0.005 |
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".