Alcohol‐attributable deaths in Thai people from 2015 to 2021 using the comparative risk assessment approach
Bibliographic record
Abstract
BACKGROUND: The alcohol-attributable mortality rate is an important health indicator for surveillance of health-related impacts of alcohol consumption. This study aimed to estimate the annual number and rate of alcohol-attributable deaths among the Thai population aged 15 years and over during 2015-2021. METHODS: Mortality data were drawn from the National Death Registry based on ICD-10. We used the standard methodology of comparative risk assessments for alcohol within the general framework of the Global Burden of Disease Studies and used alcohol-attributable fractions, derived from exposure, and relative risk compared to lifetime abstainers as the counterfactual. Age-standardization was used to adjust mortality rates which were calculated by cause, age group, and sex. RESULTS: The estimated annual number of alcohol-attributable deaths was 20,039 (men: 17,726 [6.50% of total annual deaths of the Thai population] and women: 2312 [1.11%]). The age-standardized alcohol-attributable mortality rates continuously increased from 33.8 to 37.5 deaths per 100,000 population from 2015 to 2019 and slightly decreased to 34.5 and 35.3 in 2020 and 2021, respectively. The three leading causes of death attributed to alcohol consumption were road injuries, cirrhosis and other liver diseases, and other unintentional injuries. CONCLUSION: Alcohol remains an important preventable cause of death among Thais. The alcohol-attributable mortality rate increased from 2015 to 2019 but declined in 2020 and 2021, possibly due to the coronavirus pandemic and lockdown measures. Culturally appropriate, cost-effective interventions should be used to control alcohol accessibility, particularly among young people who frequently sustain injuries from external causes and have high mortality rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".