The effect of an annual temporary abstinence campaign on population-level alcohol consumption in Thailand: a time-series analysis of 23 years
Bibliographic record
Abstract
RATIONALE: A small number of earlier studies have suggested an effect of temporary abstinence campaigns on alcohol consumption. However, all were based on self-reported consumption estimates. OBJECTIVES: Using a time series of 23-year monthly alcohol sales data, this study examined the effect of an annual temporary abstinence campaign, which has been organised annually since 2003 during the Buddhist Lent period (spanning 3 months), on population-level alcohol consumption. METHODS: Data used in the analysis included a time series of monthly alcohol sales data from January 1995 to September 2017 and the midyear population counts for those years. Generalised additive models (GAM) were applied to estimate trends as smooth functions of time, while identifying a relationship between the Buddhist Lent abstinence campaigns on alcohol consumption. The sensitivity analysis was performed using a seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model. INTERVENTION: The Buddhist Lent abstinence campaign is a national mass media campaign combined with community-based activities that encourages alcohol abstinence during the Buddhist Lent period, spanning 3 months and varying between July and October depending on the lunar calendar. The campaign has been organised annually since 2003. MAIN OUTCOME: Per capita alcohol consumption using monthly alcohol sales data divided by the midyear total population number used as a proxy. RESULTS: Median monthly per capita consumption was 0.43 (IQR: 0.37 to 0.51) litres of pure alcohol. Over the study period, two peaks of alcohol consumption were in March and December of each year. The significant difference between before-campaign and after-campaign coefficients in the GAM, -0.102 (95% CI: -0.163 to -0.042), indicated an effect of the campaign on alcohol consumption after adjusting for the time trend and monthly seasonality, corresponding to an average reduction of 9.97% (95% CI: 3.65% to 24.18%). The sensitivity analyses produced similar results, where the campaign was associated with a decrease in consumption of 8.1% (95% CI: 0.4% to 15.7%). CONCLUSIONS: This study demonstrated that the temporary abstinence campaign was associated with a decrease in population-level alcohol consumption during campaign periods. The finding contributed to a growing body of evidence on the effectiveness of emerging temporary abstinence campaigns.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".