Global statistics on alcohol, tobacco and illicit drug use: 2017 status report
Bibliographic record
Abstract
AIMS: This review provides an up-to-date curated source of information on alcohol, tobacco and illicit drug use and their associated mortality and burden of disease. Limitations in the data are also discussed, including how these can be addressed in the future. METHODS: Online data sources were identified through expert review. Data were obtained mainly from the World Health Organization, United Nations Office on Drugs and Crime and Institute for Health Metrics and Evaluation. RESULTS: In 2015, the estimated prevalence among the adult population was 18.4% for heavy episodic alcohol use (in the past 30 days); 15.2% for daily tobacco smoking; and 3.8, 0.77, 0.37 and 0.35% for past-year cannabis, amphetamine, opioid and cocaine use, respectively. European regions had the highest prevalence of heavy episodic alcohol use and daily tobacco use. The age-standardized prevalence of alcohol dependence was 843.2 per 100 000 people; for cannabis, opioids, amphetamines and cocaine dependence it was 259.3, 220.4, 86.0 and 52.5 per 100 000 people, respectively. High-income North America region had among the highest rates of cannabis, opioid and cocaine dependence. Attributable disability-adjusted life-years (DALYs) were highest for tobacco smoking (170.9 million DALYs), followed by alcohol (85.0 million) and illicit drugs (27.8 million). Substance-attributable mortality rates were highest for tobacco smoking (110.7 deaths per 100 000 people), followed by alcohol and illicit drugs (33.0 and 6.9 deaths per 100 000 people, respectively). Attributable age-standardized mortality rates and DALYs for alcohol and illicit drugs were highest in eastern Europe; attributable age-standardized tobacco mortality rates and DALYs were highest in Oceania. CONCLUSIONS: In 2015 alcohol use and tobacco smoking use between them cost the human population more than a quarter of a billion disability-adjusted life years, with illicit drugs costing further tens of millions. Europeans suffered proportionately more, but in absolute terms the mortality rate was greatest in low- and middle-income countries with large populations and where the quality of data was more limited. Better standardized and rigorous methods for data collection, collation and reporting are needed to assess more accurately the geographical and temporal trends in substance use and its disease burden.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".