Global statistics on addictive behaviours: 2014 status report
Bibliographic record
Abstract
BACKGROUND AND AIMS: Addictive behaviours are among the greatest scourges on humankind. It is important to estimate the extent of the problem globally and in different geographical regions. Such estimates are available, but there is a need to collate and evaluate these to arrive at the best available synthetic figures. Addiction has commissioned this paper as the first of a series attempting to do this. METHODS: Online sources of global, regional and national information on prevalence and major harms relating to alcohol use, tobacco use, unsanctioned psychoactive drug use and gambling were identified through expert review and assessed. The primary data sources located were the websites of the World Health Organization (WHO), the United Nations Office on Drugs and Crime (UNODC) and the Alberta Gambling Research Institute. Summary statistics were compared with recent publications on the global epidemiology of addictive behaviours. RESULTS: An estimated 4.9% of the world's adult population (240 million people) suffer from alcohol use disorder (7.8% of men and 1.5% of women), with alcohol causing an estimated 257 disability-adjusted life years lost per 100 000 population. An estimated 22.5% of adults in the world (1 billion people) smoke tobacco products (32.0% of men and 7.0% of women). It is estimated that 11% of deaths in males and 6% of deaths in females each year are due to tobacco. Of 'unsanctioned psychoactive drugs', cannabis is the most prevalent at 3.5% globally, with each of the others at < 1%; 0.3% of the world's adult population (15 million people) inject drugs. Use of unsanctioned psychoactive drugs accounts for an estimated 83 disability-adjusted life years lost per 100 000 population. Global estimates of problem gambling are not possible, but in countries where it has been assessed the prevalence is estimated at 1.5%. CONCLUSIONS: Tobacco and alcohol use are by far the most prevalent addictive behaviours and cause the large majority of the harm. However, the quality of data on prevalence and addiction-related harms is mostly low, and comparisons between countries and regions must be viewed with caution. There is an urgent need to review the quality of data on which global estimates are made and coordinate efforts to arrive at a more consistent approach.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| 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) | 0.015 | 0.012 |
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".