Trends in substance use and in the attributable burden of disease and mortality in the WHO European Region, 2010–16
Bibliographic record
Abstract
BACKGROUND: This paper examines changes in substance use, and compares the resulting attributable burden of disease in the WHO European Region between 2010 and 2016. METHODS: Data for 2010 and 2016 on the number of deaths, years of life lost (YLL) and disability-adjusted life years (DALYs) lost were obtained by sex and country from the 2016 Global Burden of Disease (GBD) study. Exposure data for all substances except alcohol were obtained from the same study, while alcohol data were obtained from the WHO. Proportional changes were calculated for the WHO European Region as a whole to identify trends and for sub-regions to identify which regions contributed most to trends. RESULTS: In the WHO European Region in 2016, substance use caused 2.1 million deaths, 48.6 million YLL and 57.9 million DALYs lost, representing 22.4, 29.0 and 20.4% of all deaths, YLL and DALYs, respectively. The substance-attributable burden of disease was higher among men than women and highest in the eastern parts of the WHO European Region. Changes in the number of deaths, YLL and DALYs lost between 2010 and 2016 were almost uniformly downward, with the largest proportional changes observed for men. Exposure to tobacco, alcohol and illicit drugs also decreased uniformly. CONCLUSIONS: Substance use and its attributable mortality and burden of disease have decreased in the WHO European Region since 2010. However, overall levels of substance use and the resulting burden of disease in the Region remain high compared with other regions of the world.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".