Alcohol Consumption and Mortality in Russia since 2000: Are there any Changes Following the Alcohol Policy Changes Starting in 2006?
Bibliographic record
Abstract
AIMS: To elucidate the possible effects of Russian alcohol control policy on alcohol consumption and alcohol-related mortality for the period 2000-2010. METHODS: Narrative review including statistical analysis. Trends before and after 2006 are compared, 2006 being the date of implementation of the Russian government's long-term strategy to reduce alcohol-related harms. Mortality data were taken from the World Health Organization (WHO) database 'Health for All'. Data on recorded alcohol consumption were taken from the WHO, based on the Russian Statistical Service (Rosstat). For unrecorded consumption, the calculations of Alexandr Nemtsov were used. Russian public opinion surveys on drinking habits were utilized. Treatment data on alcohol dependence were obtained from the Moscow National Research Centre on Addictions. Information on alcohol policy was obtained from official reports. RESULTS: Marked fluctuations in all-cause and alcohol-associated mortality in the working-age population were observed during the reviewed period. A decrease in total consumption and mortality was noted since the end of 2005, when the Russian government initially adopted the regulation of alcohol production and sale. The consumption changes were driven by decreases in recorded and unrecorded spirit consumption, only partly compensated for by increases in beer and wine consumption. CONCLUSIONS: Alcohol is a strong contributor to premature deaths in Russia, with both the volume and the pattern of consumption being detrimental to health. The regulations introduced since 2006 seem to have positive effects on both drinking behavior and health outcomes. However, there is an urgent need for further alcohol-control strategies to reduce alcohol-related harm.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".