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Volume of alcohol consumption, patterns of drinking and burden of disease in the European region 2002

2006· article· en· W2105661762 on OpenAlexaff
Jürgen Rehm, Benjamin J. Taylor, Jayadeep Patra

Bibliographic record

VenueAddiction · 2006
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsAlcohol consumptionConsumption (sociology)Environmental healthBurden of diseaseAlcoholMedicineInjury preventionDiseasePoison controlAlcohol and healthOccupational safety and healthSuicide preventionHuman factors and ergonomicsInternal medicineChemistryPathology

Abstract

fetched live from OpenAlex

AIMS: To describe the volume of alcohol consumption and patterns of drinking in the World Health Organization (WHO) European regions in 2002 and to estimate quantitatively the burden of disease attributable to alcohol in that year. METHODS: Secondary data analysis. Exposure data were taken from the WHO Comparative Risk Assessment, outcome data from the WHO Measurement and Health Information department, and used to derive three outcome measures: deaths, years of life lost (YLL) and disability adjusted life years (DALY) for 2002. All calculations were conducted according to age, sex and region. RESULTS: Alcohol consumption in the WHO regions for Europe was high, with 12.1 litres pure alcohol per capita, on average more than 100% above the global consumption. Alcohol consumption caused a considerable disease burden: 6.1% of all the deaths, 12.3% of all YLL and 10.7% of all DALY in all European regions in 2002 could be attributed to this exposure. Intentional and unintentional injuries accounted for almost 50% of all alcohol-attributable deaths and almost 44% of alcohol-attributable disease burden. Young people and men were affected the most. Geographically, the most eastern region around Russia had the highest alcohol-attributable disease burden. CONCLUSIONS: Interventions should be implemented to reduce the high burden of alcohol-attributable disease in the European regions. Given the epidemiological structure of the burden, injury prevention, including but not restricted to the prevention of traffic injuries, and specific prevention for young people should play the most important role in a comprehensive plan to reduce alcohol-attributable burden.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.135

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.251
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations130
Published2006
Admission routes1
Has abstractyes

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