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Record W2009120897 · doi:10.4314/ajdas.v7i1.46355

Volume of alcohol consumption, patterns of drinking and burden of disease in sub-Saharan Africa, 2002

2009· article· en· W2009120897 on OpenAlexaff
Michael Roerecke, Isidore Obot, Jayadeep Patra, Jürgen Rehm

Bibliographic record

VenueAfrican Journal of Drug and Alcohol Studies · 2009
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsEnvironmental healthPer capitaBurden of diseaseMedicineDisease burdenAlcoholHarmConsumption (sociology)Psychological interventionAlcohol consumptionInjury preventionPublic healthPoison controlDemographyPopulationPsychiatryPsychologyBiology

Abstract

fetched live from OpenAlex

The aim of this study was to provide an overview of the volume of alcohol consumption, type of beverage, patterns of drinking and alcohol-attributable burden of disease among adults in sub- Saharan Africa (SSA) for the year 2002. Exposure data were taken from surveys, the World Health Organization (WHO) Global Status Report on Alcohol and the WHO Global Alcohol Database. Mortality and disability data were obtained directly from WHO. The results showed that adult per capita alcohol consumption (population15 years and above) in SSA was higher than the global consumption rate (7.4 L vs. 6.2 L) and that alcohol consumption per adult drinker was 42% higher than the global rate. Alcohol was responsible for a considerable disease burden: 2.2% of all deaths and 2.5% of all DALYs could be attributed to this exposure. Intentional and unintentional injuries accounted for 53% of all alcohol-attributable deaths and almost 57% of alcohol-attributable disease burden. Among men 70% of all alcohol-attributable injury deaths occurred among 15-44 year olds (52% among women). This first attempt to quantify the health burden attributable to alcohol in SSA provides evidence of the direct health costs associated with drinking in the continent. In light of known effective and cost-effective measures, there is urgent need to implement interventions aimed at reducing levels of risky drinking and the high burden of alcohol-related harm in African countries. KEY WORDS: alcohol consumption, patterns of drinking, sub-Saharan Africa, burden of disease

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)0.001
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 score0.030
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.073
GPT teacher head0.348
Teacher spread0.276 · 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

Citations34
Published2009
Admission routes1
Has abstractyes

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