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Record W1910401102 · doi:10.1111/tmi.12618

The impact of alcohol consumption on African people in 2012: an analysis of burden of disease

2015· article· en· W1910401102 on OpenAlexaff
Carina Ferreira‐Borges, Jürgen Rehm, Sónia Dias, Thomas F. Babor, Charles Parry

Bibliographic record

VenueTropical Medicine & International Health · 2015
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsEnvironmental healthMedicineAlcoholHarmDisease burdenBurden of diseaseIncidence (geometry)Public healthAlcohol consumptionConsumption (sociology)DiseasePopulationPsychologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine the impact of alcohol consumption on deaths and disability in Africa. METHODS: We estimated alcohol exposure for 2012, and its impact on deaths and disability in Africa using estimates from the WHO Global Health Estimates for outcome data, and the WHO Global Status Report on Alcohol and Health 2014 for risk relations. We provide a scenario that includes the impact of alcohol on HIV/AIDS incidence, and qualitative predictions on future exposure and harm. RESULTS: Overall, alcohol consumption has a large impact on burden of disease and mortality in African countries. Alcohol-attributable disease burden is more important when the impact of alcohol consumption on the incidence and course of HIV/AIDS is taken into account, with alcohol being responsible, in 2012, for 6.4% of all deaths and 4.7% of all DALYs lost in the African region. Alcohol exposure is expected to increase in the next years, and thus alcohol-attributable fractions. CONCLUSIONS: The weight of new evidence, especially of alcohol's role in the incidence and course of HIV/AIDS, is particularly relevant to African countries and points to the need for a strong policy response to reduce the alcohol-related burden of disease on the continent.

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 score0.005
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.082
GPT teacher head0.434
Teacher spread0.352 · 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

Citations91
Published2015
Admission routes1
Has abstractyes

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