Global burden of tuberculosis and lower respiratory infections attributable to alcohol consumption in 2004
Bibliographic record
Abstract
Shield, K. D., Samokhvalov, A. V. & Rehm, J. (2013). Global burden of tuberculosis and lower respiratory infections attributable to alcohol consumption in 2004. International Journal of Alcohol and Drug Research, 2(1), 11-18. doi: 10.7895/ijadr.v2i1.49 (http://dx.doi.org/10.7895/ijadr.v2i1.49)Aim: To quantify the extent to which alcohol contributes to the global burden of tuberculosis (TB) and lower respiratory infections (LRI).Design: TB and LRI deaths and disability-adjusted life years (DALYs) lost due to alcohol consumption were calculated from alcohol-attributable fractions (AAFs) using various data sources.Measures: Deaths and DALYs lost were obtained from the World Health Organization (2004 revision of the Global Burden of Disease study). Alcohol consumption indicators were obtained from the ongoing Comparative Risk Assessment study. Relative risks were obtained from meta-analyses, and confidence intervals (CIs) for the AAFs were obtained using Monte Carlo simulations.Findings: In 2004 alcohol was responsible for 381,000 deaths (95% CI: 209,000–560,000), 215,000 from TB (95% CI: 135,000–295,000) and 167,000 from LRI (95% CI: 74,000–264,000); and 6,101,000 DALYs lost (95% CI: 3,463,000–8,777,000), 4,581,000 due to TB (95% CI: 2,835,000 to 6,326,000) and 1,152,000 due to LRI (95%CI: 954,000–566,000). This represents 0.65% of all deaths (95% CI: 0.36%–0.95%) and 0.40% of all DALYs lost (95% CI: 0.23%–0.23%) for people aged 15 years and older.Conclusions: The global burden of alcohol-attributable TB and LRI is substantial, and significant attention should be paid to monitoring it. Future research should focus on quantifying alcohol’s role in (1) the risk for infection, (2) disease progression, and (3) adherence to medication regimens, in order to ensure accurate descriptions of the resulting global burden attributable to alcohol consumption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".