Alcohol consumption as a risk factor for tuberculosis: meta-analyses and burden of disease
Bibliographic record
Abstract
Meta-analyses of alcohol use, alcohol dosage and alcohol-related problems as risk factors for tuberculosis incidence were undertaken. The global alcohol-attributable tuberculosis burden of disease was also re-estimated. Systematic searches were conducted, reference lists were reviewed and expert consultations were held to identify studies. Cohort and case-control studies were included if there were no temporal violations of exposure and outcome. Risk relations (RRs) were pooled by using categorical and dose-response meta-analyses. The alcohol-attributable tuberculosis burden of disease was estimated by using alcohol-attributable fractions. 36 of 1108 studies were included. RRs for alcohol use and alcohol-related problems were 1.35 (95% CI 1.09–1.68; I 2 : 83%) and 3.33 (95% CI 2.14–5.19; 87%), respectively. Concerning alcohol dosage, tuberculosis risk rose as ethanol intake increased, with evidence of a threshold effect. Alcohol consumption caused 22.02 incident cases (95% CI 19.70–40.77) and 2.35 deaths (95% CI 2.05–4.79) per 100 000 people from tuberculosis in 2014. Alcohol-attributable tuberculosis incidence increased between 2000 and 2014 in most high tuberculosis burden countries, whereas mortality decreased. Alcohol consumption was associated with an increased risk of tuberculosis in all meta-analyses. It was consequently a major contributor to the tuberculosis burden of disease.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.068 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".