Global Prevalence of Tobacco Smoking in People Living with Hepatitis C - Implications for Maximizing the Health Benefits from Antiviral Therapy: A Meta-Analysis
Bibliographic record
Abstract
Abstract Long-term antiviral treatment outcomes of hepatitis C infection may be affected by tobacco smoking. This study determined the prevalence of tobacco smoking in people living with hepatitis C virus (PLHCV) in low-and middle-income (LMICs) and high-income countries (HICs). We searched PubMed, EMBASE, PsycINFO, and ProQuest for studies published between January 1, 2008, and August 31, 2018. The quality of included studies was assessed using the Newcastle-Ottawa Scale. We performed meta-analysis using the Freeman-Tukey double arcsine transformation. We used Egger’s test to check for publication bias and performed meta-regression to identify individual-level sources of heterogeneity. The prevalence of tobacco smoking in PLHCV was 53.0%; it was 58% in LMICs and 52.0% in HICs. In subgroup analysis, the prevalence of tobacco smoking in PLHCV from clinic-based studies was 51% (95%CI 45%-57%) and it was 61% (95%CI 48%-73%) in community-based studies. In the multivariable meta-regression, study setting (coefficient=0.19, p<0.025) contributed significantly to the presence of heterogeneity between studies. Given the disproportionately high prevalence of tobacco smoking in PLHCV, addressing tobacco smoking in HCV treatment settings is recommended to maximise health benefits from antiviral therapy. That is particularly important in LMICs, where the burden of both tobacco smoking and HCV infection is growing.
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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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.078 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 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".