Hepatitis C virus infection and tobacco smoking - joint health effects and implications for treatment of both: A systematic review
Bibliographic record
Abstract
Summary Background Tobacco smoking and hepatitis C virus (HCV) infection cause many diseases independently. The interaction of these conditions on health effects has not been widely studied. There is a paucity of information on addressing tobacco smoking in HCV treatment settings. This review examines the relationship between tobacco smoking and HCV infection and health outcomes and discusses opportunities for treating both conditions. Methods A systematic review was conducted following the PRISMA 2009 guidelines (Registration No.: CRD42019127771) . We searched PubMed, EMBASE, Web of Science, and CINAHL on the health effects of tobacco smoking and HCV infection using keywords and MeSH terms for hepatitis C, tobacco smoking, hepatocellular carcinoma (HCC), chronic obstructive pulmonary disease (COPD), diabetes mellitus (DM), cardiovascular diseases (CVD), and chronic kidney disease (CKD). We used the Newcastle-Ottawa Scale, a measurement tool to assess systematic reviews (AMSTAR-2), and international narrative systematic assessment (INSA) tools to assess the methodological quality of the included studies. Findings Tobacco smoking and HCV infection share similar underlying risk factors and hence it is unsurprising that tobacco smoking prevalence is higher in people living with HCV (PLHCV) than in the general population. Tobacco smoking and HCV infection have additive or multiplicative interaction to cause HCC, COPD, DM, CVD, and CKD. Anti-HCV direct-acting antiviral (DAA) treatment is highly efficacious and widely accessible in many countries, but untreated tobacco smoking addiction may undermine the achievement of optimal health outcomes possible from HCV treatment. Interpretation The scale-up of DAA treatment programs globally is an opportunity to address the high prevalence of tobacco smoking in PLHCV by concurrently offering tobacco smoking cessation treatment. Simultaneous initiation of smoking cessation therapy at HCV treatment centres is likely to be cost-effective at maximizing the health gains afforded by DAA treatment. Studies are needed to evaluate the effect of tobacco smoking cessation on the sustained virologic response in DAA treated patients.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".