Molecular epidemiology of hepatitis C virus infection among people who inject drugs
Bibliographic record
Abstract
Background: While the factors associated with acquisition of hepatitis C virus (HCV) among people who inject drugs (PWID) are well described, an enhanced understanding of HCV transmission is required. Aims: The broad aim of this research was to investigate the molecular epidemiology of HCV infection among PWID. Specific aims included the evaluation of HCV genome sequencing in the literature; evaluation of the degree of variability in the design of HCV sequencing reactions; to investigate phylogenetic clustering of HCV and associated factors among participants in a prospective cohort of PWID in Vancouver, Canada; and to investigate whether HCV infection among younger injectors occurs as a result of few or many transmission events from older injectors to younger injectors among PWID in Vancouver, Canada. Methods: In Chapter Two, data from a systematic review of HCV genome sequencing in the literature were analysed, including production of a comprehensive database containing primers from each accepted manuscript. In Chapter Three, data from the Vancouver Injection Drug User Study (VIDUS) were analysed, using maximum likelihood phylogenetic and logistic regression methods. In Chapter Four, data from the VIDUS and At-Risk Youth Study (ARYS) were analysed, using Bayesian phylogenetic inference, compartmentalisation (Association Index) and logistic regression methods. Key Finding: From the review of HCV sequencing, there was great heterogeneity in the positioning of population sequencing amplicons in studies performed to date. Phylogenetic clustering was common in the VIDUS cohort of PWID in Vancouver, and was independently associated with recent HCV seroconversion, HIV co-infection, younger age and recent syringe borrowing. The combined analysis of VIDUS and ARYS demonstrates that HCV transmission among PWID is complex and multifaceted, with transmission occurring both between and within older and younger PWID. Conclusion: Standardisation of HCV sequencing methodologies will strengthen future HCV virological research and improve collaboration across HCV research disciplines. In the era of highly effective direct acting antivirals, targeted intervention and public health strategies will rely on evidence-based assessment of HCV transmission and acquisition to maximise outcomes. Further research should focus on the integration of phylogenetic, clinical, epidemiological and network analyses to best inform treatment as prevention and intervention studies.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".