Transmission Patterns of HIV and Hepatitis C Virus among Networks of People Who Inject Drugs
Bibliographic record
Abstract
BACKGROUND: The risk-related behaviours and practices associated with injection drug use remain a driver of HIV and hepatitis C virus (HCV) transmission throughout the world. Here we evaluated HIV and HCV transmission patterns in the context of social networks of injection drug users (IDU) recruited from a higher incidence region in order to better understand factors that contribute to ongoing transmission among IDU. METHODS: IDU recruited through a chain-referral method provided biological specimens for analysis. HIV and HCV positive specimens were sequenced and analyzed using phylogenetic methods (Neighbour-joining and bayesian) and transmission patterns of HIV and HCV evaluated in the context of the recruitment networks. RESULTS: Among the 407 recruited IDU, HCV and HIV prevalence were 60.6% and 10.1%, respectively; 98% of HIV positive individuals were co-infected with HCV. Thirty-six percent of HCV sequences were associated with clusters, compared to 67% of HIV sequences. Four (16.7%) of the 24 HCV clusters contained membership separated by 2 or fewer recruitment cycles, compared to 10 (41.6%) derived from more than one recruitment component. Two (28.6%) of the 7 HIV clusters contained membership separated by 2 or fewer recruitment cycles while 6 (85.7%) were composed of inter component membership. CONCLUSIONS: Few HIV and HCV transmissions coincided with the recruitment networks, suggesting that they occurred in a different social context or a context not captured by the recruitment network. However, among the complete cohort, a higher degree of HIV clustering indicates many are recent infections originating from within current social networks, whereas a larger proportion of HCV infections may have occurred earlier in injecting history and in the context of a different social environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".