Seroepidemiology of Hepatitis C Among Drug Users at a Detoxification Center in Southeast China
Bibliographic record
Abstract
Background: Hepatitis C virus (HCV) infection is prevalent worldwide, especially among drug users. The epidemiology of HCV is rarely reported among drug users in developing countries, including China. Objectives: We aimed to describe the seroepidemiology of HCV infection in drug users at a Detoxification Center in Southeast China. Methods: With approval from the Shantou Center for Disease Control, the archived data of drug users (n = 5,228) at the largest monitored-detoxification center in Shantou during 2011 - 2017 were analyzed for demographics, risk behaviors, and HCV serology. Results: Among HCV-tested drug users, 36.9% (1930/5228) were people who inject drugs (PWID). The mean annual HCV seroprevalence rate over the seven-year study period was 36.3% for all drug users, including 67.3% and 16.6% for PWID and non-PWID, respectively, with the highest prevalence (78.1%) in 2017 and the lowest prevalence (58.6%) in 2015 for PWID. Independent risk factors of HCV infection identified by multiple logistic regression analysis were engaging in unprotected sex (OR = 1.553, 95% CI = 1.078 - 2.236), injecting drugs (10.28, 8.98 - 11.763), and sharing needles/syringes (2.24, 1.129 - 4.445) for all drug users and sharing needles/syringes (2.062, 1.438 - 2.957) for PWID. Conclusions: This study reports the seroepidemiology of drug users in the monitored Detoxification Center in Southeast China. A relatively high HCV positivity rate, especially among PWID, their high-risk behaviors and low education, and lack of institutional interventions of HCV monitoring and transmission call for government-sponsored educational programs to raise drug users’ awareness of the risk of HCV infection and other co-infections and monitoring of the infectious status and treatment of HCV-infected drug users.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".