Seroepidemiology of Hepatitis C Among Drug Users at a Detoxification Center in Southeast China
Why this work is in the frame
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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 usersâ 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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 it