Factors Correlated With Hepatitis C and B Virus Infections Among Injecting Drug Users in Tehran, IR Iran
Bibliographic record
Abstract
BACKGROUND: In Iran, the number of injecting drug users (IDUs) has increased in recent years. The rates of hepatitis C virus (HCV) and hepatitis B virus (HBV) infections among IDUs are reportedly high. OBJECTIVES: The purpose of this study was to assess factors correlated with HCV and HBV infections among IDUs in Tehran. PATIENTS AND METHODS: A cross-sectional study included 899 IDUs recruited from the community, drug treatment centers, and drop-in-centers. The study involved interviews conducted using an adapted version of the WHO Drug Injection Study Phase II (Version 2b) questionnaire and blood testing for the HCV antibody, hepatitis B surface antigen, and hepatitis B core antibody. A logistic regression model was used to identify independent factors correlated with HCV and HBV infections. RESULTS: HCV infection was found to be primarily associated with female gender [odds ratio (OR) 5.0, 95% confidence interval (CI) 2.0-10.0)], unmarried status (OR 2.9, 95% CI 1.9-4.4), drug use for more than 10 years (OR 2.7, 95% CI 1.8-3.9), drug injection frequency of more than once per day (OR 2.6, 95% CI 1.6-4.2), history of imprisonment (OR 2.5, 95% CI 1.6-4.0)], and a history of shared injection needles in prison (OR 2.3, 95% CI 1.5-3.6). HBV infection was mainly correlated with a history of imprisonment (OR 1.9, 95% CI 1.4-2.7) and drug use for more than 10 years (OR 1.4, 95% CI 1.1-1.9). CONCLUSIONS: Because a considerable number of IDUs in Iran are receiving reduction services, tailoring services for prevention of hepatitis infection are necessary.
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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 | 0.001 | 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".