Evaluation of Harm Reduction Programs on Seroincidence of HIV, Hepatitis B and C, and Syphilis Among Intravenous Drug Users in Southwest China
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the impact of a multifaceted harm reduction program by comparing seroincidence rates of HIV, hepatitis C virus (HCV), hepatitis B virus (HBV), and syphilis before and after implementation of harm reduction strategies among intravenous drug users (IDUs) in a drug-trafficking city in Southwest China. DESIGN: This is a prospective cohort study with 24 months of follow-up. METHODS: Two prospective cohorts (cohort 2002-2004 and cohort 2006-2008) were followed up every 6 months for seroconversions of HIV, HCV, and syphilis antibodies and HBV surface antigen. RESULTS: After implementation of harm reduction strategies in Xichang city, Sichuan province, the HIV incidence rate among IDUs significantly dropped from 2.5 to 0.6 cases per 100 person-years. Subanalyses also indicated that the incidence rate of HBV significantly declined from 14.2 to 8.8 cases per 100 person-years. No significant changes in the seroincidence rates of HCV or syphilis were detected after implementation of IDU harm reduction strategies. CONCLUSIONS: Harm reduction strategies may help reduce the high incidence of certain blood-borne infectious diseases and sexual transmitted diseases among high-risk IDUs in southwest China. Additional research is needed on the implementation and evaluation of harm reduction strategies in China.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".