Prevalence of hepatitis C virus infection and its correlates in a rural area of southwestern China: a community-based cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: Hepatitis C virus (HCV) infection is a major public health problem in southwestern China. Our aim of the study was to assess the prevalence of HCV infection and its correlates in the Yi population of this region. METHODS: A community-based survey was conducted to investigate sociodemographic characteristics and other associated factors for HCV infection in a rural area of southwestern China. Blood samples were collected and tested for antibodies to HCV. Anti-HCV positive samples were further assessed for HCV RNA. RESULTS: 14 years were included in our analysis. Of them, 2.8% (95% CI 2.2% to 3.5%) were positive for HCV antibody. Multiple logistic regression analysis revealed that sex (male vs female: adjusted OR (aOR)=3.30, 95% CI 1.80 to 6.07), marital status (unmarried vs married: aOR=0.27, 95% CI 0.09 to 0.80), ever using injection drug (aOR=28.65, 95% CI 15.9 to 51.64) and ever having blood transfusion (aOR=7.64, 95% CI 1.94 to 30.16) were significantly associated with HCV infection (indicated by positive HCV antibody). Stratified analysis by HIV infection found that in HIV-negative individuals, sex (male vs female: aOR=3.84, 95% CI 1.88 to 7.85), ever using injection drug (aOR=22.15, 95% CI 8.45 to 58.04), having multiple sexual partners (aOR=2.57, 95% CI 1.26 to 5.23), and ever having blood transfusion (aOR=16.54, 95% CI 4.44 to 61.58) were significantly associated with HCV infection and in HIV-positive individuals, ever using injection drug (aOR=8.96, 95% CI 3.16 to 25.38) was associated with HCV infection. CONCLUSION: The data suggested a higher risk of HCV infection in this area when compared with the rest of China and some unique associated factors. Rapid scale-up of targeted interventions are needed to prevent further transmission and consequent morbidities.
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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".