Associations between the prevalence of chronic hepatitis B among people who inject drugs and country‐level characteristics: An ecological analysis
Bibliographic record
Abstract
INTRODUCTION: Globally, hepatitis B virus (HBV) is a leading cause of liver disease. People who inject drugs (PWID) are at greater risk than the general population of contracting HBV. This risk could depend on societal factors in different countries. We investigated the associations between country-level chronic HBV prevalence in PWID with national indicators of development and prevalence of HIV and hepatitis C virus (HCV). METHODS: We used global systematic review data on chronic HBV prevalence (hepatitis B surface antigen-positive) among PWID and country-level sociodemographic characteristics from online databases. National random-effects meta-analysis estimates of HBV prevalence were the outcome in linear regression models testing for associations with country-level characteristics. RESULTS: The study included 131,710 PWID from 304 estimates in 55 countries: the pooled HBV prevalence among PWID in the countries analysed was 4.5% (95% CI 3.9-5.1), the highest regional pooled prevalence was in East and Southeast Asia (17.6% [13.3-22.3]), and the lowest was in Western Europe (1.7% [1.4-2.1]). In multivariable models, no indicators of development were associated with HBV prevalence, but there was evidence of positive associations between HBV prevalence in the general population and among PWID, and evidence of HIV and HCV prevalence in PWID being associated with HBV prevalence in PWID: multivariable coefficients 0.03 (95% CI 0.01-0.04); p < 0.001, and 0.01 (95% CI 0.00-0.03); p = 0.01, respectively. DISCUSSION AND CONCLUSIONS: HBV prevalence among PWID was associated with HIV and HCV prevalence among PWID and background HBV prevalence in the general population, highlighting the need for improving harm reduction in PWID and implementation of HBV vaccination, especially where HBV is endemic.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".