The Prevalence and Risk Factors of Hepatitis B, Hepatitis C, and Hepatitis D Coinfection in Iran’s General Population Over the Past 25 Years: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Concurrent HBV, HCV, and HDV infections pose a global health challenge, worsening disease, treatment, and patient outcomes, and burdening healthcare. In Iran, no comprehensive review has assessed HBV/HCV and HBV/HDV coinfection rates. This study conducted a systematic review and meta-analysis per PRISMA 2020 guidelines. The study was prospectively registered in PROSPERO (CRD420251007210). A comprehensive search was conducted across international (PubMed, Scopus, Embase, Web of Science, Cochrane Library) and Iranian databases (SID, Magiran), supplemented by Google Scholar, for studies published between January 2000 and March 2025. Eligible studies reported laboratory-confirmed cases of HBV, HCV, and/or HDV coinfections using ELISA, PCR, or real-time PCR. Because HDV replication depends on hepatitis B surface antigen (HBsAg), biologically independent HCV/HDV coinfection cannot occur. Consequently, studies that reported HCV/HDV coinfection without HBV were excluded. Two researchers independently conducted screening and data extraction and assessed study quality using the Newcastle-Ottawa Scale (NOS). To account for variability across studies, a random-effects model was used to estimate the pooled prevalence and its 95% confidence interval. Our analysis included 99 studies, encompassing more than 182,000 participants from regions of Iran. The pooled prevalence rates were 3% for HBV/HCV coinfection, 7% for HBV/HDV coinfection, and 1% for triple HBV/HCV/HDV infection. Due to significant heterogeneity across studies, random-effects models were used to obtain combined estimates. Substantial heterogeneity (I² up to 98%) was observed, attributable to variations in study populations, geographic regions, and diagnostic methods, as confirmed by sensitivity analyses and meta-regression. Publication bias was evident in most analyses. Key risk factors included blood transfusions, injection drug use, incarceration, and chronic liver disease. These findings underscore the urgent need for tailored prevention and surveillance programs. The high prevalence of coinfections in Iran, coupled with marked regional and population-based disparities, calls for standardized diagnostic protocols and targeted interventions that address behavioral and healthcare-associated risk factors.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".