Estimating the prevalence of hepatitis delta virus infection among adults in the United States: A meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Suboptimal awareness and low rates of hepatitis delta virus (HDV) testing contribute to underdiagnosis and gaps in accurate estimates of U.S. HDV prevalence. We aim to provide an updated assessment of HDV prevalence in the U.S. using a comprehensive literature review and meta-analysis approach. METHODS: A comprehensive literature review of articles reporting HBsAg seroprevalence and anti-HDV prevalence was conducted to calculate country-specific rates and pooled prevalence of CHB and HDV using meta-analyses. Country-specific CHB and HDV rate estimates were combined with number of foreign-born (FB) persons in the U.S. in 2022 from U.S. Census Bureau to estimate total numbers of FB with CHB and HDV, respectively. These estimates were further combined with updated estimates of U.S.-born persons with CHB and HDV to yield the total number of persons with CHB and HDV. RESULTS: In 2022, we estimated 1.971 million (M) (95% CI 1.547-2.508) persons with CHB; 1.547 M (95% CI 1.264-1.831) were FB and 0.424 M (95% CI: 0.282-0.678) were U.S.-born. The weighted average HDV prevalence among FB persons in the U.S. was 4.20% (64 938 [95% CI 33055-97 392] persons), among whom 45% emigrated from Asia, 25% from Africa, and 14% from Europe. When combined with updated estimates of U.S.-born persons with HDV, we estimate 75 005 (95% CI: 42187-108 393) persons with HDV in the U.S. CONCLUSIONS: Including both FB and U.S.-born persons, we estimated that 1.971 M and 75 005 persons were living with CHB and HDV, respectively, in the U.S. in 2022.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.063 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".