Significant Structural Breaks in the International Incidence of Hepatitis Delta Virus
Bibliographic record
Abstract
ABSTRACT Background & Aims The international incidence of Hepatitis Delta Virus (HDV) is challenging to accurately estimate due to limited testing and lack of active surveillance for this rare infectious disease. These limitations prevent the detection of low-level and/or geographically dispersed changes in the incidence of HDV diagnoses. A study was designed to enable international active tracking and analyses of HDV epidemiology by aggregating international HDV and Hepatitis B Virus (HBV) diagnoses datasets. Methods Publicly accessible datasets containing yearly incidence for HDV and HBV diagnoses were mined from government publications for Argentina, Australia, Austria, Brazil, Bulgaria, Canada, Finland, Germany, Macao, Netherlands, New Zealand, Norway, Sweden, Taiwan, Thailand, United Kingdom, and United States. The Bayesian Information Criterion (BIC) was used to determine the best-fitting breakpoint model for the number of breakpoints and the break dates identified using the determined model. Results Aggregated analysis of these HDV and HBV datasets spanning 1999-2020 identified structural breaks in the timeline of HDV incidence in 2002, 2012, and 2017. A significant increase in the international HDV incidence, relative to reported HBV diagnoses, occurred in 2013-2017. Secondary analysis identified four distinct temporal clusters of HDV incidence, including Cluster I (Macao, Taiwan), Cluster II (Argentina, Brazil, Germany, Thailand), Cluster III (Bulgaria, Netherlands, New Zealand, United Kingdom, United States), and Cluster IV (Australia, Austria, Canada, Finland, Norway, Sweden). Conclusion Re-evaluation of the testing paradigm for HDV in HBV-positive patients and an active surveillance status of HDV are warranted to define the etiology of the structural breaks in HDV incidence timelines.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".