Conflict-induced increase in viral hepatitis and human immunodeficiency virus infection among internally displaced persons: a retrospective study in Volodymyr community, Ukraine
Bibliographic record
Abstract
OBJECTIVE: Aim: To study the trends in the prevalence of hepatitis B (HBV), hepatitis C (HCV), and human immunodeficiency virus (HIV) infection during 2020-2024 in the rear region of Ukraine, in the city of Volodymyr, and assess the impact of internal displacement on the spread of infectious diseases. PATIENTS AND METHODS: Materials and Methods: A retrospective analysis in Volodymyr Hospital (Volodymyr, Ukraine) was conducted using data from the official clinical registry and ELISA (enzyme-linked immunosorbent assay) for 2020-2024. Comparative literature and statistical data from infection prevention programmes were also analyzed. RESULTS: Results: The decline in officially reported new HIV infections (from 29 in 2020 to 7 in 2024) contrasted with a marked increase in the number of ELISA-tested cases of hepatitis B and C (7,974 and 6,705 in 2024, respectively). Internally displaced persons (IDPs) accounted for a significant proportion of new diagnoses after 2022, including 22 HBV and 17 HCV cases in 2022. The discrepancy between laboratory results and official reporting suggests underdiagnosis. Additionally, Poisson regression analysis demonstrated that IDP status was a significant factor for increased risk of blood-borne infections. CONCLUSION: Conclusions: Military conflict and population displacement significantly exacerbate the problem of blood-borne infections. Countries with successful infection control systems, such as Canada and the Netherlands, have implemented universal HBV vaccination, national HCV elimination strategies, and mobile care for vulnerable populations. These examples highlight the need to replicate such models in conflict-affected regions. National plans should be based on international recommendations, such as the WHO Global Health Strategy 2022 and the US Centers for Disease Control and Prevention's viral hepatitis elimination roadmap.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".