Travel-related hepatitis E: a two-decade GeoSentinel analysis
Bibliographic record
Abstract
BACKGROUND: Hepatitis E virus (HEV) is widely distributed worldwide and is endemic in developing countries. Travel-related HEV infection has been reported at national levels, but global data are missing. Moreover, the global availability of HEV diagnostic testing has not been explored so far. The aim of this study is to describe the epidemiology of HEV infections in returning travellers and availability of HEV diagnostic testing in the GeoSentinel surveillance network. METHODS: This was a multicentre retrospective cross-sectional study. All confirmed and probable HEV travel-related infections reported in the GeoSentinel Network between 1999 and 2018 were evaluated. GeoSentinel sites were asked to complete a survey in 2018 to assess the availability and accessibility of HEV diagnostic procedures (i.e. serology and molecular tests) throughout the study period. RESULTS: Overall, 165 travel-related HEV infections were reported, mainly since 2010 (60%) and in tourists (50%). Travellers were exposed to hepatitis E in 27 countries; most travellers (62%) were exposed to HEV in South Asia. One patient was pregnant at the time of HEV infection and 14 had a concomitant gastrointestinal infection. No deaths were reported. In the 51% of patients with information available, there was no pre-travel consultation. Among 44 GeoSentinel sites that responded to the survey, 73% have access to HEV serology at a local level, while 55% could perform (at a local or central level) molecular diagnostics. CONCLUSION: Reported access to HEV diagnostic testing is suboptimal among sites that responded to the survey; this could negatively affect diagnosing HEV. Pre-travel consultations before travel to South Asia and other low-income and high-prevalence areas with a focus on food and water precautions could be helpful in preventing hepatitis E infection. Improved HEV diagnostic capacity should be implemented to prevent and correctly diagnose travel-related HEV infection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.001 | 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".