Impact of the COVID-19 pandemic on hepatitis B and C elimination: An EASL survey
Bibliographic record
Abstract
Background & Aims The World Health Organization (WHO) HBV and HCV elimination targets, set in 2016 and based on projections to 2030, were unable to consider the impact of intervening factors. To evaluate the impact of the COVID-19 pandemic on viral hepatitis elimination programs, the European Association for the Study of the Liver (EASL) conducted a survey in liver centers worldwide in 2021. Methods A web-based questionnaire was distributed (May-July 2021) to all EASL members representing clinical units providing HBV and HCV hepatitis care. Results are expressed as absolute numbers and reduction rates for each care activity. Results Data were collected from 32 European and 12 non-European clinical centers. Between January 2019 (pre-pandemic) and December 2020 (during the pandemic), chronic HBV consultations decreased by 32% and 26%, new referrals by 38% and 39%, HBV testing rates by 39% and 21% (for HBsAg detection) and 30% and 22% (for HBV DNA detection), and new HBV treatments by 20% and 44% ( p = 0.328) in European and non-European centers, respectively. With regard to HCV during the same time frame, the overall reductions were 39% and 50% for consultations, 49% and 49% for new referrals, 11% and 38% for HCV RNA detection, and 51% and 54% for new HCV antiviral treatments for European and non-European Centers, respectively ( p = 0.071). Conclusions All steps in the viral hepatitis care cascade have been hampered by the COVID-19 pandemic, with a comparable impact across different centers. These data reaffirm the pandemic's major effect on global viral hepatitis elimination programs and suggest that actions to achieve the WHO 2030 targets should be reconsidered and revised to account for each country's progress relative to pre-pandemic values. Lay summary The EASL multinational survey conclusively shows that viral hepatitis elimination programs, expected to provide control of hepatitis B and hepatitis C worldwide by 2030, have been held back by the COVID-19 pandemic in clinical centers from several European and non-European countries, with a comparable impact across centers. Limitations in the cascade of care for both HBV and HCV were linked to limited access to screening, consultations, specific testing, and actual treatment. As restrictions for COVID-19 begin to lift, efforts to diagnose and provide treatment for viral hepatitis should remain high on the list of priorities for public health officials to maintain the WHO elimination efforts. Measures that have been put in place to control the COVID-19 pandemic could be transferred to increasing the diagnosis and linkage to care of people with hepatitis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".