Seroprevalence of hepatitis B and C viruses in moderate and severe COVID-19 inpatients: A cross-sectional study at a referral center in Mexico
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: The emergence of SARS-CoV-2, which causes the coronavirus disease (COVID-19) has caused a great impact on healthcare systems worldwide, including hepatitis B and C viruses screening and elimination programs. The high number of COVID-19 hospitalizations represent a great opportunity to screen patients for hepatitis B virus (HBV) and hepatitis C virus (HCV), which was the aim of this study. MATERIAL AND METHODS: Cross-sectional, retrospective study performed between April 2020 and 20201 at a referral center in Mexico dedicated to the care of adults with severe/critical COVID-19. We retrieved clinical, demographic, and laboratory results from each patient´s medical records, including antibodies against HCV (anti-HCV), HBV surface antigen (HBsAg), antibodies against the HBV core antigen (anti-HBcAg), and antibodies against HBsAg (anti-HBsAg). RESULTS: Out of 3620 patients that were admitted to the hospital, 24 (0.66%), 4 (0.11%), and 72 (1.99%) tested positive for anti-HCV, HBsAg, and anti-HBcAg, respectively. Of all seronegative patients, 954 (27%) had undetectable anti-HBsAg and 401 (12%) had anti-HBsAg at protective levels. Blood transfusion was the most relevant risk factor. Only 9.7% of the anti-HBc positive, 25% of the HBsAg positive, and 52% of the anti-HCV positive were aware of their serological status. CONCLUSIONS: In this study we found a prevalence of anti-HCV of 0.66%, HBsAg in 0.11%, and isolated anti-HBcAg in 1.99%. We also found that HBV vaccination coverage has been suboptimal and needs to be reinforced. This study gave us a trustworthy insight of the actual seroprevalence in Mexico, which can help provide feedback to the Hepatitis National Elimination Plan.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".