The impact of the first, second and third waves of covid‐19 on hepatitis B and C testing in Ontario, Canada
Bibliographic record
Abstract
The COVID-19 pandemic interrupted routine healthcare services. Hepatitis B virus (HBV) and hepatitis C virus (HCV) infections are often asymptomatic, and therefore, screening and on/post-treatment monitoring are required. Our aim was to determine the effect of the first, second and third waves of the pandemic on HBV and HCV testing in Ontario, Canada. We extracted data from Public Health Ontario for HBV and HCV specimens from 1 January 2019 to 31 May 2021. Testing volumes were evaluated and stratified by age, sex and region. Changes in testing volumes were analysed by per cent and absolute change. Testing volumes decreased in April 2020 with the first wave of the pandemic and recovered to 72%-75% of prepandemic volumes by the end of the first wave. HBsAg testing decreased by 33%, 18% and 15%, and HBV DNA testing decreased by 37%, 27% and 20%, in each consecutive wave. Anti-HCV testing decreased by 35%, 21% and 19%, and HCV RNA testing decreased by 44%, 30% and 36% in each consecutive wave. These trends were consistent by age, region and sex. Prenatal HBV testing volumes were stable. In conclusion, significant decreases in HBV and HCV testing occurred during the first three waves of the pandemic and have not recovered. In addition to direct consequences on viral hepatitis elimination efforts, these data provide insight into the impacts of the pandemic on chronic disease screening and management. Strategies to make up for missed testing will be critical to avoid additional consequences of COVID-19 long after the pandemic has resolved.
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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 | 0.002 |
| 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".