Impact of COVID-19 Pandemic on Testing for Hepatitis B in British Columbia, Canada: An Interrupted Time Series Analysis
Bibliographic record
Abstract
Background: Previous research suggests the COVID-19 pandemic was associated with reductions in HBV testing early in the pandemic. However, impacts of the pandemic on HBV testing in the longer-term and among people who inject drugs (PWID) are unclear. We investigated the impact of the pandemic and related policies on HBV testing from 2020 to 2022, including among PWID, in British Columbia (BC), Canada. Methods: Using population data from the BC COVID-19 Cohort, we conducted interrupted time series analyses of HBV surface antigen (HBsAg), HBV DNA, and HBV e-antigen (HBeAg) testing. The study included a prepandemic period (January 2017-February 2020), a transition period (March-May 2020), and pandemic periods in 2020 (June-December), 2021, and 2022. Results: HBsAg testing decreased by 16.5% (95% CI 13.9-18.9) and HBV DNA testing decreased by 11.6% (95% CI 9.5-13.6) in June-December 2020 relative to predicted levels, and testing remained lower than predicted throughout 2021 and 2022. Percentage reductions in HBV DNA testing were greater for PWID compared with non-PWID in 2020 (30.0% vs 11.2%) and thereafter. Changes in HBeAg testing overall were less pronounced but varied by sex and age. Conclusions: The pandemic and related policies were associated with decreases in HBsAg and HBV DNA testing in 2020, and testing remained lower than predicted throughout 2021 and 2022. Additional efforts to increase HBV testing are needed, including strategies to ensure linkage to care for PWID.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".