Global Utilization Trends of Direct Acting Antivirals (DAAs) during the COVID-19 Pandemic: A Time Series Analysis
Bibliographic record
Abstract
The 2019 novel coronavirus (COVID-19) pandemic has placed a significant strain on hepatitis programs and interventions (screening, diagnosis, and treatment) at a critical moment in the context of hepatitis C virus (HCV) elimination. We sought to quantify changes in Direct Acting Antiviral (DAA) utilization among different countries during the pandemic. We conducted a cross-sectional time series analysis between 1 September 2018 and 31 August 2020, using the IQVIA MIDAS database, which contains DAA purchase data for 54 countries. We examined the percent change in DAA units dispensed (e.g., pills and capsules) from March to August 2019 to the same period of time in 2020 across the 54 countries. Interrupted time-series analysis was used to examine the impact of COVID-19 on monthly rates of DAA utilization across each of the major developed economies (G7 nations). Overall, 46 of 54 (85%) jurisdictions experienced a decline in DAA utilization during the pandemic, with an average of −43% (range: −1% in Finland to −93% in Brazil). All high HCV prevalence (HCV prevalence > 2%) countries in the database experienced a decline in utilization, average −49% (range: −17% in Kazakhstan to −90% in Egypt). Across the G7 nations, we also observed a decreased trend in DAA utilization during the early months of the pandemic, with significant declines (p < 0.01) for Canada, Germany, the United Kingdom, and the United States of America. The global response to COVID-19 led to a large decrease in DAA utilization globally. Deliberate efforts to counteract the impact of COVID-19 on treatment delivery are needed to support the goal of HCV elimination.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".