Global disparities in access to hepatitis C medicines before and during the early phase of the COVID-19 pandemic: an ARIMA-based interrupted time series analysis
Bibliographic record
Abstract
Background: The introduction of direct-acting antivirals (DAAs) has allowed countries to reduce the health and economic burden of hepatitis C virus (HCV). However, access to DAAs remains expensive and limited in many countries globally due to wide disparities in HCV drug pricing. We assessed how global use of HCV drugs has changed over time and the effect that COVID-19 might have had on DAA utilisation. Methods: We assessed longitudinal changes in DAA sales by country income group, geographical region and drug type. We also conducted an interrupted time series analysis to assess COVID-19-related changes in the trend of DAA units sold globally. Our analysis used DAA sales data from the IQVIA multinational integrated data analysis database of 52 countries and two regions and HCV prevalence data from Polaris from 2014 to 2020. Our primary outcome was the monthly rate of DAAs sold per 100 000 people living with HCV per country, country income group and geographic region. We then compared the pre-post change in DAA units by drug type and country income group. We fitted autoregressive moving average models with a ramp function to assess the impact of COVID-19 on monthly DAA units sold. Results: Across all countries, from August 2014 to August 2020, a monthly average of 44 219 DAA units per 100 000 HCV cases was sold. High-income countries purchased more units than other groups. In terms of geographic location, North America (124 144 per 100 000 HCV cases) and Europe (81 001 per 100 000 HCV cases) had the highest DAA sales over time; the newer generation of combination DAAs was mainly used in high-income countries. In contrast, first-generation and second-generation DAAs were the predominant types of DAAs sold in lower middle-income countries (LMICs). The pre-post analysis showed a 23% (p<0.001) average decrease in global sales of DAAs during the first phase of COVID-19. The decrease in LMICs (69%, p<0.001) was approximately double that of high-income countries (33%, p<0.001), while upper middle-income countries (UMICs) had a 34% (p<0.001) increase in DAA sales. The pandemic was associated with an immediate and sustained decrease of 9263 units per month (95% CI -14 668 to -3857.46) in high-income countries, a 73.14 (-850.96 to 997.24) unit increase in UMICs and a 742.58 (95% CI -5505.91 to 4020.75) unit decrease in LMICs. Conclusion: Our study showed uneven access to DAAs globally, with higher prevalence-adjusted utilisation in high-income countries compared with lower-income countries. Our study also found that the COVID-19 pandemic has significantly decreased DAA sales in many countries. To counter these trends, additional strategies, such as price reductions, increased competition among manufacturers and licensing agreements, may help to improve access and utilisation of DAAs globally.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".