Falling treatment uptake in the hepatitis C care cascade is a growing threat to achieving elimination

Abstract

Most high-income countries are not on track to achieve the World Health Organization hepatitis C elimination targets. As elimination programmes assess growing proportions of patients in community-based pathways, rates of treatment uptake may fall. We aimed to identify factors associated with DAA treatment uptake and measure changes in their prevalence over time. We performed a time-to-treatment analysis on 2728 patients approved for hepatitis C Direct-Acting Antiviral treatment in the North Central London region between January 2016 and October 2019. We investigated the association between treatment uptake and factors including assessment/treatment setting (hospital, drug service or prison), patient age, gender, injection drug use, harmful alcohol use, cirrhosis status and previous treatment. The likelihood of treatment uptake was reduced by three independent risk factors. These included assessment setting: prison-based or drug-service pathways (aHR 0.29 or 0.81 vs. hospital outpatient pathway, 95% CI 0.21-0.40 and 0.70-0.94 respectively, p < .001); being UK-born (aHR 0.89 vs. non-UK born, 0.82-0.98, p = .01); and history of harmful alcohol use (aHR 0.84 vs. no history, 0.72-0.99, p = .04). The average number of these risk factors for not starting treatment per patient increased over time (R² = 0.66 p < .001). Independent of these, there was an additional 5% reduction in rate of treatment initiation in each successive year of the programme (aHR 0.95, 0.91-0.99, p = .02). In conclusion, disengagement from care before treatment uptake was found to be a growing threat to elimination. Despite provision of community-based test-to-cure pathways, there are persistent barriers to treatment uptake and these are increasing over time.

Keywords: antiviral agents; hepatitis C; intravenous; risk factors; substance abuse; time-to-treatment.

FIGURE 1

Progression to treatment uptake (or censor) from multidisciplinary team (MDT) approval by number of risk factors for disengagement. The numbers of patients in each group at 500, 1000 and 1500 days are shown in the underlying table. Those who had two or more risk factors (n = 477) had a 42% lower likelihood of treatment than those with no risk factors (n = 695) (HR 0.58, CI 0.51–0.65, p < .001, Cox regression).

FIGURE 2

Average prevalence of risk factors for disengagement before treatment by month of assessment. The average number of risk factors (+95% CI) that negatively impacted progression to treatment uptake (excluding month of programme, dialysis and genotype) increased by month of assessment since the beginning of the direct‐acting antiviral elimination programme (Pearson R ² = 0.66, p < .0001).

FIGURE 3

Healthcare system workload increases with delay between approval and treatment. Healthcare workload shown as number of recorded contacts between nurse specialists (triangle) and navigators (circle) and time between approval and treatment. Linear regression line shown is for all contacts, Spearman R = 0.6, p < .0001).