Forecasted impacts of a sofosbuvir-based national hepatitis C treatment programme on Egypt's hepatocellular cancer epidemic: simulation of alternatives

Abstract

Background: Egypt is experiencing a hepatocellular cancer (HCC) epidemic due to widespread hepatitis C virus (HCV) transmission. The use of sofosbuvir-related therapies producing improved treatment success has permitted an updated, nationwide, HCV treatment programme with expanded coverage. This study simulated the multidecade impacts of the new treatment programme on hepatitis and HCC.

Methods: A Markov model of HCV infection and treatment analysed the HCV-related HCC epidemic between 2009 and 2050, using parameters based on peer-reviewed studies and expert opinion. Comparing the 'new' and 'old' scenarios, and with the old treatment programme being replaced or not by the new programme in 2015, the annual number, prevalence and incidence of HCC were simulated for representative Egypt populations including HCV-infected patients aged 15-59 years in 2008, healthy people aged 5-59 years in 2008 and 5-year-old children cohorts entering the population each year beginning in 2009. Averted HCC cases were calculated, and sensitivity analyses were performed.

Results: Compared with the old scenario, the estimated number, prevalence and incidence of future HCC cases in the new scenario would peak earlier and at lower levels in 2025 (~29 000), 2023 (~28/100 000) and 2022 (~14/100 000), respectively. The new treatment programme is estimated to avert ~956 000 HCC cases between 2015 and 2050.

Discussion: By reducing cancer cases and shortening the peak epidemic period, the new programme should substantially diminish the HCC epidemic across Egypt. Our timeline forecast for Egypt's HCC epidemic, and evaluation of various disease and programme components, should be useful to other countries that are developing policies to address HCV-related liver cancer prevention.

Keywords: HCV; Liver cancer; disease burden; hepatitis C; hepatocellular cancer; simulation.

Figure 1

Markov model of HCV–HCC disease process. The process of how initially healthy people become chronically infected with HCV, go through fibrosis stages (F0–F3) to cirrhosis (F4), and eventually to HCC. Untreated patients can be successfully treated and then might become reinfected, or they could be the patients for whom the treatment fails (F). Dashed line indicates the newly included patients with HCV who are qualified for treatment in the new treatment programme. HCC, hepatocellular carcinoma; HCV, hepatitis C virus.

Figure 2

Change in number of HCV-infected patients in different stages in both scenarios. The change in number of patients with HCV in all stages in the future decades, under the old scenario (A) where the old programme is maintained and under the new scenario (B) where the new programme was initiated in 2015. HCC, hepatocellular carcinoma; HCV, hepatitis C virus.

Figure 3

Projected case number, prevalence and incidence of cirrhosis and HCC in both scenarios. The simulated number of cirrhosis cases (A) and HCC cases (B), the prevalence of cirrhosis (C) and HCC (D), as well as the incidence of cirrhosis (E) and HCC (F). For the new scenario with the new treatment programme using sofosbuvir-related therapies, high (97.5%) and low (92.5%) overall treatment success rates were assumed in order to evaluate uncertainty in the efficacy of this treatment protocol.

Figure 4

Influence on the impact of new treatment programme from single programme parameters. Changes in the forecasted number of averted cases of cirrhosis (A) and of HCC (B) that would occur as the result of differences in key input parameters under the old and new treatment scenarios. The number of averted cases refers to the difference in future cases under the scenario where the old treatment programme was maintained and that in the scenario with the new treatment programme being implemented, that is, the number of cases avoided by the implement of the new programme. Light grey bars indicate the direction and magnitude of change of the number of averted cases when given the input parameter as its maximum tested value, whereas the dark grey bars indicate the direction and magnitude of change of the number of averted cases when given the input parameter as its minimum tested value. HCC, hepatocellular carcinoma; HCV, hepatitis C virus.

Figure 5

Influence on the impact of new treatment programme from multiple program parameters. Changes in the forecasted number of averted cases of cirrhosis (A–C) and of HCC (D–F) in relation to the variation of key input parameters under the old and new treatment scenarios were presented through 1000 PSA simulation results. HCC, hepatocellular carcinoma; PSA, probabilistic sensitivity analysis.