The HepB Calculator

This calculator is intended to help determine the value of hepatitis B treatment in specific countries using country-specific information. Users enter in country-specific costs of disease and treatment and a desired time horizon. The calculator then simulates health and cost outcomes over that time horizon and presents overall results under scenarios with and without hepatitis B treatment. Users can then see how much treatment increases health for a representative person with hepatitis B. Users can also see how much the treatment costs over the time horizon (or if it ends up saving costs compared to no treatment).

More about this calculator on the Lancet: https://www.thelancet.com/journals/langas/article/PIIS2468-1253(19)30223-7/fulltext

The Hep B Calculator evaluates the cost-effectiveness of HBV treatment from a healthcare payer’s perspective. The tool uses methods compatible with the WHO CHOICE project¹, and as recommended by the Strategic Information and Modelling Reference Group of the WHO’s Global Hepatitis Programme².

Mathematical model

In the background, the Hep B Calculator runs a previously-validated mathematical model that simulates the life course of a cohort of hepatitis B patients, with and without antiviral therapy³. Treatment-naïve, chronic HBV, HBeAg-positive or HBeAg-negative patients eligible for treatment under international treatment guidelines enter the model either in the cirrhotic or non-cirrhotic health state. From the initial states patients in the model can transition to other states, response to treatment (viral suppression), loss of surface antigen, decompensated cirrhosis, hepatocellular carcinoma, and HBV related death (Figure 1). Age-specific disease progression^3-12 and treatment effectiveness^13-17 estimates govern these transitions in the Markov model. Other causes of death (background mortality) that are not related to liver disease are included in the model and are based on country-specific life expectancy from the WHO life tables. The Markov model calculates using a one-year time step, reported outcomes such as HBV-related deaths, compensated cirrhosis, decompensated cirrhosis, liver transplants, and hepatocellular carcinoma. We assigned quality-of-life weights for each liver-related health state derived from previous studies¹⁸ and aggregated the results into per-person QALYs .

 

The model compares the outcomes of two strategies – treatment with antiviral therapy versus no treatment, and returns, in real-time, the following outcomes for each strategy: the cumulative life-time incidences of compensated cirrhosis, decompensated cirrhosis, hepatocellular carcinoma, transplants, and HBV-related death, the total life-time healthcare costs (including the cost of antiviral treatment and of downstream events such as liver cancer), and QALYs. In addition, it calculates the ICERs of antiviral treatment versus no-treatment for different disease stages, and plots these as graphs to identify time duration after treatment (in years) when the net cost falls under zero to reach a point where treatment is cost-saving. The user-interactivity allows for real-time sensitivity analyses. For example, users can enter different values for local prices of antiviral therapy, and receive corresponding ICER results that can help them understand how differences in prices are likely to influence the time taken for investment in HBV treatment to become cost-effective or cost-saving. The outcomes of the Calculator can be printed or saved in an executive-summary style report.

 

Figure 1: Markov state transition model schematic showing the natural history of hepatitis B infection.

References:

1.     World Health Organization - Cost effectiveness and strategic planning (WHO-CHOICE). Retrieved from: http://www.who.int/choice/cost-effectiveness/generalized/en/ (last accesssed: Nov 20, 2018).

2.     World Health Organization – Viral Hepatitis Strategic Information and Modelling Reference Group: meeting report. Meeting report | 14–16 June 2016, WHO headquarters, Geneva, Switzerland. Available from: http://www.who.int/hepatitis/publications/strategic-information-modelling-meeting/en/ (last accessed: November 23, 2018). Geneva: WHO, 2016.

3.     Toy M, Hutton DW, So S. Population Health and Economic Impacts of Reaching Chronic Hepatitis B Diagnosis and Treatment Targets in the United States. Health Affairs 2018 Jul;37(7):1033-1040.

4.     Chu CM, Liaw YF. HBsAg seroclearance in asymptomatic carriers of high endemic areas: appreciably high rates during a long-term follow-up. Hepatology. 2007;45(5):1187-92. Epub 2007/04/28. doi: 10.1002/hep.21612. PubMed PMID: 17465003.

5.     Kanwal F, Gralnek IM, Martin P, Dulai GS, Farid M, Spiegel BM. Treatment alternatives for chronic hepatitis B virus infection: a cost-effectiveness analysis. Ann Intern Med. 2005;142(10):821-31. PubMed PMID: 15897532.

6.     Lin X, Robinson NJ, Thursz M, Rosenberg DM, Weild A, Pimenta JM, et al. Chronic hepatitis B virus infection in the Asia-Pacific region and Africa: review of disease progression. J Gastroenterol Hepatol. 2005;20(6):833-43. PubMed PMID: 15946129.

7.     Chen YC, Chu CM, Liaw YF. Age-specific prognosis following spontaneous hepatitis B e antigen seroconversion in chronic hepatitis B. Hepatology. 2010;51(2):435-44. Epub 2009/11/18. doi: 10.1002/hep.23348. PubMed PMID: 19918971.

8.     Yuen MF, Wong DK, Fung J, Ip P, But D, Hung I, et al. HBsAg Seroclearance in chronic hepatitis B in Asian patients: replicative level and risk of hepatocellular carcinoma. Gastroenterology. 2008;135(4):1192-9. Epub 2008/08/30. doi: 10.1053/j.gastro.2008.07.008. PubMed PMID: 18722377.

9.     Chu CM, Liaw YF. Incidence and risk factors of progression to cirrhosis in inactive carriers of hepatitis B virus. Am J Gastroenterol. 2009;104(7):1693-9. Epub 2009/05/21. doi: 10.1038/ajg.2009.187. PubMed PMID: 19455130.

10.  Chen CJ, Yang HI, Su J, Jen CL, You SL, Lu SN, et al. Risk of hepatocellular carcinoma across a biological gradient of serum hepatitis B virus DNA level. Jama. 2006;295(1):65-73. PubMed PMID: 16391218.

11.  Chen JD, Yang HI, Iloeje UH, You SL, Lu SN, Wang LY, et al. Carriers of inactive hepatitis B virus are still at risk for hepatocellular carcinoma and liver-related death. Gastroenterology. 2010;138(5):1747-54. Epub 2010/02/02. doi: 10.1053/j.gastro.2010.01.042. PubMed PMID: 20114048.

12.  Fattovich G, Bortolotti F, Donato F. Natural history of chronic hepatitis B: special emphasis on disease progression and prognostic factors. J Hepatol. 2008;48:335-52.

13.  Colonno RJ, Rose RE, Pokornowski K, Baldick CJ, Eggers B, Xu D, et al. Four year assessment of entecavir resistance in nucleoside naïve and lamivudine refractory patients. J Hepatol. 2007;46(Suppl. 1):S294.

14.  Colonno RJ, Rose R, Baldick CJ, Levine S, Pokornowski K, Yu CF, et al. Entecavir resistance is rare in nucleoside naive patients with hepatitis B. Hepatology. 2006;44(6):1656-65. PubMed PMID: 17133475.

15.  Tenney DJ, Rose RE, Baldick CJ, Pokornowski KA, Eggers BJ, Fang J, et al. Long-term monitoring shows hepatitis B virus resistance to entecavir in nucleoside-naive patients is rare through 5 years of therapy. Hepatology. 2009;49(5):1503-14. PubMed PMID: 19280622.

16.  Ke W, Liu L, Zhang C, Ye X, Gao Y, Zhou S, et al. Comparison of efficacy and safety of tenofovir and entecavir in chronic hepatitis B virus infection: a systematic review and meta-analysis. PloS one. 2014;9(6):e98865. Epub 2014/06/07. doi: 10.1371/journal.pone.0098865. PubMed PMID: 24905092; PubMed Central PMCID: PMC4048232.

17.  Gordon SC, Krastev Z, Horban A, Petersen J, Sperl J, Dinh P, et al. Efficacy of tenofovir disoproxil fumarate at 240 weeks in patients with chronic hepatitis B with high baseline viral load. Hepatology. 2013;58(2):505-13. Epub 2013/02/01. doi: 10.1002/hep.26277. PubMed PMID: 23364953; PubMed Central PMCID: PMC3842114.