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Background: The country of Georgia has a high prevalence of hepatitis C, with 5.4% of adults chronically infected. In April 2015, Georgia launched a national program to eliminate hepatitis C by 2020 (90% reduction in prevalence) through scaled-up treatment and prevention interventions. We evaluate the interim impact of the program and feasibility of achieving the elimination goal.

Methods: We developed a transmission model to capture the hepatitis C epidemic in Georgia, calibrated to data from bio-behavioral surveys of people who inject drugs (PWID;1998-2015) and a national survey (2015). We projected the impact of on-going treatment strategies through February 2019 and estimated treatment rates needed to reach Georgia’s elimination target.

Findings: From May 2015-February 2019, 54,313 patients were treated, with ~1,000 treated per month since mid-2017. Compared to 2015, the model projects that these treatments have reduced adult chronic hepatitis C (CHC) prevalence by median 37% (95% credible interval 3044%), reduced CHC incidence by 37% (29-44%), reduced CHC mortality by 14% (3-30%), prevented 3,516 (1,842-6,250) new infections and averted 252 (134-389) CHC-related deaths. Continuing 1,000 treatments/month will reduce prevalence and incidence by 51% (42-61%) and 51% (40-62%), respectively, by end of 2020. To reach a 90% reduction by 2020, treatment rates must increase to 4,144/month (2,963-5,322).

Interpretation: Georgia’s hepatitis C elimination program has achieved substantial treatment scale-up which has reduced the CHC burden. However, they are unlikely to meet their 2020 elimination target unless treatment scales up considerably.

Funding: CDC Foundation, National Institute for Health Research, National Institutes of Health
Original languageEnglish
Pages (from-to)PE244-E253
Number of pages10
JournalLancet Global Health
Issue number2
Early online date18 Dec 2019
Publication statusPublished - 1 Feb 2020

Structured keywords

  • Bristol Population Health Science Institute


  • chronic hepatitis C
  • antivirals
  • disease elimination
  • people who inject drugs
  • mathematical model


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