{"id":252940,"date":"2017-08-15T00:00:00","date_gmt":"2017-08-15T00:00:00","guid":{"rendered":"https:\/\/clarivate.com\/products\/research-reports\/report\/pfloid0001-biopharma-hepatitis-c-virus-epidemiology-interactive\/"},"modified":"2026-03-31T10:50:03","modified_gmt":"2026-03-31T10:50:03","slug":"pfloid0001-biopharma-hepatitis-c-virus-epidemiology-interactive-patient-flow-model-global","status":"publish","type":"report","link":"https:\/\/clarivate.com\/life-sciences-healthcare\/report\/pfloid0001-biopharma-hepatitis-c-virus-epidemiology-interactive-patient-flow-model-global\/","title":{"rendered":"Hepatitis C Virus – Epidemiology – Interactive Patient Flow Model – Global"},"content":{"rendered":"

Based on DRG<\/abbr>\u2019s gold-standard Epidemiology data, the Interactive Patient Flow Model provides insight into the epidemiology of HCV<\/abbr> for five different treatment scenarios.<\/p>\n

With the introduction of DAA<\/abbr>s, an effective cure for HCV<\/abbr>, the epidemiology and consequent future size of the HCV<\/abbr> drug market will be radically transformed. The more patients with HCV<\/abbr> who get treated with DAA<\/abbr>s, the fewer people will have HCV<\/abbr>. But the situation is more complex than this simple piece of logic. For, the fewer people that have the HCV<\/abbr> circulating in their blood, the lower the risk of anyone without HCV<\/abbr> of being infected. Meanwhile, as time passes for those older people living with undiagnosed HCV<\/abbr>, the more likely that they will become diagnosed as the disease progresses.<\/p>\n

While DRG<\/abbr> Epidemiology\u2019s HCV<\/abbr> forecast models these factors into a global HCV<\/abbr> forecast, there are other scenarios that can be examined. With DRG<\/abbr> Epidemiology\u2019s interactive patient flow model for HCV<\/abbr>, each of these scenarios can be played out across 45 countries worldwide. Each scenario comprises more than 50 patient populations, forecast to 2030. The scenarios are depicted via interactive graphical patient flow diagrams, each one showing the patient flow between different disease states from a different perspective.<\/p>\n

The model comes with extensive tabulation of data, description of methods and data sources used, and full support from our infectious disease epidemiology team. The model is available as an MS Excel deliverable, downloadable from the top right.<\/p>\n

One of the most informative of these counterfactual scenarios is if DAA<\/abbr>s were not launched, thus enabling to see their true impact on HCV<\/abbr> epidemiology apart from demographic factors such as the aging of the older generation who were exposed to the virus prior to its characterization and public health prevention policies.<\/p>\n

Other interesting scenarios to play out are those where treatment is restricted to only those high-risk HCV<\/abbr> cases. Namely, those with liver cirrhosis. This allows players in the HCV<\/abbr> space to estimate \u2013 at a global level \u2013 the minimum eligible market size on the assumption that there will be strong advocacy for treatment of these high-risk cases. On the flip side of this is where constraints on healthcare providers\u2019 budgets are loosened worldwide, so that even more resource-constrained countries in the developing world have the same treatment patterns as high-income western European countries.<\/p>\n

Finally, the WHO<\/abbr> development goals for both the diagnosis and treatment of HCV<\/abbr> also effectively specify a counterfactual scenario. Namely: if these goals were to be achieved, what would be the result in terms of the prevalence of HCV<\/abbr> worldwide?<\/p>\n

Questions Answered<\/strong><\/p>\n