Article

Simulating healthcare quality innovation based on a novel medical treatment: The case of Hepatitis-C in Europe

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Abstract

In 2014, a novel medication for treating Hepatitis C virus (HCV) infections caused severe difficulties for European decision makers in the public medical sector. Even though new drugs cure HCV in nearly all cases, related costs in the short run are extremely high. Thus, the estimation of overall costs for the national healthcare systems was of great importance for profound far-reaching decisions on policies regarding the medication and their reimbursement. As this budget estimation is extremely difficult due to the complexity of the virus spread and the existence of further discomforts that lead to additional costs, a new microsimulation model was developed that considers the problem from an individual's perspective and finally aggregates numbers on the macro level. While developing the model, general insights into the cost burden due to the new medication for the next 3 years were generated. Using the introduced model, a decision maker is able to test for impact of one financial unit in several policies in order to maximize the overall benefit for the healthcare system. As initial results imply the need to change current reimbursement strategies in Europe, further research demand is discussed at the end of this article.

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This paper presents an agent model that simulates the spread of an infection in a population. The epidemic depicted could be any attribute that is passed from a one person to others in society, for example, a disease, an idea or belief, a fad, a market or a behavioral pattern. The model was constructed to study the sensitivity of factors such as virility of the infectious agent, the “reach” of the vector and the density of the population in which the epidemic takes place. A further goal was to begin the development of a general-purpose forecasting model based on the use of agents. The model and its results are presented in this paper.
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Human immunodeficiency virus (HIV) outbreaks occur among injecting drug users (IDUs), but where HIV is low insight is required into the future risk of increased transmission. The relationship between hepatitis C virus (HCV) and HIV prevalence among IDUs is explored to determine whether HCV prevalence could indicate HIV risk. Systematic review of IDU HIV/HCV prevalence data and regression analysis using weighted prevalence estimates and time-series data. HIV/HCV prevalence estimates were obtained for 343 regions. In regions other than South America/sub-Saharan Africa (SAm/SSA), mean IDU HIV prevalence is likely to be negligible if HCV prevalence is <30% (95% confidence interval 22-38%) but increases progressively with HCV prevalence thereafter [linearly (beta = 0.39 and R(2) = 0.67) or in proportion to cubed HCV prevalence (beta = 0.40 and R(2) = 0.67)]. In SAm/SSA, limited data suggest that mean HIV prevalence is proportional to HCV prevalence (beta = 0.84, R(2) = 0.99), but will be much greater than in non-SAm/SSA settings with no threshold HCV prevalence that corresponds to low HIV risk. At low HCV prevalences (<50%), time-series data suggest that any change in HIV prevalence over time is likely to be much smaller (<25%) than the change in HCV prevalence over the same time-period, but that this difference diminishes at higher HCV prevalences. HCV prevalence could be an indicator of HIV risk among IDUs. In most settings, reducing HCV prevalence below a threshold (30%) would reduce substantially any HIV risk, and could provide a target for HIV prevention.
Article
The prevalence of chronic hepatitis C (CH-C) remains high and the complications of infection are common. Our goal was to project the future prevalence of CH-C and its complications. We developed a multicohort natural history model to overcome limitations of previous models for predicting disease outcomes and benefits of therapy. Prevalence of CH-C peaked in 2001 at 3.6 million. Fibrosis progression was inversely related to age at infection, so cirrhosis and its complications were most common after the age of 60 years, regardless of when infection occurred. The proportion of CH-C with cirrhosis is projected to reach 25% in 2010 and 45% in 2030, although the total number with cirrhosis will peak at 1.0 million (30.5% higher than the current level) in 2020 and then decline. Hepatic decompensation and liver cancer will continue to increase for another 10 to 13 years. Treatment of all infected patients in 2010 could reduce risk of cirrhosis, decompensation, cancer, and liver-related deaths by 16%, 42%, 31%, and 36% by 2020, given current response rates to antiviral therapy. Prevalence of hepatitis C cirrhosis and its complications will continue to increase through the next decade and will mostly affect those older than 60 years of age. Current treatment patterns will have little effect on these complications, but wider application of antiviral treatment and better responses with new agents could significantly reduce the impact of this disease in coming years.
Article
Treatment guidelines recommend the use of peginterferon alfa-2b or peginterferon alfa-2a in combination with ribavirin for chronic hepatitis C virus (HCV) infection. However, these regimens have not been adequately compared. At 118 sites, patients who had HCV genotype 1 infection and who had not previously been treated were randomly assigned to undergo 48 weeks of treatment with one of three regimens: peginterferon alfa-2b at a standard dose of 1.5 microg per kilogram of body weight per week or a low dose of 1.0 microg per kilogram per week, plus ribavirin at a dose of 800 to 1400 mg per day, or peginterferon alfa-2a at a dose of 180 microg per week plus ribavirin at a dose of 1000 to 1200 mg per day. We compared the rate of sustained virologic response and the safety and adverse-event profiles between the peginterferon alfa-2b regimens and between the standard-dose peginterferon alfa-2b regimen and the peginterferon alfa-2a regimen. Among 3070 patients, rates of sustained virologic response were similar among the regimens: 39.8% with standard-dose peginterferon alfa-2b, 38.0% with low-dose peginterferon alfa-2b, and 40.9% with peginterferon alfa-2a (P=0.20 for standard-dose vs. low-dose peginterferon alfa-2b; P=0.57 for standard-dose peginterferon alfa-2b vs. peginterferon alfa-2a). Estimated differences in response rates were 1.8% (95% confidence interval [CI], -2.3 to 6.0) between standard-dose and low-dose peginterferon alfa-2b and -1.1% (95% CI, -5.3 to 3.0) between standard-dose peginterferon alfa-2b and peginterferon alfa-2a. Relapse rates were 23.5% (95% CI, 19.9 to 27.2) for standard-dose peginterferon alfa-2b, 20.0% (95% CI, 16.4 to 23.6) for low-dose peginterferon alfa-2b, and 31.5% (95% CI, 27.9 to 35.2) for peginterferon alfa-2a. The safety profile was similar among the three groups; serious adverse events were observed in 8.6 to 11.7% of patients. Among the patients with undetectable HCV RNA levels at treatment weeks 4 and 12, a sustained virologic response was achieved in 86.2% and 78.7%, respectively. In patients infected with HCV genotype 1, the rates of sustained virologic response and tolerability did not differ significantly between the two available peginterferon-ribavirin regimens or between the two doses of peginterferon alfa-2b. (ClinicalTrials.gov number, NCT00081770.)
Article
With more than 170 million individuals currently infected, HCV is a global pandemic, effecting approximately 3% of the entire world's population. HCV infection is a growing infectious disease pandemic with approximately 3-4 million new cases reported each year. Due to the persistent nature of the virus, 70-90% of infected individuals will develop chronic infection, which can lead to progressive liver disease including cirrhosis and hepatocellular carcinoma. Current standard treatment with a combination of IFN-alpha and ribavirin has improved the prognosis for many HCV sufferers; however, infection is very difficult to treat successfully and the protocol for treatment is neither simple, well tolerated nor economically favorable. Standard treatment can cost an average of US$22,000, and depending on genotype, as few as 42% of treated individuals will clear the infection. This collection of treatment issues combined with new concepts in immune therapy serve to underscore an urgent need for the development of improved immunotherapies, such as novel interferons, and support the possible development of therapeutic vaccines for the treatment of chronic HCV infection.
Article
Cost effectiveness is becoming an increasingly important factor for stakeholders faced with decisions about adding a new vaccine into national immunization programmes versus alternative use of resources. Evaluating cost effectiveness, taking into account the relevant biological, clinical, epidemiological and economic factors of a vaccination programme, generally requires use of a model. This review examines the modelling approaches used in cost-effectiveness analyses (CEAs) of vaccination programmes. After overviewing the key attributes of models used in CEAs, a framework for categorising theoretical models is presented. Categories are based on three main attributes: static/dynamic; stochastic/deterministic; and aggregate/individual based. This framework was applied to a systematic review of CEAs of all currently available vaccines for the period of 1976 to May 2007. The systematic review identified 276 CEAs of vaccination programmes. The great majority (83%) of CEAs were conducted in the setting of high-income countries. Only a few vaccines were widely studied, with 57% of available CEAs being focused on the varicella, influenza, hepatitis A, hepatitis B or pneumococcal vaccine. Several time trends were evident, indicating that the number of vaccine CEAs being published is increasing; the main health outcome measures are moving away from the number of cases prevented towards quality-adjusted and unadjusted life-years gained, and more complex models are beginning to be used. The modelling approach was often not adequately described. Of the 208 CEAs that could be categorized according to the framework, around 90% were deterministic, aggregate-level static models. Although a dynamic transmission model is required to account for herd-immunity effects, only 23 of the CEAs were dynamic. None of the CEAs were individual based. To improve communication about the cost effectiveness of vaccination programmes, we believe the first step is for analysts to be more transparent with each other. A clear description of the model type using consistent terminology and justification for the model choice must begin to accompany all CEAs. As a minimum, we urge modellers to provide an explicit statement about the following attributes: static/dynamic; stochastic/deterministic; aggregate/individual based; open/closed. Where relevant, time intervals (discrete/continuous) and (non)linearity should also be described. Enhanced methods of assessing model performance and validity are also required. Our results emphasize the need to improve modelling methods for CEAs of vaccination programmes; specifically, model choice, construction, assessment and validation.
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