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IntroductionStudies evaluating the cost-effectiveness of screening for Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV) are generally heterogeneous in terms of risk groups, settings, screening intervention, outcomes and the economic modelling framework. It is therefore difficult to compare cost-effectiveness results between studies. This systematic review aims to summarise and critically assess existing economic models for HBV and HCV in order to identify the main methodological differences in modelling approaches.MethodsA structured search strategy was developed and a systematic review carried out. A critical assessment of the decision-analytic models was carried out according to the guidelines and framework developed for assessment of decision-analytic models in Health Technology Assessment of health care interventions.ResultsThe overall approach to analysing the cost-effectiveness of screening strategies was found to be broadly consistent for HBV and HCV. However, modelling parameters and related structure differed between models, producing different results. More recent publications performed better against a performance matrix, evaluating model components and methodology.ConclusionWhen assessing screening strategies for HBV and HCV infection, the focus should be on more recent studies, which applied the latest treatment regimes, test methods and had better and more complete data on which to base their models. In addition to parameter selection and associated assumptions, careful consideration of dynamic versus static modelling is recommended. Future research may want to focus on these methodological issues. In addition, the ability to evaluate screening strategies for multiple infectious diseases, (HCV and HIV at the same time) might prove important for decision makers.

Introduction: With the European Union Regulation on health technology assessment (HTA) approaching, there is concern about how to accommodate the large number of expected population, intervention, comparator, outcome(s), and study type (PICOS) questions and their uncertainty. Navigating uncertainty will prove essential to anticipate evidence requirements. This abstract presents preliminary results of the development of a metric that assesses uncertainty surrounding PICOS questions to eventually guide planning of evidence synthesis for joint clinical assessment (JCA) submission. Methods: The metric will consist of pillars representing overarching themes of uncertainty. Each pillar will contain several elements influencing uncertainty for proposed PICOS questions. This study was conducted in two phases. In phase one, targeted literature searches of peer-reviewed and gray literature were conducted to identify the overarching metric pillars. These pillars were then validated by expert opinion. Similar research methods were used in phase two to inform the content of each of the pillars. Here we present the findings for phase one. Results: The targeted literature review in phase one resulted in multiple candidate elements for the uncertainty pillars. Elements were selected based on expert opinion, resulting in five main pillars. These pillars were considered most valuable for the determination of uncertainty surrounding individual PICOS questions and were considered crucial to HTA acceptance. Phase one resulted in the following pillars: (i) indication and subpopulations; (ii) type of intervention and comparators; (iii) societal and patient unmet need; (iv) type and quality of evidence source informing evidence synthesis; and (v) methodology of evidence synthesis. Conclusions: This study lays the foundation for a metric to assess the uncertainty surrounding proposed PICOS questions for JCAs. The metric identifies existing uncertainties for PICOS questions, assesses potential issues for HTA acceptance, and guides possible evidence synthesis planning. A clear framework for anticipating uncertainty will prove essential in managing resources and expectations. [ABSTRACT FROM AUTHOR]

Byline: Elisabeth Fenwick, PhD (Public Health and Health Policy, University of Glasgow, Glasgow, UK, e.fenwick@clinmed.gla.ac.uk); Karl Claxton, PhD (Department of Economics and Related Studies, Centre for Health Economics University of [...]

Purpose. This article presents an iterative framework for managing the dynamic process of health technology assessment. The framework uses Bayesian statistical decision theory and value of information (VOI) analysis to [...]

Abstract Background The prevalence of obesity is rising in Scotland and globally. Overall, obesity is associated with increased morbidity, mortality and reduced health-related quality of life. Studies suggest that "healthy obesity" (obesity without metabolic comorbidity) may not be associated with morbidity or mortality. Its impact on health-related quality of life is unknown. Methods We extracted data from the Scottish Health Survey on self-reported health-related quality of life, body mass index (BMI), demographic information and comorbidity. SF-12 responses were converted into an overall health utility score. Linear regression analyses were used to explore the association between BMI and health utility, stratified by the presence or absence of metabolic comorbidity (diabetes, hypertension, hypercholesterolemia or cardiovascular disease), and adjusted for potential confounders (age, sex and deprivation quintile). Results Of the 5,608 individuals, 3,744 (66.8%) were either overweight or obese and 921 (16.4%) had metabolic comorbidity. There was an inverted U-shaped relationship whereby health utility was highest among overweight individuals and fell with increasing BMI. There was a significant interaction with metabolic comorbidity (p = 0.007). Individuals with metabolic comorbidty had lower utility scores and a steeper decline in utility with increasing BMI (morbidly obese, adjusted coefficient: -0.064, 95% CI -0.115, -0.012, p = 0.015 for metabolic comorbidity versus -0.042, 95% CI -0.067, -0.018, p = 0.001 for no metabolic comorbidity). Conclusions The adverse impact of obesity on health-related quality of life is greater among individuals with metabolic comorbidity. However, increased BMI is associated with reduced health-related quality of life even in the absence of metabolic comorbidity, casting doubt on the notion of "healthy obesity".

The article in this issue by Groot Koerkamp and others (1) discusses the limitations of cost-effectiveness acceptability curves (CEACs) for presenting uncertainty in cost-effectiveness analysis to policy makers and urges [...]