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Background: Stratified Medicine (SM) is becoming a practical reality with the targeting of medicines by using a biomarker or genetic-based diagnostic to identify the eligible patient sub-population. Like any healthcare intervention, SM interventions have costs and consequences that must be considered by reimbursement authorities with limited resources. Methodological standards and guidelines exist for economic evaluations in clinical pharmacology and are an important component for health technology assessments (HTAs) in many countries. However, these guidelines have initially been developed for traditional pharmaceuticals and not for complex interventions with multiple components. This raises the issue as to whether these guidelines are adequate to SM interventions or whether new specific guidance and methodology is needed to avoid inconsistencies and contradictory findings when assessing economic value in SM. Objective: This article describes specific methodological challenges when conducting health economic (HE) evaluations for SM interventions and outlines potential modifications necessary to existing evaluation guidelines /principles that would promote consistent economic evaluations for SM. Results/Conclusions: Specific methodological aspects for SM comprise considerations on the choice of comparator, measuring effectiveness and outcomes, appropriate modeling structure and the scope of sensitivity analyses. Although current HE methodology can be applied for SM, greater complexity requires further methodology development and modifications in the guidelines.

Improving health through better nutrition of the population may contribute to enhanced efficiency and sustainability of healthcare systems. A recent expert meeting investigated in detail a number of methodological aspects related to the discipline of nutrition economics. The role of nutrition in health maintenance and in the prevention of non-communicable diseases is now generally recognised. However, the main scope of those seeking to contain healthcare expenditures tends to focus on the management of existing chronic diseases. Identifying additional relevant dimensions to measure and the context of use will become increasingly important in selecting and developing outcome measurements for nutrition interventions. The translation of nutrition-related research data into public health guidance raises the challenging issue of carrying out more pragmatic trials in many areas where these would generate the most useful evidence for health policy decision-making. Nutrition exemplifies all the types of interventions and policy which need evaluating across the health field. There is a need to start actively engaging key stakeholders in order to collect data and to widen health technology assessment approaches for achieving a policy shift from evidence-based medicine to evidence-based decision-making in the field of nutrition.

There is a new merging of health economics and nutrition disciplines to assess the impact of diet on health and disease prevention and to characterise the health and economic aspects of specific changes in nutritional behaviour and nutrition recommendations. A rationale exists for developing the field of nutrition economics which could offer a better understanding of both nutrition, in the context of having a significant influence on health outcomes, and economics, in order to estimate the absolute and relative monetary impact of health measures. For this purpose, an expert meeting assessed questions aimed at clarifying the sco

The objective of this paper was to address the importance of dealing systematically and comprehensively with uncertainty in a budget impact analysis (BIA) in more detail. The handling of uncertainty in health economics was used as a point of reference for addressing the uncertainty in a BIA. This overview shows that standard methods of sensitivity analysis, which are used for standard data set in a health economic model (clinical probabilities, treatment patterns, resource utilisation and prices/tariffs), cannot always be used for the input data for the BIA model beyond the health economic data set for various reasons. Whereas in a health economic model, only limited data may come from a Delphi panel, a BIA model often relies on a majority of data taken from a Delphi panel. In addition, the dataset in a BIA model also includes forecasts (e.g. annual growth, uptakes curves, substitution effects, changes in prescription restrictions and guidelines, future distribution of the available treatment modalities, off-label use). As a consequence, the use of standard sensitivity analyses for BIA data set might be limited because of the lack of appropriate distributions as data sources are limited, or because of the need for forecasting. Therefore, scenario analyses might be more appropriate to capture the uncertainty in the BIA data set in the overall BIA model.

Objectives: To assess the cost effectiveness of palivizumab for prevention of severe respiratory syncytial virus (RSV) disease in high-risk infants in Spain, incorporating country-specific observational hospitalisation data. Methods: An existing decision tree model, designed using data from a large international clinical trial of palivizumab versus no prophylaxis, was updated to include Spanish observational hospitalisation data. The analysis was performed for preterm children born at or before 32 weeks gestational age, who are at high risk of developing severe RSV disease requiring hospitalisation. Data sources included published literature, official price/tariff lists and national population statistics. The primary perspective of the study was that of the Spanish National Health Service in 2006. Results: The base-case analysis included the direct medical costs associated with palivizumab prophylaxis and hospital care for RSV infections. Use of palivizumab produces an undiscounted incremental cost-effectiveness ratio (ICER) of €6,142 per quality-adjusted life-year (QALY), and a discounted ICER of €12,814/QALY. Conclusion: Palivizumab provides a cost-effective method of prophylaxis against severe RSV disease requiring hospitalisation among preterm infants in Spain.

Objective: The objective of this study was to determine the cost effectiveness of paricalcitol versus calcitriol for the treatment of secondary hyperparathyroidism in patients with chronic kidney disease in the United States setting. Methods: A Markov process model was developed employing data sources from the published literature, paricalcitol clinical trials and observational studies, official US price/tariff lists and national population statistics. The comparator was calcitriol, a non-selective vitamin D receptor activator (VDRA) medication. The primary perspective of the study was that of the third-party payer in the US. The efficacy outcomes (reduction in secondary hyperparathyroidism (SHPT), reduction in proteinuria, complications and mortality) were extrapolated to: number of life-years gained (LYG) and number of quality-adjusted life-years (QALYs). Clinical and economic outcomes were discounted at 3.5%. Results: The reference case analysis was a 10-year time horizon based on a comparison of paricalcitol with calcitriol, which is started in chronic kidney disease (CKD) stage 3 and continued in CKD stage 4 and CKD stage 5. The use of paricalcitol leads to a cost saving of US$1941. The inclusion of indirect costs leads to a cost saving of US$2528. The use of paricalcitol leads to an increase in life-years gained (0.47 years) and a gain in QALYs (0.43). The use of paricalcitol results in a dominant outcome from the perspective of the third-party payer, as well as from the societal perspective. One-way sensitivity analyses and probabilistic sensitivity analyses confirmed the robustness of the model. Conclusion: This model showed that the favorable clinical benefit of paricalcitol results in positive short and long-term health economic benefits. This study suggests that the use of paricalcitol in patients with early chronic kidney disease may be cost-effective from the third-party payer perspective in the US versus calcitriol. Additional comparative studies are n

Although pain can present in many different ways, acute, subacute and chronic pain are frequently used and helpful categories that cover a wide range of pain phenomena. This paper reviews five cost-minimization studies of tramadol/paracetamol compared with codeine/paracetamol, immediate-release tramadol and nonsteroidal anti-inflammatory drugs for the treatment of postsurgical pain, lower back pain and osteoarthritis. These conditions represent the main general indications for the combination analgesic. This economic modeling data is preceded by a discussion of each pain type, focusing on the published clinical literature of the efficacy of tramadol/paracetamol and relevant comparators. For the cost-minimization studies, each model employed a similar decision analytical framework, with data derived from clinical trials, Delphi panels and official price and tariff lists. Rollback analyses were used to calculate the cost of treatment per patient. The initial hypothesis in each study was that higher drug costs for tramadol/ paracetamol would be offset by a reduction of costs associated with the treatment of side effects. In addition, a cost-utility model carried out among patients with moderate or severe back pain could also underline a benefit-adjusted outcome (quality of life) for patients treated with tramadol/paracetamol. Results of the published clinical literature and these recent economic analyses suggest that tramadol/paracetamol, by reducing the incidence of adverse events, provides benefits for both patients and budgets.

Objective: To compare the costs of treating osteoarthritis (OA) pain using combination tramadol/paracetamol tablets, NSAIDs alone, NSAIDs plus proton pump inhibitors (PPIs), or NSAIDs plus histamine H2-receptor antagonists (H2RAs) from the perspective of the Dutch healthcare system. Design and methods: A decision-analytical model was constructed to model the cost outcomes of the four treatment strategies over 6 months. A cost-minimisation approach was used, which considered data related to resource utilisation, medication costs and costs for the treatment of adverse events. Data, derived mainly from the clinical literature, were supplemented by inputs from a Delphi panel as well as official price and tariff lists. The base-case analysis considered direct medical costs, including those for treating all adverse events with tramadol/paracetamol and gastrointestinal (GI) adverse events with NSAIDs. Separate scenario analyses explored costs of NSAID-based regimens: (i) according to 21 levels of risk for GI adverse events, and (ii) when renal events attributable to NSAIDs were considered. Robustness of the model was tested using univariate sensitivity analysis. Results: In the base-case analysis, costs for 6 months' treatment of OA pain using tramadol/paracetamol were €244.45, compared with €317.32 for NSAIDs + PPIs, €200.67 for NSAIDs + H 2RAs, and €125.86 for NSAIDs alone. This provided a cost saving of €72.87 per patient over 6 months for tramadol/paracetamol compared with NSAIDs + PPIs. Tramadol/paracetamol became cost saving compared with NSAIDs alone and NSAIDs + H2RAs for GI risk levels >13 and >10, respectively. When renal adverse events of NSAIDs were considered, tramadol/paracetamol was cost saving compared with all NSAID-based regimens (saving €228.40 vs NSAIDs, €418.42 vs NSAIDs + PPIs, and €302.69 vs NSAIDs + H2RAs [year of costing 2005]). Sensitivity analysis confirmed the model was robust to wide-ranging changes in the value of input parameters. Conclusion