Cost-effectiveness of infant pneumococcal vaccination in the Netherlands

Objectives: The Dutch National Immunization Program offers the 10-valent pneumococcal conjugate vaccine (PCV10). Also licensed for use in the infant population is the 13-valent PCV (PCV13). To update cost-effectiveness (CE) estimates of PCV13 over PCV10, using current epidemiological and economic data. Methods: We modeled vaccinating a birth cohort with either PCV10 or PCV13 (3+1 dose schedule), and calculated costs and effects linked to resulting disease. We modeled invasive pneumococcal disease (IPD), non-invasive pneumonia and acute otitis media, and considered death and lifetime impairments after IPD. We calculated direct effects in the vaccinated cohort and indirect effects -herd immunity for the vaccine-type (VT) serotypes and replacement for the non-VT serotypes- in the rest of the population. Since no price is available, we use a price difference of € 11 per dose and vary this price difference in sensitivity analyses. Epidemiological and economic data are taken as current as possible. A set of scenarios explore different assumptions, including different sets of epidemiological data, assumptions on vaccine efficacy and indirect effects. Results: Taking only direct effects into account PCV13 cannot be considered cost-effective, unless the price difference is much lower than € 11 per dose. In three scenarios, PCV10 dominates PCV13; in the other scenarios the ICER is between € 89000 and € 153000 per QALY gained. If indirect effects are also taken into account, the ICER of PCV13 compared to PCV10 is below € 20,000 per QALY for all scenarios. Scenarios do not have a large impact on the policy decision, unless we assume extra efficacy of PCV10 against non-typeable Haemophilus influenzae. Conclusions: Replacing PCV10 with PCV13 is not likely to be cost-effective in preventing invasive pneumococcal disease in young children. Taking potential benefits in elderly into account, PCV13 is likely cost-effective. The CE of PCV13 was highly sensitive for indirect effects our analysis

Dealing with Differences: Different populations, data sources and countries in HTA modelling

__Abstract__ At its heart, health technology assessment (HTA) is very simple. It compares two or more alternative courses of action, often pharmaceutical interventions, in terms of both their costs and health outcomes.1 One of the interventions will have better health outcomes, for example fewer number of exacerbations, longer survival or a better quality of life. This usually comes at an extra cost, often in the way of a higher price for the intervention. HTA makes this exchange between costs and effects explicit. The idea that costs are an important element to take into account, does not come naturally to many health care workers. Doctors, nurses, and other health care workers do everything they can to help patients improve their lives. The interventions these patients need are provided in a large part by companies developing and producing the necessary drugs and devices. Health care scientists and epidemiologists try to make sense of what constitutes health, what illness is and how disease is spread. Their focus is purely on the patient: what does he or she need? Choices between treatment options are usually a consideration between availability, possible side effects, and patient characteristics. If a new medication comes on the market, doctors are often eager to treat patients with this newest treatment option. With the focus on the patient in front of them, health care workers usually do not look beyond the operating room or treatment room. An oncologist wants to treat all patients to the best of his or her ability, no matter the costs of the intervention. Budgetary constraints are not, and should not, be part of the decision making process of a health care worker when dealing with an individual patient. Cost considerations should be taken into account at a more aggregate level in the clinical guidelines, written by their organizations. In this way, HTA separates health care workers from these concerns in their daily practice, which are in the public and political domain

Mix and match. A simulation study on the impact of mixed-Treatment comparison methods on health-economic outcomes

Background Decision-Analytic cost-effectiveness (CE) models combine many parameters, often obtained after meta-Analysis. Aim We compared different methods of mixed-Treatment comparison (MTC) to combine transition and event probabilities derived from several trials, especially with respect to health-economic (HE) outcomes like (quality adjusted) life years and costs. Methods Trials were drawn from a simulated reference population, comparing two of four fictitious interventions. The goal was to estimate the CE between two of these. The amount of heterogeneity between trials was varied in scenarios. Parameter estimates were combined using direct comparison, MTC methods proposed by Song and Puhan, and Bayesian generalized linear fixed effects (GLMFE) and random effects models (GLMRE). Parameters were entered into a Markov model. Parameters and HE outcomes were compare

AdViSHE: A Validation-Assessment Tool of Health-Economic Models for Decision Makers and Model Users

Background: A trade-off exists between building confidence in health-economic (HE) decision models and the use of scarce resources. We aimed to create a practical tool provid

Economics of Chronic Diseases Protocol: Cost-effectiveness modelling and the future burden of non-communicable disease in Europe

Background: The majority of chronic disease is caused by risk factors which are mostly preventable. Effective interventions to reduce these risks are known and proven to be applicable to a variety of settings. Chronic disease is generally developed long before the fatal outcome, meaning that a lot of people spend a number of years in poor health. Effective prevention measures can prolong lives of individuals and significantly improve their quality of life. However, the methods to measure cost-effectiveness are a subject to much debate. The Economics of Chronic Diseases project aims to establish the best possible methods of measuring cost-effectiveness as well as develop micro-simulation models apt at projecting future burden of chronic diseases, their costs and potential savings after implementation of cost-effective interventions. Method: This research project will involve eight European countries: Bulgaria, Finland, Greece, Lithuania, The Netherlands, Poland, Portugal and the United Kingdom (UK). A literature review will be conducted to identify scientific articles which critically review the methods of cost-effectiveness. Contact will be made health economists to inform and enrich this review. This evidence will be used as a springboard for discussion at a meeting with key European stakeholders and experts with the aim of reaching a consensus on recommendations for cost-effectiveness methodology. Epidemiological data for coronary heart disease, chronic kidney disease, type 2 diabetes and chronic obstructive pulmonary disease will be collected along with data on time trends in three major risk factors related to these diseases, specifically tobacco consumption, blood pressure and body mass index. Economic and epidemiological micro-simulation models will be developed to asses the future distributions of risks, disease outcomes, healthcare costs and the cost-effectiveness of interventions to reduce the burden of chronic diseases in Europe. Discussion: This work will help to establish the best methods of measuring cost-effectiveness of health interventions as well as test a variety of scenarios to reduce the risk factors associated with selected chronic diseases. The modelling projections could be used to inform decisions and policies that will implement the best course of action to curb the rising incidence of chronic diseases.The EConDA project is supported by the European Commission Health Programme and the Executive Agency for Health and Consumers, grant agreement n0 20121213.www.econdaproject.e

Let's go back to work: survival analysis on the return-to-work after depression

Absence from work due to mental disorders is substantial. Additionally, long-term absence from work is associated with a reduced probability of return-to-work (RTW). Major depressive disorder (MDD) is a prevalent condition in Dutch occupational health care settings. An early estimate of the prognosis regarding RTW in patients with MDD could serve both as a point of departure for the identification of high-risk cases and as an instrument to monitor the course of the disorder and of RTW. In the current study, we aimed to assess the added value of health-related quality of life (HRQoL) and severity of depression to predict the time to RTW

The Missing Stakeholder Group: Why Patients Should be Involved in Health Economic Modelling

Evaluations of healthcare interventions, e.g. new drugs or other new treatment strategies, commonly include a cost-effectiveness analysis (CEA) that is based on the application of health economic (HE) models. As end users, patients are important stakeholders regarding the outcomes of CEAs, yet their knowledge of HE model development and application, or their involvement therein, is absent. This paper considers possible benefits and risks of patient involvement in HE model development and application for modellers and patients. An exploratory review of the literature has been performed on stakeholder-involved modelling in various disciplines. In addition, Dutch patient experts have been interviewed about their experience in, and opinion about, the application of HE models. Patients have little to no knowledge of HE models and are seldom involved in HE model development and application. Benefits of becoming involved would include a greater understanding and possible acceptance by patients of HE model application, improved model validation, and a more direct infusion of patient expertise. Risks would include patient bias and increased costs of modelling. Patient involvement in HE modelling seems to carry several benefits as well as risks. We claim that the benefits may outweigh the risks and that patients should become involved

Research Costs Investigated: A Study Into the Budgets of Dutch Publicly Funded Drug-Related Research

Background: The costs of performing research are an important input in value of information (VOI) analyses but are difficult to assess. Objective: The aim of this study was to investigate the costs of research, serving two purposes: (1) estimating research costs for use in VOI analyses; and (2) developing a costing tool to support reviewers of grant proposals in assessing whether the proposed budget is realistic. Methods: For granted study proposals from the Netherlands Organization for Health Research and Development (ZonMw), type of study, potential cost drivers, proposed budget, and general characteristics were extracted. Regression analysis was conducted in an attempt to generate a ‘predicted budget’ for certain combinations of cost drivers, for implementation in the costing tool. Results: Of 133 drug-related research grant proposals, 74 were included for complete data extraction. Because an association between cost drivers and budgets was not confirmed, we could not generate a predicted budget based on regression analysis, but only historic reference budgets given certain study characteristics. The costing tool was designed accordingly, i.e. with given selection criteria the tool returns the range of budgets in comparable studies. This range can be used in VOI analysis to estimate whether the expected net benefit of sampling will be positive to decide upon the net value of future research. Conclusion: The absence of association between study characteristics and budgets may indicate inconsistencies in the budgeting or granting process. Nonetheless, the tool generates useful information on historical budgets, and the option to formally relate VOI to budgets. To our knowledge, this is the first attempt at creating such a tool, which can be complemented with new studies being granted, enlarging the underlying database and keeping estimates up to date