Economic Evaluation In Genomic Medicine

Economic Evaluation in Genomic Medicine introduces health economics and economic evaluation to genomic clinicians and researchers, while also introducing the topic to health economists. Each chapter includes an executive summary, questions, and case studies, along with supplementary online materials, including process guides, maps, flow charts, diagrams, and economic evaluation spreadsheets to enhance the learning process. The text can easily be used as course material for related graduate and undergraduate courses, providing a succinct overview of the existing, state-of-the-art application of economic evaluation to genomic healthcare and precision medicine. Interrelates economic evaluation and genomic medicine Instructs healthcare professionals and bioscientists about economic evaluation in genomic medicine Teaches health economists about application of economic evaluation in genomic medicine Introduces health economics and economic evaluation to clinicians and researchers involved in genomics Includes process guides, maps, flow charts and diagrams

Economic Evaluation in Genomic and Precision Medicine provides an in-depth examination of essential concepts, protocols and applications of economic evaluation in genomic and precision medicine. Contributions from leading international medical geneticists and health economists compile new ways to effectively assess the costs and outcomes of different genomic care pathways, implement cost-effective medical interventions, and enhance the value of genomic and precision healthcare. Foundational chapters and discipline-specific case studies cover topics ranging from the economic analysis of genomic trial design, to health technology assessment of next-generation sequencing, ethical aspects, economic policy in genomic medicine, and pricing and reimbursement in clinical genomics. Introduces clinicians, researchers and students to essential concepts, protocols and applications of economic evaluation in genomic and precision medicine Demonstrates, through foundational chapters and discipline-specific case studies, how to assess the relative costs and outcomes of different genomic care pathways and implement cost-effective budgets Establishes clear precedents on how genomic technologies can be leveraged to simultaneously reduce costs and enhance the value of healthcare Features contributions from leading international medical geneticists and health economists that are actively evolved in economic assessments of genomic and precision medicine

Although genomic medicine is still a fairly new clinical area, the history of health economics involvement in genomics has a longer history than might be anticipated. Some of the earliest health economics input into genomics was in areas such as neonatal and newborn screening, where health economists contributed to decisions about adding new conditions into newborn screening programmes worldwide. More recently, the first human genome was only sequenced in 2003, costing between US$500 million and US$1 billion. However, by 2008 costs had fallen to a level where so called 'next-generation sequencing (NGS)' approaches started to enter clinical research. NGS approaches allow either the whole genome using methods such as whole-genome sequencing (WGS) or parts of it using whole-exome sequencing (WES) or targeted panels to be sequenced in hours with increased sensitivity compared to older less advanced genetic testing approaches. These sequencing approaches provide information that can inform diagnosis, prognosis and clinical management for a variety of disorders, such as rare diseases and some cancers. However, the current costs are still too expensive for some health care providers and the benefit of the tests is largely unknown. Indeed, a lack of evidence on the cost-effectiveness of novel genomic technologies such as WGS is considered a key translational challenge. This is partly because economic evaluations of genomic technologies often fall outside the remit of health technology assessment (HTA) agencies, such as NICE and PBAC. Where they are undertaken (in a HTA context), the methods used for the assessment sometimes differ from those recommended by HTA agencies for cost-effectiveness analysis. This is against a background of uncertainty as to whether the terms precision medicine, personalised medicine or genomic medicine best capture this space in health care. Methodological challenges Some applications of genomic sequencing generate information that may not improve quality of life (as measured using preference-based health-related quality of life [HRQoL] instruments such as the EuroQol-five dimensions questionnaire) or extend life expectancy. One example is the use of WGS and WES to guide diagnosis in autism spectrum disorder. However, genomic sequencing results may influence patient wellbeing via non-clinical routes, generating 'personal utility'. This is a particular issue for individuals with rare diseases, who often have lengthy diagnostic journeys but few (if any) treatment options available once they receive a diagnosis. This could also be an issue if individuals without known health problems (healthy cohorts) undergo genomic sequencing and find out that they have an elevated risk of a disease, but no preventive action can be taken to manage this risk. With respect to costs, the costs of undertaking genomic tests are only one component of the cost of the overall genomic testing process. The costs that are incurred beyond those associated with the production of genomic information (so probably beyond the scope of any national tariffs that might be generated) include the costs of bioinformatics analysis, interpretation of results in multidisciplinary team (MDT) meetings and genetic counselling services. Such issues have raised questions about whether or not genomics is exceptional for health economists - possibly not, but the combined issues perhaps lead to it often requiring additional attention. There is also a consideration of the importance of accounting for the 'personal' when evaluating personalized medicine and considers the extent to which extra-welfarist and welfarist approaches to economic evaluation achieve this objective. Extra-welfarist approaches are currently used by many health technology assessment agencies but may not capture all of the outcomes that are important to patients in this context. Extensions to the extra-welfarist approach that might better capture the 'personal' are outlined, including multi-criteria decision analysis and the capability approach. Evidence A recent literature review identified only 36 economic evaluations of either WGS or WES, six of which were cost-effectiveness analyses using diagnostic yield as the outcome measure. Only two publications presented cost-utility analyses using quality-adjusted life-years (QALYs) as the measure of health outcomes. HTA agencies generally require data on survival and quality of life when evaluating new healthcare interventions, which, when combined, allow clinical utility to be quantified using QALYs. However, existing studies have primarily quantified the clinical utility of genomic tests in terms of changes in diagnostic yield. Methodological uncertainty among health economists is one potential explanation for the lack of evidence on the health outcomes associated with genomic sequencing. Over the past decade, health economists have repeatedly questioned whether metrics such as the QALY in genomic medicine, which focuses on clinical utility, can fully quantify the outcomes that are important to patients when they undergo genomic testing. Policy picture There are high-level discussions in several countries, including the UK, about extending the use of genomic sequencing into newborning screening, so effectively screening everyone at birth for a large range of conditions, far more than those currently being screened for and which there might not be treatments for yet. This is in addition to long term epidemiological and health economic discussions on using newborn screening for conditions such as hereditary hemochromatosis. A further area of uncertainty is the use of genomic sequencing in 'healthy populations', including direct to consumer testing (private genetic tests). In a public health care system setting, the UK Department of health is exploring the value of establishing a healthy cohort of volunteer. Furthermore, research studies are assessing the costs and effects of polygenetic risk scores in the context of primary care as an opportunistic 'health check' approach, which could incorporate risks for cardiovascular disease, diabetes, different cancers and conditions such as chrohn's disease etc. Clearly, there are health economic questions to be asked about the downstream costs and consequences of genomic tests in these newborn and 'healthy' populations. In cancer, there are discussions about how to handle the new invention of agnostic cancer drugs (which essentially target the mutation rather than the cancer, so the same drug can treat several cancers). This is an area where assessments are going through HTA agencies who are unsure about the best approaches to adopt to these assessments where drug companies are putting forward a drug for assessment that can potentially treat different cancers with very different disease profiles. These developments require careful consideration from many perspectives, including health economics. Besides highlighting some of the challenges in assessing the economic impact of genomic medicine and the use of advanced (and less advanced) technologies, the book will propose potential solutions to these key challenges. For example, in terms of data availability, one obstacle to translating genomic sequencing into routine health care has been a lack of large randomised controlled clinical trials data for health economists and others to use to populate cost-effectiveness analyses (CEAs). Arguably, in response, reimbursement decisions have moved towards lower evidentiary standards, with the development of managed access programs that hope to balance the intense pressure for patient access with the need to consider the sustainability objectives of health care systems. Single arm trials are common for assessing clinical utility of precision medicine. By excluding a counterfactual, these trials introduce outcomes uncertainty through their inability to establish causal treatment effects. In this section of the book, we illustrate the application of quasi-experimental methods for evaluating precision medicine in case studies linking real-world big data and single arm trials. A further potential option here might be provided by 'big data' can be used to partially support CEAs in genomics. Advanced genomic sequencing is considered to be a prominent example of big data because of the quantity and complexity of data it produces and because it presents an opportunity to use powerful information sources that could reduce clinical and health economic uncertainty at a patient level. The creation of large national sequencing initiatives with sequencing data linked to clinical data (including health outcomes) and resource use data such as hospital episode statistics data and claims data. Large-scale sequencing projects such as the 100,000 Genome Project in the UK and the All of Us Program in the US are collecting an unprecedented amount of genomic, clinical and healthcare resource use data on individuals with cancer or rare diseases, as well as healthy individuals. Some of these large-scale projects are now approaching completion, and national health services are deciding whether WGS and WES should be translated into clinical practice for specific disorders.

The sequencing of the human genome and the identification of links between specific genetic variants and diseases have led to tremendous excitement over the potential of genomics to direct patient treatment toward more effective or less harmful interventions. Still, the use of whole genome sequencing challenges the traditional model of medical care where a test is ordered only when there is a clear indication for its use and a path for downstream clinical action is known. This has created a tension between experts who contend that using this information is premature and those who believe that having such information will empower health care providers and patients to make proactive decisions regarding lifestyle and treatment options. In addition, some stakeholders are concerned that genomic technologies will add costs to the health care system without providing commensurate benefits, and others think that health care costs could be reduced by identifying unnecessary or ineffective treatments. Economic models are frequently used to anticipate the costs and benefits of new health care technologies, policies, and regulations. Economic studies also have been used to examine much more specific issues, such as comparing the outcomes and cost effectiveness of two different drug treatments for the same condition. These kinds of analyses offer more than just predictions of future health care costs. They provide information that is valuable when implementing and using new technologies. Unfortunately, however, these economic assessments are often limited by a lack of data on which to base the examination. This particularly affects health economics, which includes many factors for which current methods are inadequate for assessing, such as personal utility, social utility, and patient preference. To understand better the health economic issues that may arise in the course of integrating genomic data into health care, the Roundtable on Translating Genomic-Based Research for Health hosted a workshop in Washington, DC, on July 17-18, 2012, that brought together economists, regulators, payers, biomedical researchers, patients, providers, and other stakeholders to discuss the many factors that may influence this implementation. The workshop was one of a series that the roundtable has held on this topic, but it was the first focused specifically on economic issues. The Economics of Genomic Medicine summarizes this workshop.

Applied Genomics and Public Health examines the interdisciplinary and growing area of how evidence-based genomic knowledge can be applied to public health, population health, healthcare and health policies. The book gathers experts from a variety of disciplines, including life sciences, social sciences, and health care to develop a comprehensive overview of the field. In addition, the book delves into subjects such as pharmacogenomics, genethics, big data, data translation and analysis, economic evaluation, genomic awareness and education, sociology, pricing and reimbursement, policy measures and economic evaluation in genomic medicine. This book is essential reading for researchers and students exploring applications of genomics to population and public health. In addition, it is ideal for those in the biomedical sciences, medical sociologists, healthcare professionals, nurses, regulatory bodies and health economists interested in learning more about this growing field. Explores the growing application of genomics to population and public health Features internationally renowned contributors from a variety of related fields Contains chapters on important topics such as genomic data sharing, genethics and public health genomics, genomics and sociology, and regulatory aspects of genomic medicine and pharmacogenomics

It is becoming increasingly important to examine the relationship between the outcomes of a clinical trial and the costs of the medical therapy under study. The results of such analysis can affect reimbursement decisions for new medical technologies, drugs, devices or diagnostics. It can aid companies seeking to make claims about the cost-effectiveness of their product, as well as allowing early consideration of the economic value of therapies which may be important to improving initial adoption decisions. It is also vital for addressing the requirements of regulatory bodies. Economic Evaluation in Clinical Trials provides practical advice on how to conduct cost-effectiveness analyses in controlled trials of medical therapies. This new edition has been extensively rewritten and revised; topics discussed range from design issues such as the types of services that should be measured and price weights, to assessment of quality-adjusted life years. Illustrative materials, case histories and worked examples are included to encourage the reader to apply the methods discussed. These exercises are supported with datasets, programmes and solutions made available online.

Originally thought to be available only to the world's wealthiest nations, genomic medicine has developed into a broad range of clinical methods and technologies whose concrete applications are also revolutionizing health systems in many of the world's resource-limited nations. Genomic Medicine in Emerging Economies: Genomics for Every Nation provides in-depth analysis and key examples of the implementation of medical genomics in low-income nations across the globe, demonstrating how this advancing medical science has already transformed health systems and led to improved patient care in Indonesian, Chilean, Malaysian, Argentinian, Chinese, Sri Lankan, and Colombian populations among others. In addition to defining tools, diagnostics, and treatment pathways at the population-wide level for medical geneticists, genomic researchers, and public health workers to apply in their own work, this book offers an essential, case-study based approach needed to understand how genomic medicine can be used to improve disease-management in a diverse range of economic and social contexts.

In financially constrained health systems across the world, increasing emphasis is being placed on the ability to demonstrate that health care interventions are not only effective, but also cost-effective. This book deals with decision modelling techniques that can be used to estimate the value for money of various interventions including medical devices, surgical procedures, diagnostic technologies, and pharmaceuticals. Particular emphasis is placed on the importance of the appropriate representation of uncertainty in the evaluative process and the implication this uncertainty has for decision making and the need for future research. This highly practical guide takes the reader through the key principles and approaches of modelling techniques. It begins with the basics of constructing different forms of the model, the population of the model with input parameter estimates, analysis of the results, and progression to the holistic view of models as a valuable tool for informing future research exercises. Case studies and exercises are supported with online templates and solutions. This book will help analysts understand the contribution of decision-analytic modelling to the evaluation of health care programmes. ABOUT THE SERIES: Economic evaluation of health interventions is a growing specialist field, and this series of practical handbooks will tackle, in-depth, topics superficially addressed in more general health economics books. Each volume will include illustrative material, case histories and worked examples to encourage the reader to apply the methods discussed, with supporting material provided online. This series is aimed at health economists in academia, the pharmaceutical industry and the health sector, those on advanced health economics courses, and health researchers in associated fields.