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

Personalized and precision medicine (PPM)—the targeting of therapies according to an individual’s genetic, environmental, or lifestyle characteristics—is becoming an increasingly important approach in health care treatment and prevention. The advancement of PPM is a challenge in traditional clinical, reimbursement, and regulatory landscapes because it is costly to develop and introduces a wide range of scientific, clinical, ethical, and socioeconomic issues. PPM raises a multitude of economic issues, including how information on accurate diagnosis and treatment success will be disseminated and who will bear the cost; changes to physician training to incorporate genetics, probability and statistics, and economic considerations; questions about whether the benefits of PPM will be confined to developed countries or will diffuse to emerging economies with less developed health care systems; the effects of patient heterogeneity on cost-effectiveness analysis; and opportunities for PPM’s growth beyond treatment of acute illness, such as prevention and reversal of chronic conditions. This volume explores the intersection of the scientific, clinical, and economic factors affecting the development of PPM, including its effects on the drug pipeline, on reimbursement of PPM diagnostics and treatments, and on funding of the requisite underlying research; and it examines recent empirical applications of PPM.

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.

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

This book provides a unique perspective on the biomedical and societal implications of personalized medicine and how it helps to mitigate the healthcare crisis and rein in ever-growing expenditure. It introduces the reader to the underlying concepts at the heart of personalized medicine. An innovative second edition, this book functions as an update to the successful first edition to include new, state-of-the-art information and advancements in the fast-paced field of personalized medicine. Chapters examine pharmacogenomics, targeted therapies, individualized diagnosis and treatment, and cancer immunotherapies. The book also features an essential discussion on how the advent of genomic technologies gives clinicians the capability to predict and diagnose disease more efficiently and offers a detailed up-to-date compilation of clinical trials in cancer leading to breakthrough therapies. The book also addresses the impact of Big Data on personalized medicine and the newfound applications of digital health and artificial intelligence. A work that advocates for a patient-centered approach, Advancing Healthcare Through Personalized Medicine, Second Edition is an invaluable text for clinicians, healthcare providers, and patients.

Until fairly recently, genetic information was used primarily in the diagnosis of relatively rare genetic diseases, such as cystic fibrosis and Huntington's Disease, but a transformation in the use of genetic and genomic information is underway. While many predictions have been made that genomics will transform medicine, to date few of these promising discoveries have resulted in actual applications in medicine and health. The Institute of Medicine's Roundtable on Translating Genomic-Based Research for Health, established in 2007, held its first workshop to address the following questions: 1. Are there different pathways by which new scientific findings move from the research setting into health care? 2. If so, what are the implications of those different pathways for genomics? 3. What can we learn from the translation of other new technologies as we seek to understand the translation of genome science into health care? Information obtained from the workshop was then used to further discussion and exploration of the answers to these questions. This book summarizes speaker presentations and discussions. Any conclusions reported should not be construed as reflecting a group consensus; rather they are the statements and opinions of presenters and participants.

Preceded by Genomics and clinical medicine / edited by Dhavendra Kumar. [First edition]. 2008.