Personalised medicine has generated global policy interest in the past few years. In 2012, the European Union established the European Alliance for Personalised Medicine with the aim to accelerate the development, delivery, and uptake of personalised health care, broadly defi ned. In the same year, the UK’s Medical Research Council and National Institute for Health Research funded the National Phenome Centre to deliver broad access to a world-class capability in metabolic phenotyping for biomarker discovery and validation, improved patient stratifi cation, and early identifi cation of drug effi cacy and safety. In the USA, President Obama recently proposed to invest US$215 million in a Precision Medicine Initiative, with the goal to further research into patient genetics and customised treatments. There are reasons to be optimistic about these kinds of initiatives. The sequencing of the human genome and rapid advances in technology have catalysed the development of personalised medicine—so much so that reliable and aff ordable genetic analysis is well within reach of many patients and payers. Much of the research and development to date has focused on genetic mutations commonly found in cancer tumours or rare genetic diseases, and the development of targeted therapies. Most personalised therapies currently on the market are indicated for slowing tumour growth or orphan diseases. However, the promise has spawned a rapidly growing industry in which genetic markers of disease and treatment responses are searched on a larger scale. The full promise of personalised and precision medicine (PPM)—as health-care innovations involving molecular diagnostics and pharmacogenomics are called—extends beyond targeting therapies for patients who are already ill. It also includes the ability to identify healthy individuals at elevated risk of disease, enabling preventive measures to be targeted towards those who could benefi t most. Although applications of PPM aimed at prevention have the potential to generate substantial value for society, the present reimbursement environ ment, characterised by near-term budget pressures on national health systems and private payers alike, discourages their development in favour of PPM treatments that might generate less value overall, but provide greater short-run returns. On the other hand, the potential social benefi ts from prevention in the PPM context can be enormous. We used an existing health simulation model to consider the benefi ts (and costs) of PPM innovations to improve screening and risk-factor stratifi cation technologies that identify presymptomatic individuals at high risk of specifi c diseases. The model—The Health Econ omics Medical Innovation Simulation—was developed with funding from the Centers for Medicare and Medicaid Services, National Institutes of Health, and the MacArthur Foundation, and has been used to assess the long-term consequences of medical innovation in many settings, including cardiovascular disease, diabetes, cancer, and obesity. Our scenarios mirror the current research and development pursuit of PPM technology to identify patients at highest risk of high prevalence disease. These patients are then given targeted prophylactic therapy to prevent or delay disease onset. The preventive therapy itself need not be innovative—eg, the multi centre Diabetes Prevention Program trial identifi ed patients at high risk of type 2 diabetes (prediabetes), and showed that early intervention with existing therapies reduced risk of subsequent type 2 diabetes. In our scenarios, the PPM innovation allows identifi cation of the subset of patients for whom intervention is most valuable. So, for example, although diet and exercise interventions might lower the risk of heart disease among the population in general, adherence to such programmes is notoriously poor in the general population. However, aggressive preventive and inter ventional strategies targeted to patients whose genetic tests identify them as having extraordinary risks of developing cardiovascular disease have a much greater likelihood of success. In our analysis, these preventive PPM interventions are assumed to permanently reduce the incidence of six diseases (cancer, diabetes, heart disease, hypertension, lung disease, and stroke) by some fi xed percentage starting in 2012. Interventions are assumed to have effi cacy—and costs—similar to the Diabetes Prevention Program, meaning that they need to be sustained over a lifetime. Benefi ts are computed by looking at life expectancy and quality-adjusted life expectancy gains during the subsequent 50 years. Values are expressed in dollars using a very conservative $100 000 per qualityadjusted life-year. The fi gure summarises the value of health generated from 2012 to 2060 by PPM innovations that reduce incidence of six diseases in the US by 10% and 50%. Dependent on the disease, a PPM innovation that reduces incidence by as little as 10% generates anywhere from $33 to $114 billion in the form of longer, healthier lives enjoyed by the US population. A PPM innovation that reduces disease incidence by more generates commensurately larger benefi ts, for example a 50% reduction in heart disease incidence would generate $607 billion in improved health over 50 years. Of the six diseases studied, PPM innovations aimed at reducing heart disease have the greatest eff ect on public health because heart disease is highly prevalent and has relatively large eff ect on life expectancy. Other diseases such as stroke or lung disease Lancet 2015; 385: 2118–19
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