The Success of Precision Medicine Requires a Public Health PerspectivePosted on by
The announcement of a new major US Precision Medicine initiative comes more than a decade after the completion of the Human Genome Project, the ambitious project that culminated in sequencing all 3 billion base pairs of our genome. Continuous improvement in the quality of sequencing, dramatic reduction in price, and ongoing advances in multiple sectors of biotechnology all promise a new era of medicine known variably as personalized medicine, genomic medicine and more recently precision medicine. With conventional medicine, patients are treated individually but typically with the same treatment that everyone else with that condition receives. Thus an opportunity may be missed: certain medical interventions can be more effective or cause fewer side effects for some patients than for others, making it important to identify in advance which patients are more or less likely to benefit from the intervention. This is where precision medicine comes in. Precision medicine takes into account individual differences in the genes, environments, and lifestyles of people allowing the design of targeted disease interventions from the start. While genomics is often suggested as the leading driver of personalization, other factors may be equally as important. For example, health information technology can be used to integrate medical history into patient-centered approaches to improving health and treating disease.
As paradoxical as it may seem, while precision medicine focuses on individualized care for each patient, its success truly requires a population-based perspective. First, it is important to learn what works and what does not for one person, but it is impossible to infer causality by working with one person at a time. To be informative, data on an individual need to be compared with data from large numbers of people to recognize important individual characteristics and to identify relevant population subgroups that are likely to respond differently to drugs and other interventions.
Second, collecting information from large numbers of people is far more informative when these people are representative of the underlying population from which individuals are drawn. Using data from convenience samples—i.e., collected without regard to important factors such as race/ethnicity, age, and sex–can lead to substantial selection bias and unreliable disease prediction models. A strong epidemiologic foundation is needed to interpret genomics and other “big data” for applications to healthcare.
Third, while precision medicine is currently focused on treatment, a compelling case can be made for giving even more attention to early detection and disease prevention. Although personalized treatments can help save the lives of people who are already sick, disease prevention applies to all of us. “Precision prevention” then may be useful in using both science and limited resources for targeting prevention strategies to subsets of the population. For example, recent data suggest that knowing the speed with which some people metabolize nicotine, based on genetic and other factors, could lead to personalized smoking cessation interventions to complement the highly successful public health efforts that have resulted in reduction in smoking over the past few decades. Another approach to precision prevention is increased screening of people at greater risk of cancer. Family health history collection is an inexpensive tool for identifying individuals and families that require earlier and more intensive screening for breast and ovarian cancer or colorectal cancer.
Finally, implementation of precision medicine requires the full participation and education patients (all of us), communities, physicians, payers, and the healthcare community. This should be guided by strong “translational” implementation sciences which go beyond the traditional bench to bedside model (see recent paper on this topic). Society has a stake in assuring that the national investment in precision medicine research leads to tangible health benefits for all and does not worsen existing health disparities. This is where strong public health-healthcare partnerships are key in assessing the needs of individuals and communities, developing appropriate policies and guidelines, ensuring that all people have access to the intended benefits of technology, and tracking effectiveness and cost effectiveness outcomes in the real world.
An often used example of early success in precision medicine is targeted therapy for a small subset of patients affected by cystic fibrosis, a common genetic disorder that leads to premature disease and disability. However, the price tag of the drug can be around $300,000 per year per patient. Economic considerations can have major implications for differential access to such treatment for families, communities and society at large.
These are the early days of precision medicine. The road ahead is long. Let us make sure that a public health perspective is included at the outset to ensure the success of research and ultimately the effective and responsible implementation of new scientific discoveries for the benefit of all.
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Thank you, Dr. Khoury, for a most cogent editorial about the interplay between “precision medicine” (by whatever name) and public health! As a phd student in public health who works in (clinical) genetic services research, i’ve often found myself pondering how -omics medicine would collide with public health and personalized or precision medicine…and the limits to prediction as we move toward the individual. Your statement “it is impossible to infer causality by working with one person at a time…” is one of the most straightforward statements i’ve read that directly addresses the challenge of getting to truely individualized medicine (or public health genomics). G. Davey Smith (2011) addressed this issue in detail in his lengthy review: “Epidemiology, epigenetics and the ‘Gloomy Prospect’…”
Another article that readers interested in prediction medicine might consider is a recent (Collins, etal, BMJ, 2014) consensus statement on transparent reporting of multivariable prediction models which is, in essence, what precision medicine will be about.
Keep up the good work addressing interesting and thorny issues at the intersection of “the new genomics” and public health!
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