National Academies Press: OpenBook

The Economics of Genomic Medicine: Workshop Summary (2013)

Chapter: 2 Genomics, Population Health, and Technology

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Suggested Citation:"2 Genomics, Population Health, and Technology." Institute of Medicine. 2013. The Economics of Genomic Medicine: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18276.
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2

Genomics, Population Health, and Technology

Important Points Made by the Speaker

  • The incorporation of genomic sequencing into medicine will depend not just on the falling costs of genomic screening but also on the value that genomic sequencing provides.
  • Genomic testing may have important implications for people with some diseases, such as familial disorders or progressive neurological diseases.
  • For healthy people, genomic data are unlikely to have much effect on assessing the risk of common diseases.
  • Nevertheless, genomic screening could be used to find the relatively rare individuals in a population who are at high risk of preventable disease, preemptively identify genetic variants that influence the effects of drugs, provide additional information for screening of newborns, and inform a variety of reproductive decisions.
  • Genomic testing should be viewed as another available test and only used when and if the situation warrants.

Economics is not just about money, said James Evans, Bryson Distinguished Professor of Genetics and Medicine at the University of North Carolina at Chapel Hill, who provided one of the broad introductory talks

Suggested Citation:"2 Genomics, Population Health, and Technology." Institute of Medicine. 2013. The Economics of Genomic Medicine: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18276.
×

that led off the workshop. Money is a proxy for the value people ascribe to something, and value is the fundamental concern of economics. The critical issue for new technologies, such as genomics, is therefore not just how much they cost but also how much value they produce.

THE VALUE OF GENOMIC DATA

The genome contains a tremendous amount of data, said Evans. Each individual differs at millions of genetic locations from the reference human genome. Some differences influence physical traits, such as eye color, while others influence medically important characteristics. Nonetheless, only rarely do polymorphisms greatly influence health. “It is important to keep that in mind,” Evans said.

Evans divided genetic variants that affect health into two categories. In the first category are variants that occur frequently in the general population but have only a subtle impact on health. These variants raise the risk of a particular adverse health effect by only a modest amount, and geneticists do not yet know how best to aggregate such information to predict overall risk. These variants tend to have little utility in most clinical settings, said Evans.

In the second category are those variants that are found rarely in the population but that dramatically increase the risk of a health disorder. In these cases, the relatively “blunt tools” of modern medicine, such as bilateral mastectomies, annual colonoscopies, or drugs that can have substantial side effects, can be useful for preventing or treating disease on the basis of the knowledge gained from genomic information.

Because of its limited utility, genomic testing has not been widely adopted despite falling costs, said Evans. “I don’t mean to say that this isn’t marvelous technology, but we need to think about its utility to people before we rush to the conclusion that it is going to be, or should be, immediately embraced by everyone.”

THE LONG-TERM AND MID-TERM PROMISES OF GENOMICS

Genetics will eventually shed light on the underpinnings of virtually every human disease, said Evans, because virtually every disease has a genetic component. In the long run, it undoubtedly will transform medical science.

But medical science is not the same thing as medical practice. Medical science is the indispensable foundation of medical practice, said Evans, but practice is far more complex than the underlying science. Theory alone is insufficient to guide practice, and the timeline for translation of science into medicine is long. Sickle cell anemia has been understood at the genetic level

Suggested Citation:"2 Genomics, Population Health, and Technology." Institute of Medicine. 2013. The Economics of Genomic Medicine: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18276.
×

since 1949 (Neel, 1949; Pauling et al., 1949), yet treatment of patients has remained basically unchanged over this time. Medical practice is also far more expensive than medical science, and the stakes are far higher. “If you screw up, people literally suffer and die,” Evans said.

Despite the gap between medical science and medical practice, Evans noted, a current application of whole genome sequencing is proving to be exceedingly valuable. For people who have a disorder with a genetic etiology, genomic diagnostics can provide tangible benefits by giving people information about their conditions that can be used to guide treatment or prevention measures. Evans cited genomic analysis of tumors as being a specific area where these benefits could be achieved in the near term. Moreover, even if no treatment for a condition is available, many people want a diagnosis. The information can end the “diagnostic odyssey” of patients going from physician to physician, trying to find out what is wrong with them, thereby reducing anxiety and saving resources. In some cases, this information can also inform reproductive decisions and direct preventive strategies for family members who may also be at risk.

Nevertheless, this application of whole genome sequencing will be useful in only a limited number of cases, said Evans, such as children with multiple malformations, familial disorders passed among multiple generations, progressive neurological disorders, and patients with unusual presentations, such as cancer at a young age. Most common diseases, such as diabetes or hypertension, have multiple causes, including factors such as diet, smoking, exercise, and the environment, and the contribution of any one genetic variant is small. This multifactorial etiology places an inherent ceiling on the utility of genetic testing for these disorders. “I don’t think we are going to be able to get around that basic stumbling block and answer everything we want to know about, [for example], heart disease with genetic analysis,” Evans said.

GENOMIC DATA IN HEALTHY PEOPLE

A different set of considerations surrounds the use of genomic tests in healthy people, said Evans. Healthy people have less to gain and more to lose from any medical intervention, including genomic tests.

Assessing the risk of common diseases through whole genome analysis of a healthy person has received the most attention, but this attention “is somewhat misplaced,” Evans said. Currently, assessment of genetic risk alleles has “rather feeble predictive power” because the increased risks tend to be small. “From a clinical standpoint I don’t know what to do with patients who are at a 1.3 relative risk for colon cancer,” said Evans. “Am I going to hurt them by doing more intensive screening, or am I going to help them?”

Suggested Citation:"2 Genomics, Population Health, and Technology." Institute of Medicine. 2013. The Economics of Genomic Medicine: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18276.
×

In addition, few data suggest that knowledge of one’s genomic status is effective in changing behavior. Moreover, even if it is, genomic data also could be a double-edged sword, said Evans, if individuals forgo healthy diets and exercise because of a perceived decreased risk of developing a disease.

“I know what almost everybody in this room is going to die of,” said Evans. “We are going to die of heart disease or cancer.… We are all at high risk for these maladies regardless of our [genomically determined] risk. And many at decreased risk for heart disease will still die of heart disease. So we are all going to benefit from interventions that lower heart disease. We don’t really need to target people. It doesn’t do anyone much good to tweak our estimation of an individual’s relative risk for common diseases which we are all at high absolute risk of developing anyway.”

A possible application of genetic testing in healthy people is finding the relatively rare individuals in a population who are at high risk of preventable diseases—what another workshop participant called “newborn screening of adults.” Risk assessment will always be most valuable when the identified risks are high. For example, about 0.2 percent of the population carries deleterious mutations that cause Lynch syndrome (Hampel et al., 2008), placing them at extraordinarily high risk for colorectal cancer, which is a preventable disorder. Today these individuals are identified only after numerous family members have developed cancer or died. Genomic testing could make it possible to do population screening for such disorders. Altogether, perhaps 1 percent of the population might harbor genetic variants that create dramatically increased risk, Evans estimated. “That is not small change. The number needed to treat for a lot of interventions, like statins for high cholesterol, is around that number for primary prevention,” he said.

Preemptively identifying genetic variants that influence the effects of drugs in individuals is another promising application of genomic testing. Still, this application will probably be useful for a minority of drugs, Evans said. Even today, after years of development in pharmacogenomics, few loci have demonstrated unequivocal value in improving outcomes or reducing costs. Genetic testing to inform the use of abacavir (Mallal et al., 2008) is an exception to this generalization. But for other promising variants, data still are being collected regarding whether testing benefits patients. Furthermore, such testing may not require a genomic approach. Targeted genotyping at the point of care rather than advance knowledge may be preferable because pharmacogenomic information is only needed when a drug is prescribed. And retesting may be necessary for high-stakes decisions because test results can be wrong and because the tests themselves improve over time.

Genomic testing as an adjunct to newborn screening also holds consid-

Suggested Citation:"2 Genomics, Population Health, and Technology." Institute of Medicine. 2013. The Economics of Genomic Medicine: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18276.
×

erable potential, said Evans. Genomic screening will not replace the current metabolic-based screening in the near term, because it remains closer to the phenotype of interest and has much greater specificity. For example, elevated phenylalanine has much more clinical utility than a variant of uncertain significance in the phenylalanine hydroxylase gene. But genomic screening could help resolve ambiguous biochemical results and detect a subset of treatable disorders that do not have good metabolic markers, such as storage diseases, deafness, and neonatal diabetes.

Finally, genomic tests can inform a variety of reproductive decisions, which is an area that Evans believes will “take off tremendously.” Preconceptual carrier screening (see Chapter 4) is currently recommended for a few disorders, but these have been chosen essentially based on cost and mutation prevalence. Screening is conducted for cystic fibrosis or Tay-Sachs disease because it is affordable and because reliable testing is available, not because Tay-Sachs is any worse than, for example, Batten disease, said Evans. “That is not what couples really want to know. They want to know if [their] child is likely to have a really bad, untreatable disease.” Genomic sequencing can help address these concerns by potentially being used to screen for all serious diseases.

Preconceptual carrier screening for serious diseases could have “a potentially profound and very welcome impact on family planning,” said Evans. Some people will treat such information as highly actionable. Others will regard it as morally problematic. The formulation of policy in this area will be difficult, Evans warned.

CHALLENGES TO IMPLEMENTATION

Effectively harnessing genomic screening faces significant challenges. Because of the large number of bases in the complete haplotype genome, even an accuracy of 99.99 percent will produce 300,000 errors per patient, said Evans, though accuracy will gradually improve.

In addition, each person has about 4 million genetic variants, and our current understanding makes their interpretation difficult. Should information about all of them be gathered or stored? As genome sequencing becomes more accurate and cheaper, it may be more practical to do sequencing when the information is needed, Evans said.

Another significant challenge, said Evans, is that the genome is an unpredictable—and not necessarily friendly—place. For some people, whole genome sequencing will uncover things they were not looking for and might not want to know. Some people will discover that they are at high risk for untreatable and horrific conditions, such as fatal familial insomnia, Huntington’s disease, or early-onset Alzheimer’s disease. The potential for returning information when there is no medical action that can be taken

Suggested Citation:"2 Genomics, Population Health, and Technology." Institute of Medicine. 2013. The Economics of Genomic Medicine: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18276.
×

is an important externality, Evans said, in deciding whether to do whole genome sequencing on everyone. Furthermore, different people will make these decisions differently, and these decisions are even more difficult when parents and children are involved.

Evans briefly described several social challenges to genomic screening. Genetic discrimination remains a concern. In the United States, the Genetic Information Nondiscrimination Act of 2008 protects against discrimination in medical insurance and in the workplace, but no such protections exist for long-term care insurance, disability insurance, or life insurance.

Widespread genetic testing poses the threat of allelism—that people will be defined by their genetic sequences and by the traits those sequences produce rather than by the qualities that truly matter in a person.

About 20 percent of the human genome has patent claims, which means that whole genome sequencing has the potential of being interpreted as violating multiple patents, said Evans.

Widespread testing would pose privacy issues because genomic information is digital and would be easy to distribute. Who will control and have access to this information?, Evans asked. People who volunteer for genetic tests can become upset, for example, if they learn that their genomic information is the property of a private company.

ANOTHER MEDICAL TEST

In the end, Evans concluded, whole genome sequencing is just another medical test. It is a highly complex test with great potential, but claims that everyone will undergo genome sequencing are based on high perceived utility and low cost, and for now only the low cost is being realized. “The old adage that an elephant for a nickel is only a bargain if you have a nickel and you need an elephant applies here. I am not sure most of us need that elephant. Even if free, perceived low cost is an illusion, because the misapplication of medical tests—and make no mistake, whole genome sequencing is a medical test—is very expensive,” he said.

Genomic testing is likely to be applied as other medical tests are: when and if the situation warrants. Genomic analysis of a panel of variants could be useful in nondiagnostic settings. But Evans argued against burdening the health care system with a flood of extraneous information that cannot yet be interpreted and that may not be welcomed by many people. Ultimately, much more high-quality, outcome-based information on the uses of genomic tests is needed, he concluded.

Suggested Citation:"2 Genomics, Population Health, and Technology." Institute of Medicine. 2013. The Economics of Genomic Medicine: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18276.
×
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Suggested Citation:"2 Genomics, Population Health, and Technology." Institute of Medicine. 2013. The Economics of Genomic Medicine: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18276.
×
Page 10
Suggested Citation:"2 Genomics, Population Health, and Technology." Institute of Medicine. 2013. The Economics of Genomic Medicine: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18276.
×
Page 11
Suggested Citation:"2 Genomics, Population Health, and Technology." Institute of Medicine. 2013. The Economics of Genomic Medicine: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18276.
×
Page 12
Suggested Citation:"2 Genomics, Population Health, and Technology." Institute of Medicine. 2013. The Economics of Genomic Medicine: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18276.
×
Page 13
Suggested Citation:"2 Genomics, Population Health, and Technology." Institute of Medicine. 2013. The Economics of Genomic Medicine: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18276.
×
Page 14
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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.

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