NUR 610 Module 4 Precision Medicine and Population Health Discussion Example

Discussion Board: Precision Medicine in Population Health

A Million Genomes or a Thousand Blood Pressure Cuffs? Precision Medicine's Tradeoffs for Population Health

Initial Post

The three terms in this week's objectives are easy to blur. Genetics usually refers to single genes and their inheritance, such as the LDL receptor gene in familial hypercholesterolemia. Genomics refers to the whole genome and the interactions among many genes and the environment. Precision medicine is an approach to prevention and treatment that accounts for individual variability in genes, environment and lifestyle, so it uses genomics but is broader than it.

The All of Us Research Program is the clearest attempt to build precision medicine for an entire nation. Its stated aim is to enroll one million or more people in the United States, deliberately recruiting groups long left out of research, and to collect health records, surveys, physical measurements, biological samples and, increasingly, genomic data (All of Us Research Program Investigators, 2019). The program's emphasis on diversity responds to a real problem: most genomic research has involved people of European ancestry, which limits how well findings apply to others.

The tradeoff is easy to state. Precision medicine invests heavily in tailoring care to individuals, while many of the largest gains in population health come from broad measures that help everyone a little, such as controlling blood pressure, reducing smoking and improving food environments. In my county, uncontrolled hypertension affects far more people than any single genetic condition. If precision medicine draws attention and resources away from these basics, it could widen disparities, helping those already connected to sophisticated care.

But the choice is not all or nothing. The CDC's tiered approach to genomic applications classifies them by level of evidence, and identifies a small set, Tier 1 applications, with enough evidence to support implementation, such as cascade screening for familial hypercholesterolemia and for BRCA-related hereditary cancer and for Lynch syndrome (Dotson et al., 2014). These are precision medicine at population scale: targeted, evidence-based and capable of being built into public health programs. McGuire et al. (2020) describe how genomics is moving toward clinical and public health use and emphasize that realizing its benefits will require attention to diversity, data sharing and equity.

My conclusion is that precision medicine and population health need each other. Population health needs genomic tools that are proven, like Tier 1 applications, and precision medicine needs population health thinking to make sure its benefits reach everyone. For nurses in public health, that means supporting evidence-based genomic programs while continuing to champion the basic measures that matter most.

Reply to Sofia

Sofia, your example of pharmacogenomic testing before prescribing clopidogrel was a good case of precision medicine with a clear clinical benefit. I wondered about the population side. If testing is available mainly in large academic centers, patients in community hospitals may continue to receive a drug that works poorly for them because of their genotype. That seems to be the equity problem in a single example. One way forward might be to focus testing on populations where the relevant variants are more common, but that raises questions about using race or ancestry as a proxy for genotype, which can be inaccurate and stigmatizing. Do you think universal preemptive testing, where everyone's pharmacogenomic profile is stored in the record, would be fairer than targeted testing, even if it costs more? Both positions have merit, and I hope we can debate this in class. Universal testing would avoid the problem of guessing who needs testing, but it would also create large amounts of genetic data that must be stored and protected for years. Targeted testing costs less but relies on knowing in advance who is at risk, which is exactly where bias can enter.

Reply to Tom

Tom, you argued that precision medicine is mostly hype and that public health should ignore it until it proves itself. I share some of your skepticism, but the tier system suggests a middle path. A few genomic applications already meet a high bar of evidence, and ignoring them would leave people with treatable inherited conditions undiagnosed. The question may be less whether to engage with precision medicine than which parts to adopt and how to make them reach everyone. I also think your point about opportunity cost is important. Public health budgets are limited, and a dollar spent on genomic screening is a dollar not spent on blood pressure control. Perhaps the answer is that public health should adopt only Tier 1 applications and demand strong evidence for anything else. Would that standard satisfy your concern, or do you see risks even in Tier 1 programs? One risk I can see is that even proven programs can reach mainly people who are already well served, which is the equity concern from our earlier weeks. A Tier 1 standard addresses evidence but not fairness, so public health might need a second test that asks who the program will actually reach.

References

All of Us Research Program Investigators. (2019). The "All of Us" Research Program. New England Journal of Medicine, 381(7), 668-676. https://doi.org/10.1056/NEJMsr1809937

Dotson, W. D., Douglas, M. P., Kolor, K., Stewart, A. C., Bowen, M. S., Gwinn, M., Wulf, A., Anders, H. M., Chang, C. Q., Clyne, M., Lam, T. K., Schully, S. D., Marrone, M., Feero, W. G., & Khoury, M. J. (2014). Prioritizing genomic applications for action by level of evidence: A horizon-scanning method. Clinical Pharmacology & Therapeutics, 95(4), 394-402. https://doi.org/10.1038/clpt.2013.226

McGuire, A. L., Gabriel, S., Tishkoff, S. A., Wonkam, A., Chakravarti, A., Furlong, E. E. M., Treutlein, B., Meissner, A., Chang, H. Y., López-Bigas, N., Segal, E., & Kim, J.-S. (2020). The road ahead in genetics and genomics. Nature Reviews Genetics, 21(10), 581-596. https://doi.org/10.1038/s41576-020-0272-6