While researching a separate blog post on artificial intelligence in healthcare, I kept running into articles about precision medicine, a relatively new field that holds much promise for optimizing patient outcomes. As I took a detour down the precision medicine rabbit hole, I soon happened upon a related approach to public health that I’d like to explore in more depth here. It’s called “precision public health” (PPH) and despite its name, the concept is very much in line with the principles of population health, a subject I discuss often on this blog.
If you’ve heard of precision medicine, you might be wondering how a discipline whose purpose is to map out care plans based on a given patient’s genetics (i.e. medicine practiced on a molecular level) can possibly tie into public health, a field that, as the CDC Foundation notes, concerns itself “with protecting the health of entire populations.”
Aren’t the scales of these two endeavors too dissimilar to admit of any overlap?
You can be forgiven for wondering about this seeming paradox, because at first I couldn’t quite grasp it either. But stick with me and I’ll walk you through the main ideas so that by the end of this post, you’ll be armed with enough information to not be caught off guard when someone brings up PPH in conversation.
Defining Precision Medicine
Although the concept of precision medicine has only been in the public consciousness for about twenty years, it has made major inroads into healthcare delivery in the United States. With an attendant boom in the field of genomics over the past few decades, the field of precision medicine has matured at a rapid pace. Its rise to notoriety has been so swift, in fact, that in 2015, President Obama revealed the Precision Medicine Initiative, a major push to move precision medicine into common clinical practice.
As traditionally defined, precision medicine uses information about an individual’s genome, environment, and lifestyle “to guide decisions related to their medical management” with an overall goal of providing “a precise approach for the prevention, diagnosis and treatment of disease.” The term is analogous to “personalized medicine,” “personalized care,” or “individualized medicine” and, according to the American Cancer Society, “Precision medicine is a way health care providers can offer and plan specific care for their patients, based on the particular genes, proteins, and other substances in a person’s body.”
According to the Brookings Institution, precision medicine “uses personal information, such as DNA sequences, to prevent, diagnose, or treat disease. From targeting late-stage cancers to curing rare genetic diseases, precision medicine is poised to impact millions of people within the next decade.” The American Medical Association goes still further in its assessment of the field, stating that “Precision medicine is a tailored approach to health care that accounts for the individual variability in the genes, environment and lifestyle of each person.”
The emphasis here is mine, and in a bit we’ll delve into how placing more emphasis on a person’s “environment and lifestyle” can represent an area of difference between precision medicine and its cousin, precision public health.
Precision Medicine: A Brief History
As the name would suggest, precision medicine is all about tailoring medical care pathways to individual patients. Throughout most of history, medical treatments were aimed at helping the average patient. Without a detailed understanding of genetic differences between patients, a one-size-fits-all approach predominated. This makes sense given the limited tools available to physicians over the past few centuries; as a consequence, however, some patients benefited from treatments while others did not.
It’s an oversimplification to call this a scattershot approach, but that description isn’t too far off the mark. Thankfully, with the advent of the Human Genome Project (HGP) — an initiative that aimed to map the genetic makeup of humans and other organisms — researchers were able to unlock how disease states operate on a molecular level. The project, which ran from October of 1990 to April of 2003 (with the final 8% of the human genome having been sequenced just last year), resulted in sequencing “around 20,000 genes of those that make up the blueprint of our bodies.”
This advance represented a giant leap forward in human understanding, but gaps in our knowledge remained. Aside from the remaining 8% of the human genome left to be sequenced that I mentioned above, many in the scientific community were concerned about the fact that nearly 70% of the genome referenced during the HGP came from one single man who lived in the Buffalo, New York area and had his genes sequenced as part of the project.
Although the man was likely of mixed race, it didn’t sit well with some that the other HGP participants used to make reference genomes were mostly of European descent. The lack of diversity in reference data prompted a host of questions, including whether variations from the reference genome should actually be considered abnormal, and how much one genome could be relied upon to provide an accurate picture of variation among every person on the globe. Some work-arounds were employed, but evidently none of them provided the accuracy sought by experts.
Fast forward to earlier this year, when it was reported that, in an effort to create a more accurate standard measurement for the human genome, scientists had created what they called the “pangenome.”
The New York Times article linked to above states that “the new ‘pangenome’ incorporates near-complete genetic sequences from 47 men and women of diverse origins, including African Americans, Caribbean Islanders, East Asians, West Africans and South Americans.” Of particular note, the article goes on to say that the “revamped genome map represents a crucial tool for scientists and clinicians hoping to identify genetic variations associated with disease. It also promises to deliver treatments that can benefit all people, regardless of their race, ethnicity or ancestry…”
Precision medicine offers tremendous benefits, because, as a recent article in the Harvard Public Health magazine points out, “not everyone benefits equally from the same intervention. Someone at increased genetic risk for breast cancer, for instance, might benefit far more from regular mammograms than a neighbor at low risk.” This realization, says the author, is driving the development of the field.
Interestingly, with such a strong push to include non-clinical social determinants of health (SDOH) in patients’ care decisions, it is perhaps not surprising that healthcare practitioners are turning to precision medicine and the related — though more encompassing — field of “precision health” to help tailor healthcare delivery that optimizes for positive patient outcomes. But before we delve into the related but separate topic of precision public health, let’s first gain a better understanding of how genomic data is collected and analyzed.
Big Data and Precision Medicine
While advances in precision medicine represent a welcome milestone in the fight for health equity, mapping varieties in the human genome on an ever more fine-grained level has led to growing uneasiness on a number of fronts. A 2017 paper that appeared in the Journal of Community Genetics encapsulates many of these concerns. Chief among them are the following items:
- How to report genetic variants of uncertain significance (i.e. how to deal with incidental findings unrelated to a given test’s clinical indication)
- The need to expand the availability of genetic counseling services
- Training healthcare providers to interpret and better understand results from next-generation sequencing (NGS) technologies
- How best to deal with the ethical tensions inherent in practicing in this new clinical environment
Aside from these complications, there is the monumental question of how, in the era of electronic health records and wearable sensors, can clinicians best collect the avalanche of data the new technology has occasioned. This question is hard enough to answer on its own, but perhaps an even bigger challenge lies in how to analyze the treasure trove of new data to find appropriate clinical applications. With data rolling in at ever increasing rates from multiple platforms in a variety of geographic locations, how do physicians and researchers keep it all straight?
Enter big data analysis, a field that has been undergoing an explosion in recent years. According to an article in the International Journal of Molecular Sciences, “Big Data analytics uncovers hidden patterns, unknown correlations, and other insights through examining large-scale various data sets.” The article also states the following:
“While integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a Big Data infrastructure exhibit challenges, they also provide a feasible opportunity to develop an efficient and effective approach to identify clinically actionable genetic variants for individualized diagnosis and therapy.”
Put another way, as a result of this new wellspring of patient information, the need for big data analytics to search for patterns that can lead to effective medical solutions is paramount. And by “effective medical solutions,” I’m not just referring to treating disease states that have already manifested; indeed, precision medicine can also be deployed to predict illnesses before they occur.
If you want to really be dazzled by precision medicine’s promise of enhancing patient outcomes, consider this outlook on the discipline’s potential to head off diseases before they appear:
“Other genome-based technology platforms [for example, assays for RNA, proteins, metabolites] are also increasingly being used to classify disease states (as diagnostic tests) and to predict future clinical outcomes (as prognostic tests). Together, these approaches form the basis for 1) a new molecular taxonomy of disease, 2) provide more precise ways to screen for and to detect disease at its earliest molecular manifestations, often pre-clinically, and 3) allow the selection of certain drugs guided by a patient’s underlying genetic makeup.”
The authors go on to write the following: “Given that a disease’s evolution from baseline risk to clinical signs and symptoms often occurs over many years, it is likely, in the future, periodic molecular and digital profiling will shift health care strategies from acute intervention and disease management to a focus on assessing health and proactive management of disease risks and prevention.”
I don’t know about you, but I find the prospect of detecting illnesses and diseases before they show up on our doorstep to be nothing short of mind-blowing. New avenues for treatment like personalized drug therapy, creating “digital twins” to help researchers better administer treatments to a range of people, and pursuing mutation-targeted therapies for cancer will likely soon supplant older, less focused treatments and usher in a new era of medicine.
Precision Health Versus Precision Public Health
If we can get back to definitions for a minute, I’d like to offer an observation: it seems to me that the main difference between precision medicine and its closely-related cousin “precision public health” is one of scale: precision medicine applies to situations where individuals, or perhaps small patient cohorts, are targeted with healthcare interventions that conform to their individual, often molecular-level circumstances. Precision public health, on the other hand, takes a solution that’s designed to target an individual’s biological makeup and scales it up to address health problems shared by entire patient populations.
It should be noted that, as is often the case with relatively new medical fields, there doesn’t seem to be a consensus on what exactly the term “precision public health” constitutes. More to the point, multiple sources use the terms “precision population health” and “precision public health” in a way that leads me to believe that they can be used interchangeably, at least for now.
An article in the Journal of Translational Medicine makes this point directly:
“Reframing and broadening precision medicine beyond ‘omics’ (e.g., genomics, proteomics, metabolomics) has been referred to as ‘precision population health’ or ‘precision public health.'”
Since there doesn’t seem to be a unifying term for this new medical pursuit, for the duration of this blog post I’ll use “precision public health” to denote a wide-scale application of genomics to healthcare. This seems to be the favored term in the research I’ve read, so we’ll go with it.
That being said, however, we should pause to quickly examine a sort of “halfway term” that exists between the two poles of precision medicine and PPH, namely “precision health.”
Precision health can be thought of as an approach to patient care that encompasses more of the factors that impact a person’s health outcomes. According to the CDC, precision health is broader than precision medicine, taking into account not just genetic factors, “but also approaches that occur outside the setting of a doctor’s office or hospital, such as disease prevention and health promotion activities.”
Sound familiar? (Hint: this is what SDOH is all about). The CDC makes the further point that, when precision health is used to elucidate approaches that public health practitioners can take to help improve the outcomes of distinct patient populations, this is called “precision public health.”
A Public Health Approach
With that slight detour out of the way, let me add one final flourish before we discuss the nascent field of PPH: the term “population health” should be kept in mind as an adjunct to any broad approach that applies molecular-level testing to large patient cohorts. Allow me to explain.
Population health has been defined as “the health outcomes of a group of individuals, including the distribution of such outcomes within the group” and includes considerations of “health outcomes, patterns of health determinants, and policies and interventions that link these two.” Since groups targeted by public health initiatives can be thought of as discrete patient populations, it seems reasonable that the word “population” would be employed when discussing the utilization of genomics to better allocate healthcare resources.
Put another way, although the use of the term “population health” isn’t widespread in the literature focusing on PPH, I think the concept bears keeping in mind as clinicians begin to employ risk stratification and other tactics based on genetic testing that divide patients into groups.
As if to prove my point, the authors of a 2019 article in Frontiers of Public health noted the following when it comes to genomic data:
“One of the many potential roles of PPH is to use population level data to better identify how individuals can be aggregated into larger groups. This could be achieved using the increased knowledge derived from precision medicine about the biological pathways involved in disease. Such an approach may be critical to ensuring that evidence-based research methodologies can still inform decision-making in the context of increasingly smaller target groups for therapies and diagnostics.”
Remember the AMA quote from earlier about how precision medicine accounts not just for the individual variability in genes, but also for a person’s environment and lifestyle? With all due respect to the AMA, in my judgment this is actually where the fields of precision medicine and PPH begin to diverge. The authors of a 2020 paper that appeared in PLOS Medicine neatly encapsulate the field of PPH in a way that I find useful:
“Big data enable the potential for more ‘precision’ in medicine and public health. In theory, more data at the individual level can help redefine the meaning of healthy and the progression from health to disease, helping to uncover preventable disease risk factors and allowing more precision diagnostic and prognostic information. At the population level, big data can help integrate multiple social and environmental risk factors such as air pollution, neighborhood walkability, and access to healthy food.”
It’s this last part that distinguishes PPH from its antecedents: taking genetic-level data and combining it with SDOH information like the air pollution a patient experiences on a daily basis. Doing so helps physicians create a more complete picture not just of a person’s current health, but of the health risks they face in the future.
Put succinctly, the authors of the PLOS Medicine article write, “If precision medicine is about delivering the right intervention to the right individual at the right time, PPH can be simply viewed as delivering the right intervention to the right population at the right time.”
Of course, employing health surveillance in this way addresses health disparities in a more comprehensive way than has thus far been possible. One can imagine innumerable applications for this technology in fighting health inequities, including fine-tuning social vulnerability indices (which I wrote about in a previous blog post).
Although the rampant pace of development is exciting, significant hurdles to full-fledged adoption of PPH exist. Realities like a lack of incentives for health entities to share electronic health information and entrenched regulatory barriers represent just part of the range of challenges ahead. But despite these pitfalls, I firmly believe that, as the use of artificial intelligence opens up new avenues for health care delivery, PPH will flourish. For this reason, it is a field worth keeping a close eye on.