One Family, Three Disciplines: An Intergenerational Conversation on Public Health
Michael Boehnke, Betsy Foxman, and Kevin Foxman Boehnke
April 17, 2019, Alumni, Biostatistics, Environmental Health Sciences, Epidemiology, Faculty, PhD, Big Data, Biostatistics, Engaged Learning, Environmental Health, Epidemiology, Genetics, Health Communication, Health Disparities, Health Informatics, Innovation, Leadership, Pain Management, Policy, Precision Health, Social Epidemiology, Water Quality
How has the nature of public health evolved since you have been in the field and how does that relate to practices of research, teaching, and communication?
Mike: Two major changes in my work have been the extent of collaboration and the types of technology available. The kind of work I do—identifying the genetic bases for human health and disease—has always been collaborative. When I started in the 1980s, we tended to think of needing a trinity: a statistician, a molecular geneticist, and a clinical expert in the disease of interest. Even back when we were focused on simple Mendelian diseases, where one gene can cause disease, we had to collaborate. In that environment, different groups would compete, because the notion was, if somebody else finds the relevant gene, they’ve won and you’ve lost.
Bringing so many people together and being collaborative rather than competitive has been a critically important change.”
Things have totally changed. Relatively few people are affected with simple Mendelian diseases, and the rapid expansion of genotyping and sequencing technologies has made it possible to identify the genetic basis for a range of common, complex human diseases like type 2 diabetes and disease-related traits like body mass index. But we’ve been successful at harnessing this technology because of our large, cohesive collaborations. Today, we might collaborate with dozens or even hundreds of research groups working with data sets from hundreds of thousands or millions of participants. Bringing so many people together and being collaborative rather than competitive has been a critically important change.
Having interdisciplinary training makes scientists more resilient and flexible in the face of rapid change.
Betsy: The history of public health is so interdisciplinary. From the beginning, schools of public health have drawn individuals with a breadth of expertise from places like engineering, statistics, health policy, epidemiology, medicine, biology, and urban planning. As the field matured, however, there was a sort of separation, where each of these subfields became more siloed and cross-talk decreased. We’re now at an exciting time where barriers are dropping between silos and we have the technology to draw information across fields in new ways—such as connecting infectious disease genomics to clinical epidemiology. I think having interdisciplinary training makes scientists more resilient and flexible in the face of rapid change.
Mike: Remember that bumper sticker from the 1970s or 80s? “He who dies with the most toys wins.” I don’t believe that. I do believe that whoever has the best collaborators is most likely to win—and will have the most fun doing it.
Kevin: I don’t have the historical perspective you two do. But over the past six or seven years, I have also noticed public health’s ability to make connections between different fields—lab science and policy, or genomics and health—and to do it in a bigger-picture way.
Making connections through technology can broaden perspectives, but technology can also be narrowing. Tools have become so advanced and so specialized that it’s possible to work collaboratively without having to translate effectively outside your own field. For example, I study pain, and understanding the neuro-biological underpinnings and mechanisms of pain requires incredible nuance and varied expertise in behavior, neuroimaging, and so on. But such understanding is different from connecting those findings to clinical practice, insurance policy, or education about pain. We have such vast technological power but don’t necessarily think about how it’s going to be applied and how science can be brought into society in useful and ethical ways.
I’d love to see us ask questions about new technologies—like artificial intelligence—to better anticipate potential benefits and risks, as well as the resources we’re using to develop it. There’s always tension between doing something new (which may be costly and inevitably creates new challenges) and trying to address existing structural issues like poverty and environmental degradation.
I realized the most important thing to me was trying to figure how to live in a meaningful way. Much of this started with observing myself and my interactions with the world.”
—Kevin Foxman Boehnke
How do you decide which scientific questions are worthy of study?
Kevin: That’s both a balancing act and a values question. My way of approaching this has changed over time. I decided to get a degree in science because I thought it was practical, especially during the 2009 financial crisis. But my personal crisis with fibromyalgia changed my perspective. I realized the most important thing to me was trying to figure how to live in a meaningful way. Much of this started with observing myself and my interactions with the world.
For example, in 2010 my wife and I began growing a lot of our own produce. We weren’t using pesticides, our main fertilizer was organic compost, and it was a joy to eat a fresh tomato right out of the garden. In contrast, we meticulously washed all the produce we purchased from the supermarket because of residual pesticides. I was observing differences between food from our micro farm and what was going on in industrial agriculture.
This life experience—and my job at that time testing water filters—sparked my interest in water and science. I remember being struck by the fundamental fact that water is the basis of all life. In considering what that actually meant, many of my other interests crystallized into a lens through which to see my future education. This lens helped me ask simple, practical questions about my graduate school applications: How can I study water and human health? What kinds of classes would be useful, and what skills and knowledge do I need to meaningfully address my research questions? I went through this same process again after deciding to study pain. But the first part—figuring out “why”—was internal, and that helped me figure out the “how.”
Betsy: You have to take advantage of opportunities as they arise, whether it’s new technology or new colleagues who come on the scene in your discipline. In my own research studying the epidemiology of infectious disease, I started out investigating urinary tract infections but branched into studying lactation mastitis, antibiotic resistance, and most recently the microbiome. This path reinforced my belief that science is not particularly linear—science has to be able to react to the world and to move organically. A team approach, particularly in public health, helps you remain open and flexible so you can move projects forward more quickly and with more success. This approach also makes me more aware of my responsibility as a public health scientist, which is to do work relevant to both the public and to policy.
Early on, I was captivated by medical detective stories, especially outbreak investigations. The idea of using science creatively to address problems has always been appealing. Similarly, my calling to public health was centered on knowing I wanted to make a difference. I hear the same thing from students. My students often tell me that they enjoy being in the lab but need to see the bigger impact of their work, because that’s what brought them here. And that’s why I’m here too, because I can do something that impacts so many lives.
I don’t think you have to know exactly what you want to do, but you should know why you want it. I’ve heard many students worry that they aren’t ready to progress in their career or degree because they don’t know exactly what they want to research. I tell them that’s not the right question, since growing up and figuring out what you want to do is always difficult. The question should be about the depth of their motivation, since that will allow them to finish.
Mike: I agree. I think it’s silly to do science without first thinking about what we are trying to accomplish. If we’re fortunate enough to be successful, how can this new knowledge be used? If you can’t get past the why, there’s nothing else to talk about. In genetics, we want to understand the genetic basis of health and disease. We want to do better risk prediction. We want to be able to identify targets for new drugs and other therapies. We want to take the information we get from our studies and move those applications forward. And honestly, I find genetics fun.
Betsy: We all do it because it’s fun!
Mike: I don’t think I could have continued in a career spanning nearly 40 years just having fun unless I thought the fun actually had the potential to lead to something valuable. As an undergraduate, my plan was to get a degree in mathematics, but I hadn’t thought about where I’d go from there. After a remarkably valuable conversation with a mentor, I figured out that combining math and biology might be an interesting choice for me. After completing my PhD in biomathematics, I ended up in public health. That was a case where I wasn’t very purposeful, just lucky. But we increase our chances of being lucky by working with great people.
What do you make of specialization in science?
Betsy: Academics can go down rabbit holes pretty easily. We go to conferences and talk to our in-groups. It can be hard to realize how far removed we are. Education and research are not only about getting the information you need but about opening your mind to different types of information. That openness can be very difficult to maintain when people rigidly believe what they believe. Scientists are just as susceptible to circular thinking as anyone else.
Kevin: I like how you describe insular thinking happening to entire groups—like living in a big echo chamber. Say we were all studying phrenology and just continually validating each other’s work.
Mike: Whoa, that is a cool bump!
Kevin: Yeah, look at that bump! You can always find data collected using a scientific process. But if the underlying root is rotten, data can be dangerous. Exposure to other professions and disciplines can act as a safety net for science because important ideas are often resilient across fields. It’d be one thing for scientists to discuss phrenology and even study it. But outside the lab they will bump up against ideas that remind them of phrenology’s gnarly underpinnings, like racism and sexism. So contact is important, because it reminds us that our research can be used in unanticipated ways—some useful, others downright evil.
Betsy: Absolutely true. Specialization can lead to polarization—and the development of jargon, which makes communication harder because the barrier to entry includes learning a new language. People want answers to be neat and clear, but science rarely produces simple answers.
Mike: Agreed. I want to take a different riff on bumps. There are always bumps, or signals, in the data. You can almost always find signals—especially in big data sets.
In this era of big data and data science, computer scientists and statisticians sometimes compete for who owns the field of big data and who defines what big data is. To me, that’s a false dichotomy: Statistics without computing is no longer feasible, because the kinds and amounts of data we have need computation. And computing without statistics is flat out dangerous. If you’re just looking for “bumps” without the interpretive process of statistics, you’ll get results that at best are meaningless and at worst dangerous. We have to bring together multiple disciplines to check each other’s knowledge, to find the bumps and then evaluate whether those bumps are meaningful.
Betsy: And even with good statistics and computing—without substantive knowledge about what you are studying and the underlying theory—it is easy to make inter-pretive errors.
Kevin: Right. People expect science to produce useful data that will help us live better. But data are useful only when used correctly. Scientists unwilling to explain and participate in translating their data abdicate responsibility and lose a critical opportunity to participate in the ongoing human experiment.
Mike: And to Betsy’s point about communication, health education and communication are so incredibly important. If our work as public scientists is to be made useful, we need to be able to communicate and engage—to inspire action.
This article is based on an interview and edited for brevity and clarity.
ABOUT THE AUTHORS
From left in lead image:
- Michael Boehnke, Richard G. Cornell Distinguished University Professor of Biostatistics, Director of the Center for Statistical Genetics and the Genome Science Training Program
- Betsy Foxman, Hunein F. and Hilda Maassab Professor of Epidemiology, Director for the Center for Molecular and Clinical Epidemiology of Infectious Diseases
- Kevin Foxman Boehnke, PhD ’17, Research Investigator in the Department of Anesthesiology and the Chronic Pain and Fatigue Research Center, Michigan Medicine