The limits of over-reliance on the math

The second reflection in a two-part series on what we count and why.

This essay is the second in a two-part series about the role of counting in public health. Last week’s essay explored the role of metrics in strategic decision-making and the values that determine what we count and why. Today, I would like to consider the strengths and limitations of quantitative analysis itself.

Quantitative approaches seem to promise the possibility of true impartiality, a means of getting at the truth with pinpoint accuracy and using it to do the most good for the most people. In politics, for example, we can demonstrate quantitatively the general effectiveness of economic or social policies through a range of metrics, including GDP, the unemployment rate, healthcare utilization numbers, or changes in national life expectancy. We can see, for example, that after a certain number of years, national GDP has risen or declined by a certain amount and feel like we are seeing something definitive about the success or failure of the reigning economic policies, information we can then use to nudge policy towards doing the greatest possible good. This nudging reflects an ideal of utilitarianism, which is defined, notably by Jeremy Bentham, as that which does the greatest good for the greatest number of people.

I have long been drawn to utilitarianism as an idea. Doing the most good for the most people is, in many ways, a natural fit for those of us in public health. We want to create healthier populations. This is just another way of saying we want to make as many people healthy as possible, which is an essentially utilitarian statement (a statement which is, admittedly, complicated by public health’s foundational commitment to equity, which I will get to in a moment). This informed my call, in 2013, for a consequentialist epidemiology, in which the pursuit of the greatest good for the health of populations would guide our priorities in thinking and scholarship.

In recent years, utilitarianism has been embraced as a guiding philosophy for a new approach to philanthropy known as Effective Altruism. The Effective Altruist movement looks to use quantitative methods to maximize the effect of charitable giving, so that every dollar does the most good for the most people. In his book, Doing Good Better: How Effective Altruism Can Help You Make a Difference, William MacAskill, one of the originators of Effective Altruism, wrote of the movement:

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On the politics of measurement

Part one of a two-part series on what we count and why.

Lord Kelvin, the influential British mathematician (and namesake of the Kelvin scale, which he developed), once said:

“When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind: it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science, whatever the matter may be.”

Public health is concerned with precisely this—a largely quantitative mode of measurement, a process of counting which helps us better understand the world and the health of populations. On one hand, counting can seem like a simple matter—“easy,” as the saying goes, “as 1, 2, 3.” Yet counting is also complex, fraught with value judgements about what we choose to count and not count and how we go about the counting process. In the next two essays, I will tackle how counting intersects with the work of health. I will start with some thoughts on the politics of measurement—the matter of what we count and why—before moving next week to the strengths and limitations of quantitative approaches.

I will start with measurement, with the metrics that help us do what we do and evaluate how well we are doing it.

I have written previously about how this is a time to build, to shape a stronger foundation for public health. Building takes planning. It takes careful consideration of what we want to create, the pragmatic steps necessary to make this vision a reality, and the metrics by which we will hold ourselves accountable for our stewardship of this process.

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An argument for intentional moderation

Holding the center in an age of extremes.

I would like to talk about moderation. Most of us, if asked our view on moderation, would likely say it is something of which we could use more in our public life. Yet, when we look around, we often see this sentiment “more honored in the breach than the observance.” This is true in our political and cultural ecosystem, and it is true, perhaps particularly so, in our online spaces. If you have been on the internet long enough to find this blog, you have probably seen how extremism, in ideas and in their expression, is often the order of the day.

Yet it is not just at the political rally, on the social media site, on the tongues of bitter partisan foes that extremism poses a problem. It is also in the moments when a touch of immoderation, of overstatement, creates a kind of “cried wolf” paradigm in how the public is presented with key issues, complicating efforts to address them. The reasons for this presentation are often understandable, sympathetic. We may care deeply about an important issue and find ourselves frustrated with the lack of public action on it. So, when we talk about it, we may overstate the risks of inaction, presenting worst-case scenarios—which, while certainly live possibilities, are not the likeliest trajectory for the issue to take according to the data—as if they are open-and-shut representations of the future.

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Ideas about Ideas: The limits of technocratic thinking

What are the limits of expert evidence-based analysis?

This piece was co-authored by Nason Maani, and his version is cross-posted here

We are largely rationalists. After all this essay—part of our ongoing Ideas about Ideas series—is being written from within universities where we spend our careers trying to understand data to identify fact. While we are both often working on social science contexts, our disciplinary training began in medicine and biomedical science, making us even more wedded to empirically grounded, expert-led, evidence-based analysis. There is no question that this approach has done enormous good and remains the foundation of science and what we do in universities. But are there limits to the insights that can emerge from such rationalism? What would a more honest public health look like if we acknowledged the limits of this approach?

If we look closely enough there are plenty of examples where public health hit a wall, not because the evidence was wrong, but because the question that dominated the questions of the moment was not one that any kind of study can settle. Think for example about the recent pandemic, where much animus and political division was cloaked in “what do the data say?” or “follow the science” when in fact what we should have been asking was “what should we do, balancing data and other things that we value as a society?”. Think of the opioid epidemic that was fueled in part by OxyContin marketing. It was in large part built on technocratic claims about addiction risk that turned out to be an ideology about pain medications masked as data. Or think about any number of academic debates where we preserve the peace by deferring to a methodological difference in perspective when in fact the foundational question was about what we were trying to understand in the first place. Technocratic thinking emerges when we treat the answer to societal questions as though they were merely technical questions. But so very few questions that matter to societies, certainly in health, are technical in nature.

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A purple public health: Incentives and the research we don’t do

Why what we study is shaped by our motivations, and what this means for what we know

This piece was co-written by Dr Salma Abdalla and is also cross-posted here

We all create our stories. Who we are and what we do are grounded in the foundational narratives that bring coherence to our day to day. Our work in public health is no exception. The foundational story of population health science and scholarship is that we work to advance health through the force of good ideas: the right questions rise, evidence accumulates, truth wins out. It is a flattering self-image. It is not, however, entirely accurate. Yes, there is much that we do that emerges as we follow the idea-trail, informed by our best thinking about how we generate health. But there is also much that we do that is determined by far more prosaic motivations: by what is funded, fundable, publishable, and institutionally rewarded. The “marketplace of ideas” is, in practice, a market, with budgets, buyers, and incentives. This is not a story of bad actors, but of structures. It is important to understand those structures in order to see where there are gaps in our thinking, and why it is that particular populations may be left behind in having questions that matter to them answered.

It is abundantly well-established that what is studied, and what is funded, are only weakly related to what the actual population burden of disease is. Gross, Anderson, and Powe found NIH funding tracked some measures of disease burden but diverged sharply for many conditions, with clear over- and under-funded outliers relative to need. Replications across decades have shown similar findings; Gillum and colleaguesfound funding poorly predicted disease burden, and a more recent analysis found very limited correlations between NIH funding and five measures of disease burden across 27 conditions.

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Thinking and writing in the age of AI

A two-part reflection on knowledge generation and our technological future.

Before getting into today’s essay, a brief note about some events I will be doing related to the launch of my new book, Why Health? What We Need to Think About When We Think About Health. On September 24, I will join Jessica Steier, of Unbiased Science, for a Substack live conversation about the book. On October 5, I will join Katelyn Jetelina, of Your Local Epidemiologist, for a conversation in Washington, DC, at Busboys and Poets. On October 22, I will be at The Novel Neighbor in St. Louis, MO, for a reading and Q and A. I look forward to these events, to continuing the conversations that shaped the ideas in Why Health?

Now, on to today’s topic.

This is the second part of a two-part series on AI and the future of knowledge generation. Last week’s essay addressed the intersection of AI and universities, touching on how higher education can survive and thrive in the AI era. Today, I would like to move from what AI means for knowledge generation at the institutional level to what it means for individuals. How should those of us who spend our careers in the idea space approach AI in our own lives and work? What does a healthy engagement with AI look like? What standards and best practices should facilitate this engagement in academia and beyond? These are difficult questions, not to be answered lightly, but rather through a process of conversation, of trial and error, of a collective working out of how to best navigate this technological moment. In the spirit of contributing to this conversation, some thoughts on thinking and writing in the AI era, and what this new technology may mean for those who work in the academic space.

Over the last few years, AI has increasingly dominated the conversation about writing and ideas (as well as conversations about much else). Within academia, AI is the elephant in many rooms, raising questions about what this new technology means for the fundamentals of how we do what we do. Questions like: what does AI mean for the practice of research, peer review, and other core academic tasks? Should AI be used exclusively for technical work, or does it have a role to play in the actual generation of ideas? Perhaps at the more existential level, should we be afraid AI might replace us—replace the individuals and institutions whose job is to think, teach, and engage in the idea space? Is AI a tool, even a partner, to be welcomed, or a rival to be warily regarded?

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Thinking about, and with, AI

A two-part reflection on knowledge generation and our technological future.

Before jumping into the theme of today’s essay, I would like to take a moment to acknowledge, with appreciation, the reaction to last week’s announcement of the launch of my new book, Why Health? What We Need to Think About When We Think About Health. It has been a joy to encounter such enthusiasm for the book’s ideas and for having the conversation I long hoped it would help spark about shaping a new practical philosophy for public health. I am excited to continue this conversation in the weeks and months to come.

Now, on to today’s subject.

Last month, The Healthiest Goldfish featured a three-part series about the intellectual foundations of public health including essays on the role of universities, science, and how both contribute to a healthier world. It seemed a ripe moment for such a series, coming as it did at the start of a new academic year, and at a time when the intellectual scaffolding of public health is shifting. The pieces seemed to resonate, with many readers reaching out to say the essays’ themes align with their own thinking about, well, thinking—about the ideas that shape the health endeavor and the institutions that shape these ideas.

Yet, as I have had conversations about these topics, a question keeps emerging: what about AI?

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Introducing Why Health?

A practical philosophy of public health for a new global moment

Why do we wish to be healthy? What is health for? What does it mean to pursue health in this moment, with the memory of COVID still fresh and public health facing political headwinds and a collapse in public trust?

These questions have been central to my thinking over the past few years, as I have tried to reckon with this unstable time for public health. This post-pandemic moment—which I and others have characterized as a “post-war” moment—is a chance for reflection, for evaluating what is fundamental about our field, the philosophy that animates our work at the deepest level.

When we look at the sweep of public health’s history, we see a field that has had remarkable successes. The treatment, even eradication, of deadly diseases. The doubling of global life expectancy. The development, and widespread dissemination, of vaccines. These successes rank among humankind’s greatest achievements. They were won by a combination of knowledge, values, and approaches which have long served public health well.

But we are in a new era. We can no longer assume the thinking that sustained us in the past can reliably do so in the present and future. Proof of this is in the challenges public health faces, its ongoing identity crisis in which our field remains a shadow of the confident, trusted, outwardly engaged movement it once was.

This is fixable. We can not only regain our footing, we can also build new foundations on which to stand. I serve as dean of the Andrew M. and Jane M. Bursky School of Public Health at Washington University in St Louis. Each day, I have the privelege of seeing our students grow into the public health leaders this moment needs. Their talent, their comittment to the work of public health, their keen understanding of this moment give me confidence that public health will find itself again.

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