Can the “fantasy equation” of population health be solved? Should it be?

Let’s start at the end – the last sentence of “Understanding Population Health Terminology”, an article published by one of us, Kindig, in 2007: “The overarching question of population health is what is the optimal balance of investments (e.g., dollars, time, policies) in the multiple determinants of health (e.g., behavior, environment, socioeconomic status , medical care) across the lifespan that will maximize overall health outcomes and minimize population-level health inequities? This is an important challenge that will require decades of academic attention. and decision makers.

This idea stems from the 1990 Evans-Stoddart population field model it has been the intellectual foundation of our field for decades. This paper and its final figure, Exhibit 1, show the evolution from the medical model presented in the health care and disease boxes on the right to the broader model with expanded concepts of outcomes and the addition of multiple determinants of health.

Exhibit 1: A field model of population health

Source: Evans RG, Stoddart GL. Produce health, consume health care. In: Why are some people healthy and others not? New York (NY): Routledge; 1994. p. 27-66.

It is certainly a complex model that one of its creators later called a “fantasy equation”, stating that “at present we only vaguely understand the relative magnitudes of the coefficients on the independent variables that would inform specific policies rather than general directions, although we are beginning to see the variables themselves more clearly. Robert Evans and Greg Stoddart rated again in 2003 that “most students of population health cannot confidently and accurately answer the question, ‘Well, where? you put the money?'” That hasn’t stopped us from calling his solution for the past 25 years here and here but with little to show for it.

One of us, Kindig, presented this conundrum to a group of students during an invited lecture for the course “Introduction to population health” of the other, Mullahy. At this point, Kindig asked, “How is this possible? It can’t be as difficult as all the modeling and equations needed to land on the moon, can it? »

Here are the answers we found on the class whiteboard.

It’s harder

It’s social science, not physics and engineering. Causality is difficult to conceptualize and, even if well conceptualized, to demonstrate empirically.

There are several results

With the significant expansion of the model beyond disease to health and function and even well-being, the number of outcomes explodes: overall mortality, morbidity, health-related quality of life, as well as disparities and inequalities in each of them. Summary measures, while sometimes useful, add complexity to the weighting components. This seemingly constant instability led one student to wonder if the “fantastic equation” exists, is it only applicable in a steady state, where the systems or process variables are immutable over time? Since we live in a dynamic state, such a fixed solution to the “fantasy equation” probably does not exist and even if it did, it might not be applicable in a decade or two.

There are several units of analysis

Another outstanding question is: what population? What is of primary concern and relevant to clinical or social policy: individuals, communities, nations, the world, marginalized groups, separately or all together?

Many, many complex empirical problems

To speak of a “solution” to the fantasy equation is in itself a fantasy. Its essential nature is that of a complex set of cause and effect relationships. For data to illuminate these relationships, not only must specific causes and outcomes have clear definitions, but those definitions must find empirical equivalents in the available data. So what follows is a litany of additional questions:

  • What are the individual and/or population health indicators of interest?
  • What specific determinants are likely to be manipulated by policy interventions? (A reminder that, as is sometimes claimed in the literature on causation, there is “No causality without manipulation. »
  • What conceivable policies can be designed or modified to bring about such manipulation?
  • How quickly do determinants and policies take effect?

The empirical task at hand is hardly simplified when one recognizes that the confusion and interactions between determinants and between policies at a given point in time and over time are almost certainly of fundamental importance. Even if such interactions could be characterized conceptually, learning them from existing data would be a formidable task.

Another student suggested that the “fantasy equation” is too complex, too fluid, and filled with too many unknowns to solve. External forces and trade-offs add additional layers of complexity, so changing one variable or coefficient will change many other variables that affect downstream results.

Data limits

We can only review what we have data on. We know a lot about Medicare since it is a massive program in the public sector. Data on other determinants is more limited and some issues such as armed violence cannot be fully understood due to policy restrictions. Additionally, in the spirit of privacy protection, various statistical agencies, such as the Census Bureau, are increasingly creating obstacles for researchers to access data at the individual level.

At the end of the discussion, the majority of students agreed that the moon landing was much less complex.

Where does that leave us?

One of the students asked, “For how long do we weigh the pros and cons and discuss how much to invest and where? How long does an idea ruminate in a think tank before it becomes relevant to the very people it aims to help? »

We refuse to accept a political scenario in which investment decisions are based on guesswork, hunches, political whims or opinions. New datasets and new analytical approaches should bring more precision, and these efforts could potentially have an impact worthy of a Nobel Prize in medicine or economics.

Despite the slow progress, we are asking the question of the optimal balance of investments more often, and answers are beginning to emerge. New disciplines are tackling the problem from a systems science perspective. Bobby Milstein and his colleagues, for example, have asked “What are the health and wellness priorities that emerge after considering the entangled threats and costs? » and found that “poverty reduction and social support were the highest ranked interventions for all outcomes in all counties. Interventions addressing smoking, substance abuse, routine care, health insurance, violent crime, and youth education also contributed significantly to some outcomes.

After this course, we contacted Gregory Stoddart and invited him to join us in writing this piece. He declined, citing his satisfactory retirement from McMaster University, but sent this e-mail message: “Although, as you know, I think the fantastic equation may be unsolvable, that does not mean that we do not know in which directions to reallocate resources. The concept of marginal returns can and should guide us here, even within rough orders of magnitude. We don’t need precision to help more people be healthy or to be more equitable.

In other words, solid estimates of directions and orders of magnitude can be just as important in serving decision-makers as precise but unreliable results. In a clinical research setting, John Mullahy and his colleagues described this challenge this way“If the massive investment in transforming discovery into health is to bear fruit, it is essential to understand when research efforts do or do not lead to full discovery. When research fails to lead to a complete discovery, the fact that it can partially identify quantities of interest is to be celebrated, not bemoaned.

That said, there remains an equally urgent step in solving the “equation” of fantasy, whether in whole or in part. It is about studying what kinds of information about these cause and effect relationships are actually useful to know. A valuable practical step in this direction would be to engage real-world decision-makers in learning what kind of information about the causes and effects of population health would be most useful in shaping policy and practice.

George Box wrote the famous that “all models are wrong, but some are useful”. The task at hand is to determine the willingness of decision-makers to exchange the “right” for the “useful”. We assume that many will tolerate a reasonable degree of vagueness. Knowing this should usefully guide the next generation of population health research on the fantasy equation.

Author’s note

We appreciate student contributions from the Fall 2021 “Introduction to Population Health” course PHS 795 University of Wisconsin Madison School of Medicine and Public Health.

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