This blog is returning from hiatus, with a new, occasional series on thinking about complex problems.

At a recent convening of leaders in public health, Dr. David Fleming, of PATH, shared what has become a common observation regarding the relationship between spending and outcomes on US healthcare: namely, that the US spends far more than any other nation (both per-capita and in total dollars) on the health of our citizens, yet achieves results (measured in terms of life expectancy) which place us last on the list of developed nations:

This fact has featured prominently in debates over the design of the US healthcare system, fueling arguments on all sides. Yet a much more interesting picture emerges, Dr. Fleming showed, when you look at life expectancy by county, rather than nationally:

Here we see how US counties perform when compared to the world’s ten-best performing countries in terms of life expectancy. The darkest blue counties are up to 15 years ahead of the world’s best performing countries; the darkest red counties are up to fifty years behind.

What becomes immediately clear from even a casual glance is how place-based US health performance is. America’s least-well-performing communities are clustered in the American Deep South — there are counties in rural Alabama, for example, where life expectancy substantially lags that of say, Vietnam or Bangladesh. Conversely, there are certain Northeastern and California communities that are so far ahead it may take even the most advanced economies another decade just to catch up to them.

The absence of these distinctions in our everyday debates over healthcare illustrates what statisticians call the tyranny of the average, a term used describe the consequences of using averages as indicators of the performance of complex systems.

Average indicators mask the “lumpy” reality of many complex phenomena, and generally, dumb down our thinking and our debates. They suppress our understanding of both the negative and positive outliers in a given domain. Aggregate educational statistics about a city, for example, can “hide” a failing school, just as readily as they can “hide” school that is outperforming. They are an enemy of accountability and a disincentive to action.

Averages also shape our view of what constitute “normal” phenomena in the popular imagination, and they reinforce our assumptions about what “normal” responses to those phenomena should be. The real world, of course, is much more diverse. Psychologists, for example, are learning that there is a far wider array of healthy responses to adverse life events than our default cultural categories suggest. As PACE University’s Anthony Mancini writes:

 “Reliance on average responses has led to the cultural assumption that most people experience considerable distress following loss and traumatic events, and that everyone can benefit from professional intervention. After 9/11, for example, counselors and therapists descended on New York City to provide early interventions, particularly to emergency service workers, assuming that they were at high risk of developing posttraumatic stress disorder. In fact, most people—even those who experience high levels of exposure to acute stress—recover without professional help.

And we now know that many early interventions are actually harmful and can impede natural processes of recovery. For example, critical incident stress debriefing, a once widely used technique immediately following a traumatic event, actually resulted in increased distress three years later among survivors of motor vehicle accidents who received this treatment, compared to survivors who received no treatment.”

The Tyranny of the Average is given perfect mathematical expression in Anscombe’s quartet, four datasets that appear nearly identical when described with simple statistics, but reveal a completely different picture when graphed. All of these datasets have exactly the same average, variance and correlation – yet the underlying behavior of the system they represent is completely hidden until you look at the data spatially:

Each of these variations describes a different reality, and each of those realities require a different range of strategies if we wanted to drive intentional (and presumably beneficial) change. There are many strategies which might improve one variation, but actually harm the others.

For many complex social phenomena, such “lumpy” distributions are often fractal – repeating at every resolution. Just as some counties dramatically over- or under-perform their peers in healthcare, so too one community within a county may do so, and one neighborhood within that community, and one block within that neighborhood.

You can see this in evidence in the State of Mississippi, which ranks 50th in the country in terms of infant mortality, with 9.08 infant deaths per 1000 live births. (This is called the “IMR” or “Infant Mortality Rate.) Drill down on this “averaged” statewide indicator and, as you might expect, you’ll find wild disparities at the county level, with some counties doing dramatically worse (or better) than their peers:

Immediately, you may think this is a good proxy map of where the maternal health system is strongest and where it is weakest. Yet even this map hides something significant, and distorting – the shocking disparity between the IMR for black and white mothers. Here’s the relevant comparison data for the twenty most populous counties in Mississippi:

As you can see, in every single county on the list, the IMR for black mothers is higher than it is for white mothers. In Alcorn county, for example, the IMR for black mothers is 30.8 deaths per 1000 births – 275% higher than it is for white mothers who live in the same county. (The IMR in North Korea, in contrast, is ‘only’ 22.) In Pearl River County, the IMR for black mothers is 24.9, an incredible 344% higher than the IMR for white mothers of 5.6. Yet, on the above map, Pearl River only merits a severity ranking of 2 (on a scale of 1-5) because the aggregate statistic effectively “hides” this unequal racial outcome.

Two observations stand out here: first, for those seeking to encourage positive social and ecological change, having higher-resolution statistics is not only tactically essential, it’s politically and even morally essential. Without them, you can’t see the texture of the problem you’re confronting – and you can’t build the interest groups, stakeholders, accountability frameworks or political case necessary to measure and drive change.

The second is that many of our 21st-century problems may ultimately be better understood in terms of place, rather than in terms of problem-set. Poverty, limited social power, environmental degradation, underfunded education, poor health access and other issues often cluster spatially – and mutually reinforce one another. But sometimes, they don’t. Understanding the difference is essential if we’re to avoid one-size-fits-all solutions.

To do that, we need a revolution in data collection and reporting – including the production of new kinds of indicators that not only tell us the health of the overall system, but can decompose that larger picture into its constituent parts – and reveal the nuanced ‘texture’ of a place in both space and time.