There's a big problem with the pre-existing conditions argument.
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From a scientist’s point-of-view.

By Sandro Galea
March 28, 2017

The healthcare debate unfolded rapidly last week, with the story changing at a dizzying pace, until it ended with the defeat of the American Health Care Act (AHCA). Ultimately, Congress chose not to vote on “Trumpcare,” due to Republican division over the substance of the widely unpopular bill. As we watched these events play out, traditional forms of commentary often felt a step or two behind the moment. We are, after all, used to seeing situations unfold linearly, with one event leading to another, following the standard pattern of cause and effect. Yet, in the blur of last week, developments emerged almost simultaneously. We saw President Trump lobby for the AHCA, even as Republicans worked to amend the bill to make it more palatable for their caucus prior to a scheduled vote on the legislation. Republicans then delayed the vote, before, ultimately, rescheduling it for Friday, then calling it off. For those of us who are invested in navigating the complex subject of healthcare, these events begged the question: how do we make sense of such wild times?

In my book, Systems Science and Population Health, my co-authors and I discuss how to apply a systems science approach to the work of population health — the study of the conditions that shape health within and across groups of people, and of the mechanisms through which these conditions determine the health of individuals. Systems science is an interdisciplinary field that studies how the interaction of factors produces outcomes—how the causes and consequences of events can, taken together, form the basis for everything from a disease epidemic, to a pattern of human behavior. Complex systems are characterized chiefly by these elements: many interacting components, non-linearities, discontinuities (i.e. the occurrence of something unexpected), and emergent properties, which come about when systems take on a “life of their own” and develop into something that often looks quite different than the original inputs.

With this picture in mind, it is clear that the healthcare debate, particularly as it played out last week, is very much a complex system, and can be constructively viewed through a systems science lens. First, there are multiple components. These include Congress, the President, various outside stakeholders—including insurance companies and the pharmaceutical industry—and the American people.

The question then becomes: which component is key? Each attempted to influence the debate, and at various times eachseemed to be, briefly, in control of the narrative. In the days immediately following the election of President Trump, Republicans appeared to more or less have the upper hand, and the fate of the ACA looked sealed. With Republican majorities in the House and Senate, and GOP control of the Executive Branch, there were few reasons not to think that a swift repeal of the healthcare law was achievable and likely. But as the practical reality of repeal began to sink in for the millions of Americans who stand to lose coverage, the voices of concerned, angry citizens soon took center stage at town halls across the country. The challenge was compounded for proponents of repeal by the release of the AHCA. When the Congressional Budget Office reported that the AHCA stood to strip 24 million people of their health insurance, Congressional Republicans divided into two camps — those who disliked the bill for fear that it would harm their constituents (not to mention their own electoral prospects) and those who did not feel that the bill went far enough in rolling back the “socialism” of the Affordable Care Act (ACA).

It is this Republican division that most clearly illustrated the role of nonlinearity in the healthcare debate. In recent weeks, we have seen how, in politics, X does not always equal Y, but rather some function thereof, as the traditional political pressures that have been brought to bear on the healthcare fight have not yielded simple results. The most obvious example is the singular presence of President Trump, and his attempts to lobby for the AHCA. Last week, he was said to have been in “full arm-twisting mode,” as he tried to leverage his vaunted deal-making ability into the fulfillment of his frequent campaign pledge to “repeal and replace” the ACA. In the past, Presidents have pushed their legislative agendas with varying degrees of success; at present, the commander-in-chief has so far failed to control the chaos of the healthcare debate or channel his considerable electoral advantages into the swift passage of his promised initiative. From a systems perspective, this is clearly because the interplay of factors involved have shaped his input into an unlikely output — in this case, the rush to “repeal and replace” led to a bill that polarized Trump’s caucus. As a consequence, the chief source of political pressure on healthcare became less and less Trump, and more and more the ideological concerns of his fellow Republicans.

A series of discontinuities have further disrupted this debate, creating uncertainty as the process lurches into its own. These events have occurred suddenly — as, for example, when Republicans announced that a repeal vote would be delayed, before reversing course. In systems science, such disruptions give way to emergent properties, whereby a surprise break or resolution leads to an outcome that few could have predicted. In the context of healthcare, this could mean anything from a law that is even more exclusionary than the AHCA, an ACA repeal with no replacement in sight, or even — unthinkable on November 9 — the survival of the ACA in its present form, an outcome which begins to seem ever-more likely.

Looking at the healthcare debate, and the broader activities of the Trump administration, through a systems science lens helps us take perhaps a more dispassionate view — to focus less on trying to predict where each moment will lead, and more on observing the interaction of factors that will, ultimately, determine outcomes. These factors will at times seem tangled; they will proceed in fits and starts, as the ambitions of an untested President interact with the interests of a recalcitrant conservative block, a determined opposition, and the will of a divided public. The utility of a systems science approach is to wean us away from the predictive, “A plus B will likely equal C” focus of most contemporary analysis and help us become more comfortable reading the relationship between a host of factors, in real-time, to better appreciate how complexity shapes our society, and, by extension, our increasingly freewheeling politics.

Sandro Galea is the Robert A Knox Professor and Dean of Boston University School of Public Health. His book, Systems Science and Population Health, was published in February. Follow him on Twitter @sandrogalea.



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