In recent debates over health care in the US, whether it be universal insurance, single payer, or full nationalization, we often hear proponents of the system in question pointing to various studies that show the various failings of the current US system. These studies take various form, some showing greater satisfaction, others greater efficiency, and still others relying on various numeric measures, such as total lifespan or infant mortality. All of them have two features in common. First, they are superficially plausible, making a fair case that the US is inferior to a number of nations in providing its citizens with health care. Second, all of them have some serious defect.

The problem is most obvious in those studies which claim to show that one system is superior to another, or claim to rank various nations in terms of their health care. In these cases it is fairly obvious that the study is smuggling in assumptions. After all, to claim that a system ranks above or below another, or that nation A is superior to nation B, one must have a means to compare the two. Of course most studies do not simply claim A is better than B and leave it at that, they provide a detailed list of how they evaluated the two. The problem with this is that such comparisons rely upon certain assumptions, assumptions not all readers may share. For example, one study may emphasize providing care tot he greatest number while another may emphasize the number of choices available to individuals. They are both valid measures given certain assumptions, but they also produce very different results.

The problem here is that most who cite these studies do not bother to explain the assumptions underlying the study, and certainly never make clear the philosophy of government which informed each. Instead they simply name the group doing the ratings and the outcome, giving the impression that by some immutable standard, X is better than Y, while in truth all the study proved was, based on a specific set of criteria and a specific weighting of those criteria, X ranked higher than Y. That is not the same as saying "X is better than Y".

But it goes beyond that, as some criteria used are actually misleading.

One good example is the most famous figure in the entire debate. Most often we have it presented as either "46 million uninsured" or "1-in-6 without coverage". And, provided we accept the definition, the number is true. There were 46 million who were without insurance for some period in 2004 (at least that was the year for which the figure was first cited). However, that is not the way in which the number is used. While those using the number often correctly state the figure, and the meaning, they imply some things which are not true. First, there is the implication that there 46 million are chronically uninsured for whom insurance is simply unavailable, which is simply not the case. As I wrote in "A Most Dishonest Commercial" and "There ARE NOT 46 Million Uninsured!", there were 46 million who did not have insurance AT SOME POINT in 2004, which includes those whose insurance lapsed while changing jobs, when their company changed carriers, or because they missed a premium on a private policy. It also includes those who choose not to carry insurance, even if they could afford it. Second, there is sometimes the implication that not only were these 46 million uninsured, but that they were also unable to receive care, which is even less plausible.Even among those who are both chronically uninsured and unable to afford insurance, there is not an absence of care. Yes, some care, especially for elective procedures, may be absent, though less than many think, but most emergency care, and even some routine and preventative care, is available. It may not be the best care, it may require a wait, but it is available. (See "The Secret Behind the Rhetoric" for an alternate thought on this.) So, though the number may be true in a very strict sense, the way it is used is often very misleading, to say the least.

Another example of this would be the use of various numeric measures, which, on their face provide a fair measure, but when examined a little more closely do not. For example, as I described in "Lifespan", lifespan would seem a fair measure of health care, as surely health care and mortality are related. But that ignores countless factors which can change lifespan, and which vary from nation to nation, but which have little or nothing to do with health care.  In the US, for example, homicide influences lifespan and mortality figures, not just reducing overall lifespan, but also increasing the number of deaths among older children and young adults. However, while homicides point to a criminal justice problem, they do not indicate a problem with the health care system.

A similar problem exists due to the US practice of taking efforts to save extremely premature children. As many nations would simply write them off as still born, they would not figure into infant mortality or lifespan figures at all. But, as the US does try to save them, and may even keep them alive for some time, they are figured into the lifespan figures as a death at age 0. Worse still, they also serve to inflate the infant mortality figures, which are very often used to argue that US health care is inadequate.

Actually, infant mortality is a good example of a related error, ascribing meaning to figures which they do not have. Whenever infant mortality rises in the US, the assumption is that there is a lack of either services or education. Having worked in social services, I can attest that neither is the case. What has changed is both the age at which girls become pregnant and the society in which they live. Younger girls are both more prone to be irresponsible than older girls, and, like all youth, tend to imagine themselves to be indestructible. It is a combination of attitudes which makes using prenatal care much less likely. Add to that the fact that the poor are more likely to have multiple pregnancies, and poverty tends to correlate with poor health practices ("Poverty and Lifespan")*, and it should be clear there may be a number of problems with society, it would be a mistake to attribute rises in infant mortality to health care alone.

Of course, those are not the only way numbers can mislead.  I mentioned my post "Poverty and Lifespan" above. That provides one example of a misleading figure. Seeing numbers that show the poor die at a younger age than the middle class or wealthy, many jump to the conclusion that the reason is an unjust health care system. However, that is not the only possibility. As I argued in my essay, many activities, such as alcoholism and drug abuse, tend to produce both poverty and shorter lifespans, not to mention many attitudes, such a criminality, which often result in a premature death, and also tend to correlate with poverty. Finally, there is one factor I did not touch upon, but which is also a consideration. Health care is not the only factor in promoting health, exercise, diet, and many other factors play into it as well. And the wealthier one is, the more likely one has time and energy to expend on such activities. The poor, being poor, tend to need to expend most efforts on earning income, while the more affluent have more leisure. This gives more opportunity for activities which generally promote good health.

I do not claim those are the only explanations, nor do I claim the health care system has no part in this imbalance. All I am pointing out is that the assumption that every imbalance is the result of the "health care system" is absurd oversimplification, and makes studies based on that assumption suspect.

Of course, such mistaken assumptions are common in many arguments. For example, as I argued in "Violence and Culture", many people see rapists in possession of pornography and assume that pornography inspires rape, not even considering the possibility that both rape and a fondness for pornography are symptoms of a single underlying cause.

A similar error takes place in the assumption underlying many of these studies than the costs in our current system as the result of the free market. First, the assumption that our system is a free market one is incorrect. Yes, in the broadest sense, our system is free market, in that hospitals are privately owned and run for a profit. On the other hand, once you lower your gaze from the biggest picture to something a bit smaller, you can see how much government involvement there is. Thus, when studies use our present system to represent the "free market", and then compare it to various socialized schemes, it is a bit unfair, as they are really comparing a mixed system.

Which is why it is senseless to assume costs in our system come form the free market. As I showed in "High Cost of Medical Care" and "Government Efficiency", it is quite possible costs might arise form steps the government has taken. One can argue whether those costs are caused by the government intervention itself, or by the interaction of controlled and free segments of the economy (which means they would not exist in a wholly socialized system), but the fact remains that we do not represent a fully free system, so it is an assumption, and often an unwarranted one, to ascribe all the costs of our system to the free market.

 Which brings me to another species of argument, those that hope to assess the efficiency of free market systems in comparison to single payer or socialized systems. The problem with such studies is that they tend to keep their focus so narrow that they miss much of the argument. The point out money "wasted" in duplicated services, in multiple insurance forms, and paid out in profits, money that could be used in a socialized system to provide services, and they think that proves the socialized system superior. But that superiority is largely the outcome of too narrow a focus.

Let me give an example. Suppose I want to argue the government should socialize the construction trades. I argue that if the government controls all of the trade, they can set salaries, and, by lowering them 25% could build houses more cheaply. What my "savings" ignore is that the housing trade is not the only employer, and everyone whose salaries I cut would likely move to another industry, meaning my savings would likely destroy the industry, not save a fortune.

The same is true of many arguments about medical efficiency. While profits do represent a drain, they are essential to getting private capital, as well as to spurring competition. The same for duplicated services, different ways of doing things, including insurance forms, and so on. It may seem nonsensical to outsiders, and even some insiders, but each business has a reason for doing things that way, and thinks their way best. And some are right, their answer is superior, and competition will make sure the superior versions, in general, will succeed. And it is the benefits of that competition which are both lost through socialization and ignored by efficiency studies. While they can easily tally the costs of competition, they don't see the benefits.

Of course, it s possible that one could argue the benefits are still less than the costs. But to argue that one would have to realistically evaluate both the benefits and costs, a difficult task under ideal conditions, and impossible now. Nor can it be done by comparing free medical economies of today with socialized medical system, not only because of the lack of truly free medical systems, but because the more free medical systems carry the less free. For example, profits made on drugs sales in the US subsidize research for those nations with price controls, research that would not be possible if all nations imposed those controls. So, for the moment, unfree medical systems benefit from the gains of the more free systems, making it almost impossible to accurately judge the effects of socialization.

Which brings me to my final argument, the last problem form which many such studies suffer. In addition to poorly defined arguments, smuggled assumptions, mistakes about causation, and other erroneous assumptions, studies also suffer from a lack of objective evidence. While there may be many shortcomings to arguments about lifespan or mortality rates, at least those are objective numbers, with established definitions. On the other hand when we get into speculative areas, such as the benefits and costs of changing from a free market to a single payer system, the numbers become much less certain. Oh, the economists will pretend there is some support for their figures, but in reality, any economic forecasting is always somewhat based on supposition, and, in this case, as I argued above, we really have little foundation upon which to base such estimates. As a result, putting hard numbers to the benefits of competition is always going to be mostly guesswork.
 
Then there is the even less objective survey. If the economic arguments are speculative, these surveys are completely arbitrary. Those are the surveys comparing the users' subjective impressions of their nations' health care systems. As I wrote in "A Useless Measure", this is a simply absurd measure. Just think about it and the reason should be obvious. If you are used to abysmal care, and it improves to just horrible, you will be delighted. While someone used to prefect care, which decays to just "very good", they will be disappointed. And so the very good health care system will rank below the horrible one.

All of which is a rather long winded way of saying that most of these studies are much less useful than the proponents would have us believe  Many are based on silent assumptions about what is and is not desirable, often smuggled in via poorly defined terms. Others rely upon misleading assumptions about causation or the meaning of numbers, and still others rely upon speculative numbers with little foundation.

This does not mean there is no way to assess the merits of nationalized health care. Such surveys may still be of use, provided their assumptions are clearly stated, and the beliefs about causation are examined thoroughly. On the other hand, we can also approach this from the other end, asking theoretically how one is likely to respond to incentives and postulating the likely  outcomes of various actions. In either case, it is important, before anything else, that we ask what we are attempting to achieve and how such goals will be measured. As mentioned above, without such definitions and clear measures, any argument is impossible.
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* Such correlations, between poverty and shorter lifespans, are often assumed to indicate that the health care system is inequitable. However, such assumptions ignore several possibilities. First, that many activities, such as drug abuse or alcoholism, favor both reduced life span and poverty. Second, they ignore the fact that wealth tends to increase both leisure time and disposable income, both of which can be used for exercise, better diet and other activities which encourage longer lives. The list could probably be extended, but I think these two are enough to show that jumping to the conclusion that the health care system is to blame for any inequitable treatment is unfounded.

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POSTSCRIPT

This post is actually a reconstruction of an earlier one. I had almost completely finished it when a loose memory card threw an error on my computer (the first time I ever saw an NMI on a windows machine), erasing every word I had written. I know this version is quite different from the last, though it does follow the same reasoning. Hopefully it was worth recreating.