Further Discussions…

A big reservation that many express over the size of the uninsurance problem in the U.S. is that often quoted 47 million figure. The argument is that the 47 million number includes a lot of people that some do not believe should be included – namely illegal immigrants, those that have money, and those that qualify, but are not enrolled in Medicaid. So the last post and the comments that followed addressed these issues, but I have one more argument to throw out there. I have to give credit to Paul Krugman for the numbers, but the argument goes like this. There are 47 million people who are uninsured, but that number is not static. Meaning that at any given time there are 47 million people (minus whoever you don’t think should be included) uninsured, but the people who make up that 47 million is constantly changing. Here is the number that Krugman introduced me to: One in every three Americans under the age 65 (non-Medicare) were uninsured at some point in either 2006 or 2007. That’s a large number. I’m sure it can be explained away or deconstructed, but it points to a truth that the number of Americans who are put at risk (financially or health-wise) is much larger than 47 million.


14 responses to “Further Discussions…

  1. Once again, some highly misleading use of numbers. Guess it’s that Boy Scouts heritage of mine that has great difficulty with deceit and misrepresentation. Figures lie and liars figure.

    What Mr. Krugman is describing is the annual frictional uninsured number but it holds little meaning in helping to understand the magnitude of the problem…except when misrepresented to suggest that it does. Lucas, you were either a party to Mr. Krugman’s deceit or a dupe (note: I didn’t say “dope”).

    To exemplify the insignificance of the claim that 1/3 of Americans under 65 are at risk during the course of a year, you might well look at another “risk” to which Americans are subjected, that of unemployment. We keep track of unemployment numbers and report many employment/unemployment statistics every month, slicing and dicing it in every conceivable manner. What we DON’T report is the number of Americans who experience unemployment at least once during the course of a year. We don’t because it invites misinterpretation and it’s of little significance in understanding the problem.

    Which event represents a greater risk: 100% of Americans being uninsured for one day during the course of a year or 832,000 being uninsured for an entire year? That’s right…exactly the same risk (discounting the demographics of who the uninsureds are).

    Let’s see if we can’t try to stick with facts that matter, rather than digging up any old number that supports our position just to score a point. Dang, you might as well be asking POLITICIANS to weigh in on this subject!

  2. Let’s slow down for a minute and look at this number. The statistic that Mr. Krugman used, and I quoted, was that 1 out of 3 Americans under age 65 has been without health insurance at some point during 2006 or 2007. Let me make this clear for anyone reading this. The 1 in 3 number does NOT mean that one in three Americans are uninsured. Further, this number does NOT mean that there is a larger quantity of uninsured than the 47 million number claims (again, take out those who are not deserving). I want that to be clear, because stating that 1 in 3 Americans are uninsured would be deceitful. However, this number points to the fact that the risks that come with being uninsured (poorer health and financial loss) are actually spread over a larger percent of the population than the 47 million figure indicates. One in 3 Americans can expect to be uninsured at some point in the next two years (the poor more likely than the rich), and therefore, be at risk for huge financial losses and poorer health outcomes. These periods without health insurance may be short-lived – as when waiting for employer insurance to kick in or during a stint of unemployment – but a major accident or illness onset during these times can be crushing. So maybe with unemployment statistics this type of statistic makes little sense to report as the consequence of a short period of unemployment is relatively small (except that you can loose your insurance), but with health insurance the negative consequences can be much greater. The 1 in 3 statistic describes the problem more fully than the 47 million number does alone by adding a new dimension to the problem. It does not increase the number of uninsured, but indicates that some portion of the uninsured is an ever-shifting group. I understand how this number can be misused, but I don’t think Mr. Krugman was being deceitful, and I don’t think that I was duped.

  3. Regarding your conclusion that Mr. Krugman wasn’t being deceitful, I read Mr. Krugman’s article in the paper and respectfully disagree. And as to the significance of being without insurance for a day or two, I can’t appreciate the import of such a condition without knowing the probability of needing insurance on any given day (nor can anyone else). And statistics that would provide that information would necessarily be dependent on the age of the uninsureds. And we don’t know the age of those who are uninsured (other than being under 65). And we also don’t know the lengths of the periods of being uninsured.

    So I repeat, the statistic is both meaningless and misleading. But if you want to read something into it, then you’ve got a lot more explaining to do because its significance is unknowable without more data.


  4. um…aren’t we working off the assumption that having health insurance is better than not having it? so why would we need to know epistemelogically impossible probabilities to know that being in-between-uninsured is a bad thing?

  5. I first want to say that this particular statistic is not an important one in the full argument of the uninsured. However, let’s make a few things clear. When most people lose their insurance they lose it for more than “a day or two.” One or 2 days is misleading. A normal waiting time for employer insurance to kick-in is more around 3-6 months. Typical unemployment time – I don’t have the number – but I think it is fair to say that it is more than 1 to 2 days. So let’s be real. For people who move from a good job to a good job they still might be without insurance for 3 months. That is a more realistic time window of risk that a person might face. Second, a common situation is a person who moves to a freelance job that comes without benefits or person who steps out and starts a new business. These people could be without insurance for a long time. And as I understand the “American Dream,” we all want such people to be creative, innovative, and we want to promote such moves. However, these “non-conformist” most likely will be uninsured for some period of time. I’ll admit that I don’t have the time look for such numbers as the probability of needing insurance on any give day (its low) or the age of people who need the insurance (what does that matter?). The point is simple. The 47 million uninsured is an ever-changing group and the risk that this group bears is spread over more than 47 million people. There is good news in this number. For those that are currently uninsured the statistics implies that you will get insurance at some point – possibly soon. But for those with insurance, you have a larger than small risk that you will have some period without insurance. And even a small time without insurance puts one at risk (a small one) for financial ruin (bankruptcy, homelessness?) and devastating health outcomes.
    So this statistic is not pivotal, but it is not meaningless.

  6. “So this statistic is not pivotal, but it is not meaningless.”

    A statistic that can mean anything means nothing. Lucas, I was a VP of human resources and so statements like, “A normal waiting time for employer insurance to kick-in is more around 3-6 months” cause me to question either your integrity, intellect or both. Most major employers’ coverage starts from the date of hire.

    And as for the risk associated with being uninsured, let’s start by tracking down two really important statistics: 1) the probability of needing health care for a single given day and 2) the median and average costs of that care. Only these two statistics can place a price on the risk of being exposed to being without insurance. Without this information, discussions about the dangers of being uninsured are pretty meaningless.

    Now, it gets a bit more complicated than that. First, the probability of needing health care and its cost are both a function of age; the older you are, the more likely you’ll need to see a doctor and the more expensive it is. And the likelihood that the uninsured are young is probably greater than that they’re older (I don’t know for sure but it seems reasonable since younger people have less wealth are are more willing to take the risk of going without insurance). Consequently, the average “cost” of being uninsured per day (probability of needing health care times the average or median cost) is likely lower than if you took averages of probability and cost across the entire population.

    Now all that statistical analysis, for which neither you nor Krugman supplied data, is necessary for us to understand what level of risk is incurred by being uninsured. Add to that the need to know the length of uninsured status and the financial capacity of the uninsureds to accommodate the risk and you realize the irrelevance of Krugman’s statistic.

    Look, getting a handle on the extent of the health care problem is very difficult. I think your time would be better served focusing on the opportunities to improve our system, spending less time trying to fabricate doomsday situations in an effort to impel people to act…and leaving yourself open to charges of misrepresentation of reality from people like me.


  7. Bob,

    I don’t know when you stepped down from your VP position, but it seems to me (and this is just from my experience and the experience of my friends and family) that new hires often have to wait three months or more to have their insurance kick-in. I know this is a new trend, but it is real. I will not speak to the size of this trend, but it is a reality. Employers are putting employees initially on probation periods so that they are not needlessly burdened with health care costs in case the employee does not work out.

    But the bigger issue here is that you are missing my point. All I am saying is that the 47 million people who are uninsured is not a static group. It is a pretty simple concept. Sure one could look at the probabilities and average cost, but averages in this discussion are useless figures as insurance’s real value is to protect against catastrophic expenses and not in dealing with average costs. The value of insurance is not in averages or medians, but in the extremes. It is about shifting pre-sick dollars to post-sick dollars where you get both more resources (more money than you put in) and those resources now all of a sudden have more value (they’re life saving). When I am talking about “risks” I am talking about those health care costs that are so high that very few Americans could absorb the costs. As you may remember most health care dollars are spent by a few. I’m talking about becoming one of those few through car accidents, spinal cord injuries, or a new cancer diagnosis. These events are not average costs, so please let’s leave the average out in this discussion. The probability of these events actually happening are low , very low – but real, very real. All I am saying is that the gaps that our health system creates, over time, puts more people exposed to the low, but significant risks, of being uninsured than the static number of 47 million indicates.

  8. If there were no risk associated with being uninsured, why the hell would anyone bother with insurance? Give me a break! Do we have to quantify this in a statistic to make it real? Of course I said this earlier but as I’m just an ill-educated theologian type I suppose it was not worth responding to.

    And look, I know this is not my blog, but honestly, ad hominem attack would get someone barred from commenting on my blog. It’s easy to be aggressive in the blogosphere but that’s all the more reason to try harder to be respectful.

  9. Dear Mr. Parrish,

    As a fellow Boy Scout, an Eagle Scout for that matter, I would ask that if you want to bandy about your Scouting heritage, that you please remember the part of the Boy Scout Law that says “A Scout is … friendly, courteous, kind….” If you want someone like me to buy your arguments, you aren’t going to do that by attacking a person’s integrity and/or intellect.

    J. Brent Bates

  10. Lucas, I’ve been retired for seven years so I won’t presume to have seen the latest benefit surveys but I would be SHOCKED if a trend in new hire eligibility for medical benefits involved a delay of even a month. Yes, I have no doubt that some employers may have such a practice. But I have to believe that they are a small minority and/or operate in either a localized labor market or specific industry where such a practice has been established as the norm. Here again, absent any data, neither of us can say with certainty what’s going on out there so all we have is opinions (to go with our biases).

    The point that you proclaim to have been making, i.e., the uninsureds are a variable group, offers very little in allowing us to define the extent of the hardship presented by the uninsureds. And all your statistics (47 million, 1 in 3, etc.) are simply surrogates for defining that “extent of hardship” generated by being uninsured. Because of that, they’re subject to the kinds of attacks that I’ve leveled at them. They are only second-order measures of hardship and may severely overstate (yes, and maybe understate) the extent of the hardship.

    And when you then decide to focus only on protecting “against catastrophic expenses”, you’ve shifted the argument yet again. Is that what you’re really seeking: catastrophic medical insurance? If that’s the case, then numbers like 47 million and 1 in 3 become truly irrelevant. To make such a point, you might be served equally well by using only anecdotal evidence. (But a word of caution: be a bit more cautious than the Democrats who found the impoverished S-CHIP poster boy who happened to be enrolled in private school).

    I think we’ve probably beat this subject to death and I’m not simply focused on trying to get the last word. But you succeeded in touching on some points that I felt needed a response.


  11. Bob,

    First, I have personally waited three months for insurance on two occasions – both in medical fields. Second, Wal-Mart, and I believe Home Depot, make employees wait six months for insurance (it might be three for Home Depot). I read about these two this past week. It’s a trend. Third, you have to take my arguments in context. The main reason for insurance is to protect oneself from catastrophic costs and provide access to care when one really needs it. However, health care is more than that. What we all want is good health care and insurance is now equivalent to having good health care. However, and here is where the context comes in, the catastrophic aspects of health insurance play a larger role when one is uninsured for short periods of time – the context of our discussion. If I was going to be without insurance for three months I would be okay paying out of pocket for an occasional doctor visit for a chest cold, but I would still want protection for the car accident. In that context I am not shifting the argument. I’m dealing with the context of the debate. Please look at the context before you make accusations.

  12. Lucas, notwithstanding the comprehensive sample size of your employee benefits survey (4?), my former colleague at HP tells me that the overwhelmingly predominant practices in medical insurance eligibility continue to be date of hire (first) and first day of the month following date of hire (second). No evidence of the trend you describe has been seen in the surveys to which they subscribe (Mercer, et al).

    And my apologies if I missed the context surrounding your statistics. But I think you presume too much when you speak on the significance of health insurance to individuals. Each individual’s tolerance for risk varies, each has a different view of risk, each places a different value on access to health insurance (as compared to compensation and/or other benefits). So your statement of context is, in itself, somewhat presumptive. Consequently, it’s difficult to have it serve as the foundation for the evaluation of statistics (especially ill-defined ones).

    JTB, my apparent obsession with greater definition of the statistics which Lusas presents is that they are presented in support of arguments which they may or may not support. We just don’t know without more background data. And until that’s available, I think it’s disingenuous to claim that they say something they don’t. Better to say, “There’s a bunch of deserving people without insurance through no fault of their own and we should do something about it” that to say “47 million Americans…”.

    j. Brent Bates, I re-read what I might presume to be my offensive remark. Was it, “Lucas, I was a VP of human resources and so statements like, “A normal waiting time for employer insurance to kick-in is more around 3-6 months” cause me to question either your integrity, intellect or both.”? If so, I would maintain that honestly (“Trustworthy: a scout tells the truth.”) sharing my reaction to a baseless claim in the fashion I did was not unkind. Calling Lucas a stupid liar? Yes, that would be unkind. I merely said that his statement made me question whether he was intentionally misrepresenting facts and/or was ignorant of the truth (the latter appears to be the case). That was telling the truth, which Scouts are sworn to do. Ever had your spouse ask you, “Honey, do I look fat in this dress?”? Well, yes, I guess there are some times when violating the Trustworthy code in favor of kindness is prudent. I didn’t feel that was appropriate in this case. No offense intended, just honest communication.


  13. I understand that an N=4 does not have the statistical power to say anything- not to mention that my sample was not randomized, controlled, or whatever other standards you want to use. However, stating that you have a friend who says so and and giving a “mercer et al.” without year, publication, or link is just about as useless. I would love to see the survey so if you have a more expansive citation that would be great. I would love to see what industries the survey assessed (was it just technology/ computer companies such as HP or did it include the ever growing service, health care and retail industries). I imagine that industries that need to attract highly educated and skilled people have very nice benefit packages, but some of the biggest employers in this country are in the service or retail areas. What are their trends?

  14. I suppose I’m a bit impatient with statistics, for the very reason that this chain of comments demonstrates: they can nearly always be interpreted in various and nearly opposite ways by people who claim the epistemic authority to do so, and they can themselves become the object of debate–rather than the actual topic at hand. Yes, it would be great if we could just throw out some comment like, “there are people without insurance who need it and we should do something about it”–in fact that tends to be the way I might express it myself. Statement like this seems to garner responses like ‘how simplistic!’, and questions like ‘who? how many? how do we know? how can we tell the sheep from the goats?’ And there seems to be this felt need in discourse about such things to answer these questions with quantifiable quasi-empirical “evidence”…I chalk this up to our cultural lingering scientism, postmodernity notwithstanding.

    But seriously, once we’re talking numbers, how much difference is there between 47 million and 33 million? Is 47 mill enough for us to get worried and maybe act, but 33 is still in our tolerable comfort zone? It seems to be that either way that’s a hell of a lot of people, way more than enough to get across the message that something’s a bit screwy with the way things are. And if there’s no salient difference between the numbers on that matter, then why spend the time quibbling between them? Unless of course, it’s just good clean diversionary fun.

    And I never, ever ask my husband if I look fat.

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