The Incidental Economist

Sarah Kliff notes a poll this morning:

A Wolters Kluwer poll out this morning finds that not only are most consumers turning to the Internet to answer medical questions, but that they also put strong faith in their own diagnosis. Among college educated Americans, 63 percent say they have “never” misdiagnosed themselves. Add in those who have say they’ve “rarely” made a wrong call and the number jumps up to 84 percent.

As much as I complain about physicians, they are often necessary. Good ones, even more so. And in support of this, I will provide a personal and completely humiliating anecdote.

In the summer of 2006, my daughter (and third child) was born. Just two weeks later, my 2 1/2 year old son jumped off this in the backyard:

It’s just a tiny bit off the ground! But my wife, who was extremely sleep-deprived, claimed that my son had hurt himself. Rolling my eyes, I went outside to check him out. He was limping a little, but was otherwise getting along fine. I asked him if he was hurt, and he said no. Case closed.

Later that night, my wife nagged me about it again. I had already diagnosed him as fine, but as we had a two week old in the house, I wanted to be accomodating. I took  his leg and really moved it around. He laughed. I couldn’t reproduce pain. I said he was fine.

Of course, he was still limping the next day. And the day after that. And my wife wouldn’t leave it alone. She said he had to go to the doctor.

Now a sane man would have just agreed and taken him in. But I was also sleep deprived. Moreover, I had already diagnosed him. So I was done. I told her she could take him in if she wanted.

In the 100 degree heat, my wife, our 4 1/2 year old, our newborn baby, and our limping 2 1/2 year old went to the doctor. I declared that this was a waste of time. I declared that this was a waste of money. But off they want.

Hours later, my wife called to tell me they had sent Noah off for x-rays. I could hear my daughter wailing in the background. I could hear my oldest nagging her. And then I made one of the stupidest stupidest decisions of my life. I started to lecture her.

I told my her that she should have refused the x-rays. I told her that they were a waste. I told her that she should never have taken him in for the visit. I told her (I kid you not) that she was the reason that health care spending was so high in the United States.

I’m sure you can predict what came next:

In my defense, it was a small fracture, and I bet he would have been fine without the cast. But, as you can imagine, I am no longer allowed to make diagnoses in the house. If the kids appear sick, they’re going to the doctor. I’ll never say anything about that again.

The lesson you should take home from this is that we are likely terrible at diagnosing those close to us, especially ourselves. There are physicians for a reason. If you’re concerned about your health you should at least call one, if not go see one.

@aaronecarroll

About a month ago, I wrote a post congratulating Sharon Begley for her well reasoned reporting on a paper in Health Affairs that argues that the extra money we spend on cancer care is worth it because it buys us increased survival time. I also wrote a follow up post here.

On Monday, the authors posted a reply to criticism on the Health Affairs blog. I have no idea if they’re responding to my criticism, but I wanted to address some of their points anyway. Here we go:

First, some critics have argued that we should have looked at mortality rather than survival.  That is, we should have measured how many people have died from cancer, rather than how long people who are diagnosed with the disease live with it.  There is an important distinction, as we note later.  Some have argued that survival estimates suffer from a “lead-time” bias because the United States was differentially diagnosing cancer earlier.

Our Paper Looks At Mortality Estimates As Well As Survival Estimates

This criticism is somewhat puzzling since we also examine mortality trends in our paper, and the same story holds.  As discussed both in the main paper and an extensive technical appendix, we examined trends in cancer mortality rates over a similar period – 1982 through 2005 – using the WHO Cancer Mortality Database. The mortality results for the United States for the most controversial cancers (prostate and breast) fell relative to the EU.  The results implied that if the US had progressed in its cancer care at the same rate as the EU over this period, there would have been 87,000 additional breast cancer deaths and 222,000 prostate cancer deaths.   These correspond to a gain in life expectancy of 1.8 years for prostate cancer patients and 0.8 year for breast cancer—similar to our survivorship analysis. The bottom line is that looking at mortality also supports our finding of a widening gap between the United States and the European countries we investigated.

I wish they had addressed Don McCanne’s published concerns about that appendix:

Yet the data provided in the Appendix on eleven malignancies showed that four did not reach statistical significance at a p value of 0.05, three actually showed additional deaths incurred in the US, and of the four that supposedly showed additional deaths avoided, two — prostate cancer and breast cancer — are known to manifest significant lead-time bias plus overdiagnosis because of the very large numbers that would not lead to unfavorable outcomes. This hardly supplies support for their decision to dismiss lead-time bias.

Their results were dependent on the data in the Appendix, yet the errors in it suggest that it was not critically reviewed. The text includes the phrases, “prostate cancer and death cancer,” “139 prostate cancer deaths averted per 100,000 breast cancer patients,” “a prevalence of 2,400,000 prevalent prostate cancer cases,” and “a gain in life expectancy of survival of 0.8 year for breast cancer patients.” (Statistical survival time cannot be added to life expectancy because of lead-time bias.)

The Appendix is essential to their results, yet it now seems to be discredited. Therefore, their conclusion on the dollar value of survival gain is not validated.

Even so, I’ll concede that we may have lower mortality rates than many other countries when it comes to breast cancer. I’ve posted this before:

I’ll even concede prostate cancer:

But they are cherry picking. Here’s cervical cancer:

Here’s lung cancer:

So they’re focusing on cancers where we do better and, to a lesser extent, ignoring those where we don’t. Moreover, the differences in mortality rate aren’t nearly as big as the differences in survival rates. If they’d done their calculations on these differences, the cost might not have appeared to be worth it. Here again are their data, using survival rates, showing how much they are cherry picking:

Almost all the gains are from prostate and breast cancer. Cherry picking. And there’s a reason we may look better for those cancers. It’s lead time bias. They disagree:

So why did we analyze survivorship rather than population cancer mortality?  Naturally, mortality and survivorship convey equivalent information if one conditions on diagnosis.  However, at a population level, mortality also depends on the number of people who get cancer each year (incidence).  So mortality – unlike survival – is sensitive to underlying trends in behavior affecting cancer that have little to do with the health care system.  For example, if the United States stopped smoking at a greater rate than the EU, we would expect mortality to fall, but we should give credit to public health efforts rather than health care delivery.  Furthermore, mortality analyses can itself be subject to bias due to changes over time in attribution of cause of death.

Ultimately though, people with cancer—and their physicians—are most concerned about their survival chances once they are diagnosed. While mortality rates in the population may be a focus of epidemiological research, they are not the statistics of greatest interest to those diagnosed with cancer.  Once diagnosed, a patient and her physician care more about how long she will live—hence our choice of survival as the primary endpoint. Put another way, researchers do not abandon measuring survival in oncology trials because there is a well-recognized issue of attrition bias.  The bottom line here is that one wants to compare differences in health care treatment, survival and not mortality is the appropriate outcome.

Lead-Time Bias Is Not A Plausible Explanation For Better US Survival Rates

As I have argued, survival is absolutely of use when a doctor is talking to a patient. They have that correct. The first question a patient will ask when diagnosed is “how long have I got”? But that doesn’t mean it’s appropriate for cross country comparisons. If you diagnose a cancer earlier in one country than in another, almost by definition survival time is increased, even if they die at the same rate at the same time. Survival estimates absolutely will be subject to lead time bias when there are different methods of diagnosis. And, as I’ve said before, in the US the American Cancer Society recommends women are screened by mammography every year starting at age 40;  in the UK, women are screened every three years starting at age 50. It’s almost certain women in America are going to be picked up earlier. This will absolutely increase survival time, even if it prevents no deaths.

Again, look at the chart above from their data. We have massive screening programs for prostate cancer and breast cancer, but not for most of those other cancers. Do you really think that has nothing to do with the massive differences in results between those two cancers and the others?

Go read their whole response so you can rest assured I’m not leaving something out. But they still didn’t address what I consider to be the most damning criticism. Nothing in their study – nothing – proves that increased spending accounts for the differences in survival. Was it a randomized controlled trial? No. Can we know anything about causality? No.

This study showed that survival rates are increased for two cancers that are massively screened for in the US. Then it declared, in the manuscript’s discussion, that these differences are likely due to the increased health care spending in the US, specifically citing pharmaceuticals. (Side note – you wouldn’t know that this research was funded in part by a pharmaceutical company unless you could access the gated manuscript). And then in the title, they imply this spending is “worth it”.

That’s far from proven. Even after their response.

@aaronecarroll

(h/t longtime reader Brad Flansbaum)

I have a friend who loves to send me links to studies he knows I’m going to start screaming about. This would be a good example of such a study (but that came from a different friend). Yesterday, he sent me “Ovulation and Perceived Paternal Investment“:

Why do some women pursue relationships with men who are attractive, dominant, and charming but who don’t want to be in relationships – the prototypical sexy cad? Previous research shows that women have an increased desire for such men when they are ovulating, but it is unclear why ovulating women would think it is wise to pursue men who may be unfaithful and could desert them. Using both college-aged and community-based samples, in three studies we show that ovulating women perceive charismatic and physically attractive men, but not reliable and nice men, as more committed partners and more devoted future fathers. Ovulating women perceive that sexy cads would be good fathers to their own children, but not to the children of other women. This ovulatory-induced perceptual shift is driven by women who experienced early onset of puberty. Taken together, the current research identifies a novel proximate reason why ovulating women pursue relationships with sexy cads, complementing existing research that identifies the ultimate, evolutionary reasons for this behavior.

Here’s that in chart form:

Here’s a quote from the author:

“Previous research has shown in the week near ovulation women become attracted to sexy, rebellious and handsome men like George Clooney or James Bond,” said Durante. “But until now it was unclear why women would ever think it’s wise to pursue long-term relationships with these kinds of men.”

Evidently, she really has a problem with George Clooney. And that’s all I’m going to say about this.

@aaronecarroll

If you’re the score-keeping type and a fan of health economics, you might like the recent paper by Adam Wagstaff and Anthony Culyer, titled “Four decades of health economics through a bibliometric lens.” (An ungated working paper pdf is here.) It includes lists of the top health economics papers (300 of them), health economists (100), journals (100), and institutions (100) , and more, all ranked by a publication/citation-based metric of influence, generated from a database of 33,000 papers in the field from 1969-2010. Unsurprisingly, neither I nor my work are on them. Not even close. However, I am proud to say that one of my frequent co-authors, Roger Feldman, is number 18 among health economists. See if you can guess who’s at the top of the list.

Then there’s this frightening chart:

This is clearly unsustainable! Something must be done to stop the exponential growth of health economics. Even our peer field, education economics, has lower growth. I can only shake my head in shame sarcasm.

@afrakt

I’m pleased to announce that Aaron and I will be posting several times a month on the AcademyHealth blog. My first post appears today. It’s a lot about what insurers and a little about what hospitals do and don’t do with market power. It summarizes some recent publications and includes a must-see chart. Go read it!

@afrakt

Last week, in Inquiry, my latest paper with Steve Pizer and Roger Feldman was published. An ungated, working paper version is also available. Note also that I wrote a bit about a portion of it in a prior post, though even that does not describe what the paper is about.  I’ll write more about the results in the paper in another post. If you can’t wait, click through for the abstract. For now, I want to focus on another technical detail, which is likely to interest all of five readers. You know who you are from the title of the post.

Until fairly recently, my colleagues and I thought overidentification tests of instruments were worth doing. We no longer feel that way. Still, in order to be published, we have little choice but to do them when a reviewer demands them, but we still think they’re not very valuable.

Though these are typically discussed as tests of excludability, they are, in fact, joint tests of excludability and homogeneity of treatment effects (Angrist 2010). Consequently, instruments that are excludable may be rejected due to local average treatment effects.

Passing overid tests may convince some reviewers that one’s instruments are excludable from the second stage model, but it shouldn’t. Failing to pass doesn’t prove they are not. This is a rather weak case for their scientific value. Many papers in top economics journals using IV methods do not include overid tests. That’s just fine.

“Angrist 2010″ is a personal communication with Josh Angrist.

@afrakt

I gave two talks at the end of last week. The first was on Accountable Care Organizations for a local chapter of the AAP. The second was on health care reform and the health care system in general. By the end of the second talk on Friday, I was pretty much feeling despondent.

As I reviewed ACOs on Thursday, I was talking about how many hoped they would bring about cost control at the physician level. But as I reviewed the progress of how the rules have been modified over time, I could feel cynicism creeping into my voice. There was the removal of sticks in October. The elimination of rules requiring EHRs. The reduction in quality oversight. The ability of patients to seek care outside the ACO. And let’s not forget the counterproductive side effect of provider concentration that the formation of ACOs encourages (Austin helpfully posted a FAQ on this balance of power earlier today).

So I’m skeptical that ACOs will be what gets us out of the ditch in the future. Not that I see many other helpful ideas from others. Tort reform won’t do what people say it will. Medicaid block grants are just passing the buck. Medicare “sorta” premium support is just cost-shifting. None of these things will get us there.

So what will, I was asked? I wish I had a magic wand to just fix things. But, more and more, I’m becoming convinced that we need to identify areas where we can save money at the local level. Wennberg’s work shows us that there are huge variations in care that don’t seem linked to quality. That stuff has to go. Berwick and Hackbarth went further and identified how much “waste” there is in the US health care system right now:

I’m drawn to the third line alone. We’re spending somewhere betweeen $156 billion and $226 billion just on overtreatment. If we could just figure out a way to stop doing that, we’d be well on our way to bending the curve. Not that we can’t fix the other things as well. But stopping overtreatment seems like it would be the easiest to do, and it would have the added benefit of not requiring that much new added infrastructure.

We may need to focus on where these things are occurring locally, though, instead of thinking there’s some large lever to pull at a national level. We also need to identify ways to change physician behavior, which isn’t easy. I’ve been talking much more about that in my own corner of the world. I hope others are, too.

@aaronecarroll

I’m grand rounding Tuesday noon at the Washington DC  VA. I’ll be discussing methadone dosage and HIV prevention practices in outpatient substance abuse treatment. This talk draws on two decades of data from the National Drug Abuse System Survey (NDATSS), a nationally-representative panel study of outpatient treatment facilities across the U.S. It’s not a general audience talk. It should be quite accessible to people interested in HIV prevention and injection drug use. I draw on these two papers, among others.

The event will take place in the nursing education room (BL 105U) at the DC VA Medical Center (50 Irving Street NW, Washington, DC 20010)  from 12:00PM – 1:00PM.

(HAP)

This is a FAQ entry. See the main FAQ index for others.

• How insurance functions like bulk purchasing, achieving discounts on volume
• If insurers suffer cost shifting, they lack sufficient market power [see also the other cost shifting FAQs]
• Insurer market power tells us nothing about premiums
• The co-evolution of insurer and hospital market power explains a lot  [charts]
• How market concentration can be good
• Managed care and hospital bargaining
• Read Austin’s paper on insurer-hospital balance of power

Plenty more here.

Earlier today, Sarah Kliff reported that “nurse practitioners are rolling out a campaign this week to explain what, exactly, nurse practitioners do — and why patients should trust them with their medical needs.” Anticipating a shortage of physicians, nurses are asserting that they can help fill the void.

Though not the same as nurse practitioners, the ranks of registered nurses have swelled due to the poor economy and, somewhat paradoxically, high unemployment. Staiger, Auerbach, and Buehaus explain (ungated),

Whereas the national economy lost 7.5 million jobs, the health care industry gained 428,000 jobs. In particular, hospital employment of registered nurses (RNs) increased by an estimated 243,000 full-time equivalents (FTEs) in 2007 and 2008. [...]

This sharp rise in RN employment is probably attributable to several factors. During economic downturns, demand for health care continues unabated, and RNs tend to fill existing job vacancies because of their concerns about their personal (or their family’s) economic uncertainty and diminished alternative opportunities. In addition, because approximately 7 in 10 RNs are married women, an economic downturn may have a particularly large effect, since many RNs who were not working or were working part-time may rejoin the workforce or change to full-time status to bolster their household’s economic security.

The final sentence quoted is a bit odd. Is it the fact that the RNs are mostly women or mostly married, or both, that is important? Why? Yes, I can speculate, but I think the authors should have devoted one more sentence of explanation.

In any case, what goes up, comes down. When the unemployment rate falls, RN employment will fall with it, all other things held constant. That may exacerbate the anticipated physician shortage, increasing an opportunity for registered nurses.

@afrakt