A contentious point of debate is the role of physicians in running health care organizations. Some argue that doctors should be in charge of hospitals, given their firsthand knowledge of the realities of clinical medicine and the day-to-day happenstances of caretaking. Others argue that physicians are hopeless at leadership activities in general, that outsiders sometimes have fresh perspectives that sweep away the inertia inherent in a hierarchically structured field like medicine, and point to high profile examples of how executives from other sectors/industries have swept in to save ailing hospital systems (they often refer specifically to Paul Levy, the former CEO at Beth Israel Deaconess in Boston).
So what does the evidence say? Unfortunately, there is very little in the way of hard data on this issue, except for this new paper by Amanda Goodall:
Although it has long been conjectured that having physicians in leadership positions is valuable for hospital performance, there is no published empirical work on the hypothesis. This cross-sectional study reports the first evidence. Data are collected on the top-100 U.S. hospitals in 2009, as identified by a widely-used media-generated ranking of quality, in three specialties: Cancer, Digestive Disorders, and Heart and Heart Surgery. The personal histories of the 300 chief executive officers of these hospitals are then traced by hand. The CEOs are classified into physicians and non-physician managers. The paper finds a strong positive association between the ranked quality of a hospital and whether the CEO is a physician (p<0.001). This kind of cross-sectional evidence does not establish that physician leaders outperform professional managers, but it is consistent with such claims and suggests that this area is now an important one for systematic future research.
As the author suggests, this is but a first step into understanding the returns to a physician versus a non-physician leader. Here are a few thoughts:
1. The main threat to inference in this study is selection into leadership positions. That is, physician and non-physician leaders are not randomly assigned. What if hospitals that are doing poorly, are more desperate, tend to "go outside the box" and hire non-physicians (supposedly, Beth Israel was in this position a decade or more ago). This would create the appearance in the data that non-physician managers do worse, when it reality it is not the case.
One way to push this point is to augment the regression slightly: add a measure of historical hospital quality on the right hand side. That is, regress current quality against current leadership and a measure of quality before that leadership went into place. This would control for selection into quality.
2. Of course, a better design would be to use longitudinal data on quality and leadership and track outcomes over time. A problem with implementing this is that effects only are identified off of those hospitals that change leadership regimes. In addition, rankings need to change over time, too. It's not hard to imagine inertia in both leadership and rankings, limiting the utility of this potential research design.
3. Everyone seems to refer to US rankings as gospel while at the same time denouncing them for their inaccuracy. I think better measures of quality (process elements, for example, like door-to-balloon time, patient satisfaction, etc) may be more informative in better delineating the effectiveness of different kinds of leaders.
4. Finally, there is a growing cadre of physicians who have obtained MBAs, MHAs, MPPs, MPHs. Are these dual-degreed souls better leaders than MD only physicians or non-MDs (I suspect the answer is yes)? I'd be interested to know.
Thursday, July 28, 2011
Tuesday, July 19, 2011
Random Links
1. "Frying big fish" - My colleague and good friend Paul Lagunes has a wonderful piece on the problem of, and solutions to, police corruption.
2. A trip across one of the bridges crossing Chennai's Buckingham Canal brings the familiar site of people defecating along the side of the road. Clearly a public health program. Karen Grepin on how the Gates' Foundation is bringing this to public attention.
3. A piece on sportswriter Bill Simmons' new website "Grantland" about the genius that is Friday Night Lights. I love how the article is structured as an "oral history."
2. A trip across one of the bridges crossing Chennai's Buckingham Canal brings the familiar site of people defecating along the side of the road. Clearly a public health program. Karen Grepin on how the Gates' Foundation is bringing this to public attention.
3. A piece on sportswriter Bill Simmons' new website "Grantland" about the genius that is Friday Night Lights. I love how the article is structured as an "oral history."
Tuesday, July 12, 2011
Expanding Medicaid - Good, Bad, or Ugly?
Possibly the most important health economics paper of the year, especially as it relates to the debates surrounding Obamacare. Here is the abstract:
In 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides a unique opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. In the year after random assignment, the treatment group selected by the lottery was about 25 percentage points more likely to have insurance than the control group that was not selected. We find that in this first year, the treatment group had substantively and statistically significantly higher health care utilization (including primary and preventive care as well as hospitalizations), lower out-of-pocket medical expenditures and medical debt (including fewer bills sent to collection), and better self-reported physical and mental health than the control group.
Some quick thoughts:
-Possibly one of the first randomized studies to show a positive impact of insurance on self-reported well-being. While some may pooh-pooh at the fact that the effects were on self-reported health rather than objective measures, I would argue that such subjective measures are equally, if not more, important.
-The randomized design obviously gives you a solid estimate of the average treatment effect for this population. However, Oregon is a unique place and the people targeted were unique, as well (low-income people who were aching for insurance). It remains to be seen if this result would generalize elsewhere.
-These effects are for 1 year out. It would be interesting to see how this all fares in the medium and long-run. Would increased preventative and primary care utilization now lead to cost-savings down the road? One would hope.
In 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides a unique opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. In the year after random assignment, the treatment group selected by the lottery was about 25 percentage points more likely to have insurance than the control group that was not selected. We find that in this first year, the treatment group had substantively and statistically significantly higher health care utilization (including primary and preventive care as well as hospitalizations), lower out-of-pocket medical expenditures and medical debt (including fewer bills sent to collection), and better self-reported physical and mental health than the control group.
Some quick thoughts:
-Possibly one of the first randomized studies to show a positive impact of insurance on self-reported well-being. While some may pooh-pooh at the fact that the effects were on self-reported health rather than objective measures, I would argue that such subjective measures are equally, if not more, important.
-The randomized design obviously gives you a solid estimate of the average treatment effect for this population. However, Oregon is a unique place and the people targeted were unique, as well (low-income people who were aching for insurance). It remains to be seen if this result would generalize elsewhere.
-These effects are for 1 year out. It would be interesting to see how this all fares in the medium and long-run. Would increased preventative and primary care utilization now lead to cost-savings down the road? One would hope.
Sunday, July 10, 2011
Global Health Data Exchange [!]
For your viewing and researching pleasure. The data exchange is courtesy of the University of Washington's Institute for Health Metrics and Evaluation. The goal is to collect all the random and not-so-random datasets floating around out there, thereby creating a "one-stop shopping" space for those interested in both tabulated and raw (census, survey, macro-health) data.
I found out about this just today while reading Sanjay Basu's latest blog post (a good one on global health data sources), and spent a better part of the browsing the site. At a first pass, the data exchange seems really comprehensive. As a grad student, I prided myself on knowing about every random dataset out there, something that took a lot of effort and time. Now, there is a nice, comprehensive external brain for such an endeavor. I hope this project continues along its current trajectory because it has a ton of promise. I would say that even in its current state it will prove quite useful for interested lay-people, policymakers, and hard-core researchers alike.
I found out about this just today while reading Sanjay Basu's latest blog post (a good one on global health data sources), and spent a better part of the browsing the site. At a first pass, the data exchange seems really comprehensive. As a grad student, I prided myself on knowing about every random dataset out there, something that took a lot of effort and time. Now, there is a nice, comprehensive external brain for such an endeavor. I hope this project continues along its current trajectory because it has a ton of promise. I would say that even in its current state it will prove quite useful for interested lay-people, policymakers, and hard-core researchers alike.
Friday, July 8, 2011
Noisy/Bad Information and Health Care Decisions
There was an interesting post on the Wall Street Journal's Health Blog about medical professionals and the use of social networks a few days ago. Much of it dealt with issues related to privacy (don't tweet about interesting cases in a manner that might identify patients, etc). However, I thought the most interesting part came at the end:
Montori says institutions and practitioners can raise awareness about conditions or available treatments, and also to counteract misinformation floating around online [using social networks]. “A lot of my colleagues say they don’t have time for distractions” like social media, he says. “But if folks who are really on the front lines of care cannot engage in this space, their thoughts, insights and experience will not be flowing through the network.”
And meantime, Montori says, “the thoughts of those who aren’t that busy, or who are paid to be in that space” will dominate. “Patients are receiving what they think is a signal but in fact it’s noise,” he says.
That last bit, about noisy signals, is an important one. It turns out that when health care professionals provide incorrect information, people learn from it in a way that is counterproductive. One of the most poignant illustrations of this comes from my friend and colleage Achyuta Adhvaryu, an economist who works on global health issues at Yale University. Adhvaryu was struck by how slowly people adopted new, highly effective anti-malarials in Tanzania after a brisk rate of uptake in the first year they were available. This is all the more weird given what we know about what malaria does to economic productivity.
Using an elegant and convincing set of theoretical and empirical techniques, he uncovers an interesting phenomenon: adoption rates are far lower in areas where the rate of misdiagnosis is higher. The story goes something like this: you have a fever, and go seek treatment. You get diagnosed with malaria and handed antimalarials. Now, if you actually have malaria, the treatment will make you feel better and you'll learn from that experience. If you don't have malaria, the treatment won't really help you and you'll lose belief in the new therapy. Adhvaryu's estimates suggests that this misdiagnosis effect is quite large and important.
We remain very interested in why people in developing countries don't adopt things like better vaccinations, malarial bednets, circumcision, etc. At a first glance, failure to adopt these cheap but potentially life-saving/enhancing interventions seem irrational. However, in a world where people respond to information, good or bad, accuracy in education and diagnosis can go a long way in encouraging socially optimal behaviors.
By the way, this is not just a developing country issue. When the medical journal Lancet published a startlingly dubious study linking measles vaccines to autism, a non-trivial number of people stopped vaccinating their kids. It all seems silly, but it emphasizes greatly the role of information, good or bad, in the decision making process.
Montori says institutions and practitioners can raise awareness about conditions or available treatments, and also to counteract misinformation floating around online [using social networks]. “A lot of my colleagues say they don’t have time for distractions” like social media, he says. “But if folks who are really on the front lines of care cannot engage in this space, their thoughts, insights and experience will not be flowing through the network.”
And meantime, Montori says, “the thoughts of those who aren’t that busy, or who are paid to be in that space” will dominate. “Patients are receiving what they think is a signal but in fact it’s noise,” he says.
That last bit, about noisy signals, is an important one. It turns out that when health care professionals provide incorrect information, people learn from it in a way that is counterproductive. One of the most poignant illustrations of this comes from my friend and colleage Achyuta Adhvaryu, an economist who works on global health issues at Yale University. Adhvaryu was struck by how slowly people adopted new, highly effective anti-malarials in Tanzania after a brisk rate of uptake in the first year they were available. This is all the more weird given what we know about what malaria does to economic productivity.
Using an elegant and convincing set of theoretical and empirical techniques, he uncovers an interesting phenomenon: adoption rates are far lower in areas where the rate of misdiagnosis is higher. The story goes something like this: you have a fever, and go seek treatment. You get diagnosed with malaria and handed antimalarials. Now, if you actually have malaria, the treatment will make you feel better and you'll learn from that experience. If you don't have malaria, the treatment won't really help you and you'll lose belief in the new therapy. Adhvaryu's estimates suggests that this misdiagnosis effect is quite large and important.
We remain very interested in why people in developing countries don't adopt things like better vaccinations, malarial bednets, circumcision, etc. At a first glance, failure to adopt these cheap but potentially life-saving/enhancing interventions seem irrational. However, in a world where people respond to information, good or bad, accuracy in education and diagnosis can go a long way in encouraging socially optimal behaviors.
By the way, this is not just a developing country issue. When the medical journal Lancet published a startlingly dubious study linking measles vaccines to autism, a non-trivial number of people stopped vaccinating their kids. It all seems silly, but it emphasizes greatly the role of information, good or bad, in the decision making process.
Thursday, July 7, 2011
Bad Epidemiology
While in South Africa a few months ago, an irritating yet clever radio announcer, during a joke-based interlude between songs, made the following comment:
"Research has shown that insomnia leads to depression. Other research has shown that depression leads to insomnia. Still other research has shown that research leads to more research."
Seems like a great indictment of some of less-than-careful, data mining-y studies that often find their way into decent journals and on the evening new. (Note: I'm not anti-epidemiology.)
"Research has shown that insomnia leads to depression. Other research has shown that depression leads to insomnia. Still other research has shown that research leads to more research."
Seems like a great indictment of some of less-than-careful, data mining-y studies that often find their way into decent journals and on the evening new. (Note: I'm not anti-epidemiology.)
Wednesday, June 29, 2011
More on Corruption in the Public Sector
This time the relationship between elections and corruption. Suprise surprise, but elected officials respond to incentives, too:
We show that political institutions affect corruption levels. We use corruption audit reports in Brazil to construct new measures of political corruption in local governments and test whether electoral accountability affects the corruption practices of incumbent politicians. We find significantly less corruption in municipalities where mayors can get reelected. Mayors with re-election incentives misappropriate 27 percent fewer resources than mayors without re-election incentives. These effects are more pronounced among municipalities with less access to information and where the likelihood of judicial punishment is lower. Overall our findings suggest that electoral rules that enhance political accountability play a crucial role in constraining politician’s corrupt behavior.
Great paper, and in the June 2011 issue of the American Economic Review.
We show that political institutions affect corruption levels. We use corruption audit reports in Brazil to construct new measures of political corruption in local governments and test whether electoral accountability affects the corruption practices of incumbent politicians. We find significantly less corruption in municipalities where mayors can get reelected. Mayors with re-election incentives misappropriate 27 percent fewer resources than mayors without re-election incentives. These effects are more pronounced among municipalities with less access to information and where the likelihood of judicial punishment is lower. Overall our findings suggest that electoral rules that enhance political accountability play a crucial role in constraining politician’s corrupt behavior.
Great paper, and in the June 2011 issue of the American Economic Review.
Tuesday, June 28, 2011
The Persistence of Inequalities at Birth
The Economix blog at the New York Times has a great post on how differences in birth weight early in life lead to persistent differences in well-being (measured any way you'd like) in adulthood.
The article does a great job of highlighting studies exploring the causes of birthweight differences. Some of them are somewhat unexpected: did you know that EZ-pass is associated with higher birth weights and less risk of prematurity? (Hat tip: AKN)
The article does a great job of highlighting studies exploring the causes of birthweight differences. Some of them are somewhat unexpected: did you know that EZ-pass is associated with higher birth weights and less risk of prematurity? (Hat tip: AKN)
Sunday, June 26, 2011
Comparative Effectiveness Research - What is it Good For?
One oft floated solution to rising health care costs is the use of comparative effectiveness research (CER) to guide use of more efficient/efficacious therapies from the outset, reducing the need for costly readmission, diagnostic tests and trials of different therapies. CER involves a set of tools that help compare two or more different treatment strategies with each other, often in the context of a randomized clinical trial. An added wrinkle to all this is the the (in)famous Cost Effectiveness Study (CEX), where the outcome returns to different treatments are scaled/compared by their cost.
While proponents of CER are gung-ho about its clinical and policy utility, there are potential downsides to such research. In general, most of our clinical trials recover average effects for a population of interest. That is, we compare drug X against drug Y in randomized groups of 15-75 year olds with certain manifestations of disease Z. This is great for getting an average effect estimate for a particular population. That is, if we randomly draw a 15-75 year old with certain manifestations of disease Z, on average we can expect drug X and Y to work a certain way.
However, there is an increasing realization that drugs work differently for different people. Individuals may vary in the manner in which they metabolize certain drugs or the nature of their underlying illness, while equivalent to the average clinician, may differ in its responsiveness to treatment (see here for a great discussion on this.) If this is the case, widespread use of CER and CEX may not make people better off. In some cases, it might make some people worse off. For example, if some people are better off with drug X, but the average person benefits more from drug Y, the use of the latter will make some people worse off.
In a very interesting paper (see here for a non-gated, older version), Anirban Basu, Anupam Jena,and Tomas Philipson provide a real clinical example of this latter point from psychiatry. They build a model where CER and CEX information is used by insurers/payers to guide clinical care. That is, when a study comes out showing that drug Y > X, these parties are only willing to pay from drug Y. They then show that, in the case of schizophrenia, overall health may have been reduced because people who were formally doing well on drug X were forced to take drug Y, which was actually worse for their health and well-being. The authors go on to call for a more nuanced understanding of how CER and CEX research can be used to guide treatment, especially in an era where individualized treatments are becoming more popular (Basu has a great essay on this point here; see here for a technical paper on how CER can be individualized). Certainly, a regime where CER/CEX can be maximally useful will involve directed clinical trials that take heterogeneous treatment effects into account in the a priori design.
(PS: A great summary essay on CER/CEX, which covers many of the above points, can be found in a recent issue of the Journal of Economic Perspectives. Also, hat tip to AKN for bringing several of these papers to my attention.)
While proponents of CER are gung-ho about its clinical and policy utility, there are potential downsides to such research. In general, most of our clinical trials recover average effects for a population of interest. That is, we compare drug X against drug Y in randomized groups of 15-75 year olds with certain manifestations of disease Z. This is great for getting an average effect estimate for a particular population. That is, if we randomly draw a 15-75 year old with certain manifestations of disease Z, on average we can expect drug X and Y to work a certain way.
However, there is an increasing realization that drugs work differently for different people. Individuals may vary in the manner in which they metabolize certain drugs or the nature of their underlying illness, while equivalent to the average clinician, may differ in its responsiveness to treatment (see here for a great discussion on this.) If this is the case, widespread use of CER and CEX may not make people better off. In some cases, it might make some people worse off. For example, if some people are better off with drug X, but the average person benefits more from drug Y, the use of the latter will make some people worse off.
In a very interesting paper (see here for a non-gated, older version), Anirban Basu, Anupam Jena,and Tomas Philipson provide a real clinical example of this latter point from psychiatry. They build a model where CER and CEX information is used by insurers/payers to guide clinical care. That is, when a study comes out showing that drug Y > X, these parties are only willing to pay from drug Y. They then show that, in the case of schizophrenia, overall health may have been reduced because people who were formally doing well on drug X were forced to take drug Y, which was actually worse for their health and well-being. The authors go on to call for a more nuanced understanding of how CER and CEX research can be used to guide treatment, especially in an era where individualized treatments are becoming more popular (Basu has a great essay on this point here; see here for a technical paper on how CER can be individualized). Certainly, a regime where CER/CEX can be maximally useful will involve directed clinical trials that take heterogeneous treatment effects into account in the a priori design.
(PS: A great summary essay on CER/CEX, which covers many of the above points, can be found in a recent issue of the Journal of Economic Perspectives. Also, hat tip to AKN for bringing several of these papers to my attention.)
Saturday, June 25, 2011
Random Links
1. Al Gore comes out in favor of access to better health care, family planning services, and education, especially targeted towards women, as a strategy towards improving well-being in the developing world. All sensible stuff. Unfortunately, echoing the vitriol of family planning debates over the last half century or more, he was mistakenly, hilariously, and sadly criticized for being a eugenicist and/or Malthusian by some conservatives.
2. Chris Blattman on a great new paper linking weather disturbances/changes faced early in life to long-run outcomes. He makes some great points about the mechanisms underlying these relationships as well as appropriate practices for statistical work when researchers have abundant data points but little theory guiding exactly what the relationship between two variables might be.
3. Some time ago, I wrote about tennis rackets, lamenting the disappearance of one model in particular as if it were a lost love. Apparently, that tone was appropriate since the racket a pro tennis player chooses seems to say a lot about their personality and preferences - at least as it relates to the tennis court . (Hat tip: MG)
4. I just found out that Sanjay Basu, an MD/PhD epidemiologist doing an internal medicine residency at UCSF, has a great thing going with his new(-ish) blog, epianalysis. Sanjay has got to be one of the most talented, insightful and prolific researchers around. His work spans the mathematical modeling of infectious diseases that incorporate insights from fields as diverse as economics and epidemiology, all the way to deep political economy issues related to global health. He's produced a body of work while in residency that I would be proud of if it formed the entirety of my research career. Seriously. His blog is phenomenal and highly recommended. (Hat tip: PC)
2. Chris Blattman on a great new paper linking weather disturbances/changes faced early in life to long-run outcomes. He makes some great points about the mechanisms underlying these relationships as well as appropriate practices for statistical work when researchers have abundant data points but little theory guiding exactly what the relationship between two variables might be.
3. Some time ago, I wrote about tennis rackets, lamenting the disappearance of one model in particular as if it were a lost love. Apparently, that tone was appropriate since the racket a pro tennis player chooses seems to say a lot about their personality and preferences - at least as it relates to the tennis court . (Hat tip: MG)
4. I just found out that Sanjay Basu, an MD/PhD epidemiologist doing an internal medicine residency at UCSF, has a great thing going with his new(-ish) blog, epianalysis. Sanjay has got to be one of the most talented, insightful and prolific researchers around. His work spans the mathematical modeling of infectious diseases that incorporate insights from fields as diverse as economics and epidemiology, all the way to deep political economy issues related to global health. He's produced a body of work while in residency that I would be proud of if it formed the entirety of my research career. Seriously. His blog is phenomenal and highly recommended. (Hat tip: PC)
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