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.
Wednesday, June 29, 2011
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)
Wednesday, June 22, 2011
Sex and Measurement
We know with a good deal of certainty that unprotected sex exposes individuals to potentially life-threatening illness. We also know that all sexual encounters are not the same and, especially since the HIV/AIDS epidemic, researchers have been trying to figure out what sexual behaviors are riskiest and how to use this information towards better micro and macro-focused prevention efforts.
As with all research, a key issue is measurement. Our models to predict individual behavior are usually only as good as our data. As you might imagine, sex can be a personal topic. One may be reluctant to tell a survey interviewer/doctor/friend about their sexual activities, obscuring the whos, hows and whens that are oh-so-important for public health.
Some recent work provides insight into the scale of the measurement problem. A paper by Alexandra Minnis and colleagues compared self-reported sexual activity with biomarkers of exposure (a test based on PSA which can detect exposure to semen in the previous two days) in a sample of Zimbabwean women. The results were sobering: 52% of women who had positive biomarkers said that they engaged in protected sex in the last two days; 23% reported having no sex at all!
In another paper, Brendan Maughan-Brown and I looked at a sample of young adults in Cape Town, South Africa. Our study focused on concurrent sexual partnerships, intuitively defined as the presence of (temporal) overlap between sexual relationships with two distinct partners. There is a hot debate right now on whether such partnerships have been driving the HIV/AIDS epidemic in sub-Saharan Africa. Unfortunately, this debate has been held back by the availability of good data.
Recently, UNAIDS came out with some guidelines on how to standardize and better measure concurrency. We assessed the effectiveness of these guidelines by assessing whether individuals who reported having concurrent relations also reported more than one sexual partner. What we found was surprising: among those who reported only one sexual partner in the last year, nearly 1 out of 6 reported having concurrent sexual relations during this period! We conclude that the UNAIDS methods, which involves asking individuals about each sexual partner they've had and the start and end dates of those partnerships, may actually underestimate the prevalence of concurrency by a significant amount by not fully accounting for all sexual partners.
As both these papers suggest, we have a long way to go before we can credibly claim that we have precise, unbiased estimates of sexual behavior. It would be useful to divert some of time we all spend on linking specific sexual behaviors to health outcomes to figuring out how to get the measurements of those behaviors right in the first place.
As with all research, a key issue is measurement. Our models to predict individual behavior are usually only as good as our data. As you might imagine, sex can be a personal topic. One may be reluctant to tell a survey interviewer/doctor/friend about their sexual activities, obscuring the whos, hows and whens that are oh-so-important for public health.
Some recent work provides insight into the scale of the measurement problem. A paper by Alexandra Minnis and colleagues compared self-reported sexual activity with biomarkers of exposure (a test based on PSA which can detect exposure to semen in the previous two days) in a sample of Zimbabwean women. The results were sobering: 52% of women who had positive biomarkers said that they engaged in protected sex in the last two days; 23% reported having no sex at all!
In another paper, Brendan Maughan-Brown and I looked at a sample of young adults in Cape Town, South Africa. Our study focused on concurrent sexual partnerships, intuitively defined as the presence of (temporal) overlap between sexual relationships with two distinct partners. There is a hot debate right now on whether such partnerships have been driving the HIV/AIDS epidemic in sub-Saharan Africa. Unfortunately, this debate has been held back by the availability of good data.
Recently, UNAIDS came out with some guidelines on how to standardize and better measure concurrency. We assessed the effectiveness of these guidelines by assessing whether individuals who reported having concurrent relations also reported more than one sexual partner. What we found was surprising: among those who reported only one sexual partner in the last year, nearly 1 out of 6 reported having concurrent sexual relations during this period! We conclude that the UNAIDS methods, which involves asking individuals about each sexual partner they've had and the start and end dates of those partnerships, may actually underestimate the prevalence of concurrency by a significant amount by not fully accounting for all sexual partners.
As both these papers suggest, we have a long way to go before we can credibly claim that we have precise, unbiased estimates of sexual behavior. It would be useful to divert some of time we all spend on linking specific sexual behaviors to health outcomes to figuring out how to get the measurements of those behaviors right in the first place.
Tuesday, June 14, 2011
A Poignant Opening to the Innings
Today was the second of a 10-day orientation to my internal medicine residency. It's a bit like summer camp right now: the schedule is friendly, the people are even more friendly, and everyone is smiles and giggles. It's been celebratory as well, as if a continuing acknowledgment of our finishing medical school. However, I snapped out of this post-medical school graduation reverie this afternoon when I met one of my future patients.
While I was visiting my to-be clinic site with three to-be colleagues, an African-American gentleman in a baseball cap, who had been watching me while on a tour of the facility, came up to me and plainly stated: "So, you're my doctor." I must have looked at him blankly because he followed it up by saying: "You're the guy with the really long name right? You're my doctor."
I was a bit taken aback, until I realized that this nice gentleman was indeed going to be one of my patients. Every year, my internal medicine program graduates a class of residents. Each resident has a panel of outpatients that they have taken care of over the three years of the program. At the end of residency they turn their panels over to one of the incoming interns. This particular patient is one of the 100 or so I'll be "inheriting" from my senior.
"My doc told me that she's leaving and that you're the new guy," he went onto explain, "So...what is your name?" I started out by saying, "Hey, I'm Atheen" - and then I caught myself. "I'm Doctor Atheendar" I told him, steadying my voice. I gave him a firm handshake, too, instinctively, yet still theatrically moving my left hand over to additionally grasp his right, as if to say "yeah, I'm new - but I got this." Being so unsure about my abilities, knowledge and competence as a physician-in-training, I thought I saw a hint of skepticism in his eyes. But it couldn't have been, because he suddenly smiled broadly and stated proudly, as he looked at the nurse nearby, "HE is my doctor."
And so I am. And so it begins - humbling and inspiring, all at once.
While I was visiting my to-be clinic site with three to-be colleagues, an African-American gentleman in a baseball cap, who had been watching me while on a tour of the facility, came up to me and plainly stated: "So, you're my doctor." I must have looked at him blankly because he followed it up by saying: "You're the guy with the really long name right? You're my doctor."
I was a bit taken aback, until I realized that this nice gentleman was indeed going to be one of my patients. Every year, my internal medicine program graduates a class of residents. Each resident has a panel of outpatients that they have taken care of over the three years of the program. At the end of residency they turn their panels over to one of the incoming interns. This particular patient is one of the 100 or so I'll be "inheriting" from my senior.
"My doc told me that she's leaving and that you're the new guy," he went onto explain, "So...what is your name?" I started out by saying, "Hey, I'm Atheen" - and then I caught myself. "I'm Doctor Atheendar" I told him, steadying my voice. I gave him a firm handshake, too, instinctively, yet still theatrically moving my left hand over to additionally grasp his right, as if to say "yeah, I'm new - but I got this." Being so unsure about my abilities, knowledge and competence as a physician-in-training, I thought I saw a hint of skepticism in his eyes. But it couldn't have been, because he suddenly smiled broadly and stated proudly, as he looked at the nurse nearby, "HE is my doctor."
And so I am. And so it begins - humbling and inspiring, all at once.
Sunday, June 5, 2011
Good Articles on US Health Care
The most recent issue of the Journal of Economic Perspectives contains some excellent articles related to health care reform. The articles cover everything from the effects of medical malpractice reform to the impacts of payment structures to physicians on cost growth. All of the articles are written by top health economists with a great deal of research and policy experience. My two favorite pieces examine the role of administrative costs in explaining cross-country differences in health care expenditures and the future of comparative effectiveness (and cost-effectiveness) research in health care decision making. Definitely check it out!
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