Health Expenditures in the US: Are We Not Spending Enough?

According the Elizabeth Bradley, a Professor of Health Policy and Administration at Yale, the answer is no. As she and Lauren Taylor point out in a recent New York Times editorial:

We studied 10 years’ worth of data and found that if you counted the combined investment in health care and social services, the United States no longer spent the most money — far from it. In 2005, for example, the United States devoted only 29 percent of gross domestic product to health and social services combined, while countries like Sweden, France, the Netherlands, Belgium and Denmark dedicated 33 percent to 38 percent of their G.D.P. to the combination. We came in 10th.

Bradley and Taylor put forth the argument that the things that make people healthy go beyond what we typically think of as health care. That is, access to employment, good housing, food security, and educational institutions all contribute to population health. I don't think this is a revolutionary thought.

But what is revolutionary is that they authors imply that the answer to our central question for US health care - "Do we get what we pay for?" - might not be the "no" we've always assumed, but a "yes." We just aren't spending enough, at least not on the proximal things that really matter. I don't think that it is that simple - it's hard to know what portion of social service spending actually improves health. But the discourse does need to move in this direction.

Furthermore, another neat aspect of this piece is that Bradley and Taylor's contention doesn't just apply to the macro-level health policy sphere. Imagine a primary care system that takes into account the socioeconomic realities of patients and creates interventions that use these insights to better provide care. A developing country example: subsidizing the transportation fees for HIV/AIDS patients who would otherwise find this to be a barrier and be unable to seek much needed health care. This sort of intervention may be equally important to any medications or lab tests in advancing the health of these patients. I'll talk more about this sort of "economic hotspotting" in a later post.

Great Harvard Med Class Show Parody

As an intern, I get to work side by side with Harvard medical students. I have to say that they have all been very, very good in terms of their clinical knowledge and ability to efficiently get things done. No wonder I got rejected when I applied.

It turns out that Harvard med students are pretty funny, too. Check out this great parody of medical students' experiences while on their third year clinical rotations by members of the Class of 2014. I'm sure you'll recognize the Saturday Night Live short this is based on. (HT: the awesome and hilarious Camila Fabersunne).

Male Circumcision, HIV/AIDS and the "Real World"

This past week, PLoS Medicine put forth multi-piece expose (start with this lead/summary article) on medical male circumcision, its cost-effectiveness in combating HIV/AIDS and methods and challenges to scaling up this practice in Sub-Saharan Africa, where the epidemic is at its worst. The upshot of this series of papers was covered in a recent Scientific American piece (which quotes yours truly). To summarize, the argument is that medical male circumcision works (as demonstrated in three large randomized clinical trials, all conducted in Africa) and is cost-effective. Indeed, it may even be cost-saving, with high upfront costs that are easily recovered over a 10 year period. Challenges to scale-up include finding health care workers to carry out circumcisions (in a way that doesn't crowd-out provision of other important health care services), getting people to adopt the practice in a respectful, non-coercive yet effective way, especially in areas where there are strong traditional norms over circumcision, and dealing with any risk compensating behavior (if circumcised individuals think circumcision is protective, they may be more likely to engage in riskier sexual behaviors than they otherwise would - more on this in a later post).

Circumcision is one of those topics that seems to always bring with it a vociferous debate. Those opposed to the practice make their stance known quite vehemently. In my opinion, much of what is being spouted against medical male circumcision as a tool for HIV prevention is based on an incomplete understanding of the available evidence and already strong negative priors against the practice that are almost impossible to shift (for example, see this clip or refer to any of the comments to the aforementioned Scientific American article).

However, I think there is one oft-cited argument against medical male circumcision that is worth discussing further. In particular, opponents point to evidence from a 2009 UNAIDS study that uses recent survey data from 18 African countries and concludes that "there appears no clear pattern of association between male circumcision and HIV prevalence—in 8 of 18 countries with data, HIV prevalence is lower among circumcised men, while in the remaining 10 countries it is higher." This is contrast to the large randomized clinical trials mentioned above which show that circumcision reduces HIV rates by greater than 50%. The fact that the clinical trial results are not borne out in the sample survey data, opponents argue, means that circumcision does not work in "real world settings."

In a recent study, Brendan Maughan-Brown, Nicoli Nattrass, Jeremy Seekings, Alan Whiteside and I offer a different explanation for this differential set of findings. It has to do with the fact that the UNAIDS study looks at population that were circumcised in a multitude of settings (clinics, traditional healers) whereas the clinical trials focus on medical circumcision only. In practice, there great deal of heterogeneity in traditionally circumcising populations: some people do not have all of their foreskin removed, and others are circumcised several years after their peers. In our study population of blacks living in the Cape Town metro area, when we don't account for this heterogeneity, we find only a weak negative effect of circumcision on HIV positivity. However, once we "unpack" circumcision, we find that the practice actually has a strong negative association with the probability of testing HIV positive, provided it is done earlier and that there is complete removal of the foreskin.

These results suggest that the UNAIDS results may simply be due to measurement error. In a traditional setting, a circumcision is not a circumcision is not a circumcision. Treating every circumcised person the same introduces measurement error, and statistically it is well known that this would deflate the estimates of the impacts of the practice towards zero. So, the differential results between the UNAIDS findings and the randomized clinical trial findings is not that circumcision doesn't work in the real world. Rather, it is that we really need to understand better the heterogeneity in male circumcision and what can be done to ensure better outcomes for everyone involved.

Do Financial Incentives Induce Physicians to Provide More (Unnecessary) Care?

About two years ago, I posted something on my now non-existent Facebook account about how medical tests and treatments, especially those that are elective, are more likely to be offered if doctors are reimbursed well for them. My point was that there was a strong financial incentive to test and treat, even in cases where doing so would confer only little benefit to the patient's health, at best. A bunch of people (mainly physicians) responded on my wall pointing out how misguided I was. It was actually a bit more vociferous than this, but I digress.

Anyway, it turns out that I was right (in this case, NOT shocking). I just came across a great study by Joshua Gottleib, an economics job market candidate from Harvard. His study uses a natural experiment in physician incentives to examine whether payment drives care offered. Specifically, he takes advantage of a large scale policy change by Medicare in 1997. Previously, Medicare created different fee schedules for each of around 300 small geographic areas. This was done because production costs and other realities of providing a given service obviously varied across space. In 1997, they decided to coalesce these regions into 80 larger areas. For some smaller areas, there may have been large payouts for certain services which fell after 1997 because the average payout for their new larger group was lower. For others, it went the other way. In any case, comparing pre and post 1997 gives you a nice experiment as to what would happen to health services provision when payouts are changed for reasons other than local health outcomes or demand for care.

Whether you hold my priors or shared those of my misguided Facebook friends, the results remain astounding. Across all health services, Gottleib finds that "on average, a 2 percent increase in payment rates leads to a 5 percent increase in care provision per patient." Predictably the price response of services with an elective component (such as cataract surgery, colonoscopy and cardiac procedures - don't huff and puff, I said elective COMPONENT!) but not so much for things like dialysis or cancer care, where it is easy to identify who needs it and you need to do it no matter what. Furthermore, in addition to disproportionally adjusting the provision of relative intensive and elective treatments as reimbursements rise, physicians also invest in new technology to do so; this is beautifully illustrated by the examination of reimbursement rates and MRI purchases.

So what's the upshot of all this? Is this a good thing? Probably not. Despite scaling up technology, Gottleib is unable to find any impacts on health outcomes or mortality among cardiac patients (for which he explored more deeply the relationship between payouts and treatment). Furthermore, he asserts that "that changes in physician pro fit margins can explain up to one third of the growth in health spending over recent decades."

Ultimately, some good lessons here. First, if we are interested in bring down costs and increasing health care efficiency, we need to pay for things that actually help maintain and increase health. Second, we can't rely on physicians do be the gatekeepers of rising costs as it is clear that, given incentives, they may not always behave in a way that actually improves health outcomes (thankfully, for cases like fractures, cancer or end-stage renal disease treatment, docs aren't sensitive to prices and do the right thing clinically). Finally, we need to stop universally and blindly lauding the US health care system as a bastion of health care technology if that technology does little to improve outcomes.

Infections and IQ

A well known fact about our world is that there are great disparities in average IQ scores across countries. In the past, some have tried to argue that this pattern be explained by innate differences in cognition across populations - some people are just innately smarter than others. Others have tried to attribute these to cultural factors. However, genetics and culture are likely not driving these differences in any meaningful sense. After all, another stylized fact is that average IQ scores have been going up markedly, within one or two generations, within any given country. These changes, also known as the Flynn Effect after the researcher who painstakingly documented them, speak against the genes story because they occurred far more quickly than one would expect from population-wide changes in the distribution of cognition-determining genes. The have occured too quickly to be explained by paradigm shifting social changes, as well.

So what gives? Enter Chris Eppig, a researcher at the University of New Mexico. In a recent piece in The Scientific American , he proposes that cross-country differences in IQ, as well as changes in IQ rates within a country over time, can be explained by exposure to infectious diseases early in life. The story goes something like this: infections early in life require energy to fight off. Energy during this age is primarily used for brain development (in infancy, it is thought that over 80% of calories are allocated to neurologic development). So if energy is diverted to fend off infections, it can't be used to develop cognitive endowments, and afflicted infants and children end up becoming adults that do poorly on IQ tests.

In the piece, Eppig cites some of his work linking infectious disease death rates in countries to average IQ scores. His models control for country income and a few other important macroeconomic variables. His evidence, while not proof of a causal relationship, is certainly provocative. So provocative in fact that I ended up trying to build a stronger causal story between early childhood infections and later life cognitive outcomes. In a recent paper (cited in the above Scientific American article), I examine the impact of early life exposure to malaria on later life performance on a visual IQ test. I use a large-scale malaria eradication program in Mexico (1957) as a quasi-experiment to prove causality. Basically, I find that individuals born in states with high rates of malaria prior to eradication - the area that gained most from eradication - experienced large gains in IQ test scores after eradication relative those born in states with low pre-intervention malaria rates, areas that did not benefit as much from eradication (see this Marginal Revolution piece for a slightly differently worded explanation).

My paper also looks at the mechanisms linking infections and cognition. One possibility is the biological model described above - infections divert nutritional energy away from brain development. However, I also find evidence of a second possibility: parents respond to initial differences in cognition or health due to early life infections and invest in their children accordingly. In the Mexican data, children who were less afflicted by malaria thanks to the eradication program started school earlier than those who were more afflicted. Because a child's time is the domain of parental choice, this suggests that parents reinforce differences in the way their children are (- erhaps they feel that smarter children will be smarter adults, and so investments in their schooling will yield a higher rate of return - and that this can modulate the relationship between early life experiences and adulthood outcomes.

Good and Bad Epidemiology and Other Interesting Links

1. The incomparably intelligent and eloquent Sanjay Basu on how doctor house calls of the olden days could return - in the form of targeted efforts to reduce preventative disease and hospitalizations on the basis of cutting edge epidemiology.

2. A man after my own heart, Dr. Ben Goldacare, an evidence based medicine expert, rails against bad epidemiology studies (you know, the kind in the news that like say coffee is protective against cancer or something like this, only to be overturned 180 degrees two months later) in this entertaining TED talk. Some useful pointers about how to differentiate between junk and good research as well as a good summary of causal inference. (HT: Jeremy Green)

3. Apparently there is enough sense to go around for all of us.

4. Asif Mandvi of The Daily Show lampoons the Republican candidates position on science and scientific knowledge in this great clip. Its pretty funny, until you realize that the candidates are actually serious. Then its a little scary. (HT: Kim Kopecky)

5. How does the recession and being out of work influence our physical activity? And what are its implications for health? Gregory Colman and Dhaval Dave explore these issues in an interesting recent NBER working paper.

Discrimination in the Shadows

Two new papers looking at various aspects of discrimination in product and labor markets. The first, by Ian Ayres and coauthors, examines baseball card sales:

We investigate the impact of seller race in a field experiment involving baseball card auctions on eBay. Photographs showed the cards held by either a dark-skinned/African-American hand or a light-skinned/Caucasian hand. Cards held by African-American sellers sold for approximately 20% ($0.90) less than cards held by Caucasian sellers, and the race effect was more pronounced in sales of minority player cards. Our evidence of race differentials is important because the on-line environment is well controlled (with the absence of confounding tester effects) and because the results show that race effects can persist in a thick real-world market such as eBay.

The second looks at skilled immigrant labor in Canada. Canada, like many other first world countries, has made it a policy to strongly select for skilled immigrants to augment their workforce. Unfortunately, these immigrants do not do as well as one would hope in the labor market. Philip Oreopoulos explores this issue in greater depth:

Thousands of randomly manipulated resumes were sent in response to online job postings in Toronto to investigate why immigrants, allowed in based on skill, struggle in the labor market. The study finds substantial discrimination across a variety of occupations towards applicants with foreign experience or those with Indian, Pakistani, Chinese, and Greek names compared with English names. Listing language fluency, multinational firm experience, education from highly selective schools, or active extracurricular activities had no diminishing effect. Recruiters justify this behavior based on language skill concerns but fail to fully account for offsetting features when listed.

While they speak for themselves, here are a few collective comments on these papers:
1. Both illustrate the power of audit studies, where researchers elicit real-time behavioral responses in the field to some often innocuous stimuli. Resume experiments have a long history in economics and sociology. The EBay thing is new, and quite innovative.
2. Audit studies give us an example of behavior, but can further be extended to think about mechanisms and policy. One reason I like the Oreopoulos paper is that his randomization involved an explicit countersignal to having an immigrant last name. Unfortuntately, it didn't work to reverse the discriminatory effect, but it is informative that signaling language skills failed. What would be nice is to further extend this, both to other policies, but also to a further elucidation of mechanisms. I think qualitative work could be very useful in this regard. (My colleagues and I did this in a paper about corruption).
3. Both of these studies reveal that taste-based or statistical discrimination is pretty deep seated, though it may lurk in the shadows. So what to do about this? Jumping from (2), I hope the next wave of experiments look at different sorts of policies. Are there other nudges that can be used to counteract these forces? Or will immigrants have their change from Shankaranarayan to Steve in order to get jobs?

Building Reflexes

So I am now a third of the way into my intern year in my internal medicine residency. The whole enterprise started off as a string of stress and self-doubt inducing thought after another: There is a lot to know and I don't think I'll ever know any of it...Wow, that senior resident is really smart. I'm never going to be that good...Dr. So and So is a great attending, I'm never quite going to get there...I've never done a thoracentesis before. What is I mess up?...What if I kill someone?

Of all of the above, a green intern seeing the sheer confidence and competence of the junior and senior residents was somehow the scariest. It was a mystery how someone could go with my fund of knowledge to their fund of knowledge in a year or two. I was convinced I was the imposter in my intern class, the one for whom the physician production process would fail.

Along these lines, as I've moved through intern year, I've learned three important things. All of these have served to keep me sane in the storm of self-doubt:

1. A scared intern is an intern who has an appropriate level of confidence, and therefore thinks harder and is quicker to ask for help. He/she is therefore a safe intern.
2. Every intern feels the same fear coming into residency.
3. The goals of intern year should be modest. Not, "I want to read everything become a master clinician after 1 month on an inpatient service" but "I want to develop quickly implementable algorithms for common clinical situations that will ensure that I am thorough and efficient."

This last aspect is what I call "reflex building." There is a set of clinical situations that interns and residents will face time and time again. Getting good at working up and troubleshooting those problems builds confidence, saves time and allows you to devote your precious tired brain to more intransigent clinical issues.

I remember my first call night where a gentleman became short of breath. I walked into the patient's room, following a frantic nurse, with a veneer of calm but with the insides of a rookie quarterback facing his first test against a wild, aggressive defense. Thoughts scattered, I correctly asked for a stat chest x-ray, ABG kit, had the angle of the bed increased, and called for stat labs. I listened to the chest and thought it sounded wet, and ordered a diuretic. Prior to all this, I paged the teaching senior resident on call.

It turns out I did alright, but I forgot to get an EKG. In my stressed state, I forgot to think about acute cardiac issues (like heart attacks) as precipitants for this new shortness of breath. Clearly, ruling out heart attacks is an absolute must. Luckily the nurses and the night senior all knew what to do and the EKG machine was in the room and humming before I'd even thought to call out for it. (The patient was not having a heart attack.)

Two months later, I was in the same situation. I walked in to the room, again with some outward swag, but this time with also with an organized work-up plan. I got all the tests I needed to get. It turned out the gentleman had missed his diuretic pill for two days. He sounded "wet" and I diuresed him. His EKG was fine and the patient got better quickly. I then had time to leave and deal with the four new admits that hit the floor all at once, then later check in on this patient before I handed off the service to the day team.

It was at that point I realized that I had actually learned a lot during intern year. At any given point in time, the marginal output of the physician production function is hard to observe. That is, at most points in your training, you are faced with such a huge knowledge base that the distance between you and the ideal always looks limitless – you don’t really feel like you are getting anywhere. However, in situations like the one I just described, or where a patient has new urinary retention, new chest pain, a new GI bleed, we've all now seen enough of these to know how to handle these problems efficiently and safely. It’s a great feeling to have these new clinical reflexes.

Some time later, I took on the roll of "running plans" in our inpatient service. Basically, our inpatient service has four interns that take on different roles every day. The "plan runner" goes through each of the twenty or so patients and decides what needs to be done for that day based on that mornings exam and labs, the previous nights events, and all the accruing data about the clinical course. The junior resident, who runs the team, watches over all of this and chimes in and teaches when necessary (which is a lot, early in the year). The first few times I ran plans I got a lot of much needed and much appreciated input from the junior ("Do you really want to do that? And have you thought about this?"). More recently, I’ve been hearing less from them. And I also have a better sense of the nuances the junior needs to know to run the team effectively - how to discharge patients, when not to get labs, how to deal with a difficult consult service. There was one moment, though brief, where I thought to myself “I think I can do that. I think I can be a good junior.”

That was a small, but important, victory.

Ultimately, perhaps the best lesson from all this is to trust in the production process. As our program director put it during orientation, "[The residency program has] been doing this for years. Sit back, put on your seat belt, and let us do our thing. You'll be fine."

Battling the Bulge

Some innovative social policy this week in Mexico, where the federal government been expressing some palpable alarm over rising obesity among children and adults. I'm sure that Mexico now is the fattest nation in the world and, if they aren't, they are right behind the US in this dubious regard. From a country that was worried about infectious disease deaths just a generation ago to one that is increasingly burdened by diabetes and heart disease comes a new social policy that aggressively seeks to reduce obesity in children by banning junk food, increase hours of physical education, and provide nutritional education in a school based setting. Only time will tell whether they can get people to substitute agua for refrescas, but I like where they are headed with this multi-pronged approach. I know there is some evidence that each of these interventions could provide positive benefits alone (see, for example, here), so perhaps there will be a bigger kick from all three together.

On our side of the border comes some new evidence that your neighborhood matters as far as obesity goes. A study in this week's New England Journal of Medicine finds that poor households randomized to receiving housing vouchers enabling them to move to nicer neighborhoods were significantly less likely to be obese and have elevated hemoglobin A1c levels (a marker of blood sugar content used to diagnose, and track response to treatment for, diabetes). This experiment validates a long-standing hunch that neighborhoods matter for obesity. The question now is what exactly matters, i.e., what is the mechanism behind this causal pathway? We obviously need to know this in order to design targeted policies? Is it that better neighborhoods have better designed streets that encourage walking? The presence of parks? Are there positive peer effects from health nuts? Better grocery stores and more healthy food options relative to junk food options? Better access to primary care docs? I'm awaiting the follow up study which tries to tease these different possibilities apart.

R.I.P Steve Jobs

"Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work. And the only way to do great work is to love what you do" - Jobs, during a 2005 commencement address.

Foreclosures and Health

Great paper by Janet Currie and Edral Tekin on the housing crisis and its impacts on health outcomes:

We investigate the relationship between foreclosure activity and the health of residents using zip code level longitudinal data. We focus on Arizona, California, Florida, and New Jersey, four states that have been among the hardest hit by the foreclosure crisis. We combine foreclosure data for 2005 to 2009 from RealtyTrac with data on emergency room visits and hospital discharges. Our zip code level quarterly data allow us to control for many potential confounding factors through the inclusion of fixed effects for each zip code as well as for each combination of county, quarter, and year. We find that an increase in the number of foreclosures is associated with increases in medical visits for mental health (anxiety and suicide attempts), for preventable conditions (such as hypertension), and for a broad array of physical complaints that are plausibly stress-related. They are not related to visits for cancer morbidity, which arguably should not respond as rapidly to stress. Foreclosures also have a zero or negative effect on elective procedures, as one might expect. Age specific results suggest that the foreclosure crisis is having its most harmful effects on individuals 20 to 49. We also find that larger effects for African-Americans and Hispanics than for whites, consistent with the perception that minorities have been particularly hard hit.

This study makes a few key contributions to the literature on economic vulnerability and health outcomes. First, it utilizes very detailed data that both enable precise estimates and a number of statistical techniques that can better address potential confounders. Second, the authors make great use of falsification tests. Basically, in theory you would expect stressful events to impact some diseases and not others (especially those that take time to evolve). The authors follow up this idea and actually demonstrate it in their data, which makes the results more believable.

Experiential Learning Versus Thinking Outside the Box

A typical phone call with my mom involves learning about some of our family friends back home. Invariably, a given set of family friends would have someone my age who I either went to high school with or happened to socialize with in community events. I enjoy these updates because its great to see everyone carving out interesting lives for themselves.

In any case, I recently heard from her about a friend of mine who is now doing an internal medicine residency in Cleveland, OH. This chap was a year ahead of me in high school and someone I really looked up to as I tried to temper and use for good my pre-college pre-med craziness. Like everyone else, I ended up googling him thereafter and came across this really interesting article about a medical school experience of his in the New York Times.

As a physician in training, I am working on developing two skill sets. The first is building enough experiential knowledge in order to recognize important aspects of clinical situations more quickly and thereby reach a diagnostic and treatment pathway earlier on in the clinical encounter. The second is to "think outside the box" and develop a "broad differential diagnosis", meaning one thinks of all of the possibilities why someone might be coming into the hospital to start with and then using the available data to parse out what is more likely to be happening than what is not. Experience is important here, too. As you see more patients, you'll see some weird things, and those will get added to your possibility space as you move forward.

However, I've always wondered whether there are situations where these two skills aren't complementary. The first skill brings forth a form of pattern recognition: "this looks like acute heart failure because I've seen hundreds of these and that was it has to be." This is what experienced clinicians bring to the table. On the other hand, "thinking outside the box" involves convincing yourself that there is a non-zero probability that a rare diagnosis may be driving the picture. This is what medical students are good at, in part because they aren't really taught much about population prevalence and probabilities, evening the playing field between the common and the rare. But I wonder, at least at some points in time, if part of this is also because medical students are not "encumbered" by experiential knowledge. As a result, they can "go outside of the box" because they don't have tunnel vision? They come into a clinical situation with fresh eyes, in some sense like an outside consultant.

The NYT piece about my family friend discusses a situation where more experienced clinicians zeroed in on one particular diagnosis but then were at a loss when they didn't find the evidence to support this. My friend, a fresh wide-eyed third year medical student, was able to think outside the box about a rare illness that could unify all the laboratory results and physical exam findings. He turned out to be right. The piece goes on to discuss the value of a fresh, unadulterated pair of eyes.

I'm barely two months in to my residency, but I've been wondering how to optimize returns to my experiences while at least keeping one eye outside the box. On the other hand, I think, in general, the returns to experience are positive with almost all patients. That is, having more clinical experiences under your belt is usually better. In the other cases, which may be rare, maybe my best bet then is to rely on the fresh supply of medical students and doctors-in-training. Or maybe it actually does make sense for a hospital to have some weirdo like House, MD to outsource bizarre, intractable cases to. We'll each have our comparative advantage and can capitalize on "returns to specialization." Thoughts?

Absolutely Lovely Paper About Esther Duflo

Esther Duflo, hands down, is one of my favorite economists. Her work spans a rich swath of development economics, touching subjects as diverse (and, ultimately, as related!) as microfinance, education, health care, corruption, women's agency, and, central to a good deal of her work, randomized field experiments. She is also the founder of the Jameel Poverty Action lab and co-wrote the incredible Poor Economics.

Recently, she won the American Economic Association's John Bates Clark Medal, an bi-yearly award given to the topic economist under the age of forty. Chris Udry, one of my favorite professors at Yale, ruminates on Duflo's work in a beautiful essay in the Journal of Economic Perspectives. Reading it, one is amazed about the impact one person can have on an entire field - disruptive technological change at its finest! My favorite part about the piece is how Duflo has remained an incredible academician while also serving as a public intellectual and activist. Excellent stuff.

Great Paper on the Impact of Cancer Screening

A good number of health care professionals, though perhaps not enough, are obsessed with screening to catch various diseases early - "when we can do more about it." Cancer screening, in particular, is an area of great interest, one that the public has really latched on to over the last 20 years or so. As the row over breast cancer screening illustrates, there is a huge debate on when to screen people. Should we screen at, say, age 40 for breast cancer, or start yearly mammograms at age 50?

It turns out that the evidence to motivate one set of screening guidelines over another isn't all that great. That is, while we know that certain kinds of screening (breast and colon, for example) work, randomized clinical trials of screening tests have too few people in each age group to definitively assess the best age cutoff for these modalities.

Enter this neat paper by Srikanth Kadiyala and Erin Strumpf. They utilize our existing screening guidelines as "natural experiment" to study the effectiveness of screening at the population level. Specifically, while the person aged 39 years old and the person aged 41 years old may be similar in terms of their cancer risk, our national guidelines lead to one person being screened and the other not. Is the 41 year old better off as a result?

U.S. cancer screening guidelines recommend that cancer screening begin for breast cancer at age 40 and for colorectal cancer and prostate cancers at age 50. What are the marginal returns to physician and individual compliance with these cancer screening guidelines? We estimate the marginal benefits by comparing cancer test and cancer detection rates on either side of recommended initiation ages (age 40 for breast cancer, age 50 for colorectal and prostate cancers). Using a regression discontinuity design and self-reported test data from national health surveys, we find test rates for breast, colorectal, and prostate cancer increase at the guideline age thresholds by 78%, 65% and 4%, respectively. Data from cancer registries in twelve U.S. states indicate that cancer detection rates increase at the same thresholds by 25%, 27% and 17%, respectively. We estimate statistically significant effects of screening on breast cancer detection (1.3 cases/1000 screened) at age 40 and colorectal cancer detection (1.8 cases/1000 individuals screened) at age 50. We do not find a statistically significant effect of prostate cancer screening on prostate cancer detection. Fifty and 65 percent of the increases in breast and colorectal case detection, respectively, occur among middle-stage cancers (localized and regional) with the remainder among early-stage (in-situ). Our analysis suggests that the cost of detecting an asymptomatic case of breast cancer at age 40 is approximately $100,000-125,000 and that the cost of detecting an asymptomatic case of colorectal cancer at age 50 is approximately $306,000-313,000. We also find suggestive evidence of mortality benefits due to the increase in U.S. breast cancer screening at age 40 and colorectal cancer screening at age 50.

This is a neat, well-crafted study. The methodology the authors use, called regression discontinuity, utilizes sharp cutoffs in decision rules/policies/etc in the context of otherwise inconsequential changes in the variable used to determine this cutoff (i.e., in the immediate neighborhood of the cutoff). It is a useful way to get quasi-experimental evidence where there is no other way to get it. Indeed, regression discontinuity is now considered second below the gold standard randomized clinical trial in the pantheon of statistical approaches.

Of course, the one problem with this study is that, while we know our existing cutoffs are useful, we don't know if there is some other cutoff that would be better (one of the motivating questions of the paper). Here is a weakness of their research design: unless there is a policy change to a different age cutoff, regression discontinuity will only allow us to evaluate our current guidelines. Either we'll have to turn to evidence from other countries, evidence from different eras - both of which have problems insofar as that the epidemiology and treatment of cancer likely varies across time and space - or turn to larger randomized controlled trials where we can be sure to have large numbers of individuals around the cutoff ages we want to test. Alternatively, we could perhaps exploit current confusion over breast cancer screening guidelines, taking advantage of the fact that some providers may choose age 40 and others 50 to commence yearly mammograms, to assess whether one or the other is better.

Work Hour Limitations for Residents - What Are They Good For?

I have the (dubious?) distinction of being part of the first cohort of interns who can only work 16 hours at a stretch in the context of an 80 hour work week. These new restrictions were put in place by the American Council for Graduate Medical Education (ACGME) for the purported reasons of reducing medical errors and improving education for first year residents.

These changes come on the heels of a previous set of work hour restrictions put into place in 2004. Prior to that time point residents were pulling long(er) work weeks. Some high profile cases and research into medical errors pointed to the sleepy residence who'd been up 30 hours or something as the culpable party. Hence, residency programs were mandated to have residents work no more than 80 hours on an average week (over some time frame like four weeks).

Interestingly, the data on whether these work hour restrictions produced better outcomes for patients is mixed. Some studies show positive effects on mortality and complications and some show no effect (here's a systematic review from this year covering a whole bunch of studies in the US and UK).

All of this begs two questions: Why did the original work hours restriction not give unambiguously positive effects on patient outcomes? And why the further restriction in 2011, limiting the length of shift to 16 hours?

On the first point, one theory is that compliance to the 80 hour work week was low in residency programs: there was no effect because work hours really didn't decrease. Another explanation is that work hour reductions led to increases in patient "hand-offs." Resident A can't work anymore so resident B might have to take over. If resident A does a poor job telling resident B about a complicated patient, mistakes and bad outcomes become more likely when resident B takes over the caregiving role.

Growing concerns over poor handoffs - and there is some kind of evidence base (see here for an example) now suggesting that handoffs are indeed poor - have prompted many to criticize the ACGME's latest work hour restrictions. If there original work hour restrictions didn't affect patient outcomes, why make any further changes, especially if the number of handoffs increase?

An interesting piece in the NYT a few days ago really gets at the heart of the matter, I think. The author, Darshak Sanghavi, begins deconstructing our obsession with work hour restrictions by examining the case of Libby Zion. In 1984, Zion, a college student, died in an NYC emergency department while being take care of by two residents, one of which was an intern on little sleep. Essentially, a medication error was made that led to a drug interaction that cost Zion her life. The incident became the test case for resident work hour reform, with New York State instituting such reforms first, and the ACGME following suit nationally.

Sanghavi examines this case in detail and notes that there were a multitude of different factors - fragmented outpatient care, poor electronic medical records and decision support, among others - that also could have credibly contributed to Zion's tragic demise. This deconstruction makes a powerful point - sleepy residents may indeed make more errors, but we should be wary of tunnel vision. Other factors deserve consideration. It's a great article and I encourage you to read it (hat tip to OKS for sending it to me).

That said, it may be that the latest work hour restrictions may have some positive effects on patient outcomes. It could be that the real margin of improvement is reducing a given work stretch from 30 to 16 hours, not reducing the total weekly work hours from 100+ to 80. It may also be that less lengthy shifts may reduce burnout and actually allow interns to go home and read something medical. Certainly, there will be a good deal of empirical work on these latest restrictions, and I'll be interested to see which way the axe falls.

(PS: Not sure how much time I'll have to read. One disadvantage of the new restrictions is that it looks like I'll be in the hospital more often during inpatient rotations. In the end, they get you somehow!)

Can Research on Measurement Provide Insights into the Poverty Experience?

Great paper, forthcoming in the Journal of Development Economics on how the length of recall periods in surveys leads to different measurements of health, wellness and health care seeking behavior. Also interesting is how the recall period length effect differs by income status. The authors use their findings to suggest that experiences with illness have become disturbingly become the normal among the poor vis-a-vis the rich:

Between 2000 and 2002, we followed 1621 individuals in Delhi, India using a combination of weekly and monthly-recall health questionnaires. In 2008, we augmented these data with another 8 weeks of surveys during which households were experimentally allocated to surveys with different recall periods in the second half of the survey. We show that the length of the recall period had a large impact on reported morbidity, doctor visits; time spent sick; whether at least one day of work/school was lost due to sickness and; the reported use of self-medication. The effects are more pronounced among the poor than the rich. In one example, differential recall effects across income groups reverse the sign of the gradient between doctor visits and per-capita expenditures such that the poor use health care providers more than the rich in the weekly recall surveys but less in monthly recall surveys. We hypothesize that illnesses--especially among the poor--are no longer perceived as "extraordinary events" but have become part of “normal” life. We discuss the implications of these results for health survey methodology, and the economic interpretation of sickness in poor populations.

Inaccurate Public Health Messages from Politicians = Very Bad

Those of you who either follow public health and/or know South Africa have certainly heard about the "AIDS-denialist" bent of former President Mbeki and his Health Minister, Manto Tshabalala-Msimang. If you don't, basically the two of them (mainly the latter with support from the former) put forth a view that HIV does not cause AIDS and that anti-retrovirals on balance confer negative health benefits (see this earlier post). Clearly, this flies in the face of science and common-sense. But what are the effects of these espousals on risky behaviors? Do people actually listen to this stuff? Did these beliefs lead to changes in behavior and, ominously, more HIV infections, in the general public?

A recent paper by Eduard Grebe and Nicoli Nattrass at the University of Cape Town strongly suggests that denialist claims played a role in reducing condom use among a sample of young adults in South Africa. Here's the abstract:

This paper uses multivariate logistic regressions to explore: (1) potential socio-economic, cultural, psychological and political determinants of AIDS conspiracy beliefs among young adults in Cape Town; and (2) whether these beliefs matter for unsafe sex. Membership of a religious organisation reduced the odds of believing AIDS origin conspiracy theories by more than a third, whereas serious psychological distress more than doubled it and belief in witchcraft tripled the odds among Africans. Political factors mattered, but in ways that differed by gender. Tertiary education and relatively high household income reduced the odds of believing AIDS conspiracies for African women (but not men) and trust in President Mbeki's health minister (relative to her successor) increased the odds sevenfold for African men (but not women). Never having heard of the Treatment Action Campaign (TAC), the pro-science activist group that opposed Mbeki on AIDS, tripled the odds of believing AIDS conspiracies for African women (but not men). Controlling for demographic, attitudinal and relationship variables, the odds of using a condom were halved amongst female African AIDS conspiracy believers, whereas for African men, never having heard of TAC and holding AIDS denialist beliefs were the key determinants of unsafe sex.

The study makes a few good points:
1) Bad information can lead to bad public health outcomes. (The ridiculous measles vaccines-autism scare did something very similar, more on that later)
2) These negative effects can depend on the level of education. (Here it is decreasing in education. For the measles vaccine-autism link, more educated people were more likely to decline the vaccine for their kids. Again, more on that later)
3) Social organizations, NGOs and activists can play a major role in reducing the effects of noisy or bad information.

Should Physicians Mind Their (Own) Business?

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.

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."

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.

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.

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.

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.)

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.

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)

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.)

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)

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.

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.

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!

Poor Economics

I am working through this great book by MIT economists Abhijit Banerjee and Esther Duflo called Poor Economics. This beautifully written tome goes through various problems in economic development and discusses how evidence from the fast growing array of randomized field experiments in development economics can be used towards designing incisive policy interventions. What I love about this book is that it is theoretical and practical all at once. While there is still a healthy debate over the utility of experiments in development economics (see this recent post by Chris Blattman, and this one), what can't be argued is the importance of this methodology as at least a complementary tool in our quest to understand why some places are poor and others are not.

One of my favorite aspects of this new book is the accompanying website (linked above). In addition to access to various tables and datasets for 18 different countries, the website has a link to lectures on Banerjee and Duflo. The lectures on health, in particular, are quite interesting: they cover prevention, deworming, the importance of information, and the role of health in development. Some of these are practical resources that would be highly useful for health care practitioners who are interested in global health.

Lost in the Mail

I just read an interesting paper by Marco Castillo and coauthors on crime in Peru. The study involves a field experiment where the researchers sent out a bunch of envelopes to people involved in the experiment. They signaled the presence of valuable items in the envelopes by making them thicker and/or implying that the letters were sent between relatives (who might be more likely to send valuable things).

This is a clever paper with three really interesting findings:
1) 18% of the envelopes never made it to their destination.
2) Thicker envelopes and those addressed to putative relatives were far less likely to make it.
3) Mail sent to poor neighborhoods did not make it to its destination 18% of the time and mail sent to really rich neighborhoods failed to arrive about 10% of the time. Where most of the mail was lost is in middle income neighborhood. Apparently, this is where the trade-off between the expected value of the envelope contents and the risk of facing retribution due to complaints from influential people is maximized.

Here's the kicker: Peru's mail system is privatized. While privatization is often tossed around as a solution to inefficiencies in developing countries, this paper makes the great point that such changes may have little impact if employees in the system are not held accountable. Ultimately, bad incentives are bad incentives are bad incentives.

Write Our Future...

...is the name of a fantastic NGO that my good friends Brendan and Rebecca Maughan-Brown have started in South Africa (you may recognize Brendan as a frequent co-author of mine in previous posts). The broad goal of WoF is to intervene on disadvantaged children in South Africa to improve health, nutrition and education.

Currently, the Maughan-Browns are working towards providing school-based meals for 100 children for an entire year in the Eastern Cape, where budget issues have led to the cessation of a government-funded program doing the same. It's a great cause, as school meals have been shown to increase attendance and perhaps even test scores, both of which can have important long-run benefits.

I encourage you to donate if you are interested. The Maughan-Browns are smart people looking for evidence-based, high impact interventions. They are running WoF with low overheads, guaranteeing that your money is well spent.

The Internet and Prescription Drug Abuse

Abuse of prescription drugs has grown markedly over the last decade or so. Some have argued that this is due to the growth in online pharmacies, particularly ones that do not require physician visits prior to dispensing medications or, more ominously, ones that do not require any physician approval or prescription or even questionnaires to assess medical histories.

A recent paper by Anupam Jena and Dana Goldman argues that this connection might be quite real. The authors find that a 10% increase in the use of high speed internet - which increases access to online pharmacies - at the state level is associated with a 1% increase in admissions to treatment facilities for prescription drug use. Importantly, this finding is robust to a variety of falsification checks. In particular, the authors show that admissions for abuse of other drugs, such as cocaine and alcohol, whose purchase is unlikely to be linked to access to internet, did not rise with the proliferation of internet during the same time period.