On Friday I wrote about Jerome Groopman’s book How Doctors Think. On Monday Dr. Groopman appeared on the Colbert Report (you can view the clip here).
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Last month’s Wall Street Journal (“Faltering Family MDs get Technology Lifeline“) has an interesting article about how small-practice physicians are using technology as a weapon against the economies of scale which physicians working for large-scale practices enjoy. The article tells the story of Dr. Gordon Moore. When he worked at a large, hospital-owned medical practice, Dr. Moore soon became fed up with the time pressure to see over 30 patients per day and decided to start his own solo practice.
Patients at [Dr. Moore's] “micropractice” can call or email to get appointments the same day. Visits last 30 minutes. Dr. Moore can be reached day or night on his cellphone. To refill a prescription, he walks “zero feet,” he says, and taps a few keys on his laptop. “I was able to build a Norman Rockwell practice with a 21st-century information-technology backbone,” he says…
In the summer of 1998, Dr. Moore attended a talk on how technology could be used to improve medical “work flow,” and another by a Massachusetts Institute of Technology professor who talked about how a local bike shop got lean by cutting its area by 75% and still thrived. The professor suggested similar stripping of overhead would work in other fields.
In early 2001, after considerable anxiety about the decision, Dr. Moore opened a solo practice in an airless 150-square-foot room in a small office building. By keeping a tight lid on overhead costs, he hoped to see fewer patients — no more than about a dozen a day — and provide them better care, and still earn a decent wage for a doctor.”
The American Academy of Family Physicians has an interesting chart demonstrating that fewer U.S. medical School graduates are filling family-medicine residency programs.
If you think creating a survey which will compel respondents to answer in an unbiased manner is easy, check out this article originally published in the Wall Street Journal in February (“Census 2010 plays six not-so-easy questions“). The six questions proposed to be asked in 2010 Census short-form questionnaire are as follows:
- Name of person
- How is this person related to Person 1*? [Person 1 is defined to be the head of household]
- What is this person’s sex?
- What is this person’s age and what is this person’s date of birth?
- Is this person of Hispanic, Latino or Spanish origin?
- What is the person’s race?
These seems pretty self explanatory, right? Well the questions are not as clear as they seem. Examples of problems from each category are below.
- Name: This field can be confusing for migrants. Chinese names are written with the surname name first and the given name last (e.g.: Yao Ming should be formally addressed as Mr. Yao). Latin-American immigrants typically have two Spanish surnames, one from the father’s family name and one from the mother’s family name.
- Relationship: Respondents can choose among 14 possible answers regarding their relationship to the head of household, but a 15th answer–foster child–has been deleted since the 2000 census. How are these poor foster kids going to respond to the 2010 census?
- Sex: While this field seems the most self-explanatory, in the 2000 census 0.05% of respondents (or 150,000 of 300 million Americans) checked both the male and female boxes.
- Age: According to the WSJ, “Question No. 4 asks age — and for a computer double-check, date of birth — because so many people seem to get it wrong. Adding instructions to ‘report babies as age 0′ when they’re less than a year old, offends some people, census research suggests. But in the 2005 trial it improved the response rate among people who otherwise couldn’t decide how to answer for a six-month old.”
- Latino: (see “Race” below)
- Race: Again from the WSJ, “But in trial tests, the Census Bureau also found that Asian and Hispanic immigrants could be baffled when asked to lump themselves with other nationality groups. ‘The whole concept of being Latino is a very American construct,’ says Mr. Vargas. ‘People might not know what’s being asked of them.’ Under a 2005 order from Congress, question No. 6 also allows people to call themselves ‘some other race’ and identify that race on a fill-in line. In census tests, respondents declared themselves Creole, Aryan, rainbow and cosmopolitan, among others. Other federal data users, like Social Security and the federal Education Department, don’t recognize those races, though. So in data that the Census Bureau will send to those departments, the bureau will impute a race. ‘Maybe I get it right and maybe I get it wrong. It’s not something I like to do,’ says Mr. Waite.”
To sum up, designing a good survey instrument is harder than you think.
Approximately one year ago today, I wrote about whether or not non-profit hospitals should be tax exempt (“Should Non-Profit Hospitals get a Tax Break?“). Generally, I concluded that they should not.
Flash forward to March 2007 and we see that The New York Times has an article titled “In Shriner Spending, A Blurry Line of Giving.” John Goline was struck by polio as a child, but can walk today due to treatment in a Shriners hospital. Mr. Goline was a huge supporter of the Shriner cause. This article continues:
“But [Mr. Goline's] faith was shaken when he joined the leadership of the Suez Shriners in San Angelo, one of 191 temples affiliated with the order. He found that much of the money collected to support the hospitals was commingled with money used for liquor, parties and members’ travel to Shrine events. The Shrine’s national auditor largely confirmed his findings, but not before Mr. Goline was forced out of office.”
Other problems in the Shriner organization include:
- “More than 57 percent of the $32 million the Shriners raised in 2005 through circuses, bingo games, raffles and a variety of sales went to costs of the fraternity, including keeping temple liquor cabinets full and offering expenses-paid trips to Shrine meetings and other events.”
- “A top Shrine official told a meeting of temple treasurers that poor accounting for cash coming into the organization was ‘an increasingly common problem,’ and that more than 30 temples had discovered fraud — like theft of money and inventory, altered bank statements, padded payrolls and fake invoices — amounting to as much as $300,000 and involving members of their ‘divans,’ the five-member boards that govern each temple.”
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This week on NPR’s “Fresh Air” radio program is an interesting interview with Dr. Jerome Groopman. Dr. Groopman has recently written a book titled How Doctors Think. The interview is available at the NPR website, but below I have a brief excerpt from the book.
“This book is about what goes on in a doctor’s mind as he or she treats a patient. The idea for it came to me unexpectedly, on a September morning three years ago while I was on rounds with a group of interns, residents, and medical students. I was the attending physician on “general medicine,” meaning that it was my responsibility to guide this team of trainees in its care of patients with a wide variety of clinical problems, not just those in my own specialties of blood diseases, cancer, and AIDS. There were patients on our ward with pneumonia, diabetes, and other common ailments, but there were also some with symptoms that did not readily suggest a diagnosis, or with maladies for which there was a range of possible treatments, where no one therapy was clearly superior to the others.”
“Medical students are taught that the evaluation of a patient should proceed in a discrete, linear way: you first take the patient’s history, then perform a physical examination, order tests, and analyze the results. Only after all the data are compiled should you formulate hypotheses about what might be wrong. These hypotheses should be winnowed by assigning statistical probabilities, based on existing databases, to each symptom, physical abnormality, and laboratory test; then you calculate the likely diagnosis. This is Bayesian analysis, a method of decision-making favored by those who construct algorithms and strictly adhere to evidence-based practice. But, in fact, few if any physicians work with this mathematical paradigm. The physical examination begins with the first visual impression in the waiting room, and with the tactile feedback gained by shaking a person’s hand. Hypotheses about the diagnosis come to a doctor’s mind even before a word of the medical history is spoken. And in cases like Anne’s, of course, the specialist had a diagnosis on the referral form from the internist, confirmed by the multitude of doctors’ notes in her records.”
It’s the first day of “March Madness” and already my alma mater, the University of Pennsylvania, has been bounced from the tournament. If you’re looking for an interesting article about how you should choose your NCAA tournament bracket for next year, check out the N.Y. Times article “Top Seeds can often mislead in NCAA bracket.”
Consumer-directed health plans (CDHP) have been a topic of much interest in the United States of late. In order to increase competition between health plans, in 1996 the German government began a program to allow consumers to have a free choice of their social health insurers plan. Since the federal government sets a high minimum benefit level for each insurance company, 95% of the benefits are equivalent across plans. Thus, the only main difference is price. In Germany, the insurance premium paid by the worker is equal to a set contribution rate multiplied by their salary. If benefits are truly identical, one would expect an infinite elasticity of demand for health plans with respect to price (i.e.: the contribution rate). A paper by Tamm, Tauchmann, Wasem and Greß (2007) aim to estimate these price elasticities in Germany using a panel data between 2001 and 2004.
Econometrics
The basic econometric specification is to use a conditional logit model (McFadden Frontier in Econometrics 1973). The authors wisely realize, however, that there are likely transaction and information costs to change insurance plans and thus there may be some persistence in the data. Thus a more general dynamic model is estimated of the form:
- log(sit)=αlog(sit-1)+βxit+γi+εit
The variable s represents the insurance plan’s market share, x gives the contribution rate, γ is a fixed effects term and ε is the error term, distributed Type I extreme value. If α=0 then this model is the same as the static case, and if α=1 then market shares are non-stationary and follow a random walk. Since using a lagged dependent variable often means that the lagged term is correlated with the error term, the authors use the Arellano and Bond (1991) GMM estimator.
Results
The authors find that there is significant persistence, (i.e.: α is approximately one). The elasticity of market shares with respect to contribution rates is found to be either -0.55 or -1.09 depending on the specification. These results are similar to those found by studies in Switzerland, but much less than the -2.90 elasticity found by the Schut et al. (2003) study also analyzing German data, but in a static setting.
Criticism
The two major points of criticism is that this paper only analyzes individuals who are enrolled in the social insurance scheme. Individuals with salaries above a certain threshold, the self-employed, and civil servants can all opt-out of social insurance scheme in order to purchase private insurance. Also, although the benefits are relatively constant across the insurers, there is no attempt to control for doctor quality or any other quality measure of the insurance plan. Plan quality may explain why the price elasticity is much lower than expected.
- Tamm, Tauchmann, Wasem and Greß (2007) “Elasticities of market shares and social health insurance choice in Germany: a dynamic panel data approach” Health Economics, vol 16, pp. 243-256.
A 2007 NBER working paper by Banerjee, Iyer and Somanathan presents the following facts: “In Nepal, access to schools is ten times better in the best districts compared to the worst. For Kenyan provinces, this ratio is 8:1; it is more than 2:1 for both Indian states and Russian regions and slightly over 1.5:1 for Chinese provinces. In contrast, regional differences are small in Mexico and Thailand and negligible in Vietnam.” Why is the case? What determines these differences in public good allocations? The Banerjee, Iyer and Somanathan paper reviews some of the root causes of why public good provision can be so variable within and across countries.
- Land Ownership. Foster and Rosenzweig (2000) investigate 245 villages in India between 1971 and 1982 and find that “investments in schooling were greatest in areas with a high fraction of landed relative to landless households.”
- Cultural Norms and Religious Beliefs. The Brahman caste are traditionally considered to be priestly elite in India. A paper by Banerjee and Somanathan (2006) finds that “in the early 1970s, the population share of Brahmans in a constituency is positively correlated with access to primary, middle and secondary schools, to post offices and to piped water.”
- Sex of Politician. The 73rd Amendment to the Indian Constitution requires that one third of all local election seats be reserved for women. A paper by Chattoppadhyay and Duflo (2004) concludes that “political reservation for women in local government results in greater provision of goods which women value, such as drinking water and roads.”
- Ethnic Fragmentation. There has been a lot of work done to see whether or not ethnic diversity will increase or decrease the amount of public goods provided in a given area. Alesina, Baqir and Easterly (1999) find that U.S. cities with more ethnic fragmentation “spend proportionally less on schooling, roads and trash pickups but more on health and police.” Banerjee and Somanathan look at data in India and “construct a fractionalization index of social heterogeneity using population shares of non-Hindu religions (Muslims, Christians, Sikhs, Jains, Parsis), as well as 185 distinct Hindu caste groups…Of the fifteen different public goods considered in 1971, they find that the social heterogeneity measure has a significant negative effect in six cases and positive in two.”
- Monitoring and Communication. A general finding for the experimental literature is that individuals generally give more money to public goods than predicted by the Nash Equilibrium, but if games are repeated, the donations converge to the stingy Nash equilibrium. An experiment by Cason and Khan (1999) allowed the experimental group to converse for a few minutes before they decide on their contributions. Individuals receive information on others donations levels after either every round or every six rounds. The authors find that “in the absence of communication, contributions with both types of monitoring are fairly similar and decline over time. In the presence of communication, overall contributions are much higher (about 80% of the tokens were invested in the group activity as opposed to a high of 40% in the no-communication case) and did not decline in later rounds.” Thus one can conclude that a monitoring mechanism operated within a group where communication is frequent can lead to high levels of public good contribution.
- Autocratic Rule. Many public good expansions have taken place under colonial or autocratic rule. The European powers often helped to provide schooling for the people living in their colonies. The British, French and Dutch invested more in local public goods than did the Belgian or Portuguese. During the 19th century, autocratic princely states in India often invested heavily in public goods despite no political pressure to do so. “The Travancore state in present-day Kerala is particularly well-known for its long tradition of enlightened rulers. In 1817, the Regent Gauri Parvathi Bai declared, ‘The state should defray the entire cost of the education of its people in order that there might be no backwardness in the spread of enlightenment among them, that by diffusion of education they might become better subjects and public servants and that the reputation of the state might be enhanced thereby.’ “
- Political Atmosphere. “After independence, the Kenyan leadership played up tribal loyalties for political reasons and little effort was put into building a Kenyan identity; in contrast, the Tanzanian leadership put a lot of emphasis on creating a single Tanzanian identity. This seems to have implications for public good provision: in the Busia region of Kenya, ethnic heterogeneity at the local level is negatively correlated with the quality of public goods (mainly schools), while in the nearby Meatu region of Tanzania, they are slightly positively correlated.”
Note: The term “public good” is used in a very general sense in the paper. Using a stricter definition, education has some public good elements if working with more educated individuals increases one’s productivity. Medical care generally is not seen to be a public good with the exception of services such immunization, but Public Health efforts—such as maintaining a clean water source—are more likely to fall within the public good arena.
- Banerjee, Iyer, Somanathan (2007) “Public Action for Public Goods” NBER Working Paper, #12911.
This week’s edition of the Cavalcade of Risk is up at MSSP Nexus Blog.
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