May 2008

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Explaining the Mortgage Meltdown

May 30, 2008 in Economics - General | Permalink

NPR’s This American Life has a great episode (“The Giant Pool of Money“) explaining in a non-technical, entertaining manner how the “credit crunch” came upon us. The episode looks at all the parts of the mortgage-backed securities chain: home owners and borrowers, brokers, banks, rating agencies, Wall Street, and foreign and domestic investors.

A special program about the housing crisis produced in a special collaboration with NPR news. We explain it all to you. What does the housing crisis have to do with the turmoil on Wall street? Why did banks make half-million dollar loans to people without jobs or income? And why is everyone talking so much about the 1930s? It all comes back to the Giant Pool of Money.

This program even made the list of one of the top 10 pieces of journalism of the decade.

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Health Insurance, Physician Compensation and the Quantity of Medical Care

May 30, 2008 in Health Insurance, Hospitals, Physician Compensation, Supply of Medical Services | Permalink

Many studies have attempted to determine how the manner in which physicians are compensated by health insurance companies affects the quantity of medical care provided. Today I will summarize some seminal studies in this field.

Epstein, Begg and McNeil (NEJM 1986)

In this study, the authors examine whether or not there is a difference in the rate of ambulatory testing between physicians in a large fee-for-service (FFS) group and a large prepaid group. The physicians were internists who were caring for patients with uncomplicated hypertension. The authors found that 50% more electrocadriograms (EKGs) were obtained in the FFS group and 40% more chest radiographs were obtained in the FFS group [compared to the prepaid group]. There did not seem to be any difference in testing rates between FFS and prepaid doctors for blood counts and urinalyses. This is not surprising, however, because the profit physicians earned from EKGs and chest radiographs was significantly higher than the profit they made from each blood count or urinalysis. Further, the cost of preforming EKGs and chest radiographs was much higher than the cost to preform blood counts and urinalyses.

Thus, this study concludes that patient testing rates are different between FFS and prepaid doctors, but this effect is only observed for high cost and/or high margin ambulatory tests.

Newhouse and Marquis (JHR 1978)

Physicians may treat patients differently based on how the patient’s insurance company pays them. However, the patient base of most physicians includes a wide variety of health plans and thus it may be difficult to discriminate care levels by insurance type. The “norms hypothesis” predicts that physicians treat patients in accordance with the average or modal insurance coverage in a given metropolitan area. In other words, “the level of a community’s insurance coverage determines physician norms.” If this were true, it would imply that there would be significant variation in medical care quantities across geographic regions but very little variations by patient for each physician.

The authors use data from the RAND Health Insurance Study in Dayton, Ohio. The authors test whether or not the patients own insurance rate will affect hospital admissions, the length of a hospital stay or the number of physician office visits. The authors find no evidence that community-level coinsurance rate impact hospital admissions, while the patient’s own coinsurance rate significantly affects hospital admission. In the 1963 data, neither the community insurance variable nor individual insurance variable had any affect on the length of a hospital stay or the number of physician visits, however in the 1970 data, individual coinsurance rates had a large impact on the length of a hospital stay or the number of physician visits, while the community level coinsurance rate had no impact on these dependent variables.

Hellerstein (RAND J Econ 1998)

Most health policy wonks believe we could significantly reduce health care costs without sacrificing quality by using more generic drugs. Many states have passed “permissive substitution laws” which allow pharmacists to substitute generic drugs in place of name-brand ones unless the physician explicitly instructs them not to do so. Other states use a “two-line” prescription method. With these prescription pads, doctors can sign their name in one of two spots: the first indicates that generic substitution is allowed and the other indicates that the brand name option is medically necessary.

Hellerstein uses data from the 1989 NAMCS, and finds that “30% of the unobserved (residual) variance in the prescription choice is physician-specific, rather than patient specific.” Does an individual’s health insurance influence the physician’s prescribing behavior? This paper finds that an individual’s health insurance does not influence prescribing decisions. However, “conditional on a patient’s insurance status, a patient who switches to a physician with a marginally greater fraction of HMO patients is 10.12% more likely to receive a generically written prescription.”

Summary

Why does the composition of the physician’s patient base seem to matter for Hellerstein (1998), but not for Marquis and Newhouse (1978) or Epstein, Begg and McNeil (NEJM 1986)? The main reason is likely that Hellerstein examines medical care in which physician do not receive any compensation. Physicians receive no more or less revenue from prescribing name brand compared to generic drugs and thus have no incentive to find out how they are being paid by each individual’s insurance company. On the other hand, Marquis and Newhouse found that hospital admissions, the length of the hospital stay, and the number of office visits is affected by an indvidiual’s health insurance variables since this type of medical care is high margin and high cost. Similarly, Epstein, Begg and McNeil (1986) found that how an individual’s insurance company compensates physicians matters for high margin, high cost EKGs and chest radiographs, but does not seem to matter for low margin, low cost blood counts and urinalyses.

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Custom-made versus ready-to-wear treatment

May 29, 2008 in Pharmaceuticals, Physician Compensation, Supply of Medical Services | Permalink

Many patients have an idealized view that physicians customize their treatments for each individual patient.  For instance, do physicians tailor prescription dosage based on individual characteristics and responses over time, or will they simple prescribe the standard dosage?

A paper by Frank and Zeckhuaser (JHE 2007) find that norm-following behavior (rather than patient-by-patient customization) is very prevalent.  The authors claim that there are 4 reasons why a physician would strictly adhere to a professional norm rather than customizing their treatments to maximize the quality of care for each individual patient.

  1. Communication costs. In order to prescribe treatment outside of the norm, the physician must communicate their reasons for doing this to the patient.  If patients have preconceived notions of how they want to be treated, physicians may have to expend significant effort to convince the patient that this individualized treatment is the best way to proceed.
  2. Cognition costs. As every person knows, exerting mental effort is costly.  Physicians can cut down on the cost of diagnosis by using heuristics.  These shortcuts may not be optimal for every patient, but they generally “do a reasonable job for a broad array of cases” and also cut down on the physicians mental computing costs.
  3. Coordination costs.  Physicians often have to work with other physicians.  The more physicians customize their treatment, the more difficult it is to communicate this alteration in care levels with specialists and thus more difficult to coordinate care.
  4. Capability costs.  Physicians are trained in certain treatments.  If a new, better treatment comes along, the physician has a choice of either 1) doing the old treatment, 2) learning the new treatment poorly and performing the new treatment, or 3) learning the new treatment well and performing the new treatment.  Choice (3) may be optimal from the patient’s point of view, but for the physician it may involve significant fixed costs involved in acquiring the human capital necessary to preform the new procedure.  If the physician decides not to incur the cost to learn the new technique well, it may in fact be optimal to choose option (1) over option (2) and thus old techniques will persist.

While these four costs will push physicians towards following norms, superior patient outcomes may compel doctors to customize their care.

Results

Frank and Zeckhuaser use data from the 2004 NAMCS to determine whether or not physicians customize the length of the patient visit or prescribing behavior.  The authors find that customization in prescribing behavior occurs most frequently for patients with chronic conditions.  This is likely because altering the “standard” treatment has more benefit for ‘repeat-visit’ patients than those with simply an acute illness.  However, race, gender, number of physician visits and insurance type do not affect prescribing behavior.

Regarding length of the office visit, the most important factor is whether or not the patient is a new patient.  Individuals who were self-pay had shorter visits while those with had Medicare insurance had longer visits, but these results were fairly small in magnitude.  Twenty-eight percent of the differences in the length of an office visit was due to physician specific factors.

Overall, the evidence shows that physicians often follow norms rather than customize care.  Also, it seems that the manner in which physicians are paid has no bearing on how they treat patients.  However, this is likely due to the fact that 1) it is very difficult to customize visit length especially when physicians are dealing with eleven managed care contracts on average [see other evidence in "Time Allocation" post on Tai-Seale et al. (2007) or the "Doctors Behave" post on Glied and Zivin (2002)], and 2) physicians do not receive compensation for pharmaceuticals and thus have no financial incentive to tailor treatment to patients based on their individual insurance.

My “Operating on Commission” paper does find that physicians tailor surgery treatment based on how they are compensated, but this is likely because 1) these are high margin procedures where it is worth the physicians time to find out how they are being paid, and 2) unlike pharmaceuticals, the surgeon is the one who receives the compensation directly.

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Why Economists support Free Trade

May 28, 2008 in Economics - General | Permalink

Economist Greg Mankiw reveals the answer in the “Trade: why not?” post on The Free Exchange blog.

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Employers must be involved in health insurance?

May 28, 2008 in Health Insurance | Permalink

At least Joe Paduda thinks so.  His post today gives an example from the employer GTE.  GTE was worried about ER and inpatient admissions rate for children with asthma.  Why?  Because employees who were single parents would miss work to take care of their children when they were sick.

Mr. Paduda’s argues convincingly that employers care about the health of their employees in order that they come to work and make profit for the company.   Of course, individuals also have an incentive to maintain their health for personal reasons.  Employers may be more worried about employees missing work if they are paid on a salaried basis; the cost of the lost time at work accrues mostly to the company.  On the other hand, if an employee paid on an hourly basis without sick days, then the cost of missing work may fall more upon the employees.

The argument that employers prefer insurance benefits which return individuals to work and do not encourage them to stay at home is a key point.  However, employers likely care more about maintaining healthy people’s health.  This way they won’t miss work and the firm can recoup the training costs they invested in the employee.  On the other hand, if an employee is stricken with a long-term, debilitating disease, the employer would prefer to get rid of the employee and not cover his medical costs whereas for the individual, the case of a drawn out, expensive, debilitating illness is exactly the reason they want insurance.

Thus, while there are many compelling reasons why insurance should be employment based (e.g., risk pooling, economies of scale, more choice than a single payer system), believing that employer’s can design a superior health insurance benefit schedule is not one of them.

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Sample Selection vs. Two-part Model

May 28, 2008 in Econometrics | 3 comments

Much of health care data is characterized by a large cluster of data at 0, and a right skewed distribution of the remaining outcomes. For instance, people who do not get sick generally use $0 of medical care. Those who do get sick, use a varying amount of medical care dollars, but there are a large number of outliers with extremely expensive medical care. How do health economists take these anomalies into account?

David Madden looks at two alternatives to correct for the shape of the distribution in his 2008 JHE paper: sample selection and two-part models. Zero consumption of medical can be caused from two different decisions: a participation decision and a consumption decision. For instance, in the case of smoking, individuals may decide not to smoke no matter how cheap cigarettes get (participation decision). On the other hand, some smokers may decide not to smoke during a given time period because cigarettes are very expensive or they have low income (consumption decision). Since people can not smoke negative cigarettes, there still may be a cluster of observations around zero.

Assume that individuals utility from participation is equal to w=α’Z + v. If w>0, then d=1, (the individual participates) and if w<0, then d=0, (the individual does not participate). For consumption, individuals will choose y**=max[0,y*]; y*= β’X + u. A general model can be written as follows:

  • L0 = Π0 [1-P(v>-α'Z) P(u>-β'X |v>-α'Z)] Π+ P(v>-α’Z) P(u > -β’X|v>-α’Z) g(y|v>-α’Z,u > -β’X)

If u and v are independent, then we have the Cragg model:

  • L1 =Π0 [1-P(v>-α'Z) P(u>-β'X)] Π+ P(v>-α’Z) P(u > -β’X) g(y|u > -β’X)

If we assume that the participation constraint dominates the consumption constraint (which is likely in the smoking example, but maybe not for drinking), then we have P(y*>0|d=1)=1 and g(y*|y*>0,d=1)=g(y*|d=1). This means that if you are a smoker you will have at least one cigarette per period. When the participation constraint dominates, we ignore the consumption decision and we have the following likelihood function which corresponds to the Heckman Selection model.

  • L2 =Π0 [1-P(v>-α’Z) Π+ P(v>-α’Z) g(y|v>-α’Z)

If independence is assumed, then we are left with probit for participation and OLS for consumption. This is the two part model:

  • L3 =Π0 [1-P(v>-α’Z) Π+ P(v>-α’Z) g(y)

Which of these models works best empirically?

Results

Madden looks at the fit of regressions trying to model smoking and drinking behavior using a wide variety of covariates. In general, the two-part model seems to be perform better in the data used for this study, but the author wisely notes that deciding between the Heckman selection and the two-part model should be done on a case-by-case basis.

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Do we finally know how vaccines work?

May 27, 2008 in Vaccinations | Permalink

Vaccines work well because of an adjuvant. The adjuvant boosts immunity but physicians did not know how it worked until now. The Economist reports (“A shot in the dark not more“) that Stephanie Eisenbarth, Richard Flavell an co-authors have discovered that the adjuvant “works by stimulating bits of the immune system called NOD-like receptors.”

Why is this discovery important?

“The value of that is shown by another piece of news. This week GlaxoSmithKline, a big British drug company, won the European Union’s approval for a ‘pre-pandemic’ vaccine that promises protection against multiple strains of bird flu. This vaccine depends, according to Emmanuel Hanon, who helped develop it, on an oil-in-water-emulsion adjuvant so good that only a twentieth of the normal amount of antigen is needed. So how does this amazing adjuvant work? Dr Hanon admits that his team does not actually know.“

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Kaiser’s Blog Watch

May 27, 2008 in Carnivals | Permalink

The Kaiser Family Foundation has a new BlogWatch feature.  Every Tuesday and Friday, the site will highlight some of the best health blogging on the net.  Check out the inaugural edition here.

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Checking in on my 96-year old grandmother

May 27, 2008 in Information, Technology | Permalink

My grandmother is 96 years old and incredibly lives on her own.  My mother drops off packages of food she prepares for my grandmother and gets her mail, but my grandmother still does her laundry and gets herself ready in the morning. Bringing in some help for her or moving her to an assisted living facility are options, but my grandmother loves her home, sees herself as fiercely independent, and a change would be difficult for her at this age.

Lately, however, it has been getting tougher for my grandmother to live on her own, which is why a New York Times article on high-tech elderly monitoring systems caught my attention.  The article talks about how some sons and daughters have installed motion sensors and a remote monitoring systems to check up on their aging parents.

“Sensors attached to the wall are able to register when Mrs. Trost [an elderly parent] gets out of bed and whether she stops at her medication dispenser, and to alert her daughters to any deviations from her routine that might indicate an accident or illness. The family is updated by electronic report every morning.”

This technology not only is beneficial for the elderly individual (who gets to stay in their home), and for their family (who can more quickly check up on their loved ones), but can also saved costs by delaying the time when the elderly are moved to an assisted living facility.  Elderly concerns with privacy is a problem and people (like my grandmother) would likely resent the monitoring…at least at first.

Nevertheless, as people around the world continue to live longer, monitoring technology can help keep the elderly in their homes and out of assisted living facilities.

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Decreased Mortality leads to increased investment in Education

May 27, 2008 in Health Care in Developing Nations, Labor Economics | Permalink

Economists predict that longer life expectancy leads to more investment in education. For those who live a short time, sacrificing working years for education is not worthwhile if the payback period is short. For those with a longer life expectancy, an individual can reap the monetary rewards from education over a longer period of time.

An NBER working paper by Seema Jayachandran looks at a what happens to educational investment after there was a 70% reduction in maternal mortality risk in Sri Lanka. The decreased maternal mortality rate was due to a number of factors: an increase in the number of hospitals, clinics and health centers, an increase in the number of trained birth attendants, transportation improvements such as free ambulances, and increased adoption of western technologies such as sulfa drugs and penicillin. Further, there was significant success in eradicating malaria during this time. Also, most of the medical services were provided for free.

Jayachandran uses a difference-in-difference-in-difference (DDD) strategy. “The first difference is over time, since maternal mortality fell between 1946 and 1953. The second difference is across geographic areas; the magnitude of the MMR declines varied considerably across Sri Lanka’s 19 districts. The third difference is between genders; maternal mortality is quite unique among major causes of death in that it exclusively pertains to women.”

The results of the study are that the decline in “…maternal mortality risk over the sample period increased female life expectancy at age 15 by 4.1%, female literacy by 2.5%, and female years of education by 4.0%.”

  • Seema Jayachandran (2008) “Life Expectancy and Human Capital Investments: Evidence From Maternal Mortality Declines” NBER WP #13947.

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