January 2007

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Preparing and Presenting Effective Research Posters

January 31, 2007 in Economics - General | Permalink

For researchers who are planning on presenting research posters in the near future, Jane Miller (2007) has an excellent capacity-building article in this month’s edition of the Health Services Research (HSR) journal. Miller’s advice covers all the major facets of the poster presentation: poster design, concise phrasing of statistical methodologies, the narrative to accompany the poster and handouts. The article can be found on the HSR website (subscription required for full article).

Mandatory Health Insurance Now

January 30, 2007 in Current Events | Permalink

The Reason magazine makes an compelling argument in favor of mandatory private health insurance (“Mandatory Health Insurance Now!“). A subsidy could help all individuals to afford the insurance. Some of the funds for this subsidy could be raised by eliminating the tax-deductibility of health insurance. This program funnels more than $140 billion a year in federal tax breaks to workers.

The article includes comments from Wharton economist Mark Pauly and a review of a recent New America Foundation health policy paper.

Insurance Status and Access to Urgent Ambulatory Care Follow-up Appointments

January 29, 2007 in Academic Articles, Health Insurance, Medicaid/Medicare, Medical Studies | Permalink

In 2005, approximately 114 million visits were made by Americans to the hospital emergency departments. Of these, more than eighty percent concluded with a discharge and a recommendation for follow-up care. Receiving prompt and adequate post-ER care is imperative for the resolution of many illnesses and temporary disabilities. Is timely care available for these patients?

A study by Asplin, et al. (2005) and a subsequent paper by Neath and Carlin (2006) look at how easy it is to schedule an appointment after an ER visit. To collect the data, clinics were phoned by a graduate students posing as patients just released from a hospital emergency department. Callers had four (made-up) medical conditions: pneumonia, elevated blood pressure, vaginal bleeding in the first trimester, and symptoms of depression. The depression observations were excluded from the study because many primary care physicians do not feel qualified to treat depression.

In each call, the individual claimed to have either: 1) private insurance, 2) Medicaid insurance, 3) no insurance and could not pay, or 4) no insurance but would pay for the visit out-of-pocket. A call was deemed successful if an appointment was made within 7 days and the out-of-pocket payment for the appointment was $20 or less.

Results

Asplin, et al. preform a simple paired comparison in which the same clinics are compared where the only difference between the observations is the unit of insurance the phony patient had. Neath and Carlin directly incorporate other covariates – such as the medical condition, safety-net status of the clinic, city dummy variables, etc. The results are similar in both studies, but the table below gives Neath and Carlin’s findings.

Clinic Type Insurance Status P(Success)
Non-Safety Net Private 68.4%
Non-Safety Net Medicaid 26.3%
Non-Safety Net Uninsured 14.6%
Safety Net Private 41.5%
Safety Net Medicaid 38.5%
Safety Net Uninsured 20.0%

We can see that the “overall success probabilities in Asplin et al. were distressingly low.” One also notices that it is much easier to get an appointment if one has private insurance, but these differences are less severe at “safety net” clinics. Finally, the authors note that the majority of clinics made no attempt to determine the severity of the caller’s condition. Having trained staff answering the phone calls and preforming triage is costly, but is likely worth the cost for patients needing immediate assistance. Put more concisely, Asplin states: “Financial screening is trumping medical triage.”

A Dynamic Analysis of GP Visiting in Ireland

January 26, 2007 in Academic Articles, International Health Care Systems | Permalink

Many studies have looked at which variables determine medical utilization rates. Specifically, most of the studies have focused on primary care or general practitioner (GP) visits. A recent study by Nolan (2007) employs panel data from Ireland to reach some new conclusions on this topic.

Ireland is an interesting location to preform the study. All individuals are eligible for the universal public health insurance, but the coverage insists on significant out-of-pocket payments for GP visits. Only the poor and the unemployed are eligible to receive a ‘medical card’ which entitles them to free medical treatment. Also, 50% of Irish hold private insurance. However, private insurance is used mostly to pay for private or semi-private hospital care and does not cover the large copayments for primary care visits.

Generally, studies using cross-sectional data find that non-need factors such as education level, employment status, income, and marital status are important determinants of GP visitation. Causality, however, is difficult to ascertain from these findings. Does low income and education cause poor health or does poor health inhibit wage growth and the accumulation of human capital? Dolan also finds that 30% of individuals do not see a GP each year. However, when we look at the entire panel, only 2.5% of individuals had not visited a GP during sample time period.

Econometric Specification

Since the dependent variable, GP visits, is a count variable, a poisson regression is used. The function λ() is altered from the standard procedure to account for the panel nature of the data.

  • λ=αi*exp[zitγ+g(yit-1)Ï?] (1a)
  • αi=ci*exp[α0+ri0α1+Zia2] (1b)

The variable zit represents a variety of covariates and is Zi is the within-individual means of the covariates. The vector of initial conditions is represented by ri0. Five dummies for lagged values of the number of patient visits (0, 1, 2, 3-5 and 6+ visits) is represented by g(yit-1). The λ equation includes an individual effect, αi, and the lagged dependent variables. The individual effects are parameterized by equation (1b). Although the above is the preferred regression, Nolan also uses a static random effects model (2) and a static random affects model with correlated individual affects (3a, 3b).

  • λ=αi*exp[zitγ] (2)
  • λ=αi*exp[zitγ] (3a)
  • αi=ci*exp[α0+Zia2] (3b)

The data set the paper uses is the Living in Ireland Survey between 1995 and 2001. The survey is part of European Community Household Panel (ECHP).

Results

Using the panel framework, the author concludes that “the only significant non-need factors are medical card eligibility and employment status.” Nolan finds that more physician visits in the prior year lead to more GP visits in the current year. Also, the initial level of GP visits (in 1995) also had a significant impact on current primary care visits. Other findings include:

  • Unemployment lead to 0.18 fewer GP visits
  • As expected, health-related covariates such as old age, ill-health, stress, and a birth in a family lead to higher GP usage.
  • Having a “medical card,” which entitles the patient to completely free care, leads to an increase of 0.33 GP visits per year.
  • Covariates such as income, education level, and marriage status had no impact on GP visits.
  • Having private insurance actually decrease GP visits by 0.17 per year.

This paper supports the case of both economists and patient advocates. Economists will point to the medical card finding that giving away medical care for free will increase utilization rates. Nolan point out the RAND health insurance found that decreasing copayment lead to an increase of both ‘appropriate’ and ‘inappropriate’ medical services. On the other hand, patient advocates will point to the fact that the most important covariates determining GP visits are health-related.

This paper answers important questions regarding the determinants of GP utilization. The panel nature of the data allows the researcher to control for individual heterogeneity as well as condition on past GP visitation rates.

Health Wonk Review #24 is up

January 25, 2007 in Current Events | Permalink

The Health Wonk Review has been posted at the Health Affairs blog.

A Zyprexa rep tells all

January 25, 2007 in Pharmaceuticals | Permalink

Courtesy of Kevin, M.D. blog: an interesting interview with a former drug rep at Eli Lilly employee who sold Zyprexa between 1998 and 2000.

Expected Value of Information and Decision Making in Health Technology Assessment

January 25, 2007 in Academic Articles, Information | Permalink

Health decision makers often have to decide whether to adopt a new health care intervention (e.g.: pharmaceuticals, new procedures, etc.) or keep the existing practice. If one assumes that the new intervention has positive but uncertain net benefits over the existing procedure, should the new technology be adopted?

A paper by Eckermann and Willan (2007) looks at this problem and create a theoretical framework to find which actions are best suitable for which situations. They claim that decision makers face three options:

  1. adopt the new intervention without further research (A);
  2. adopt the new intervention and undertake a trial (AT); or
  3. delay the decision and undertake a trial (DT).

The authors adopt the notation of the expected net gain (ENG) where ENGA gives the expected gain from choose AT over A and ENGD gives the expected gain from choosing DT over A. ENGA represents the difference between the value of the trial (sample) information (assuming adoption) minus the cost of the trial. ENGD is the difference between the value of the trial (sample) information (assuming delay) minus the cost of the trial.

Delaying the decision allows time for more information to be collected, but creates direct costs (the cost of the trial) and opportunity costs (the cost of non-treatment of affected individuals during the delay). Deciding to adopt the technology and preform a trial has the benefit of providing more information to the decision maker but the additional direct cost of the trial as well as reversal costs (discussed later). The expected value of the of preforming the trial (expected value of sample information-EVSID) is calculated as follows.

  • EVSI=N[∫ -b*{f0(b)-f1(b)} db] = N*[E0(b|b<0) - E1(b|b<0)]

The argument inside the integral is integrated between negative infinity and zero. The distributions f0 and f1 represent the predicted distribution of benefits at the present (0) and after the trial (1). The benefit level is given by the variable b, and N is the number of people affected by the disease. The information is only valuable if researchers find that there are more ‘bad’ outcomes than previously expected. If the new treatment proves safer than expected, choice A would have been optimal. Also, the trial is more valuable when the number of people affected with the disease, N, is larger since the decision will be a more important one to society.

Eckermann and Willian also add to the model the concept of the cost of reversal. After a new treatment is adopted, a subsequent reversal has costs. These costs include reversing information flows (e.g.: public health messages, changing med school training curriculum, etc.) and sunk cost investments in specific equipment or training. Taking into account these reversal costs makes option A seem (relatively) more attractive to the DT and AT cases since a reversal is impossible if a trial is not conducted.
Using a cost benefit analysis, the following decision rules are established.

  • choose A when ENGA and ENGD<0;
  • choose AT when ENGA>0 and ENGD<0;
  • choose DT when ENGA<0 and ENGD>0;

The authors give a more intuitive explanation as well.

  • AT is preferred where expected costs of reversal per patient are small relative to the expected distribution of net benefit below 0, E(b|b<0).
  • A is preferred where there is little uncertainty of the positive benefits and costs of reversal are large.
  • DT is preferred when there is significant uncertainty, the opportunity costs of delay are small and data collection and analysis proceeds quickly.

The authors use the framework constructed above to analyze the prospect of adopting external cephalic version (ECV) treatment for pregnant women presenting in the breech position. ECV attempts to manipulate the fetus into a cephalic presentation and avoid a caesarian delivery.

This paper is interesting and possibly useful. The theoretical model is enlightening and warns decision makers to evaluate opportunity costs and reversal costs in addition to simply the direct cost of conducting a trial. The researchers also advise decision-makers when to choose which course of action in a comparative sense. Further, in the example using ECV, the authors actually mathematically calculate which course of action is preferred in this real-life situation. Much of the value of the framework, empirically, depends and having accurate information. Do decision-makers know how many people are affected by the disease? Are the cost of referrals known? Can we accurately estimate the value of information gained from a future trial? If the answer to these questions is ‘yes’, then this paper is very useful; if not, it is still a clever model.

The State of the Union

January 24, 2007 in Current Events, Health Insurance, Taxes | Permalink

Yesterday, President Bush gave the State of the Union Address. In this post, I 1) analyze Bush’s new health care plan, 2) review some commentary from various blogs on the net, and 3) give a excerpt from the speech which directly relates to health care.

Healthcare Economist’s Analysis
The heart of the Bush proposal is as follows:

  • Families With Health Insurance Will Not Pay Income Or Payroll Taxes On The First $15,000 In Compensation And Singles Will Not Pay Income Or Payroll Taxes On The First $7,500.

On the positive side, since this is a fixed deduction regardless of the generosity of the health insurance, there is less incentive for individuals to purchase “too much” insurance. In the case where each dollar worth of health insurance decreases one’s tax liability, insurance only costs (1-τ)*A dollars per year with the tax deduction when the true cost to society is A. The proposal is also good in that it the tax break does not discriminate between employer-provided and individual-based insurance. This will help (somewhat) to reduce the phenomenon that individuals often choose their job based on the type of insurance offered rather than actual job characterisitcs or the wage offered. According to the President, the health insurance deduction will decrease the taxes for most available which will free up more disposal income for them to spend on other items.

On the negative side, the plan is very inequitable. Since this is a tax deduction, if you are poor and do not owe any taxes, you will not receive any financial help with the deduction. Since tax rates are progressive, the deduction is most valuable to individuals in the high tax bracket, the rich. Look at the example below for a single individual.

Taxable Income Deduction Value Marg Tax Rate Deduction Value
$5,000 $5,000 10% $500
$10,000 $7,500 15% $1,125
$35,000 $7,500 25% $1,875
$10,000,000 $7,500 35% $2,625

We can see that the value of the health insurance tax deduction is worth more than 2x the value for the individual making $10,000,000 as for the person making $10,000.

Other problems with the proposal is the possibility of ‘fake’ health insurance. It seems that the government does not establish a minimum level of health insurance. Thus, someone who wants the tax deduction, but does not want to buy health insurance could buy a policy for $1 which pays for all medical expenses over $1 trillion. Of course, the person will never be able to use this policy but since they technically have insurance, they will receive the deduction. If the person gets sick, however, they can still go to the emergency room and get free care, paid for by the American taxpayers. Thus, some level of minimum insurance should be established in order to qualify for the deduction.

Also, while treating the employer-provider and individual-based insurance groups equivalently may be more fair, it may exacerbate the problem of adverse selection in the individual markets. People with pre-existing conditions may find it even more difficult to afford insurance in the individual market under this reform.

Overall, I think the plan does little to help those who need insurance most. The dollars saved from eliminating the deductibility of employer-provided health insurance could be used in a much more productive fashion to provide health care to more Americans.

Around The Blog-o-sphere
Below I have tried to give a diverse review of some early feedback about the SOTU plan from around the blog-o-sphere:

  • SameFacts.com “Maybe I’m missing something here, but this just seems laughable. The idea of a deduction for the uninsured is silly: the value of deductions increases with tax rate, and most of the uninsured either don’t pay income taxes or at the lowest bracket.”
  • Cato-at-Liberty: “the president’s proposal mirrors the proposal for “large HSAsâ€? that I introduced.”
  • Managed Care Matters: “But it won’t do anything to fix the underlying problem – people who need insurance can’t get it, and if they can, many can’t afford it, leaving the rest of us to pay for their health care.”
  • Paul Krugman at Economist’s View: “…the actual plan is to penalize workers with relatively generous insurance coverage…”
  • The Heritage Foundation: “It would treat all Americans equally by ending the tax discrimination against families who buy their own health insurance, either because they do not have insurance offered by employers or because they prefer other coverage.“

Transcript
Below is a transcript of the section of Bush’s State of the Union speech which refers to health care. The full transcript is available at the White House website and there is a section on the Bush health care policy initiative as well.

A future of hope and opportunity requires that all our citizens have affordable and available health care. (Applause.) When it comes to health care, government has an obligation to care for the elderly, the disabled, and poor children. And we will meet those responsibilities. For all other Americans, private health insurance is the best way to meet their needs. (Applause.) But many Americans cannot afford a health insurance policy.

And so tonight, I propose two new initiatives to help more Americans afford their own insurance. First, I propose a standard tax deduction for health insurance that will be like the standard tax deduction for dependents. Families with health insurance will pay no income on payroll tax — or payroll taxes on $15,000 of their income. Single Americans with health insurance will pay no income or payroll taxes on $7,500 of their income. With this reform, more than 100 million men, women, and children who are now covered by employer-provided insurance will benefit from lower tax bills. At the same time, this reform will level the playing field for those who do not get health insurance through their job. For Americans who now purchase health insurance on their own, this proposal would mean a substantial tax savings — $4,500 for a family of four making $60,000 a year. And for the millions of other Americans who have no health insurance at all, this deduction would help put a basic private health insurance plan within their reach. Changing the tax code is a vital and necessary step to making health care affordable for more Americans. (Applause.)

My second proposal is to help the states that are coming up with innovative ways to cover the uninsured. States that make basic private health insurance available to all their citizens should receive federal funds to help them provide this coverage to the poor and the sick. I have asked the Secretary of Health and Human Services to work with Congress to take existing federal funds and use them to create “Affordable Choices” grants. These grants would give our nation’s governors more money and more flexibility to get private health insurance to those most in need.

There are many other ways that Congress can help. We need to expand Health Savings Accounts. (Applause.) We need to help small businesses through Association Health Plans. (Applause.) We need to reduce costs and medical errors with better information technology. (Applause.) We will encourage price transparency. And to protect good doctors from junk lawsuits, we passing medical liability reform. (Applause.) In all we do, we must remember that the best health care decisions are made not by government and insurance companies, but by patients and their doctors. (Applause.)

How do doctors behave when some (but not all) of their patients are in managed care?

January 23, 2007 in Academic Articles, Physician Compensation | Permalink

Today, I will review an interesting article by Glied and Zivin (2002) examining how physicians adjust their practice style when the HMO share of patients changes (note: the authors implicitly assume all HMOs pay physicians via capitation). First we will look at the three theoretical models the authors develop.

Models

1. Excess capacity model. The excess capacity model assumes that HMOs seek to pay the marginal costs for any excess physician capacity. This is possible so long as HMOs make up a small percentage of the physician’s patient base. When the HMO makes up a large portion of the doctor’s practice, the doctor will eventually refuse to accept HMO payments at marginal costs since, in the long run, this will not cover his/her fixed costs and the doctor will go out of business. Thus, practices with larger HMO shares will be paid higher reimbursement rates, which will lead to greater effort levels to managed care patients while leaving indemnity patients unaffected. Glied and Zivin predict that patterns of care between FFS and HMO patients will converge when HMO penetration increases within a practice.

2. Demand inducement model. If physicians are able to influence patient demand, the demand inducement will only occur for FFS patients. HMO patients cost the practice money and thus no demand inducement will occur for them. The authors thus posit that treatment intensity will increase for FFS patients as HMO penetration increases.

3. Fixed cost model. “…Fee-for-service patients receive variable cost effort that reflects marginal reimbursement rates for this effort. Any HMO patients seen in this practice, however, will receive lower variable cost effort to compensate for the “excessive�? fixed cost effort with which they are provided. Variable cost effort will be reduced to the point where total effort just satisfies the minimum service constraint. In a practice that treats mainly managed care patients, the optimal fixed cost investment will be smaller. HMO patients will then receive higher levels of variable cost effort than in FFS-dominated practices; again ensuring that total effort is large enough to satisfy the participation constraint.” In the empirical section of the paper, the fixed cost is the duration of the patient visit. The authors give convincing evidence that it is difficult for the doctor to schedule different visit durations for different patients based on their insurance holdings. The physician can, however, easily alter the visit intensity in terms of either effort, test ordering or medicine prescription. The authors predict that if the fixed cost model holds, treatment intensity of FFS and HMO patients will increase as HMO penetration increases with in a practice.

Data

The data used is the 1993 to 1996 National Ambulatory Medical Care Survey.

Results

  1. Moving from 0% to 50% HMO penetration within a practice reduces the average visit duration (i.e.: fixed cost) by 1.3 minutes (7%).
  2. Holding constant the share of HMO penetration, there is no change in visit duration based on the type of insurance the individual has. This lends some credibility that visit duration is a fixed cost.
  3. The authors find more tests and medicine for all patients when there is a high HMO share.
  4. Controlling for HMO penetration, FFS have more medicine prescribed and tests run than HMO patients.
  5. The above evidence suggests that the fixed cost model most accurately describes reality.

Economic theory would predict a physician would provide different types of care based on the manner in which they patient’s insurance compensate them. The authors conclude that physicians may be limited in their ability to adjust care on a patient by patient basis due to some real-world practicalities of operating an office (e.g.: visit duration is generally fixed). The authors do find that when doctors have a large HMO caseload, however, they change the way they do business altogether by reducing visit duration and increasing visit intensity.

Some problems with the study are the possibility of adverse selection. HMO patients may be healthier than FFS patients and this may be driving the difference. Glied and Zivin do try to control for this by incorporating diagnosis based variables into their regression. Also, the authors assume all HMOs pay physicians via capitation, but this is not true in reality. HMO compensation type often depends on the idiosyncrasies of each regional market. Physician selection may also be a problem; physician may choose to accept more or less HMO patients since they prefer to have a given practice style. Overall, however, the paper is clear and concise, and shows that measuring the impact of FFS versus capitation physician compensation is more complex in reality than economic theory would suggest.

Quote(s) of the day

January 22, 2007 in Miscellaneous | Permalink

Here are some inspirational quotations for some motivation at the beginning of your week.

“The discipline of writing something down is the first step toward making it happen.”
—Lee Iacocca

“Success comes to the person who does today what you were thinking about doing tomorrow.”
—Unknown

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