February 2008

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Flu shot overhaul next year

February 22, 2008 in Current Events, Vaccinations | 1 comment

According to the USA Today, the influenza vaccine may receive a complete overhaul for the 2008-2009 flu season.

All three flu viruses in this year’s vaccine should be swapped for others next year because of a dramatic change in the mix of circulating flu bugs…

The decision launches a “time-critical, highly orchestrated” effort by public health agencies and vaccine makers to produce roughly 100 million doses of vaccine — a dated process that involves growing the virus in eggs, he said. “From egg to vial, it takes six to eight months,” Baylor said in a background briefing with reporters this week.

The egg-growing process is done only in the U.S. In Europe, vaccines can be produced faster since they are developed artificially in a lab.

This year’s vaccine protects against just one of three viruses that are dominating this year’s flu season, now reaching its peak. The Centers for Disease Control and Prevention said last Friday that 44 states are reporting widespread flu activity, with cases mounting in five others.

Scientists are unable to explain why all of a sudden, new flu strains have grown so much in prevalence.

Health Wonk Review

February 21, 2008 in Carnivals | Permalink

The latest edition of the Health Wonk Review is available at Merrill Goozner’s GoozNews.

I noted in earlier posts (4 June 2007 and 2 Aug 2006) that email and telephone communication between doctors and patients is not compensated.  In this week’s edition of the HWR, I learned that this is about to change.

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Google Health

February 21, 2008 in Current Events, Healthcare IT | Permalink

Google is everywhere. CNN reports that Google is venturing into health records biz.

“Google Inc. will begin storing the medical records of a few thousand people as it tests a long-awaited health service that’s likely to raise more concerns about the volume of sensitive information entrusted to the Internet search leader.

The pilot project to be announced Thursday will involve 1,500 to 10,000 patients at the Cleveland Clinic who volunteered to an electronic transfer of their personal health records so they can be retrieved through Google’s new service, which won’t be open to the general public…

The third-party services are troublesome because they aren’t covered by the Health Insurance Portability and Accountability Act, or HIPAA, said Pam Dixon, executive director of the World Privacy Forum, which just issued a cautionary report on the topic.

Passed in 1996, HIPAA established strict standards that classify medical information as a privileged communication between a doctor and patient. Among other things, the law requires a doctor to notify a patient when subpoenaed for a medical record.

That means a patient who agrees to transfer medical records to an external health service run by Google or Microsoft could be unwittingly making it easier for the government or some other legal adversary to obtain the information, Dixon said.”

The New York Times blog BITS also has a post about Google Health as well.

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And you think delays at airports are bad…

February 21, 2008 in International Health Care Systems | Permalink

The NHS has a target that all patients should be treated within four hours of arriving at the emergency room. This is a laudable goal, but it has lead to some unexpected consequences: patient stacking. According to the Daily Mail, (‘A&E patients left in ambulances…‘), “Thousands of people a year are having to wait outside accident and emergency departments because trusts will not let them in until they can treat them within four hours, in line with a Labour pledge.” According to the Daily Mail figures, 43,576 patients waited longer than one hour before being let into emergency units.

A department of health spokesperson noted that there are 4.3 million patient emergency journeys per year so according to the Daily Mail figures, just over 1% of individuals waited more than one hour due to patient stacking. While 1-2% of emergency journeys does not seem like a lot, it does mean that about 120 people every day wait more than one hour to enter the emergency room.

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Shingles Vaccine

February 20, 2008 in Medicaid/Medicare, Medical Studies | Permalink

The USA Today reports on the development of a shingles vaccine. According to the article, “The vaccine reduced shingles cases by 51% in people given the vaccine vs. those given the placebo. Vaccination reduced the burden of illness, a measure of pain and discomfort, by 61%.”

So why aren’t people getting this vaccine? One reason is how the vaccine is paid for. The vaccine, priced around $150 by the manufacturer, is covered by the part of Medicare that pays for prescriptions, not doctor visits. That means doctors are not automatically paid for shots given in their offices. Some send patients to pharmacies to get the shots or pick up prescription vials, adding steps that may reduce use, Oxman says. Others stock and give the vaccine, but require patients to pay upfront and seek their own reimbursement.

Calculating Marginal Effects with Discrete Variables

February 20, 2008 in Economics - General | Permalink

Sometimes, a coefficient isn’t what it seems to be. When using an ordinary least squares (OLS) regression, the regression coefficients indicate the proportion by which the dependent variable changes when the independent variables increases by one unit. Regression coefficients are more difficult to interpret, however, for more complicated regression specifications such as probit, mutlinomial logit, negative binomial, etc.

Let us assume that a regression coefficients are fitted to the following equation:

  • y=f(Xβ)=f(β0+β1x1+…+βkxk)

For instance, in the probit model, f(·)=Φ(·), where Φ(·) is the normal cdf. In the OLS setting,∂y/∂xk=βk. Thus, there are no interaction terms–unless explicitly states as one of the variables in X–in the OLS. However, in the probit case, ∂y/∂xk=φ(xβ)βk., where φ(·) is the pdf of the normal distribution. Thus, the marginal effects differ depending on the value of x.

The standard solution to this problem is to calculate the marginal effects when x is set equal to its mean value. When xk is a dummy variable (i.e.: xk∈{0,1}), the marginal effects are calculated by setting x-k equal to their mean and then finding the difference in y when xk increases from 0 to 1. This difference is the marginal effect for the discrete variable.

Sometimes this method will lead to results that are difficult to interpret. For instance, one could calculate the marginal effect on health from taking a new drug for someone with a gender=.051, and minority status=0.24. Since someone can either be male or female, they can be a minority or not, finding the marginal effect for this hypothetical person may not be the very revealing.

Another method would set continuous variables equal to their mean (e.g.: age, income) and then we could calculate the marginal effects for a the typical white female, a typical black male, etc.

A final methodology is given in Boonen, Schut and Koolman (2008). The authors investigate whether or not health insurance company financial incentives are effective in directing enrollees towards preferred provider pharmacies. In the results section of their paper, they state:

“The marginal effects for discrete variables are computed by calculating the change resulting from a change in the discrete variable from 0 to 1 holding all other variables fixed at their mean (see, for example, McGuirk and Porell, 1984; Madden et al., 2005). An average individual does not exist, however, and in our research we are interested in the probability that a certain consumer does or does not visit the preferred supplier. The marginal effects are thus not computed over the average individual but represent the mean of the marginal effects over each individual. This is done by computing the effect of, for example, a one-year increase in age on the probability of visiting the preferred provider for each individual and then averaging these probabilities across all individuals in the sample (Strombom et al., [JHE] 2002; Greene, 2003). The standard errors for the marginal effects are computed through bootstrapping. ”

This post should give researchers some idea of how to calculate marginal effects for complex regression models. It should be noted that there is no one optimal method, but one should determine how best to analyze the data and then use the marginal effects method most appropriate for your data analysis.

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Ezra Klein vs. Andrew Sullivan

February 19, 2008 in Current Events, Social Security | Permalink

There is an interesting pair of blog posts by Ezra Klein and Andrew Sullivan.  Mr. Klein advocates a more centralized health care system while Mr. Sullivan is opposed to expanding the government’s role in health care.

Who do you agree with?

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Consumer Channeling and Preferred Providers

February 19, 2008 in Health Insurance, Managed Care, Pharmaceuticals | Permalink

Doctors often complain that health insurers are squeezing their profit margins. These insurers offer the physicians access to patients as part of their network in exchange for discounted fees. Physicians can decide not to join the network and charge higher prices, but may be left with fewer patients. The bargaining power of the health insurer depends on how many patients they are able to channel towards these physicians.

In the U.S., most health insurers restrict provider choice ex ante by using either prohibiting patients from visiting providers outside the network or charging the patients significantly higher co-payment rates if go to a provider outside the network. In the Netherlands, almost all care is free to patients so insurer need to use ex post incentives (e.g.: bonuses, gift certificates, and extra services) in order to entice the patients to use the services of the preferred provider.

A paper by Boonen, Schut and Koolman in the most recent edition of Health Economics examines how well the ex post incentives function in the Netherlands’ pharmacy market. Since pharmacies are regulated and prescription drugs are a homogeneous commodity, quality differences between pharmacies are negligible. The authors use data from two health insurers who attempt to direct their enrollees to specific pharmacies.

Using a multinomial logit framework, the authors find that convenience (i.e.: distance to the pharmacy) has a large impact. The financial incentives offered by health insurer A and B cause many enrollees to use the preferred provider. Health insurer A, however, gave a 10 € for the patient’s first visit to the pharmacy and 5 € for their second visit to the pharmacy. Under this incentive structure, individuals were more likely to switch to the preferred provider and then return to their original pharmacy after the incentives had disappeared. Only 25% of those who switch to the preferred provider continue to use them after the financial incentives disappear.

Health insurer B offered a discounts on products offered at the preferred pharmacy and these incentives were made permanent. Unsurprisingly, enrollees also were more likely to go to the preferred provider after the financial incentive regime was enacted.

One interesting item of note is that Health insurer B’s preferred pharmacy was in the same building as a general practitioner (GP). Since GPs function as gatekeepers in the Dutch system (i.e.: one cannot a prescription without the GPs approval), having the GP in the same building as the pharmacy was a huge convenience. Further, the GP could influence the patient to use the preferred pharmacy.

In summary, it was shown in the Dutch setting that even small incentives can have a large effect on provider choice.

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Product Design and Consumer Choices in the Individual Insurance Market

February 18, 2008 in Health Insurance, HSA | Permalink

What is the cost of the last article of clothing you bought. This is easy to determine, just check your credit card statement.

Which is cost of health insurance? This answer is more difficult to find. Sure, there is the price of the premium, but different insurance plans have different co-pay/co-insurance levels and different deductible amounts. How do these insurance product design parameters affect the demand for insurance?

This is the question tackled by Marquis et al. (2007). The authors use a nested logit model to examine plan characteristics within the individual insurance market. In their nested logit, the authors assume individuals first choose whether to be insured or not. Then, they must choose which type of insurance (PPO, POS, HMO, etc.). After they choose which type of plan they prefer, then a carrier is chosen. This methodology is based on the work of McFadden (1978).

The authors find an elasticity estimate of -2.0 for plan choice among purchaser. This means that those who are insurer are very price sensitive. However, Marquis and co-authors also find that once a particular company is selected, there are significant switching costs to changing companies. The elasticity of switching companies once a plan type (PPO, HMO etc.) is chosen is only -0.4.

The authors also find that:

…a 3 percent decrease in the actuarially adjusted price (or a 4 percent decrease in the nominal premium) would induce a healthy consumer to switch to a plan with a 50 percent higher deductible. For a riskier consumer, however, it would take a 4.5 percent decrease in the actuarially adjusted premium (or a 5.5 percent decrease in the nominal premium) to make the switch. This suggests that there is potential for selection in consumer-directed health plans—an outcome that concerns many critics of these new plans. In addition, the findings suggest that introducing new high-deductible products is unlikely to play a major role in reducing the number of uninsured.

Consumer education regarding the choice of different plans and help to expand coverage by introducing consumers to low-cost (actuarially) insurance options.

Can HilaryCare end regional variation in healthcare spending?

February 18, 2008 in Current Events | Permalink

Michael Cannon of Cato-at-Liberty cites a study from the Congressional Budget Office (CBO) that it is difficult for centrally planned medical care to eliminate local norms.

“…the centrally budgeted VA system does not display much less geographic variation in spending than is exhibited in the unbudgeted Medicare program . . . . In addition to exhibiting geographic variation in spending, the VA system shows substantial variation in patterns of clinical practice despite the fact that VA’s management tracks providers’ compliance with national guidelines for the treatment of many medical conditions . . . . The implication is that local norms can influence practice patterns, even in a relatively centralized system that places a strong institutional emphasis on adherence to clinical guidelines for care”

Cannon also concludes that pay-for-performance mechanisms may have a similar difficulties eliminating regional variations.

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