April 2010

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Effect of Decreasing Medicare Reimbursement Rates on Cataract Surgeries

April 19, 2010 in Medicaid/Medicare, Physician Compensation | No comments

What if Medicare spends reimburses physicians too generously for a certain service. Will reducing reimbursement for that service decrease utilization and cost?

A study by Mitchell, Hadley and Gaskin (2002) attempts to answer this question by examining the impact of changing Medicare reimbursement for cataract surgeries.  Between 1992 and 1994, Medicare decreased fees paid for cataract extractions by 17.4%.  After the fee decrease, the authors observe the following:

…we found that the Medicare cross-price [elasticity] is significant and negative, implying that a 10% reduction in the fee for a cataract extraction will cause ophthalmologists to supply about 5% more non-cataract services.  Second, the income variable is highly significant, but its impact on the supply of non-cataract services is trivial. This suggests that physicians behave more like profit maximizing firms than target income seekers.

What this means is that when Medicare decreases one specific fee, physicians will substitute other services in its place.   In this case, ophthalmologists replace cataract surgeries with non-cataract services. The authors do not estimate the total effect on prices, but if non-cataract services are less expensive than cataract surgeries, than this almost certainly decreases Medicare expenditures.

The generalizability of this finding, however, is unknown. A cataract surgery is fairly elective meaning that it can be easily postponed temporarily or indefinitely. When necessary, postponing a cataract surgery will not affect patient health, but will influence their quality of life through worse vision.  Thus, ophthalmologists can easily substitute non-cataract services for cataract services with little effect on patient health.  In cases where the fee-reduction does not have such a clear substitute for services, it is unclear how physicians will respond.

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Developing a pre-existing condition

April 17, 2010 in Health Insurance, Miscellaneous | No comments

I have recently read in the press a number of mentions of the phrase “developing a pre-existing condition.”  For instance, a Cato Institute paper discusses this phenomenon and how you can buy insurance against developing a pre-existing condition.

This phrase seem paradoxical however.  How can you develop a pre-existing condition?  Before you “developed” the condition, it was not pre-existing.  Once the condition comes into existence, at what point is it pre-existing?  Immediately?

The phrase is more likely derived from the language of insurance companies rather than common sense.  If you get sick during a given year and have to renew your insurance over the subsequent year, the illness you developed during the past year will become a pre-existing condition next year when you need to buy insurance.  Thus, by becoming sick, you immediately have a pre-existing condition which will affect your future health insurance premiums.

Only in the crazy language of insurance-speak would it be  possible to develop a pre-existing condition.

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Friday Links

April 16, 2010 in Current Events | 2 comments

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Health Wonk Review

April 15, 2010 in Carnivals | 2 comments

The latest edition of the Health Wonk Review is up at David Harlow’s HealthBlawg.

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Substitution and Price Indices

April 15, 2010 in Economics - General | No comments

In this post, I discussed how to construct the Laspeyres, the Paasche, and the Fisher price index.  In practice, the Laspeyres tends to overstate the price increase and the Paasche tends to understate the price increases.  Let us look at the following example to see why this is the case.

In this spreadsheet, I use the example of a doctor’s visit to an internist and a doctor’s visit to a nurse practitioner.  Assume that there is no health insurance.  In the first period, it costs $200 to see the internist and $100 to see the nurse practitioner.  In this case, 10 patients visit each type of provider (20 total visits).  However, in the second period, the price for an internist visit rises to $350 (75% increase) while the price of a visit to a nurse practitioner only rises to $125 (25% increase).  Because of the difference in both the rate and level of price increases, some patients will stop seeing the internist and will instead visit the nurse practitioner.

Because the change in prices is between 25% (for the NP) and 75% (for the internist), we know the resulting price index value will be between 1.25 and 1.75.  If we use the Laspeyres index and weight the price changes by the initial quantities, the value of the price index in period 2 is 1.58.  However, if we use the Paasche index and weight the price changes by the terminal quantities, then the price index value in the second period is only 1.45.

The Laspeyres overstates the price increase, because it does not take into account the fact that people switched from the expensive internist to the cheaper nurse practitioner.  On the other hand, the Paache index understate the price increases because it ignores the fact that some people had to switch from their preferred provider (internist) to a less preferred provider (NP) because of the price change.  It ignores that some people are getting lower quality care when they switch to the NP.  [Disclaimer! This is just a simple example where I assume internists care is superior to NP care when I realize that in reality, this may or may not be the case].

One could apply the Fisher index as well which is the geometric mean of the Laspeyres and the Paasche.   The Fisher index takes into account the substitution between goods or services over time.  In this case, the price index value is a more reasonable 1.52.

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The Impact of Comparative Effectiveness Research on Health Care Spending

April 14, 2010 in Health Insurance, Public Policy, Quality | 3 comments

Comparative Effectiveness Research (CER), as it name suggests, compares how well different medicines treat a given disease.  Politicians claim that using CER findings can help improve quality and decrease cost.  If one treatment produces better health outcomes on average than another and also costs less, we should always make people use that treatment, right?

Not according to a working paper by Basu and Philipson (2010).  Let us assume that health outcomes for Drug A are better than the health outcomes for Drug B.  If the results from this research were released, the public’s demand for Drug A would increase and the demand for Drug B would decrease.  However, this may not save money.  The demand for Drug B will decrease since fewer people want to buy it; this will reduce expenditures.  As the demand for Drug A increases, however, the price and quantity purchased will increase.  Thus, the net effect on spending is indeterminate.

In addition, if insurance companies or the government decided to subsidize or cover the entire cost of treatment using Drug A, the demand will increase even more.

Basu and Philipson, however, assume that the marginal cost to produce a drug increases as the quantity rises (i.e., the supply curve is upward sloping).  However, because there are economies of scale in the production of pharmaceuticals, the price of Drug A could actually decrease (increasing returns to scale) or stay the same (constant returns to scale) as demand increased.

The key assumption in the above analysis is that it assumes that all people with a given disease respond in a homogeneous way to Drug A.  If two-thirds of people have better health outcomes when treated with Drug A and one-third have better health outcomes when treated with Drug B, then it may be suboptimal to cover Drug A, but not Drug B.

To prove this, Basu and Philipson look at the Clinical Antipsychotic Trials of Intervention Effectiveness project (CATIE).  They find that “if Medicaid would have eliminated coverage for the least cost-effective treatments of the CATIE trial then under homogeneous effects, it would save about 90% of the $1.3B Medicaid class sales annually in non-elderly adult patient with schizophrenia. However, taking into account the observed heterogeneity in treatment effects, it would incur a loss of health valued annually at about 98% of class spending and thus a net loss of about 8% of annual class spending.”

However, one of their key assumptions is that not covering the less effective medicines means that no patients will take the uncovered drug.  This may be a fair assumption for the Medicaid population, but not for the population at large.  There is a big distinction that needs to be made between not covering a drug (but allowing for the purchase to purchase it out of of their own pocket) and prohibiting the drug entirely.  Basu and Philipson are looking at the most extreme case where not covering the drug means, de facto, that the drug will not be taken, but this need not be the case.

What is important to take from this research, however, is that the drug that is most effective on average may not be the best drug for everyone.  One must take into account heterogeneous treatment effects when designing any insurance benefit plan.

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Defining Opportunity Cost

April 13, 2010 in Economics - General, Miscellaneous | No comments

As an economist and an avid reader I certainly appreciated Marketplace Money’s description of the concept of opportunity cost:

“The late Robert Eisner, an economist at Northwestern University, somewhat tongue-in-cheek illustrated opportunity cost this way. The cost of buying and reading his book–The Misunderstood Economy–was not only the dollars spent on it, but also the value of the time spent reading it and the alternative use of that time. In other words, his book should only be read if you believe your return, both in enlightenment and enjoyment exceeds its opportunity cost, that is, money spent on the book and the time required to read it.“

Tuesday Links

April 13, 2010 in Current Events | No comments

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U.S. vs. Canada: Health outcomes and heart disease

April 12, 2010 in International Health Care Systems | No comments

This blog has posted frequently on comparisons between the U.S. and Canadian healthcare systems (see here, here, here and here).  Although there are many points of contention, it is clear that the Canadian system is less expensive than the American.  According to the OECD, in 2006 Canada spent $3,678 per person on health care and the U.S. spent $6,714.  From this additional expense, do Americans receive better health outcomes?

A paper by Pozen and Cutler (2010) examines this question for individuals with heart disease.  Using data from the Joint Canada-U.S. Survey of Health between 2002 and 2003, the authors compare health outcomes between Americans and Canadians who are aged aged 45 and older and who have heart disease.  “Past analyses using these data have found that wealthier Americans and Canadians report similar overall health status, while poorer Americans report worse health status than poorer Canadians.”

The results of the study are as follows:

“Being Canadian was positively associated with fair or poor health, but negatively associated with disability and functional impairment. None of these coefficients, however, was statistically significant from zero at the 5 percent level (though the coefficient on disability was significant at the 10 percent level). Results that were significant in some cases were income, education, and risk factors such as hypertension and smoking status.”

One problem with this simple analysis is that Canadians may simply be more or less optimistic about their health state than Americans.  If could be the case that Canadians report poor health even though–based on objective measures—they may have the same quality health.  A difference in difference estimate would be useful where one could compare the health self-reports of clinically healthy Canadians and clinically healthy Americans and see if the health self-report difference is higher or lower for cohorts in each country with heart disease.

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Incentivizing wins: A basketball digression

April 10, 2010 in Miscellaneous | 1 comment

Although this blog deals mostly with topic related to health economics, today I will digress to another one of my passions basketball.  This post deals with an issue of incentives: Do all basketball teams have an incentive to win games?

As we are now nearing the end of the NBA regular season, the Toronto Raptors and the Chicago Bulls are fighting for the last playoff seat in the Eastern Conference.  Fifteen other playoff contenders are jockeying for playoff seeding.  Philadelphia 76ers fans, however, are praying for losses.  The following comes from a discussion of an upcoming 76ers-Bucks game from the blog of hard-core 76ers fans:

“The Sixers need a loss in the worst way. A win pretty much destroys all hopes at a top 6 pick, and puts the 7th in jeopardy as well. The Sixers must, lose, out.”

Because the 76ers have been eliminated from playoff contention they  have little incentive to win games.  In fact, they have an incentive to lose as many games as possible since the more losses they have, the higher their draft pick will be.  The NBA instituted a lottery system whereby the top 3 picks are determined randomly among the non-playoff contenders.   The odds of getting these top 3 picks, however, increase with the number of losses each team has.  Additionally, the NBA orders all remaining picks outside the top 3 from worst to best record.  Thus, teams like the 76ers still have an incentive to lose games.

A better system would be to hand out draft picks based on each team’s record after 60 games.  After about three-quarters of the games have been played, the best and worst teams will have generally separated from each other.  By calculating draft picks based on the record only after a certain share of the games, teams will not have an incentive to lose games on purpose.  Even if the players try as hard as they can (which is not certain), teams are still able to lose games by resting marginally injured stars, or giving more playing time to lesser quality players.

Although this system does not incentivize teams to win, at least it does not incentivize them to lose.  Plus, by winning more games at the end of the season, teams may be able to attract more fans.  The teams who will be hurt are those that played well over the first 60 games only to falter over the last 22.  Also teams with unusually difficult schedules in the first 60 games would be hurt.  However, it is worth punishes these few teams to improve the overall play at the end of the season.

One could even add a mini-playoff among the teams with the 10 worst records in the NBA.  Among these 10 bad teams, the team with the best record over the remaining 22 games should be able to increase their draft pick by one spot.  This would encourage the worst teams to still try to win without unnecessarily rewarding teams still in the playoff hunt with an improved draft pick.

To summarize:

  • Base draft picks on each team’s record after 60 games.
  • Among the teams with the 10 worst records after 60 games, increase the draft position by one slot for the team with the best winning percentage over the remaining 22 games.

If the NBA implements my strategy, fans like those in Philadelphia will experience a more exciting brand of basketball at the season’s end.

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