Unbiased Analysis of Today's Healthcare Issues

Reimbursement Rates and Quality

Written By: Jason Shafrin - Sep• 29•14

How do reimbursement rates affect quality?  One school of thought holds that decreased reimbursement decreases quality in the short-run and decreases innovation in the long-run.  Another school of thought believes that there is so  much inefficiency in the health care system, that reducing reimbursement rates will have no affect  on quality.  Which answer is correct?

A study by Wu and Shen (2014) aims to answer this question by examining the long-run impact on quality of the reimbursement rate reductions from the Balanced Budget Act of 1997 (BBA).  Why is the BBA so important?

With the exception of the Prospective Payment System (PPS), the BBA is the only legislation that reduced Medicare inpatient payments in nominal terms, rather than just slowing down the growth rate.  Second, BBA payment cuts could have a long-lasting effect on hospitals because the legislation not only reduced diagnosis-related group (DRG) payment levels between 1998 and 2002 but also permanently altered the formula for special add-on payments [i.e., DSH and IME payments]…Third, Medicare BBA reductions occurred after a sustained period of declining inpatient admissions and lengths of stay, as well as aggressive payment negotiations from managed care plans (Wu 2009) that limited hospital ability to cost shift to private payers.

Using MedPAR data between 1995 and 2005, the author uses a DDD design.  They compare “…the relative change in mortality trends between large- and small-cut hospitals before and after the BBA.”  They find that:

…the BBA generated long-term financial pressure to hospitals that extended beyond the BBA implementation period. While there is a general declining trend in AMI mortality rate, Medicare patients treated at hospitals facing a large degree of such fi nancial pressure experienced smaller improvement in mortality outcomes relative to patients treated in small-cut hospital, not in the short run, but in the longer run post-BBA period. The elasticity  of the effect, while small ( 0.2), is constant and consistently observed from 7-day to 1-year post hospitalization…hospitals responded to BBA cuts by reducing operating costs per bed immediately after the BBA took effect, and such effort involved a reduction in staffing, particularly among registered nurses.


Rankings and Kendall’s W

Written By: Jason Shafrin - Sep• 28•14

How can you compare how similar two rankings are.  For instance, US News and Consumer Reports may both rate hospitals.  If they have identical ratings, then they are obviously the same.  However, what if the rankings differ for 2 hospitals?  For 4 hospitals?  How can one quantify the similar of rankings?

One method for doing so is Kendall’s W (or Kendall’s coefficient of concordance).  Kendall’s W is a not parametric measure of how similar two or more rankings are.  Whereas the Spearman rank correlation measures the similarity of  any two sets of rankings, Kendall’s W can be used to measure similarity for more than two raters.

A common heuristic for judging Kendall’s W is the following:

  • ≤0: poor agreement
  • >0 to ≤0.2: slight agreement
  • >0.2 to ≤0.4: fair agreement
  • >0.4 to ≤0.6: moderate agreement
  • >0.6 to ≤0.8: substantial agreement
  • >0.8 to ≤1.0: almost perfect agreement.

Do demonstrate how to use Kendall’s W, I look at last year’s college basketball rankings.  I choose the top 5 teams according to the AP poll and see how other polls (RPI and Ken Pomeroy’s rating) would rank these same teams.  You can see the calculations of Kendall’s W for this example HERE.

End of week links

Written By: Jason Shafrin - Sep• 25•14

HWR and Recess

Written By: Jason Shafrin - Sep• 25•14

Billy Wynne has posted the  Thank God It’s Recess” edition of Health Wonk Review at Healthcare Lighthouse.  Check it out.


Stimulating Health Care Innovation

Written By: Jason Shafrin - Sep• 24•14

How do policymakers increase the speed of healthcare innovation in the US? A 2014 report by RAND gives five proposals which I list below with my own commentary.

  1. Enable more creativity in funding basic science. Increasing funding for basic science is a clear way to spur innovation. The opportunity cost of additional funding for science is other government programs that often have better short-term returns. However, few societal investments pay off in the long term like investments in science. RAND also proposes adopting the a funding model “used by the Howard Hughes Medical Institute, which funds scientists rather than
    projects…” This proposal would decrease the administrative burden on applicants and could target more funds to the most promising scientists. On the other hand, this policy would likely move research funds even more towards senior researchers, decrease the probability that junior researchers receive funding, and increase the cost of allocating funds incorrectly. Thus, the marginal payoff of this change is likely low.
  2. Offer prizes for inventions. Prizes for innovation is a good idea but of limited applicability. In cases where it is clear that innovation is needed, prizes can be highly useful. For instance, there is a clear need for an effective treatment for Ebola. Prizes could be useful here to spur innovation while lowering the marginal cost of the products near zero. On the other hand, much innovation occurs in unexpected medicine. No one has a clear idea of what the best ideas in digital health will be in 10 or 20 years. Thus, prizes for fast changing areas may reward innovations that quickly become obsolete.
  3. Buy out patents. In theory, buying out patents could create value to society. Pharmaceutical firms will charge a price not less than the estimated profits they would make over the lifetime of the patient. Society will benefit because the marginal cost of the innovation will fall to 0 and thus use of the product will increase above what would occur under monopoly prices. However, the true value of a patent is likely uncertain and pharmaceutical firms likely have more information about the true value of a product than the government. Thus, this policy will likely lead to the government systematically overpaying to buy out patents.
  4. Establish a public-interest investment fund. “The market rewards for inventing products that reduce spending are often too low to be attractive to private investors. A public-interest investment fund (PIIF) could finance such inventive efforts.” This approach could be beneficials but–RAND correctly worries–that this relies on government or quasi-government public-private partnerships to pick winners. Further, the government may have incentive to pick suboptimal treatments funded by the investment fund in order to receive a return on their investment even if superior products are available in the market.
  5. Expedite FDA reviews and approvals. Faster FDA reviews would certainly increase innovation. Lowering the cost to bring a product to market will improve innovation. This is especially important for digital medicine devices and smartphone diagnostic apps that the FDA is regulating.

Healthcare Economists’ Take

In summary, I believe the RAND propositions will have a small effect on innovation. Reducing barriers to entry by speeding up FDA review and increasing basic science fund would clearly improve innovation. Creating prizes, buying out patients, or creating a government investment fund would likely work in selected cases, but they will not have a major impact on innovation.


The Angelina Jolie Effect

Written By: Jason Shafrin - Sep• 23•14

You may know that Angelina Jolie is an actress but do you know that she also is influencing the health care decisions of millions of women worldwide?  At least that is the findings from a recent article in Breast Cancer Research.  The Guardian reports:

Referrals for genetic breast cancer tests more than doubled in the UK as a result of what doctors have described as the “Angelina effect”.

In May last year, actor Angelina Jolie revealed that she had undergone a double mastectomy to avoid breast cancer.

She took the decision after testing positive for the BRCA1 gene mutation that greatly increases the risk of developing the disease.

A study has measured the impact of her announcement on women in the UK. It shows that in June and July last year, the number of GP referrals for genetic counselling and DNA tests for breast cancer mutations increased two and a half times on the same period in 2012.

In a superstar economy, it seems like celebrities’ decisions affect all kinds of behavioral choices, including health care decisions.

Jolie has had double mastectomy

Longer trials or larger sample size?

Written By: Jason Shafrin - Sep• 22•14

Developing drugs is expensive. Some estimates have estimated that the cost of bringing a drug to market is $1 billion. In addition, payers are now reimbursing based on the perceived value of a treatment. That is, treatments that provide more health benefits receive higher reimbursements.

In this world of value-based pricing (VBP), pharmaceutical companies have an incentive to show strong efficacy not only to secure approval, but also to secure higher reimbursement rates from payers. A question for these firms is, should they invest in trials with a longer duration or in increasing the trials’ sample size?

A paper by Breeze and Brennan (2014) relies on the following methodology to answer this question:

We modify the traditional framework for conducting ENBS [expected net benefit of sampling] and assume that the price of the drug is conditional on the trial outcomes. We use a value-based pricing (VBP) criterion to determine price conditional on trial data using Bayesian updating of cost-effectiveness (CE) model parameters.

Using this approach and parameterizing the model using treatments for systemic lupus erythematosus, the authors find that:

…shorter trials with a large sample size are associated with greater profit forecasts for the pharmaceutical company. Although there is substantial uncertainty in the long-term effectiveness of treatments in chronic diseases, increasing sample size is a more efficient method of data collection in this illustrative example.


What can Geographic Variation in Health Care Spending Tell Us About Efficiency?

Written By: Jason Shafrin - Sep• 21•14

In a project with the Institute of Medicine (IOM), I examined the sources of regional variation in Medicare and Medicaid spending and spending growth. The IOM wisely concluded that policymakers should target decision-makerss rather than geography when attempting to improve the efficiency of the healthcare system.

I recent paper by the Louise Sheniner of the Brookings institute claims that patient characteristics may drive most of the regional variation in healthcare spending. Her conclusion is:

But the paper has several findings that suggest that the variation in Medicare spending does not represent wasteful spending that could be easily eliminated without significant effects on the health system. First, population characteristics have more explanatory power for Medicare spending than measures of social capital, indicating that the variation in patient characteristics is more important than variation in provider characteristics. Second, health measures are significantly more correlated at the state level than at the individual level, making it likely that state level regressions do a better job of controlling for unobserved variation in population health. Third, there does not seem to be a significant relationship between the use of “preference-sensitive procedures” and the level Medicare spending. Fourth, states with high levels of Medicare spending tend to have lower levels of non-Medicare spending. Providers in these states may face greater financial difficulties, and may “volume shift” to Medicare patients in order to cover costs.

First, I would agree that population characteristics do make up a large share of the differences across states. However, they cannot explain all the differences. Since Dr. Sheiner relies on state-level data rather than individual level data, and the Dartmouth Group wisely points out the issue of an ecological fallacy of assigning individual-level inference based on group level data. However, the examination of average patient characteristics misses the point. In our research for IOM, we found that variation in median spending across regions is much less than regional variation in average spending. In essence, it is how difference physician practices treat the sickest patients in the tails of the distribution that determines whether an area is low or high cost; regional policies to treat low or medium cost patients have little affect on an areas relative spending ranking due to the skewed nature of health care data.

This finding is consistent with Dr. Sheiner’s conclusion that preference-sensitive procedures are not driving the regional variation in spending. Just because one area is more likely to give hip or knee replacements than another, this practice pattern will be dwarfed by how a region treats end of life patients or patients with multiple serious comorbidities.

Overall, however, I do agree with Dr Sheiner’s conclusion that geography is a straw man here. We need to focus on how policymakers treat the sickest patients and identify treatments that are most cost-effective to help improve their health and the nations’ fiscal bottom line.



Written By: Jason Shafrin - Sep• 18•14

Here are some links to check out for the end of the week:

CoR is up

Written By: Jason Shafrin - Sep• 17•14

Rebecca Shafer of AMAXX turns in another great roundup of risky posts in this week’s Cavalcade of Risk.