Unbiased Analysis of Today's Healthcare Issues

Does private ownership improve quality of care?

Written By: Jason Shafrin - Sep• 19•16

In the case of elderly care services in Sweden, the answer is ‘yes’.  In Bergman et al. (2016), the authors…

assemble a large data set on elderly care services in Sweden between 1990 and 2009 and estimate how opening to private provision affected mortality rates – an important and not easily contractible quality dimension – using a difference-in-difference-in-difference approach. The results indicate that privatization and the associated increase in competition significantly improved non-contractible quality as measured by mortality rates.

Clearly, privatization does not guarantee improved quality in all countries and in all care settings, but this study provides some evidence that competition in the health care field results in better care for patients.


Links to Start your Week

Written By: Jason Shafrin - Sep• 18•16

How do we measure the value of and pay for biomedical innovation?

Written By: Jason Shafrin - Sep• 15•16

Dana Goldman, Samuel Nussbaum, and Mark Linthicum have an interesting post on the Health Affairs blog about innovation, value measurement and pricing.  The article mentions the new Innovation and Value Initiative, where I serve as the Director of Research.  An excerpt is below.

New pricing mechanisms are needed to effectively link prices to value; we need innovation in pricing, not just treatment. Potential pricing mechanisms include outcomes-based contracts and indication-specific pricing, but other approaches are possible, such as selling licenses for pharmaceuticals like we do for software.

Our challenge is to design a system that: (1) measures value appropriately; (2) links reimbursement to that value; and, (3) does so in a way that stimulates innovation in high-need areas. European-style HTA is not really up to the task. Moving forward will be daunting and will require multiple viewpoints, which is why we are helping to lead the Innovation and Value Initiative to advance the field. The result, we hope, will be an approach to technology assessment that reflects multiple perspectives and defines a new role for the United States as an engine of value-based innovation.

Physicians moving to larger groups

Written By: Jason Shafrin - Sep• 14•16

This is the finding of Muhlestein and Smith (2016):

The proportion of physicians in groups of nine or fewer dropped from 40.1 percent in 2013 to 35.3 percent in 2015, while the proportion of those in groups of one hundred or more increased from 29.6 percent to 35.1 percent during the same time period.

Initiatives requiring quality reporting and use of electronic medical records impose significant cost on small practices, whereas large practices can spread these costs across more patients and physicians.  However, whether the increase in large group practices results in better patient outcomes is unclear.

Evaluating Matching-Adjusted Indirect Comparisons in Practice

Written By: Jason Shafrin - Sep• 13•16

One of my papers on matching-adjusted indirect comparisons (MAIC) was published today, with co-authors Anshu Shrestha, Amitabh Chandra, M. Haim Erder, and Vanja Sikirica.  The title is: “Evaluating Matching-Adjusted Indirect Comparisons in Practice: A Case Study of Patients with Attention-Deficit/Hyperactivity Disorder” and the abstract is below.  Check it out!

Differences in patient characteristics across trials may bias efficacy estimates from indirect treatment comparisons. To address this issue, matching-adjusted indirect comparison (MAIC) measures treatment efficacy after weighting individual patient data to match patient characteristics across trials. To date, however, there is no consensus on how best to implement MAIC. To address this issue, we applied MAIC to measure how two attention-deficit/hyperactivity disorder (ADHD) treatments (guanfacine extended release and atomoxetine hydrochloride) affect patients’ ADHD symptoms, as measured by the ADHD Rating Scale IV score. We tested MAIC sensitivity to: matched patient characteristics, matched statistical moments, weighting matrix, and placebo-arm matching (i.e., matching on outcomes in the placebo arm). After applying MAIC, guanfacine and atomoxetine had similar reductions in ADHD symptoms (Δ: 0.4, p < 0.737). The results were similar for three of four sensitivity analyses. When we applied MAIC with placebo-arm matching, however, guanfacine reduced symptoms more than atomoxetine (Δ: −3.9, p < 0.004). We discuss the implication of this finding and advise MAIC practitioners to carefully consider the use of placebo-arm matching, depending on the presence of residual confounding across trials.

Shafrin, J., Shrestha, A., Chandra, A., Erder, M. H., and Sikirica, V. (2016) Evaluating Matching-Adjusted Indirect Comparisons in Practice: A Case Study of Patients with Attention-Deficit/Hyperactivity Disorder.

Should patients pay high-cost sharing for treatments for the Hepatitis C Virus?

Written By: Jason Shafrin - Sep• 12•16

Treatments for the hepatitis C virus (HCV) are expensive.  At one point they cost over $80,000 per year, although costs have decreased since then.  To prevent moral hazard, should insurance companies rely on cost sharing to decrease utilization?  An article by Lakdawalla, Linthicum, and Vanderpuye-Orgle (2016) argues that they shouldn’t.

Cost sharing appears even less efficient when one considers that treatment makes long term financial sense for society: the treatment of HCV today will lead to lower medical expenditures for covered individuals in later years. Combined with the health and longevity gains from treatment, this is likely to lead to a return on investment within 8 years. Curing more patients with HCV in the present also prevents more cases of infection in the future, and the reduced rate of transmission will accelerate the return on investment even further. Further, there is no good economic reason to discourage patients with HCV from using novel agents and every reason to protect them against financial risk.

Even though HCV treatments are costly. because the returns to patients, payers and society is so large, reducing utilization is suboptimal.

Developing new antibiotics is vital

Written By: Jason Shafrin - Sep• 11•16

Antibiotics are one of the greatest inventions.  They rank in ABC News’s top 10 health advances of all time.  However, bacteria are not static.  They can change and mutate to become anti-biotic resistant.  In this constant battle against disease, new antibiotics are needed.

Harvard’s Mega-Plate Petri Dish experiment shows in vivid detail why the development of new antibiotics will likely be a war humans will need to continue to fight forever.

HWR is up

Written By: Jason Shafrin - Sep• 08•16

David Williams has posted Health Wonk Review: Back to School Daze at Health Business Blog

David debuts the fall season with a serious roster of posts, just the thing to get us back to business.

Is Precision Medicine ready for prime time

Written By: Jason Shafrin - Sep• 07•16

According to two researchers, the answer is no.  As they write at Stat:

As we wrote recently in Science, three key barriers are impeding the drive toward truly transformational precision medicine: researchers often don’t rigorously test the biological theories that supposedly explain why a targeted treatment should work; they haven’t fully determined the accuracy of the diagnostic tests used to figure out if a patient is a good candidate for the therapy; and there’s little coordination between investigators, which has led to inefficient research.

Can you give me an example of the problem?

An evaluation of 33 studies of ERCC1 and lung cancer chemotherapy showed so much heterogeneity in the diagnostic methods and scoring rules that the results are essentially incomparable. So after more than a decade of research, we still don’t know if measuring ERCC1 makes a difference. Nevertheless, numerous ERCC1 test kits are commercially available, and are being used to help “guide” lung cancer chemotherapy.

Precision medicine is still a laudable goal.  Finding the best treatment on average is less useful than finding the best treatment match for each patient. However, rigorous science needs to back up any claims that treatments are more or less effective for specific biomarkers or lab values.

Precision medicine is still the future…the future may just be a few more years away than we had previously hoped.

The problem with step therapy

Written By: Jason Shafrin - Sep• 06•16

Step therapy is good in theory, but often not in practice.  In step therapy, patients are required to try one drug first–typically a low cost and/or high-value treatment–before moving on to more expensive alternatives.  In theory, this is a great idea.  The first drug patients should try should be the highest value one.

In practice, however, step therapy does not always work out as planned. Stat describes some of these issues. Broadly, step therapy doesn’t work due to asymetric information.  Typically there is private information that patients and physicians have that insurers do not have.  For instance, how does one define whether a patient “failed” a therapy.  Clinically this may be easy to do in many cases, but for payers this information is difficult to observe.

The practical issues described above highlight some shortcomings when step therapy is administered well.  Oftentimes, however, it is not.

Dr. Kenneth B. Blankstein, an oncologist in Flemington, N.J., is treating a woman for lung cancer. She responded well to the first chemotherapy drugs he prescribed. When her health was stable, he gave her a “temporary break” from chemo to spare her some of its side effects.

But when he tried to return her to the treatment, the insurer balked, saying that the “temporary break” was evidence that the treatment had failed. Despite Blankstein’s protests, the insurer said she would have to move next to Tarceva, another treatment.

“She had under a 5 percent chance of a response on Tarceva,” he said. “Yet they insisted, so we had to.”

More broadly, Blankenstein summarizes the problem with step therapy as follows.

“The patient’s being told to use a drug we know isn’t going to work, but we have to use it anyway for someone with terminal illness? To me that’s just insane, but it’s the way they do things…It’s taken away clinical judgment. It’s managing by algorithms.”