Unbiased Analysis of Today's Healthcare Issues

Presentation at 2017 British Society for Rheumatology

Written By: Jason Shafrin - Apr• 24•17

For those attending the British Society for Rheumatology Annual Meeting in Birmingham, England, I will be giving a podium presentation on Wednesday, April 26 at 12pm BST titled “Economic Burden of Rheumatoid Arthritis is Higher for Anti-Citrullinated Protein Antibody-Positive Patients.”  Come by Hall 6 to check it out along with other presentations in the Clinical and cost effectiveness oral session (Session OAS2).

How does payment reform affect providers in competitive vs. non-competitive markets?

Written By: Jason Shafrin - Apr• 23•17

How does payment reform affect access to care?  And what does payment reform mean?

Payment reform can mean manythings but in this context we will mean substituting fee-for-service or cost-plus reimbursement schemes for fixed reimbursement for a fixed episodes of care or fixed bundles of services during a specific time frame.

One example of how payment reform worked, is Medicare’s Inpatient Prospective Payment System (IPPS).  One study using data from California in the early 1980s (Meltzer et al., 2002) found that costs fell more for providers in the most competitive markets after theIPPS was implemented.  More generally, there is evidence that the less sensitive the reimbursement system is to differences in marginal costs across patients, the more providers tend to reduce costs (see Grabowski et al., 2011; Huckfeldt et al., 2014; Newhouse and Byrne, 1988; Sood et al.,2013)

To further examine the interaction of payment reform and changes in provider cost, a recent paper by Sood et al. 2017 examines the effect of changes to home health payments in the late 1990s.  The specific reform evaluted was the 1997 Interim Payment System (IPS).

The authors use Medicare claims data between 1996 and 2000 to examine how the implementation of IPS changed provider cost.  The study focuses patients discharged from a hospital for a stroke admission who used home health services.  Cost data come from Medicare cost reports.  Using these data, the authors run an OLS regression measuring the relationship between home health agencies competition (measured using the Herfindahl-Hirschman Index) and various outcomes including home health days, cost or the probability of any home health use.  The key variable is the interaction between the HHI and whether the IPS was instituted in the given year.

Using this specification, the authors find that:

…there was significant variation in costs by level of competition in the pre-IPS period, with more competitive markets having higher costs. After the IPS, costs declined in all markets but there were larger declines in costs in more competitive markets. The decline in costs was driven by both changes in the probability of any home health use (extensive margin) and a decline in the number of home health days among existing users (intensive margin). As a result of the heterogeneous response to the payment reform, costs and the number of home health days converged in more and less competitive markets and the significant variation in costs or intensity of care by level of com-petition in the pre-IPS period nearly disappeared in the post-IPS period.

Why would competitive markets have larger decreases in cost?  Shouldn’t these markets already have a lot of discipline?  We would expect payment reform to have less of an effect on competitive markets, right?

The authors claim that average treatment intensity (and thus cost) is higher in more competitive markets.  Second, they propose that payment reforms that reduce reimbursement lead to a larger reduction in profit margins in more competitive markets as equilibrium intensity is higher in more competitive markets.  The authors do find evidence that in more competitive markets, there was more firm exit and home health agencies that did exit were the ones that were high-cost.

One issue with this analysis is that it is very short-term.  They authors correctly make the point that “payment reform serves to eliminate some of the most inefficient providers”.  This sounds like a good thing.  However, payment reductions generally are positive in the short run.  The highest cost providers exit.  In the long-run, however, less entry will occur with lower reimbursement, there will be less competition, and access to care could be reduced.  Ideally, one would hope high-quality, low-cost firms would enter, but payment reform may incentivize only low-quality, low cost firms to enter.


Who’s on First Health Wonk Review + Links

Written By: Jason Shafrin - Apr• 20•17

Brad Wright has posted the  Health Wonk Review: Who’s On First? Edition at Wright on Health.  

Check it out along with some other interesting links from the week.

FDA Hedges

Written By: Jason Shafrin - Apr• 19•17

Pharmaceutical companies face major risk.  There is risk that the drugs they are researching don’t work (e.g., lack of efficacy) or are not safe.  There is risk that health insurers or government payers will not cover their treatment.  And there is risk that the FDA will not approve a drug after a Phase III clinical trial.

One approach to address the last source of risk is what Alex Tabarrok of Marginal Revolution calls FDA Hedges.  He explains the concept below:

In the words of a recent article, the FDA’s rejection of a recent drug application was a stunning setback. Stunning setbacks are by definition unpredictable and unpredictable risks aren’t correlated with other risks which means that they can be easily priced and bought and sold. The all-star team of Adam Jørring, Andrew W. Lo, Tomas J. Philipson, Manita Singh and Richard T. Thakor propose just this in Sharing R&D Risk in Healthcare via FDA Hedges.

The idea is to create FDA Hedges that pay out a fixed fee if a drug fails to be approved and zero otherwise. Pharmaceutical firms could then buy some of these contracts and reduce their risk exposure which in turn would increase their incentive to invest in R&D.

This concept of FDA Hedges is very novel.  However, a key question is how robust the FDA Hedges market would be.  Drug companies with insider information that their drug would fail could buy up these hedges whereas companies that have strong evidence that their drug will succeed will not buy them.  This could create adverse selection in the market whereby the FDA Hedges unravel.  In his article, Tabarrok mentions a few other reasons to be skeptical, but concludes that because R&D is so valuable, even a marginal increase in R&D would be worthwhile.


Access to cancer care in the UK

Written By: Jason Shafrin - Apr• 18•17

Being sick in the United Kingdom has advantages and disadvantages.  Supporters will cite that out-of-pocket costs are generally low, coverage is universal, and the price of health care to the government is lower. One question is, what is the quality of care received?  Critics cite that access to cutting edge, innovative treatments is often restricted or delayed.  This is particularly true of cancer treatments. The Guardian reports:

More than 100,000 patients a year are “having their worst fears dragged out” by having to wait longer than the stated maximum of two weeks to see a cancer specialist to find out if they have the disease, new NHS figures obtained by the Guardian reveal.

A total of 102,697 people in England did not get to see a consultant within 14 days of being urgently referred by their GP last year – a key patient right in the NHS constitution. Some 25,153 people had to wait more than the official target of 62 days to start their treatment.

One questions is whether these delays are just inconveniences or if they really affect patient health.  The president of the Royal College of Radiologists, Dr Nicola Strickland, said:

“Any delay in diagnosis or time to start therapy risks a growth in the cancer, potentially making it incurable. These delays increase the anxiety experienced by patients and their relatives at this difficult time.”

It is easy to cite statistics that the UK’s National Health Service has lower cost of care than the U.S..  However, most sick patients–and healthy individuals who may become sick one day–want access to the best available treatment.  These options are limited in the NHS.  Further, restrictions on reimbursement for novel, breakthrough treatments lead to less investment and fewer treatments developed for future generations of patients.  Although making sure treatments are affordable to patients is important, affordability to payers is not useful if patients do not get access to the treatments they need.

How do patients and physicians make good treatment decisions?

Written By: Jason Shafrin - Apr• 17•17

The answer seems to be just find the “best” treatment.  In theory this sounds easy, but in practice it is difficult.  What does best mean?  What if one treatment has better outcomes on average but is riskier?  What if one treatment is more effective than another but requires injections rather than pills?  What if patients have a strong preferences for avoiding side effects compared to efficacy?

The Ottawa Decision Support Framework (ODSF) represents one way of getting to a good treatment decisions.  The content below summarizes the ODSF approach.

Clarifying Decisional Needs

First, physicians and other providers needs to determine what information is needed to make a good decision.  For instance, if the patient already has their mind set on a treatment, it likely is not worth a lot of time explaining their options.  On the other hand, if they are still in the process of searching for a treatment, that is a good time to discuss what factors would play into the treatment decisions.  The factors could include:

  • Personal/Clinical characteristics: Decision support should be tailored to a person’s physical, emotional and cognitive capacities.
  • Decisional conflict.  Is the patient expressing clear uncertainty about treatment choice, verbalizing concern about undesired outcomes, or wavers between choices?  If so, additional discussion of treatment choices is likely needed.
  • Lack of knowledge.  If patients are not aware of treatment choices or their attributes, the provider should inform them of these options.
  • Unrealistic expectation.  The patient may believe that a treatment works better than it does (e.g., 100% cure for everyone), underestimate it’s benefits (e.g., this treatment never works), or misunderstand side effects (e.g., vaccines cause autism).  Providers should provide information to set more realistic expectation based on available evidence.
  • Unclear values.  Treatment decisions depend on the factors that patients value most.  If physicians do not know these values or patients themselves are ambivalent, additional discussion is needed to clarify these values.
  • Unclear perceptions of others or social pressure.  Some people may not opt for a treatment they would prefer in isolation because they fear what others might think.  Or patients may ask what other patients often due in tis situation.

In these cases, providers should offer facts and set the probabilities of specific outcomes based on existing scientific information.

Patient Decision Aids.

According to Maine Quality Counts, patient decision aids are tools that translate research evidence by providing information on the options, benefits, risks and associated probabilities; helping patients clarify their values for outcomes; and providing guidance in the process of decision making.

Decision Coaches

Decision coaches are third parties that facilitate discussions between patients and physicians.  For instance, decision coaches can assess patients’ decisional needs, provide decision aids or coaching to address known needs, evaluate the decision quality and screen for factors influencing implementation of the decision.  Coaches could play a key role in ensuring that patient preferences are reflected in the treatment decision-making dicussion between patients and providers.

Why aren’t there more cures?

Written By: Jason Shafrin - Apr• 16•17

The answer is money, reimbursement, and incentives.  Treating chronic disease gives innovators payoff over a long period of time.  If innovators created a cure for that disease, they could of course charge the net present value of this same stream of payments.  Health plans, patients and the media, however, are often shocked at the high sticker price of such a large one-time payment for a cure.   Mark Trusheim, a visiting scientist at MIT’s Sloan School of Management and one of my co-attendees at the NBER Conference on Economic Dimensions of Personalized and Precision Medicine stated:

Insurers are “used to paying rent for health, and we’re asking them to buy a houseful of cure.

Spark Chief Executive Officer Jeff Marrazzo, whose company spent $400 million creating a genetic therapy to cure retinal disease, said in a Bloomberg article:

Why is it that there are not more cures?” Marrazzo says. “It’s not because there are bad actors. It’s an industry full of people who react to incentive structures.” If compensation could be redesigned to reward one-time treatments over chronic treatments, “that’s where people would play,” he says.

If we want cures, we clearly need a different payment structure than is needed for treatments for chronic diseases.  One solution is health care mortgages–proposed by Tomas Phillipson and Andrew von Eschenbach–where innovators could receive a high one time payment for cures, but this payment could be financed over time, similar to a home mortgage.  These types of innovative payment schemes are needed to ensure that innovators start creating cures to treat the sickest patients in society.


Applying Cost Effectiveness Analysis to all Health Care Interventions

Written By: Jason Shafrin - Apr• 14•17

That is the topic of a Health Affairs blog post published today by James Baumgarder and Peter Neumann.  An excerpt is below.

Cost-effectiveness analysis (CEA) is an important tool for assessing and pointing the way toward better health care efficiency. The number of published CEAs on health care interventions has blossomed, averaging 34 per year in the 1990 to 1999 time frame and increasing to more than 500 per year in the 2010 to 2014 period. Advances in computing and data storage technology, along with a workforce with the appropriate statistical, health economic, modeling, and computational skills, will enable continued growth in the application of CEA, which can be used for the benefit of providers, payers, patients, and governments. The founding and rapid increase in membership of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR), for example, is a testament to the growth and potential for the use of CEA and other quantitative assessments of the value of various health care technologies.

Researchers have noted, however, that the application of CEA across different types of health care interventions is imbalanced, with CEAs disproportionately conducted on pharmaceuticals. Data from the Tufts Medical Center Cost-Effectiveness Analysis Registry indicate that 46 percent of recent CEAs evaluated pharmaceuticals, yet less than 15 percent of personal health care expenditures in the United States are devoted to prescription drugs. In contrast, only 22 percent of CEAs evaluated surgical or medical procedures.

The rest of the article focuses on the reasons why cost effectiveness analysis occurs more frequently among pharmaceutical interventions compared to medical or surgical interventions and what steps could be taken to better inform key stakeholders of the value of these medical and surgical interventions.  Interesting throughout.

Note that the research underlying the article was funded by the Innovation and Value Initiative, where I serve as the Director of Research.

Orphan Medical Products

Written By: Jason Shafrin - Apr• 12•17

Should health insurers cover orphan drugs?  Although the clear answer appears to be yes, the issue is tricky.  An orphan drug is one which treats a limited number of people.  In Europe, this designation generally applies to patients with a disease with an overall prevalence between 5 and 10,000 individuals.

In order to incentivize innovators to create treatments for these rare diseases, prices need to be high.  Even if a drug costs $100,000 per year, if there are only 10 people with the disease it is unlikely a life sciences firm would be able to recoup their investment.  On the other hand, payers do not want to cover treamtents that are a poo rvalue, even if it is for an orphan disease.  Society could prioritize covering drugs that affect a large number of people which could deliver a bigger bang for the buck, but also perhaps less social equity.

To address the issue of ophan medical products, the European Working Group for Value Assessment and Funding Processes in Rare Diseases (ORPH-VAL) proposes nine principles to guide the assessment of orphan drugs and medical products. These recommendations are largely for European regulators and payers and may be less relevant for a U.S. context.  Regardless, the nine principles are:

  1. Orphan medical product (OMP) assessments should consider all relevant elements of product value in a multi-dimensional framework. In particular, decision-makers should consider the perspective of patients, the healthcare system and wider society.
  2. Pricing and reimbursement decisions should be adjusted to reflect other considerations beyond product value.  ORPH-VAL recommends loosening the cost-effectiveness thresholds for rare diseases in order to promote innovation in these areas.
  3. Those making pricing and reimbursement decisions at a national level should take into account regulatory and HTA undertaken by other countries
  4. Healthcare professionals and patient perspectives should be included in the assessment
  5. Value assessment, pricing and reimbursement decisions should be adaptive based on patient need and the availability of new information over time.  In particular, any assessment should take into account, disease prevalence, disease severity and unmet need.
  6. All eligible patients within the authorized OMP label should be considered in any pricing and reimbursement decisions, but decision-makers should also evalaute value for sub populations when relevant.
  7. Funding should be provided to ensure patient access to OMPs
  8. Evidence based funding mechanisms should be developed to guarantee long-term availability of these treatments.
  9. Coordination of OMP value assessment should be better coordinated across European countries.

These guidelines are helpful. However, the question still remains, how much is too much (or alternatively, how much is not enough).   The balance between measuring treatment value for individual patients and incentivizing innovation for rare treatments is a fine line to walk.


How to measure preferences in health

Written By: Jason Shafrin - Apr• 11•17

Which treatment is the best?  This is a seemingly simple question, but there are many answers.  Some people would say whatever the clinical evidence says.  Others would contend that patient preferences are paramount and patient preferences should rule the day.  In our current world of health care largely paid for by insurance, how should the preferences of third-payers (e.g., over treatment cost) play a role.  Further, some healthcare treatment decisions affect others, such as an individuals preference to vaccinate themselves or their children.

To account for these differences, by Dolan, Olsen, Menzel and Richardson (2003), outline six different perspectives on how to measure value.  These vary along two dimensions.  The first is who’s preferences are measured (i.e., personal, social, and socially inclusive personal). The second dimension is when preferences are measured: ex ante and ex post.  For instance, do you measure someone’s preferences for certain types of cancer treatment before they get cancer (e.g., general population survey) or after (i.e., surveying only people with cancer)?

A recent paper by Tsuchiya and Watson (2017), however, claim that this framework is incomplete.  Instead, they propose five points of view from which preference can be measured: personal, non-use, proxy, socially inclusive personal (SIP), and social.  The first perspective is the patient’s perspective.  However, the patient’s perspective could be evaluated by treatment users, payers, or health technology assessment bodies.   The second would be the perspectives of healthy people who do not use the treatment but could in the need it in the future (e.g., healthy people who are at risk of contacting cancer in the future).  This perspective is often used by payers and health technology agencies.  The “proxy” category is also important.  For instance, the parents of young adults with schizophrenia may have strong preferences over treatments and those preferences may or may not coincide with the wishes of the patient. The social perspective is often taken by payers or health technology assessors. Finally, the SIP perspective includes both payer or assessors and the patient perspective.

Additionally, the Tsuchiya and Watson article also adds some nuance to the ex-ante time perspective.  Whereas Dolan et al. say there is only a single ex ante perspective, Tsuchiya and Watson provide four cases:

  1. There will be exactly 50 patients for certain and we already know who they are;

  2. There will be exactly 50 patients for certain but we do not know who they are;

  3. Each of the 1000 individuals has a 5% chance of becoming illex post there will be around 50 patients;

  4. There is a 5% probability that all 1000 people will become illex post there will be either exactly zero or exactly 1000 patients.

The first example could be the case where biomarkers determine disease progression with perfect accuracy. The second provides an alternative where the exact prevalence level is known whereas the third case the prevalence rate is known.  The fourth situation is a case where the uncertaintly around the prevalence rate is not normally distributed; in this example it is bimodal.  For instance, there could be a contagious disease where if people develop an immunity, no one becomes ill, but if the society is not used to this strain, everyone becomes ill.

While this nuance is helpful for analytic purposes, having exact knowledge of any of these probabilities is unlikely in reality and there will always be some uncertainty.  Nevertheless, providing some more description of how this uncertainty is being characterized is helpful. (more…)