Unbiased Analysis of Today's Healthcare Issues

Health insurance expansion and physician supply

Written By: Jason Shafrin - Dec• 07•17

When new bills pass in Congress or state legislatures that expand health insurance coverage, most researcher look at the demand side effect.  How does the insurance expansion affect the number uninsured?  How does it affect access to care?  How does it affect out of pocket cost?

What is less frequently studied is the supply side effects.  Namely, does expanding insurance coverage increase the number of doctors, nurses, medical assistants and other stuff in the U.S?  This is the question that Chen et al. (2017) examine.  In particular, they look at the re-authorization and expansion of the Children’s Health Insurance Program (CHIP) in 2009 and they test whether the number of pediatricians increase after the CHIP expansion.  They find:

…newly trained pediatricians are 8 percentage points more likely to subspecialize and as much as 17 percentage points more likely to enter private practice after the law passed. There is also suggestive evidence of greater private practice growth in more rural locations. The sharp supply-side changes that we observe indicate that expanding public insurance can have important spillover effects on provider training and practice choices.

Very interesting study.


CVS-Aetna merger

Written By: Jason Shafrin - Dec• 06•17

Many of you have already heard the news: CVS is buying Aetna for $69 billion.  As the New York Times reports:

Together, the companies touch most of the basic health services that people regularly use, providing an opportunity to benefit consumers. CVS operates a chain of pharmacies and retail clinics that could be used by Aetna to provide care directly to patients, while the merged company could be better able to offer employers one-stop shopping for health insurance for their workers.

But critics worry that customers could also find their choices sharply limited. The deal risks leaving patients with less choice of where to get care or fill a prescription if those with Aetna insurance are forced to go to CVS for much of their care.

I have been a fan of retail clinics.  Offering more choice and convenience is a good thing, especially if it is less expensive.  Increasing access hours to care is also a positive.

Cost savings

However, I am skeptical that there will be substantial savings from increased use of retail clinics.   Most of health care spending, however, isn’t from taking care of kids with coughs or annual check-ups.  The real cost of health care is treating patients with severe diseases and/or multiple comorbidities.  These more severely ill people–who make up the lion share of Aetna’s costs–need to see specialists, not PCPs.  Secondly, the thought that there will be significant efficiencies from merging to very different business entities, markets and cultures likely is wishful thinking.

The one key area where there could be cost savings is through lower drug prices.  As Austin Frakt argues, oftentimes, drug prices rise with PBMs, and PBMs negotiate rebates for payers.  By reducing these rebates and cutting out the middleman, drug prices may be able to fall as CVS would now internalize these savings as part of Aetna.

One benefit of the merger is that health plans can internalize cost offsets.  If there are expensive pharmaceuticals that reduce hospitalizations, by owning a pharmacy benefit manager (PBM) and health plan, the combined company can realize these savings and make informed decisions on pharmaceuticals taking into account cost offsets.

The business of health care

What does CVS get out of the deal? Likely, CVS sees that getting relatively healthy people into CVS (i) builds trust in their brand, (ii) people will buy stuff while getting their health care, (iii) patients will be required/encouraged to fill their prescriptions with CVS.  As described above, Aetna will likely get lower drug prices by cutting out (i.e., internalizing) the PBM middleman.

On the business front, it is likely that Express Scripts–another PBM–will be a target for acquisition.  Bloomberg posits that the deal could portend a run health care businesses takeovers.  Could Wal-mart buy Humana? That is what one analyst proposes.

One thing is certain: the health care world is not going to stay the same for long.



Inequality in mortality is not as bad as you think

Written By: Jason Shafrin - Dec• 03•17

There have been numerous articles (e.g., Krugman in the NY Times) stating that disparities in life expectancy is growing.  It is known that income inequality has grown in recent decades but some claim that health inequality is also growing.  Janet Currie argues that the truth is not as bad as you think in a forthcoming article in Contemporary Economic Policy.

The main point here is that for the group with less than 12 years of education, life expectancy seems to be falling precipitously, whereas for the other groups, it is going up, so therefore, you have increased inequality in life expectancy. What is the problem with that? The problem is that…the population denominator is not staying the same, so you have a huge reduction in the share of White females who have less than 12 years of education.

Focusing on this particular subgroup is somewhat like taking a good news story, that is, that in the United States we now have many fewer high school dropouts in the White female population than we had in the past, and reporting it as a bad news story.

Currie also makes some technical arguments regarding how the U.S. Census’ decision to allow respondents to code multiple races has changed the reported racial composition of the country over time.  It may be more accurate, but racial definitions in the Census data have changed over time (which clearly any one individuals race is fixed in time). Currie investigates county level mortality rates and conducts a sensitivity analysis using 1990 racial definitions and 2100 Census racial definition plus combination racial individuals.  She finds that although mortality does increase with poverty, over this 20 year period:

…there is a strong reduction in mortality across the county-poverty spectrum. There are very large reductions for African-Americans, and those are even larger when multiple-race people are included. The reductions are largest in the poorest counties, which implies decreasing inequality in mortality for children.



Written By: Jason Shafrin - Nov• 30•17

What does “value” really mean?

Written By: Jason Shafrin - Nov• 30•17

An interesting article in Stat makes the important point that although there is much talk about “value” in the media and health policy world, the exact definition of “value” depends on your presepective.

But a survey released Wednesday by the University of Utah shows that, in health care, value has no universal meaning — 88 percent of doctors equated value with quality care, while patients and employers provided a more nuanced definition, mixing in measures of cost, customer service, and worker productivity.

The lack of consensus is not merely a philosophical matter. It is a huge stumbling block in the effort to deliver more bang for the buck in American health care, said University of Utah chief medical quality officer Dr. Bob Pendleton…

The diversity of perceptions of value–while clearly complicating treatment and policy decisions–should not be seen as a problem to solve but a reflection of reality.  Different stakeholders have different perceptions of value.  As I stated in a previous Forbes article, the goal of measuring value “…should not be to identify the highest value treatment on average and then restrict access to all other treatments. Rather, value…should be used to promote the optimal matching of patients to existing therapies based on each patient’s unique characteristics [and preferences].”


The effect of medication nonadherence on progression-free survival among patients with renal cell carcinoma

Written By: Jason Shafrin - Nov• 28•17

Along with my co-authors Jeff Sullivan, Jacki Chou, Michael Neely, Justin Doan, and Ross Maclean I am happy to announce to publication our paper “The effect of medication nonadherence on progression-free survival among patients with renal cell carcinoma” in Cancer Management and Research.  The abstract is below.

Objective: To examine how observed medication nonadherence to 2 second-line, oral anticancer medications (axitinib and everolimus) affects progression-free survival (PFS) among patients with renal cell carcinoma.
Methods: We used an adherence–exposure–outcome model to simulate the impact of adherence on PFS. Using a pharmacokinetic/pharmacodynamic (PK/PD) population model, we simulated drug exposure measured by area under the plasma concentration–time curve (AUC) and minimum blood or trough concentration (Cmin) under 2 scenarios: 1) optimal adherence and 2) real-world adherence. Real-world adherence was measured using the medication possession ratios as calculated from health insurance claims data. A population PK/PD model was simulated on individuals drawn from the Medical Expenditure Panel Survey (MEPS), a large survey broadly representative of the US population. Finally, we used previously published PK/PD models to estimate the effect of drug exposure (i.e., Cmin and AUC) on PFS outcomes under optimal and real-world adherence scenarios.
Results: Average adherence measured using medication possession ratios was 76%. After applying our simulation model to 2164 individuals in MEPS, drug exposure was significantly higher among adherent patients compared with nonadherent patients for axitinib (AUC: 249.5 vs. 159.8 ng×h/mL, P<0.001) and everolimus (AUC: 185.4 vs. 118.0 µg×h/L, P<0.001). Patient nonadherence in the real world decreased the expected PFS from an optimally adherent population by 29% for axitinib (8.4 months with optimal adherence vs. 6.0 months using real-world adherence, P<0.001) and by 5% (5.5 vs. 5.2 months, P<0.001) for everolimus.
Conclusion: Nonadherence by renal cell carcinoma patients to second-line oral therapies significantly decreased the expected PFS.

Do read the whole article as it is published on an open access journal.

Off-label prescribing

Written By: Jason Shafrin - Nov• 27•17

How frequently are pharmaceuticals used off label?  Perhaps more than you think.  Although these figures are a bit dated, Tabarrok (2000) details the extent of off-label prescribing in the U.S. as follows:

According to a study by the U.S. General Accounting Office, 56 percent of cancer patients have been given non-FDA-approved prescriptions, and 33 percent of all prescriptions in cancer treatment were off-label (General Accounting Office [GAO] 1991). Another survey, of AIDS patients, found that 81 percent of patients received at least one drug off-label, and 40 percent of all reported drug use was off-label (Brosgart and others 1996). Experts have estimated that nearly all pediatric patients (80 to 90 percent) are prescribed drugs off-label (Jaffe 1994; Kauffman 1996; Goldberg 1996).

If on-label prescribing has the best available evidence, why would a physician use off-label prescribing?  Tabarrok gives three reasons:

  1. Evolving Evidence: Best practices may change the standard of care faster than the FDA approves new uses for existing drugs.
  2. Efficacy may not match individual effectiveness. The treatment currently deemed as “best practice” may still fail to stem a given patient’s disease.  Further, “patients with terminal diseases may rationally demand ‘experimental’ treatment.”
  3. Cost. The FDA approval process for new indications is expensive and lengthy

Tabarrok’s article also highlights the important need to trade off Type 1 and Type 2 errors when regulating pharmaceuticals.  He posits that FDA is likely biased towards avoiding Type 2 errors (the approval of drugs that are actually harmful) and is less concerned with Type 1 errors (not approving drugs that are helpful).  However, Type 1 errors are clearly costly although receive less attention.

If the FDA approved a drug that killed thousands of people, that story would make the front page of every newspaper in the nation. Congressional hearings would certainly he held, the head of the FDA would probably lose his or her job, and the agency would be reorganized. But if the FDA rejected a drug that could save thousands of people, who would complain? When a drug kills a patient, that person is identifiable, and family and friends may learn the cause of the death. In contrast, the patient who would have lived, had new drugs been available, is identifiable only in a statistical sense.

Clearly, off-label prescribing is helpful in some cases and harmful in others.  The key is to maximize the frequency of the former and decreases the frequency of the latter; which is easier said than done.

How reimbursement affects innovation

Written By: Jason Shafrin - Nov• 26•17

Below are some excerpts from seminal papers examining how changes in reimbursement or market size affect pharmaceutical innovation.

Acemoglu and Lin (2004):

Our estimates suggest that a 1 percent increase in the size of the potential market for a drug category leads to a 6 percent increase in the total number of new drugs entering the U.S. market. Much of this response comes from the entry of generics, which are drugs that are identical or bioequivalent to an existing drug no longer under patent protection.

More important, there is a statistically significant response of the entry of nongeneric drugs, which more closely correspond to new products and “innovation”: a 1 percent increase in potential market size leads to approximately a 4 percent increase in the entry of new nongeneric drugs.


Blume-Kohout and Sood (2013):

…we estimated effects of Part D on each stage of clinical R&D, Phase I through Phase III…we estimate the number of drugs entering Phase I trials in 2004–2005 increased by 27% versus expected trends. By 2006–2007, for the average drug class, the number of drugs entering Phase I trials had increased by about 34% versus expected trends, and by 2008–2010 Phase I trials had increased by a little over 50%. At the means, this translates to about 2–3 additional drugs reported as entering Phase I trials, per drug class…

In a Poisson regression with class-specific time trends, we do find a significant effect of Part D beginning in 2008, with a MedicareShare*(Year>2007) coefficient estimate of 0.74 (p=.013), corresponding to an elasticity of new drug approvals with respect to market size of about 2.8….

Our results indicate that the increase in outpatient prescription drug coverage provided through Medicare Part D has had a significant impact on pharmaceutical R&D. We observe evidence of a structural break in established R&D trends after passage and implementation of Part D, with greater percentage increases in drug trials for therapeutic classes that are most used by Medicare beneficiaries. In addition, we find stronger effects of Part D for protected classes,


Dubois, de Mouzon, Scott-Morton, Seabright (2015):

This article quantifies the relationship between market size and innovation in the pharmaceutical industry using improved, and newer, methods and data. We find significant elasticities of innovation to expected market size with a point estimate under our preferred specification of 0.23. This suggests that, on average, $2.5 billion is required in additional revenue to support the invention of one new chemical entity. This magnitude is plausible given recent accounting estimates of the cost of innovation of $800 million to $1 billion per drug, and marginal costs of manufacture and distribution near 50%.


Dranove, Garthwaite, Hermosilla (2014):

In this paper, we use a novel data set to explore the impact of the introduction of Medicare Part D on the development of new biotechnology products. We find that the law spurred development of products targeting illnesses that affect the elderly, but most of this effect is concentrated among products aimed at diseases that already have multiple existing treatments. Moreover, we find no increase in products targeting orphan disease or those receiving either fast track or priority review status from the FDA. This suggests that marginal changes in demand may have little effect on the development of products with large welfare benefits.


Kyle and McGahan (2009):

We examine the relationship between patent protection for pharmaceuticals and investment in development of new drugs. Patent protection has increased around the world as a consequence of the TRIPS Agreement, which specifies minimum levels of intellectual property protection for members of the World Trade Organization…We find that patent protection is associated with increases in research and development (R&D) effort when adopted in high income countries. However, the introduction of patents in developing countries has not been followed by greater investment. Particularly for diseases that primarily affect the poorest countries, our results suggest that alternative mechanisms for inducing R&D may be more appropriate than patents.

These articles were identified by a recent Health Affairs blog post by Frank and Ginsburg.

Friday Links

Written By: Jason Shafrin - Nov• 24•17

Progress on Obamacare Sign-ups

Written By: Jason Shafrin - Nov• 22•17

According to the N.Y. Times:

Despite the Trump administration’s efforts to scale back the health law, about 300,000 more people have signed up for health insurance in the Affordable Care Act marketplaces in the first weeks of this enrollment period than last year.

This is about a 25% increase from last year at this time.  However, it may not be good news.

Because the Trump administration cut the enrollment periodin half this year, to 45 days from three months, weekly sign-ups would need to double to match the number of sign-ups in previous years.

Thus, this surge in enrollment may or may not be enough to make up for the shorter enrollment period and likely overall enrollment will fall in 2018.