Unbiased Analysis of Today's Healthcare Issues

Is the physicians labor market recession proof?

Written By: Jason Shafrin - Feb• 18•18

According to a Health Economics paper by Alice Chen, Anthony Lo Sasso, and Michael R. Richards, the answer is ‘yes’.  According to their study:

We leverage a unique dataset on New York physicians to analyze if and how the Great Recession impacted the labor market of physicians who have completed their residency and fellowship training and are seeking their first job. We find that these physicians do not delay labor market entry and their job searches and other employment outcomes are unaffected by the business cycle. The collage of evidence demonstrates that new graduates were largely unfazed by the recent downturn, which sharply contrasts with other highly educated, high remunerating occupations.

Although being a physician may be getting less enjoyable with more paperwork, more pressure on earnings, and more oversight by regulators and managers, it does appear that physicians do enjoy high levels of job security.


Friday Links

Written By: Jason Shafrin - Feb• 15•18

HWR Going for the Gold Edition

Written By: Jason Shafrin - Feb• 15•18

The latest edition of the Health Wonk Review–the Going for the Gold Edition— has been posted at HealthInsurance.org.  Thanks to Steve Anderson for hosting.  Check it out!

Bayes vs. Fisher

Written By: Jason Shafrin - Feb• 15•18

An interesting People of Science video compares the approaches of two titans of statistics, Ronald A. Fisher and Thomas Bayes.

Do expensive drugs reduce the price of health?

Written By: Jason Shafrin - Feb• 14•18

The answer is ‘yes’ according to a report by the Council of Economic Advisors (CEA) titled Reforming Biopharmaceutical Pricing at Home and Abroad.

Innovations such as new drugs often reduce the price of health even when the new drug is very expensive relative to other goods. Consider the example of a patient diagnosed with HIV in the early 1990s. Before new breakthrough therapies for HIV emerged, the price of a longer life was prohibitively high because a longer life could not be bought at any price anywhere in the world. Once new HIV drugs were developed and marketed in 1996, the price of a longer and healthier life for HIV-positive individuals decreased dramatically: it reached the equivalent of the price of the new, patented drugs. Generally, the price of better health falls further as competing drugs enter the market, then fall even further to competitive price levels upon patent expiration when cheaper generics become available. The example of innovative HIV drugs makes the essential point that even though the price of the drug was considerable and drug spending rose, the effective price of better health declined.

The report notes that most funding for biopharmaceutical medical innovation comes from the U.S.

The U.S. market makes up 46 percent of OECD sales of brand name innovative drugs, funds about 44 percent of world medical R&D, invests 75 percent of global medical venture capital,

The U.S. GDP makes up 22% of the world economy so clearly the U.S. is funding a disproportionate share of medical innovation.

The CEA report also discusses how to balance two goals: (i) reduce drug costs now, (ii) ensure financial incentives for innovators to invest in R&D for new treatments for future generations. Proposed reforms to reach these goals include:

Reducing drug costs

  • Increased use of value-based contracts,
  • End cost-plus pricing for physician administered Part B drugs
  • Potentially shift all Medicare Part B drugs to be covered by Part D
  • Allow for Low-Income Subidsidy (LIS) beneficiaries to pay copayments for low-value pharmaceuticals
  • End Medicare Part D Coverage Gap Discount Program
  • Allow health plans more discretion of what treatments to cover on formulary among covered classes
  • Discouraging plan formulary design that speeds patients to the catastrophic coverage phase of benefit and increases overall spending.
  • Lowering co-pays for generic drugs for patients
  • Expanding FDA expedited review for new molecular entities that are second or third in a class, or second or third for a given indication for which there are no generics.
  • Advocates for policy to increase competition in the PBM market

Incentivizing innovation

  • Incentivize other countries to raise their prices
  • Reforming Medicaid best price regulations
  • More restrictive hospital eligibility for the 340B program
  • Speed up the FDA approval process, particularly for generic drugs and biosimilars


Collecting data on health state utilities

Written By: Jason Shafrin - Feb• 12•18

If you had a given disease, how bad would it be?  Can we quantify people’s happiness based on different diseases?  One way to do this is to measure patient preferences using a concept known as health state utilities.  As stated in a recent ISPOR guidelines, health state utilities are “estimates of the preference for a given state of health on a cardinal numeric scale, where a value of 1.0 represents full health, 0.0 represents dead, and negative values represent states worse than death.”

Health technology assessment (HTA) agencies often use health state utilities for making resource allocation decisions. For instance, consider the case of a serious disease that makes people wheelchair bound for multiple years.  Would you rather pay for a treatment that could extend healthy individual’s 1 year or a treatment that eliminated their need for a wheelchair but had no effect on survival?  If you feel that being in a wheelchair is not too bad (health state utility closer to 1), you would be relatively more likely to choose the treatment that extended people’s life; on the other hand, if you though living in a wheelchair was a horrible fate (health state utility close to 0), then you would be relatively more likely to prefer the treatment that eliminated the need of the wheelchair without any survival gain.  The health state utilities are a way to quantify the degree of these preferences.

How are these health state utility data collected?

These health state utilities–often used in economic models–can be collected in a number of ways.  For instance,

  • Review of published estimates. This approach would conduct a literature review of published literature to identify high-quality health state utility estimates. The review can be targeted or systematic, as some HTA agencies (e.g., Canadian Agency for Drugs and Technologies in Health, Haute Autorité de Santé, and NICE) require a systematic literature review as part of their submission process.
  • Prospective data collection in clinical trials. Clinical trials are one easy way for stakeholders to collect health state utilities.  There are a number of methodological considerations to consider.  For instance, it is important to insure that the data “…captures profiles of change around the relevant health states”. Collecting health state utlity data as part of a clinical trial may also be more cost effective.
  • Prospective longitudinal or cross-sectional observational studies. “These studies may offer the greatest flexibility in terms of the data that can be collected; and, for many health states, it may be more appropriate to collect HSU data in observational or routine data sets rather than in trials.”  These approaches, however, are often expensive and collecting these data may take time.
  • Early-access or compassionate-use–type programs, phase 4 studies, registries, and other postlicensing commitments. These studies may better reflect health state utilities in real world settings.  For life sciences firms, however, these approaches have significan drawbacks since these studies typically are performed to late to be included in submissions to HTA bodies.
  • Vignette studies. “In this approach, detailed descriptions of each health state are developed from different sources of information (e.g., patient and physician interviews, trial data, and published literature) [18–20]. Members of the public are asked to rate these states in a stated-preference experiment (such as time trade-off or standard gamble). These methods are limited because the resulting estimates are entirely dependent on the validity of the vignette descriptions…”

What are common preference based measures used to estimate health state utilities?

These preference measures can be divided into general measures–those relevant for any diesase–and those that are condition-specific.  Some examples of general preference based instruments include:

  • AQoL, Assessment of Quality of Life;
  • EQ-5D, EuroQol five-dimensional questionnaire
  • HUI, Health Utilities Index;
  • SF-6D, six-dimensional health state short form (derived from 36-item short form health survey).

Some condition specific examples of preference based measurement instruments include:

  • AQL-5D, Asthma Quality of Life Utility Index;
  • EORTC-8D, European Organization for Research and Treatment of Cancer cancer-specific instrument

There is more information in the article on how to estimate health state utilities so do read the whole article.

Health care in Peru

Written By: Jason Shafrin - Feb• 11•18

Located in South America, Peru is almost twice the size of Texas and has 31 million people.  About one third of these live along the coast, largely in Lima.  About half of the population–largely Amerinidan population–live in the Andean highlands, with the rest spread on the eastern slopes of the Andes and the adjoining rainforest.  About 45% of the population is aged 24 or younger.  Peru is a middle income country, with GDP per capita of $13,300, which ranks 118/224 countries in the world.

Life expectancy in Peru is 74 years, which ranks 126 out of 224 countries.  Health care spending is 5.5% of GDP (128/224 countries), far below the United States’ 17.1% of GDP.  There are 1.12 physicians per 1000 people and 1.5 hospital beds per 1,000 individuals.  The World Health Organization recommends 2.3 health workers per 1,000 people so this figure is far below that recommendation.  However, access to physicians and hospitals is highly variable depending on where you live, particularly across rural regions.

Before 2007, health care spending was largely financed by the government (54% of health expenditures) or patients paid out-of-pocket (40%).  Over 60% of the population had no health insurance coverage.  As stated in Neelsen and Donnell:

From 2002, poor children (<18 years) and poor pregnant women were exempted from paying user fees for basic healthcare at National Health Service (NHS) facilities.5  Providers were reimbursed through the tax-financed Seguro Integral de Salud (SIS), which covered 16% of the population in 2006. This programme also covered basic emergency care for poor uninsured adults with life-threatening or potentially permanently damaging conditions.

Despite the perceived generosity of the National Health Service at his time, the budget only allowed for spending of $18 per person. Further, often times claims exceeded Peru’s budget and in these cases regional governments simply refused to pay these bills.

In 2007, the Peruvian government passed the Seguro Integral de Salud (SIS), which provided 6 million Peruvian adults (21% of the population) access to free basic health care.  Previous user fees were eliminated and coverage was intended to be more comprehensive. Budget shortfalls, however, have made accessing some services difficult, especially inpatient treatment.

Both before and after the reform, workers in the formal economy were covered by El Seguro Social en Salud (EsSalud).  This program was financed by a 9% payroll tax.  Worker retain this benefit during retirement as well as for up to 1 year of unemployment.


How to become a centaur

Written By: Jason Shafrin - Feb• 08•18

Interesting article on how human intelligence and artificial intelligence can be seen as complements rather than substitutes.  Human+AI combinations have been shown to perform better than human or AI alone.  Why?

AIs are best at choosing answers. Humans are best at choosing questions.

The advent of AI is not a zero sum game.  The symbiotic relationship between humans and AI may lead to great advances in the coming decade in technology, medicine and a wide variety or fields.

AI, Robots and the Future of Health Care

Written By: Jason Shafrin - Feb• 08•18

Interesting video from Wired on how artificial intelligence (AI) software may help improve patient care in the future.

How restrictive value assessments affect R&D and innovation

Written By: Jason Shafrin - Feb• 06•18

Health technology assessment (HTA) is growing in popularity. Already widely entrenched in Europe, in the U.S. value frameworks are being used to measure the cost effectiveness of different therapies.  Some of these frameworks, however, take a narrow view of societal benefits and value to include components of value to patients or society such as caregiver burden, the value of hope, insurance valueoption value or other components of value that take into account uncertainty or non-payer value components.

The question is, does this matter? Well, if prices are being set based on a treatments benefits, prices will not reflect true value if not all benefits are included. More importantly, research and development (R&D) may move towards relatively safer investments. That is the argument made by Cook and Golec (2017).  They summarize their study as follows:

This paper presents a real options model to value biopharmaceutical R&D investment and shows how excluding option benefits from HTA can affect R&D.We show that it discourages certain investments in high risk (volatile) R&D projects and encourages investments in safer (positively skewed) ones. The model helps to explain recent changes observed in biopharmaceutical R&D investments that are coincident with growing application of HTA.

Robust policymaking requires understanding not only how drug pries affect current patients and payers, but also how these prices and treatment valuations affect R&D for future treatments.  This paper provides a nice contribution to better understand the mechanism for the latter.