Unbiased Analysis of Today's Healthcare Issues

Static and Dynamic Effects of Vaccine Policy on R&D

Written By: Jason Shafrin - Jan• 02•17

How does health policy and pricing affect investment in innovation? This is the research question investigated in Amy Finkelstein’s 2004 QJE paper on Static and Dynamic Effects of Health Policy. She examines three policy changes:

  • 1991 CDC recommendation that all infants be vaccinated against Hepatitis B
  • 1993 decision for Medicare to cover (without any copayments or deductibles) the cost of influenza vaccination for Medicare recipients.
  • the introduction of the Vaccine Injury Compensation Fund (VICF) in 1986, indemnified manufacturers from lawsuits stemming from potentially adverse reactions to childhood vaccines against polio, diphtheria-tetanus (DT), measles mumps-rubella (MMR), and pertussis.

The effect of these policies on vaccine research were categorized into four streams of research: (i) basic research (which may result in a patent), (ii) preclinical trials (testing in animals), (iii) clinical trials (testing in humans), and (iv) FDA approval. Finkelstein looks at whether the adoption of one of the three policies listed above affected the number of new clinical trials controlling for year and disease specific effects. She compares “control” diseases unlikely to be affected by the policy changes with “treatment’ diseases where the policy changes are more likely to have a large effect.

Finkelstein finds large effects on clinical trial investment, but less investment in basic research or patents.

These estimates imply that a $1 increase in annual expected market revenue for vaccines against a particular disease stimulates an additional 6 cents in annual present discounted value investment in that vaccine…For most of the affected diseases, I find that the induced innovation is entirely socially wasteful business stealing, although the magnitude of the dynamic social costs is small. In one case, however, I estimate that the “dynamic” welfare benefits from the induced innovation are not only positive, but also larger than the “static” welfare benefits from the policy’s effect on vaccination with the preexisting technology. These findings underscore the inadequacy of the near-exclusive focus in economic evaluations of health policy on the policy’s “static” effects.


…in the case of the Flu vaccine, I estimate that the “dynamic” social welfare benefits from the induced innovation are not only positive, but also larger than the “static” social welfare benefits of the Flu
policy from increasing utilization of the existing vaccines

Finkelstein does mention that for other diseases, the dynamic welfare implications are less because they represent “business stealing”. However, to the extent that additional R&D produces alternative treatments, this could drive down prices of patented products which would be beneficial to society. Further, if vaccine efficacy varies by person of patients have different preferences over vaccines–e.g., if one vaccine is administered orally and another administered through an injection–then this “business stealing” concept obscures the value of having more choice and more competition in the vaccine market.

Nevertheless, this is an interesting paper and makes the important point also highlighted in Acemoglu and Linn that market size and profit potential drive R&D investments.


Last Links Post of 2016

Written By: Jason Shafrin - Dec• 29•16

What is a Pseudo R-squared?

Written By: Jason Shafrin - Dec• 28•16

When running an ordinary least squares (OLS) regression, one common metric to assess model fit is the R-squared (R2). The R2 metric can is calculated as follows.

  • R2 = 1 – [Σi(yii)2]/[Σi(yi-ȳ)2]

The dependent variable is y, the predicted value from the OLS regression is ŷ, and the average value of y across all observations is ȳ. The index for observations is omitted for brevity.

One can interpret the R2 metric a variety of ways. UCLA’s Institute for Digital Research and Education explains as follows:

  1. R-squared as explained variability – The denominator of the ratio can be thought of as the total variability in the dependent variable, or how much y varies from its mean. The numerator of the ratio can be thought of as the variability in the dependent variable that is not predicted by the model. Thus, this ratio is the proportion of the total variability unexplained by the model. Subtracting this ratio from one results in the proportion of the total variability explained by the model. The more variability explained, the better the model.
  2. R-squared as improvement from null model to fitted model – The denominator of the ratio can be thought of as the sum of squared errors from the null model–a model predicting the dependent variable without any independent variables. In the null model, each y value is predicted to be the mean of the y values. Consider being asked to predict a y value without having any additional information about what you are predicting. The mean of the y values would be your best guess if your aim is to minimize the squared difference between your prediction and the actual y value. The numerator of the ratio would then be the sum of squared errors of the fitted model. The ratio is indicative of the degree to which the model parameters improve upon the prediction of the null model. The smaller this ratio, the greater the improvement and the higher the R-squared.
  3. R-squared as the square of the correlation – The term “R-squared” is derived from this definition. R-squared is the square of the correlation between the model’s predicted values and the actual values. This correlation can range from -1 to 1, and so the square of the correlation then ranges from 0 to 1. The greater the magnitude of the correlation between the predicted values and the actual values, the greater the R-squared, regardless of whether the correlation is positive or negative.

So then what is a pseudo R-squared? When running a logistic regression, many people would like a similar goodness of fit metric. An R-squared value does not exist, however, for logit regressions since these regressions rely on “maximum likelihood estimates arrived at through an iterative process. They are not calculated to minimize variance, so the OLS approach to goodness-of-fit does not apply.” However, there are a few variations of a pseudo R-squared which are analogs to the OLS R-squared. For instance:

  • Efron’s Pseudo R-Squared. R2 = 1 – [Σi(yi-πˆi)2]/[Σi(yi-ȳ)2], where πˆi are the model’s predicted values.
  • McFadden’s Pseudo R-Squared. R2 = 1 – [ln LL(Mˆfull)]/[ln LL(Mˆintercept)]. This approach is one minus the ratio of two log likelihoods. The numerator is the log likelihood of the logit model selected and the denominator is the log likelihood if the model just had an intercept. McFadden’s Pseudo R-Squared is the approach used as the default for a logit regression in Stata.
  • McFadden’s Pseudo R-Squared (adjusted). R2adj = 1 – [ln LL(Mˆfull)-K]/[ln LL(Mˆintercept)]. This approach is similar to above but the model is penalized penalizing a model for including too many predictors, where K is the number of regressors in the model.  This adjustment, however, makes it possible to have negative values for the McFadden’s adjusted Pseudo R-squared.

There are a number of other Pseudo R-Squared approaches that are listed on the UCLA IDRE website.


Quotation of the Day

Written By: Jason Shafrin - Dec• 27•16

I think everybody should get rich and famous and do everything they ever dreamed of so they can see that it’s not the answer.

– Jim Carrey

Quality of care and prices

Written By: Jason Shafrin - Dec• 26•16

There have been a number of studies that have examined how publicly reporting quality ratings (for health plans, physicians, hospitals or other health care providers) affects market share.  Less attention has been paid to the effect of measured quality on health care prices.  A paper by Huang and Hirth (2016) aim to answer just this question.

We use the rollout of the five-star rating of nursing homes to study how private-pay prices respond to quality rating. We find that star rating increases the price differential between top- and bottom-ranked facilities. On average, prices of top-ranked facilities increased by 4.8 to 6.0 percent more than the prices of bottom-ranked facilities. We find stronger price effects in markets that are less concentrated where consumers may have more choices of alternative nursing homes. Our results suggest that with simplified design and when markets are less concentrated, consumers are more responsive to quality reporting.

Setting higher prices at high quality facilities should be seen as a good thing as allowing high-quality providers to raise prices leads to move investment in quality.


Quotation of the Day

Written By: Jason Shafrin - Dec• 25•16

I would rather have questions that can’t be answered, than answers that can’t be questioned

Richard Feynman

A Christmas present?

Written By: Jason Shafrin - Dec• 24•16

Good news reported from the NY Times:

In a scientific triumph that will change the way the world fights a terrifying killer, an experimental Ebola vaccine tested on humans in the waning days of the West African epidemic has been shown to provide 100 percent protection against the lethal disease.

The vaccine has not yet been approved by any regulatory authority, but it is considered so effective that an emergency stockpile of 300,000 doses has already been created for use should an outbreak flare up again.

The results of the study are available in Henao-Restrepo et al. (2016).  For many, this finding may signify an early start to the holiday season.

Staying healthy during the holidays

Written By: Jason Shafrin - Dec• 22•16

Christmas, Hanukah and Kwanza bring a sense of joy to many.  At the same time, the holiday times are often a time of stress as well.  There is financial pressure to buy presents for your children, pressure to make Christmas perfect, family members not getting along, etc.

These and other challenges around the holidays problems can be especially problematic for individuals with mental illness.  The holidays often involve a break in regular routines, lots of eating and drinking, and unrealistic expectations for a perfect holiday.

The Canadian Mental Health Association of Nova Scotia has a list of Tips for Mental Wellness during the Holidays.  These tips include maintaining a routine, not indulging too much, staying positive, reaching out for help if someone feels isolated, and many other tips.

To all Healthcare Economist readers around the world, I want to wish you happy holidays and a very healthy 2017!


Mid-week links

Written By: Jason Shafrin - Dec• 21•16

Are new anti-cancer drugs worth the cost?

Written By: Jason Shafrin - Dec• 21•16

The high price of cancer treatment often grabs headlines. But how much have patients benefited from these new treatments.  A paper by Howard et al. (2016) look at new cancer treatments for chronic myeloid leukemia (CML), metastatic kidney, breast, or lung tumors and they generally find that the answer is ‘yes’, recent anti-cancer treatments have delivered significant value to patients.

The authors use SEER-Medicare data looking at patients diagnosed with these cancers between 1996-2011; the authors can track survival through 2013 with these data.  The benefits of the treatment are captured based on survival gains; the cost of the treatments is measured from the point of diagnosis until death using the so-called phase-of-care approach.  The authors compared changes in life expectancy and lifetime medical costs for patients with breast or lung cancer who were treated with physician-administered anticancer drugs and those who were not treated with such drugs.

Using this approach, the authors found:

Breast cancer: “…life expectancy increased over time by 13.2 months, and lifetime medical costs increased by $72,200…Among those who did not receive physician-administered drugs, life expectancy increased by 2.0 months, and costs increased by $8,900. The life expectancy increase in this group could be attributable to improvements in supportive care or lead-time bias.

Lung cancer: “…life expectancy increased over time by 3.9 months and $23,200 dollars [for those receiving anti-cancer drugs]…while remaining basically unchanged for patients who did not receive such drugs.”

Kidney Cancer: “Life expectancy among patients with kidney cancer increased by 7.9 months, and lifetime costs increased by $44,700.”

CML: “Patients with CML experienced the largest gain in life expectancy (22.1 months)…lifetime medical costs…increased by $142,200, of which $126,300 was attributable to Part D spending.”

The gains in life expectancy and cost for CML were due almost entirely to the introduction of Gleevac (imatinib) in 2001.

Overall, the incremental cots per QALY generally ranged between $100,000 and $150,000; which is generally seen to be cost effective for cancer care treatments.

The study is interesting throughout.  It will be interesting to re-run a similar analysis using treatments released in the last few years as well.

The authors conclusion is interesting as well:

Our results also raise the question of whether back-of-the-envelope calculations based on drugsprices and the survival benefits reported in clinical trials provide an accurate measure of value.