# Sam Watson’s journal round-up for 26th March 2016

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Martin Shkreli has been frequently called “the most hated man in America“. Aside from defrauding investors and being the envied owner of a one-of-a-kind Wu-Tang Clan album, the company of which he was chief executive, Turing Pharmaceuticals, purchased the sole US approved manufacturer of a toxoplasmosis treatment, pyrimethamine, and hiked its price from $13 to$750 per tablet. Price gouging is nothing new in the pharmaceutical sector. An episode of the recent Netflix documentary series Dirty Money covers the story of Valeant Pharmaceuticals whose entire business was structured around the purchase of drug companies, laying off any research staff, and then hiking the price as high as the market could bear (even if this included running their own pharmacies to buy products at these inflated prices). The structure of the US drug market often allows the formation of monopolies on off-patent, or generic, medication, since the process for regulatory approval for a new manufacturer can be long and expensive. There have been proposals though that this could be ameliorated by allowing manufacturers approved by other trusted agencies (such as the European Medicines Agencies) to sell generics in the US while the FDA approvals process takes place. The aim of this paper is to determine how many more manufacturers this would allow into the US drugs market. The authors identify all the off-patent drugs that have been approved by the FDA since 1939 and all the manufacturers of those drugs that were approved by the FDA and by other trusted agencies. No analysis is given of how this might affect drug prices, though there is a pretty obvious correlation between the number of manufacturers and drug prices shown elsewhere. The results show that the proposed policy would increase the number of manufacturers for a sizeable proportion of generics: for example, 39% of generic medications could reach four or more manufacturers when including those approved by non-FDA bodies.

Why internists might want single-payer health care. Annals of Internal Medicine [PubMedPublished 20th March 2018

The US healthcare system has long been an object of fascination for many health economists. It spends far more than any other nation on healthcare (approximately $9,000 per capita compared to, say,$4,000 for the UK) and yet population health ranks alongside middle-income countries like Cuba and Ecuador. Garber and Skinner wondered whether it was uniquely inefficient and identified or questioned a number of issues that may or may not explain the efficiency or lack thereof. One of these was the administrative burden of multiple insurance companies, which evidence suggests does not actually account for much of the total expenditure on health care. However, Garber and Skinner say this does not take into account time spent by clinical and non-clinical staff on administration within hospitals. In this opinion piece, Paul Sorum argues that internists should support a move to a single-payer system in the US. One of his four points is the administrative burden of dealing with insurance companies, which he cites as an astonishing 61 hours per week per physician (presumably spread across a number of staff). Certainly, this seems to be a key issue. But Sorum’s other three points don’t necessarily support a single-payer system. He also argues that the insurance system is leading to increasing deductibles and co-payments placed on patients, limiting access to medications, as drug prices rise. Indeed, Garber and Skinner note also that high deductibles limit the use of highly cost-effective measures and actually have the opposite effect of reducing productive efficiency. A single payer system per se would not solve this, it would need significant subsidies and regulation as well, and as our previous paper shows, other measures can be used to bring down drug prices. Sorum also argues that the US insurance system places an unnecessary burden from quality measures and assessment as well as electronic medical records used to collect information for billing purposes. But these issues of quality and electronic medical records have been discussed in the context of many health care systems, not least the NHS, as the political and regulatory framework still requires this. So a single-payer system is not a solution here. A key difference between the US and elsewhere that Garber and Skinner identify is that the US permits much more heterogeneity in access to and use of health care (e.g. overuse by the wealthy and underuse by the poor). Significant political barriers stand in the way of a single payer system, and since other means can be used to achieve universal coverage, such as the provisions in the Affordable Care Act, maybe internists would be better directing their energy at more achievable goals.

Social ties in academia: a friend is a treasure. Review of Economics and Statistics [RePEcPublished 2nd March 2018

If you ever wondered whether the reason you didn’t get published in that top economics journal was that you didn’t know the right people, you may well be right! This article examines the social ties between authors and editors of the top four economics journals. Almost half of the papers published in these journals had at least one author with a connection to an editor, either through working in the same department, co-authoring a paper, or PhD supervision. The QJE appears to be the worst offender with (if I’ve read this correctly) all authors between 2000 and 2006 getting their PhD in either Harvard or MIT. So don’t bother trying to get published there! This article also shows that you’re more likely to get a paper into the journals when your former PhD supervisor is editing it. Given how much sway a paper published in these journals has on the future careers of young economists, it is disheartening to see the extent of nepotism in the publication process. Of course, one may argue that it just so happens that those that work at the top journals associate most frequently with those who write the best papers. But given even a little understanding of human nature, one would be inclined to discount this explanation. We have all previously asked ourselves, especially when writing a journal round-up, how this or that paper got into a particularly highly regarded journal, now we know…

Credits

# “Doing the math” on the distribution of healthcare expenditures: a Pareto-like distribution is inevitable

Yesterday I explored one of the major challenges to affordable, universal health insurance, namely the high cost of providing care to the sickest patients. The extreme distribution of healthcare costs means that “Targeting the highest spenders represents the greatest opportunity to have a significant impact on overall spending”, an opportunity for insurance carriers  to reduce costs by risk selection, as well as for public policy. Here is a deeper look into the math behind the distribution of healthcare expenditures, using 2012 US data as a model.

One can fit a Pareto (power law, 80/20) distribution with scale coefficient $\alpha$ – that is, $prob(expenditure)\sim 1/expenditure^{\alpha+1}$ – to the data in several ways. For a Pareto distribution with scale coefficient $\alpha$, the per-capita expenditure at a given percentile from the top scales as $1/\%ile^{1/\alpha}$. The first two of these approaches yield a scale coefficient $1/\%ile^{0.893}$, with expenditures scaling as :

1. Use the 80/20 rule modified to fit the data: the top 25% ranked by healthcare expenditures account for 86.7% of costs; thus $\alpha=1.115$.
2. Use the ratio of mean to median expenditure, 5.05:1; thus $\alpha=1.119$.
However, a graphical analysis finds that the data does not follow such a Pareto distribution, shown as a black dashed line in the following figure (representing a Pareto distribution with $\alpha=1.117$ and median expenditure $854, the actual median expenditure). 3. Use data for the most expensive patients (10% through 30% percentiles from the top), for these patients, per-capita expenditure scales as $1/\%ile^{1.24}, (R^{2}=0.994)$, shown as a dashed red line in the figure above; thus $\alpha=0.806$. 4. Use the fraction of total expenses paid by the most expensive patients. A comparison of the fraction of expenses paid by the most expensive 1%, 5% and 10% finds that this scales as $x^{0.4228}, (R^{2}=0.987)$, shown as a dashed black line in the figure below. This scaling exponent is $1-1/\alpha$; thus $\alpha=1.733$. (Scaling added to figure modified from Cohen, 2014) Thus, there really is no typical patient. For discussion and implications, see Feyman, who called the empirical distribution of healthcare costs “worse than Pareto”. The Pareto-like (hyper-Pareto?) empirical distribution of expenditures presents a severe challenge to risk pooling through insurance without limiting the highest expenditures through risk selection (illegal!). Pareto distributions differ sharply from normal distributions, with important consequences for payment models. For a Pareto-like distribution with $\alpha\leq2$ at large expenditures, the variance is not defined, and sample variance approaches infinity with increasing sample size. Therefore, unlike the case of distributions with finite variance, variability in the mean of a sample of size N does not decrease with N. This violates a standard requirement for insurance; that risk pooling over a large sample reduces variability in the mean expenditure, and thus, standard insurance models cannot effectively price health insurance when the highest per capita expenditures follow Pareto distributions. Moreover, a Pareto-like distribution may be a natural consequence of advances in healthcare: our growing ability to manage multiple simultaneous chronic conditions, with consequent exponential growth in costs, while extending life expectancy, so that the probability of dying is not only not reduced, but may actually increase. In a mathematically limiting case, with no bound on healthcare costs, these dynamics yield a Pareto distribution. In fact, if one extrapolates the power law for a broad range of the sickest patients (the 10th through 30th percentiles of expenditures from the top), obtaining a Pareto distribution with $\alpha\leq1$, even the mean is not defined and the sample mean approaches infinity with increasing sample size. The actual distribution of healthcare cost for the very sickest patients clearly falls below the empirical Pareto distribution with $\alpha=0.806$, such a distribution predicts a cost at the 1st percentile of$178,194, well above the average for the top 1% of \$97,956. Deviations from this distribution for the very sickest patients may reflect current limits on healthcare and thus healthcare expenses. These limits may be relaxed with advances in healthcare, causing further growth in costs.

A Pareto-like distribution of healthcare costs is here to stay, and must be reflected in how we share the burden of healthcare and provide care to our sickest patients.

Credits