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.

Affordability and availability of off-patent drugs in the United States—the case for importing from abroad: observational study. BMJ [PubMedPublished 19th March 2018

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…


Sam Watson’s journal round-up for 1st May 2017

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.

Systematic review of health economic impact evaluations of risk prediction models: stop developing, start evaluating. Value in Health [PubMed] Published April 2017

Risk prediction models are pervasive in clinical medicine. For example, one 2012 review of type 2 diabetes (T2DM) models identified 16 studies with 25 models. There was not much difference between the models in ability to predict T2DM and models including biomarkers were slightly better. But, obviously no model is perfect, the T2DM risk prediction tools generally overestimated the risk of development of diabetes. One could see parallels here with screening. When subjected to cost-benefit analyses, many screening programs become somewhat controversial. False positives can cause harm to patients both psychologically and through further procedures they may be subjected to. Such concerns thus may also apply to risk prediction models. This review surveys the literature on health economic evaluations of risk prediction models. Forty studies examining 60 risk models were included. Compare this number with the total of T2DM models above and you will see how the authors might arrive at the conclusion that economic evaluations of risk prediction models are rare. Another key finding, and one I empathize with as I am currently reviewing economic evaluations in another area of heath economics, is that there is a large amount of methodological heterogeneity and quality differences between studies. This makes comparisons difficult if not impossible. This limits the utility of these findings to decision makers. A routine, standardised approach to economic evaluation is needed.

The fading American dream: trends in absolute income mobility since 1940. Science [PubMed] [RePEc] Published 28th April 2017

This one is not strictly health. But it’s findings may have important implications for how we understand the relationship between income and health, and the inter-generational transmission of health. And, it’s not everyday an economics paper gets into Science. Economic mobility is a key goal for many societies – children should earn more than their parents. One way of examining this quantitatively is the proportion of children who earn more than their parents. This paper shows that this can be estimated using (i) the marginal income distribution of children, (ii) the marginal income distribution of parents, and (iii) the joint distribution of child and parent income ranks. The key finding is that mobility has declined over the 20th Century. While around 90% of children were earning more than their parents in 1940, by 1980 this is only around 40%. The authors look at what would happen to these estimates if GDP growth were more equally distributed and find much of the decline in mobility would be reversed.

Economic consequences of legal and illegal drugs: the case of social costs in Belgium. International Journal of Drug Policy [PubMed] Published 23rd April 2017

Put ten economists in a room and you’ll get 11 different opinions. Or so the saying goes. But while there is division on a number of topics in economics, some issues find a strong consensus. Drug prohibition is one of those issues many economists agree on. As a policy is has high costs and reasonably little benefit, especially when harm reduction is the goal. David Nutt, whose work we’ve discussed before, is a prominent critic of the UK government’s policy on drugs. Just this week he has discussed how the recent increase in the use of and health problems due to ‘spice’ (synthetic cannabinoids) may well be attributable to the prohibition of natural cannabis. However, recreational drug use, whether illegal or legal, does bear a societal cost. This paper attempts to quantify both the indirect and direct costs of drug use in Belgium. They take a ‘cost of illness’ approach, a term I think is a little unsuitable for the topic – most drug use causes no harm so could hardly be called illness. They also refer to the drugs as ‘addictive substances’, which is also a stretch for what they consider. Costs are further divided into health care and crime costs. The headline finding is that the total cost is 4.6 billion Euros annually. Interestingly, for illegal drugs, law enforcement expenditure was higher than the health care costs. In my mind this further undermines a prohibition policy. However, I think this study reveals the difficulty of taking an objective stance on these matters. Recreational substance use is an ‘illness’ and ‘addictive’ and bears a cost to society – the word ‘benefit’ is mentioned only once.

New metrics for economic evaluation in the presence of heterogeneity: focusing on evaluating policy alternatives rather than treatment alternatives. Medical Decision Making [PubMed] Published 25th April 2017

Cost-effectiveness analyses (CEA) are a key aspect of the evaluation of medical technologies and pharmaceutical products. Typically, the main output of these analyses is an incremental cost-effectiveness ratio (ICER) or other summary measure of incremental costs and benefits. However, these ICERs typically use an average treatment effect and complete adoption. This is unlikely to be realistic, though, from a policy perspective. Both effectiveness and adoption rates may differ between sub-groups. This paper proposes a ‘policy’ framework that takes this heterogeneity into account. In essence, the paper advocates a weighted average ICER taking into account adoption rates and heterogeneous effectiveness. It takes this idea a step further and considers uncertainty about all the parameters. Conceptually, the framework is a straightforward extension of CEA, but the paper is clear and lucid and it certainly makes sense to evaluate technologies on the basis of how they will actually be used. Similar ideas have been used to take forward clinical trial design: with more information patients will make different treatment choices, for example. The trouble is, innovative and sensible ideas can be very slow to catch on.


Chris Sampson’s journal round-up for 26th September 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.

Health economics as rhetoric: the limited impact of health economics on funding decisions in four European countries. Value in Health Published 19th September 2016

We start on a sombre note, with a paper that begs the question: why do we bother? A key purpose of health economic evaluation is to prevent the use of low-value, high-cost technologies. Influence on funding decisions is arguably a good basis on which to judge the impact of health economics. This study looks at funding decisions in England, Germany, the Netherlands and Sweden. The paper identifies key features of the HTA institutions and processes in each country. For all countries, there is very little evidence of economic evaluation having been the basis for the restriction of high-cost drugs. England found ways to support the funding of drugs for multiple sclerosis and cancer, despite high-cost and apparent low value. One positive impact might be in facilitating the negotiation of reduced prices – for example, through NICE’s patient access schemes. While the different countries have quite different processes, they have produced similar decisions in practice. The authors suggest that, despite having had limited impact on the outcome of funding decisions, health economics has influenced the process of decision making and the language of health care prioritisation. In this sense, health economics has value in rhetoric, increasing transparency and rational decision making. It’s an interesting idea that I’d like to see developed further, as the authors only provide a limited discussion of it. Personally, I think some distinction needs to be drawn between ‘health economics’ – as identified in the title – and ‘agency-mandated health technology assessment’. While many readers of this blog might do the former on a daily basis, I’d bet not many of us deal in the latter. I certainly don’t. So there’s a lot of ‘health economics’ that can’t – at least not directly – be judged on the basis of funding decisions. Yes, high-cost drugs backed by money-hungry Pharma evade HTA defences. But what about the other end of the spectrum? What about high-value interventions that have been commissioned because the economic evidence has been so compelling. Wishful thinking? Maybe not. Either way, we shouldn’t understate the value of health economics as rhetoric when dealing with capricious and myopic governments.

Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: Second Panel on Cost-Effectiveness in Health and Medicine. JAMA [PubMed] Published 13th September 2016

What do you mean you haven’t yet pre-ordered the new edition of the ‘Gold’ book from the famous Panel on Cost-Effectiveness in Health and Medicine? The original Panel was a big deal (not that I remember it, of course, as I was 8 years old), and so, presumably is the Second Panel. Maybe less so as relative consensus has developed in the use of health technology assessment in practice around the world. But we still need guidance. It’s ironic that the Panel was convened and funded by US organisations in a country that lags far behind in its use of economic evaluation in health technology assessment. This article in JAMA outlines the Panel’s recommendations. I can’t summarise them all here, so you probably need to go and read it all yourself. But know that there isn’t anything radical or unexpected. This Panel updated the original recommendations and created new ones where necessary. Threatening the validity of many a joke at economists’ expense, the Panel was able to reach consensus on all recommendations. Readers are chastised for not appropriately adopting a societal perspective as recommended by the first Panel, but then we are offered a compromise: “All studies should report a reference case analysis based on a health care sector perspective and another reference case analysis based on a societal perspective”. The Panel also recommend use of an “impact inventory”. This is a nice suggestion and I like the terminology. Including a disaggregated list of costs (and outcomes) improves transparency and makes studies more useful to future researchers. One new recommendation is that we should include unrelated future costs, which is something we saw discussed in a recent journal round-up. Another departure from the first Panel is that we are told to include productivity costs in the cost side of our equation. A suggestion that’s dropped in is that protocols should be written in advance of a study. I wish the panel had been more forceful with this one, as published protocols could go a long way in improving consistency, transparency and quality.

The Load Model: an alternative to QALY. Journal of Medical Economics [PubMedPublished 7th September 2016

OK, I admit it: I went into this paper with a lot of scepticism. The QALY – that is, the combination of the quality and quantity of life – fundamentally makes sense. I’m not sure we need ‘an alternative’. The paper introduces some interesting ideas, but they aren’t as revolutionary as the author suggests and I’m not sure that it gets us anywhere. There are some problems from the outset. The article jumbles up positive and normative matters, criticising the QALY on the basis of its capacity to indicate what we might consider to be inequitable results. The author hints that the need for a new model derives from the QALY’s inappropriate combination of quality and duration of life. The most obvious criticism is that the constant proportional trade-off assumption does not hold. But then there’s no discussion of CPTO. The Load Model is presented as “radically different”, but it isn’t. Equations are shuffled so that we’re dealing in rates rather than time, but this adjustment appears to be inconsequential. It might be a more useful way to think about morbidity and mortality, but no argument to that end is presented. The main difference in the Load Model is that a ‘load’ is added for the negative impact of death (as opposed to being dead). Now, I have big problems with the way we handle ‘dead’ in health state valuation. I think it’s a more serious issue than we know (and we know quite a bit), so I am always glad to see attempts to fix it. Once you get past the superficial adjustments to the QALY, what’s really going on is that the Load Model is adding a third dimension to the valuation process; in addition to length of life and quality of life (in the Load Model it’s disease burden) we also have quality (or rather the burden) of death. But this could be incorporated into a QALY framework; I’ve spoken before about the notion of a 3- or otherwise multi-dimensional QALY. Given that death is so key to the distinction between the Load Model and the QALY, it’s unfortunate that in the worked example an entirely arbitrary value of questionable meaning is attributed to it. So the subsequent comparison between the two approaches seems meaningless. There may be more merit in the Load Model than I can see – perhaps I lack the immagination. But it seems to solve none of the problems associated with the QALY framework, while introducing new ones.

Associations between extending access to primary care and emergency department visits: a difference-in-differences analysis. PLOS Medicine [PubMedPublished 6th September 2016

We’ve had quite a bit of discussion of 7 day services here on the blog. But the papers continue to flood in, much to the chagrin of Jeremy Hunt. This study doesn’t look at the most controversial case of extending hospital services, but investigates whether extended (evening and weekend) opening of GP practices reduces hospital attendance. The context is that providers in Manchester (England) were invited to bid for funding to roll out extended hours from December 2013. In total we’re looking at 56 practices who succeeded in the bid and 469 practices who provided services as normal. The analysis uses routinely collected hospital administrative data for almost 3 million patients from 2011 to 2014. A difference-in-differences OLS regression was used with propensity score matching to try and deal with the obvious selection problem. Of course, there was an increase in the number of GP visits: 33,519 in total. The main finding is that patients registered at practices with extended hours exhibited a 26.4% relative reduction in attendances for minor problems at A&E. So in this sense, extending opening hours seems to have satisfied its purpose. Though each emergency attendance ‘avoided’ corresponded to around 3 additional GP appointments. Unfortunately the study wasn’t able to determine the set-up and running costs of the extended GP services, so couldn’t carry out a proper cost-effectiveness analysis. And as we’ve discussed before in this context, that’s the question that really matters.