# Sam Watson’s journal round-up for 12th June 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.

Machine learning: an applied econometric approach. Journal of Economic Perspectives [RePEcPublished Spring 2017

Machine learning tools have become ubiquitous in the software we use on a day to day basis. Facebook can identify faces in photos; Google can tell you the traffic for your journey; Netflix can recommend you movies based on what you’ve watched before. Machine learning algorithms provide a way to estimate an unknown function $f$ that predicts an outcome $Y$ given some data $x$: $Y = f(x) + \epsilon$. The potential application of these algorithms to many econometric problems is clear. This article outlines the principles of machine learning methods. It divides econometric problems into prediction, $\hat{y}$, and parameter estimation, $\hat{\beta}$ and suggests machine learning is a useful tool for the former. However, this distinction is a false one, I believe. Parameters are typically estimated because they represent an average treatment effect, say $E(y|x=1) - E(y|x=0)$. But, we can estimate these quantities in ‘$\hat{y}$ problems’ since $f(x) = E(y|x)$. Machine learning algorithms, therefore, represent a non-parametric (or very highly parametric) approach to the estimation of treatment effects. In cases where functional form is unknown, where there may be nonlinearities in the response function, and interactions between variables, this approach can be very useful. They do not represent a panacea to estimation problems of course, since interpretation rests on the assumptions. For example, as Jennifer Hill discusses, additive regression tree methods can be used to estimate conditional average treatment effects if we can assume the treatment is ignorable conditional on the covariates. This article, while providing a good summary of methods, doesn’t quite identify the right niche where these approaches might be useful in econometrics.

Incorporating equity in economic evaluations: a multi-attribute equity state approach. European Journal of Health Economics [PubMedPublished 1st June 2017

Efficiency is a key goal for the health service. Economic evaluation provides evidence to support investment decisions, whether displacing resources from one technology to another can produce greater health benefits. Equity is generally not formally considered except through the final investment decision-making process, which may lead to different decisions by different commissioning groups. One approach to incorporating equity considerations into economic evaluation is the weighting of benefits, such as QALYs, by group. For example, a number of studies have estimated that benefits of end-of-life treatments have a greater social valuation than other treatments. One way of incorporating this into economic evaluation is to raise the cost-effectiveness threshold by an appropriate amount for end-of-life treatments. However, multiple attributes may be relevant for equity considerations, negating a simplistic approach like this. This paper proposed a multi-attribute equity state approach to incorporating equity concerns formally in economic evaluation. The basic premise of this approach is to firstly define a set of morally relevant attributes, to secondly derive a weighting scheme for each set of characteristics (similarly to how QALY weights are derived from the EQ-5D questionnaire), and thirdly to apply these weights to economic evaluation. A key aspect of the last step is to weight both the QALYs gained by a population from a new technology and those displaced from another. Indeed, identifying where resources are displaced from is perhaps the biggest limitation to this approach. This displacement problem has also come up in other discussions revolving around the estimation of the cost-effectiveness threshold. This seems to be an important area for future research.

Financial incentives, hospital care, and health outcomes: evidence from fair pricing laws. American Economic Journal: Economic Policy [RePEcPublished May 2017

There is a not-insubstantial literature on the response of health care providers to financial incentives. Generally, providers behave as expected, which can often lead to adverse outcomes, such as overtreatment in cases where there is potential for revenue to be made. But empirical studies of this behaviour often rely upon the comparison of conditions with different incentive schedules; rarely is there the opportunity to study the effects of relative shifts in incentive within the same condition. This paper studies the effects of fair pricing laws in the US, which limited the amount uninsured patients would have to pay hospitals, thus providing the opportunity to study patients with the same conditions but who represent different levels of revenue for the hospital. The introduction of fair pricing laws was associated with a reduction in total billing costs and length of stay for uninsured patients but little association was seen with changes in quality. A similar effect was not seen in the insured suggesting the price ceiling introduced by the fair pricing laws led to an increase in efficiency.

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# Alastair Canaway’s journal round-up for 5th June 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.

Redistribution and redesign in health care: an ebbing tide in England versus growing concerns in the United States. Health Economics [PubMed] Published 4th May 2017

Health Economics included an editorial that will be of interest to a wider readership. It focusses on the similarities and differences between the US and the UK’s health care systems, particularly in terms of (re)design, redistribution, and the challenges facing each. The UK system is characterised by a preference for collectivism in funding and access, and in the US, a pluralism of funding. In both countries, groups seek to reverse their existing approach (the grass is always greener). The editorial outlines recent changes in healthcare design, notably, the impact of the affordable care act (ACA). The main focus of the editorial is twofold: i) a discussion of the efforts in England to limit public spending whilst increasing hospital sector efficiency, ii) discussion of the US’s attempt to reduce the growth in the role of government in financing and delivering healthcare. In respect to the UK, the diagnosis is worrying yet unsurprising: chronic underfunding combined with a plethora of unevidenced reform proposals has left the NHS on a knife-edge; the prognosis is that it is uncertain whether the NHS will survive the next few years. In the US, the picture is more complex and the paper discussed possible repeal components of the ACA. A key point of the discussion relates to the assumption that US healthcare is much more expensive than any OECD country due to American’s using too much medical care. In fact as the authors note, the evidence points to the contrary, and the high expenditure is due to a myriad of factors including high wages, high drug prices, and a system which requires many more lawyers, administrators and consultants. The paper discusses various nuances with both systems in the current political context and is well worth reading for a quick overview of some the key issues facing both countries.

Statistical alchemy: conceptual validity and mapping to generate health state utility values. Pharmacoeconomics – Open Published 15th May 2017

With a passing interest in mapping and counting myself as a bit of a mapping skeptic, this paper discussing mapping in terms of ‘statistical alchemy’ obviously caught my eye. As most will know, mapping is a frequently used technique to obtain utility estimates by predicting utility values from data collected using other measures. The focus of the paper is ‘conceptual validity’: ‘the degree to which the content of two different instruments reflect one another when used for mapping’. There were three aims i) explain the idea of conceptual validity in relation to mapping, ii) consider the implications of poor conceptual validity when mapping for decision making in the context of resource allocation, and iii) provide suggestions to improve conceptual validity. The paper successfully achieves the first goal with an exposition of the (many) issues with mapping in relation to conceptual validity. The paper highlights that poor conceptual validity will result in systematic biases in the preferences for health when mapped estimates are used. This is aptly demonstrated through an example using a multiple sclerosis measure, and the EQ-5D. A number of ways for improving the conceptual validity are also presented, these include: i) response mapping, ii) assessment of ‘conceptual decision validity’ (which draws upon face, construct and criterion validity) to determine whether there is a prima facie case that a mapping function may lead to a valid decision, and iii) the need to examine ‘what is lost’ should mapping be used. I found it to be a thoughtful paper, and echoed some of my concerns with existing mapping functions. For those interested in conducting a mapping exercise this is an essential read as an introduction to some of the pitfalls you will encounter.

Is there additional value attached to health gains at the end of life? A revisit. Health Economics Published 1st June 2017

Following NICE’s (2009) guidance for the acceptability of higher cost-per-QALY thresholds for life extending treatments, the past eight years has seen an increase in research examining whether the general public actually have an appetite for this. That is, do the general public have a preference for an end of life premium? Many studies have sought to answer this, with mixed results. All previous attempts however, have tackled this issue from an ex-post perspective: respondents are asked to choose between providing treatment after the diagnosis when they face a shorter life expectancy without treatment. The issue highlighted in this paper is that by presenting life expectancy as certain and salient (e.g. 2 years, or 10 years), it may be interpreted as a life sentence regardless of length. This paper goes down an alternative route by adopting an ex-ante insurance approach. Additionally a new comparator is used, end of life treatment is compared with a preventative treatment that offers life extension with the same expected health gain. It also explores whether preferences depend on recipient age. The paper found that preventative treatments were prioritised over end of life treatments, and thus a dearth of justification for the end of life premium exists. This is another addition to the mixed literature regarding preferences for end of life treatments. The paper does have its limitations which it readily admits. It is however another useful addition this tricky research area.

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# Meeting round-up: 18th European Health Economics Workshop (EHEW)

I attended the European Health Economics Workshop (EHEW) in Oslo. The workshop has been running for almost 20 years and it shows. Most participants have attended many editions of EHEW, which has and continues to shape the field of health economics theory. This is “the theory workshop”. The atmosphere is one of great friendship and constructive feedback, based on long-term collaborations that set the tone of the workshop. I am definitely not a theorist but found a very welcoming group of people, interested in fostering collaboration between theory, experiments, and empirical work.

EHEW is also a perfect example of the law of small numbers. The smaller the workshop, the more useful the feedback. The smaller the workshop, the larger the potential for fruitful research co-authorship.

Over two days, we went through 15 papers, building up to a total of not more than 30 participants, all of whom had an active role. The author presents in 25 minutes, followed by 10 minutes from the discussant and floor debate, a format that has become the golden rule.

We started off the proper way, with a wine reception at our headquarters hotel in downtown Oslo. I have to say, the organizers – Tor Iversen, Oddvar Kaarboe and Jan Erik Askildsen – did a terrific job. We all know what people remember from a workshop or conference: food and venue. It will be hard to beat EHEW Oslo (although we are possibly headed to Paris next year). We spent Friday and Saturday in an old stable, transformed into a delightful meeting room (see below). The catering was also on point, but what really stood out were the dinners. I think we can all agree that the dinner on Friday night was the best conference meal of all time; a 4-course dinner with paired wine at Restaurant Eik (I leave this here in case you ever go to Oslo – trust me, you want to go there.)

What about scientific content, you might ask? Jonathan Kolstad set the tone with an opening keynote lecture on the role of IT in physician response to pay for performance. The lecture combined theory with empirics, and I was rapidly drawn into a data-envy generating process. Tremendous physician and patient level data from the largest provider in Hawaii. Can you imagine the hardships of field work?

As for the presentations, we covered a broad range of topics. Luigi Siciliani, Helmuth Cremer and Francesca Barigozzi teamed up for a session on long-term care. Their theoretical approaches ranged from a standard IO two-sided market approach to strategic bequests and informal caregiving within the family. We had sessions on the regulation of drugs and unhealthy food, hospital, pharmaceutical and insurance markets, and on GP and health behavior. The paper by Marcos Vera-Hernandez (Identifying complementarities across tasks using two-part contracts. An application to family doctors) was a fantastic example of how to combine theory and empirical analysis. Johannes Schunemann gave a thought-provoking talk on The marriage gap: optimal aging and death in partnerships. I don’t quite agree with the assumptions and conclusions of the study, but then again I think that’s why I’m not a theorist… The main problem, in this case, is that there is nothing about the model that is specific to the variables being studied. We also covered the hot topic of antibiotic prescribing, with a model for prescription under uncertainty about resistance that got us all guesstimating our risk aversion.

The discussions within the workshop highlighted the potential benefits from having cross-field feedback. Empirically-minded researchers provided very useful feedback for theory articles, and vice-versa (for the few exceptions to the theory rule). In retrospect, I am convinced this arises from getting less caught up in technicalities of the theoretical model or the econometric specification, and placing a stronger emphasis on the basic assumptions of the models and the corresponding story.

All in all, we had a terrific time in Oslo. I was impressed by the level of collegiality amongst long-term participants, as well as their welcoming attitude towards newbies like myself. We worked hard and partied hard – even brought back dancing to EHEW – and I look forward to meeting up with the theorists in the near future. Lise, it’s on you!