Sam Watson’s journal round-up for 27th March 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.

The minimum legal drinking age and morbidity in the United States. Review of Economics and Statistics Published 23rd February 2017

Governments have tried multiple different policies to reduce the physical and social harms of alcohol consumption. In the United Kingdom, a minimum price per unit alcohol has been investigated recently, and in 2003 opening times for licensed premises were extended. Neither policy was overwhelmingly judged to be an effective way to reduce harms. In the United States, the legal minimum age for purchasing alcohol is 21, notably higher than other Western nations. This legal age resulted from the National Minimum Age Drinking Act of 1984, which threatened states with a reduction of 10% in their funding for federal highways if they did not raise the legal age to 21. The Act was ostensibly in response to evidence of increased traffic fatalities associated with a lower legal age. This study adds evidence to this ongoing debate. The legal cut-off provides a natural discontinuity for the authors to investigate. Regression discontinuity can be abused, with some researchers controlling inappropriately for high powers of the variable, ‘forcing’ a difference to appear. This paper takes a more sensible approach adopting a quadratic form. For some variables, such as ED admission for alcohol intoxication, the discontinuity is obvious, as you would expect. But for others, such as accidental injury or deliberate injury by another person, the difference is not so apparent if you ignore the fitted lines. One wonders then how much their effect size is driven by their functional form. The authors write that their model is to ‘determine if an increase in the morbidity rate visible in a figure is statistically significant’. Oh dear.  Theoretically, the effect makes sense, alcohol does lead to physical and social harms. But I’m not convinced by the magnitude of the effect they’ve estimated: some sensitivity analyses wouldn’t have gone amiss.

A re-evaluation of fixed effect(s) meta-analysis. Journal of the Royal Statistical Society: Series A Published 16th March 2017

Meta-analysis is the frequently used method to combine results from multiple studies. Evidence synthesis is frequently required in health economic analyses to estimate parameters for models. Practitioners typically either consider ‘fixed effects’ or ‘random effects’ meta-analysis. The latter is used when it is assumed the estimated effects differ between studies, leading many authors to shun fixed effects analyses if there’s any suspicion of heterogeneity. But, as this article argues, there are multiple interpretations of fixed effects analyses. They can provide useful results even in the presence of between study heterogeneity. There are three key assumptions about the parameters estimated in different studies. First, there could be the same common effect underlying all studies. Secondly, each study could have a its own separate fixed effect. Or thirdly, each estimate is a draw from an underlying sampling distribution, an exchangeable parameters assumption. This latter assumption is the basis of random effects meta-analysis. The fixed effects meta-analysis estimator is consistent for the common effect parameter. For the multiple fixed effects assumption the fixed effects meta-analysis is a consistent estimator for the parameter that would have been estimated if the samples in each study were amalgamated. The key point of the paper is that under both the common effect and fixed effects assumptions the fixed effects meta-analysis estimator is useful.

Insurer competition in health care markets. Econometrica. Published 21st March 2017.

Given the gestation length of an economics paper, it is perhaps somewhat fortuitous that this one should land just as major health care market legislation is being discussed in the US. Health care provision differs notably between the US and other high income countries. Health care is predominantly left up to the market with ‘consumers’ purchasing insurance or health care directly. This, despite it being long recognised that health care markets are likely to fail (see our recent piece on the late Kenneth Arrow). But a single payer system is politically unpalatable. The Affordable Care Act (ACA; Obamacare) aimed to ensure universal coverage of health care through a system of subsidies, regulations, and mandates. The ACA brought about changes to the insurance market with a number of providers merging and consolidating. The consequences of these mergers may be deliterious as increased monopoly power within states may lead to higher premiums, but equally increased monopsony power may mean lower prices negotiated with health care providers. This article attempts to simulate what will happen to premiums and health care prices when insurers of different sizes are removed from the market. I can’t give a fair review to the methods in the time I’ve had to read this paper as there is a lot going on including econometric models of household choice and game theoretic models of insurer bargaining. But I put it here as it appears at first glance to be a solid analysis of what is an incredibly large and complex market in the US and is likely worth more time to understand.




Alastair Canaway’s journal round-up for 20th March 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.

The use of quality-adjusted life years in cost-effectiveness analyses in palliative care: mapping the debate through an integrative review. Palliative Medicine [PubMed] Published 13th February 2017

February saw a health economics special within the journal Palliative Medicine – the editorials are very much worth a read to get a quick idea of how health economics has (and hasn’t) developed within the end of life care context. One of the most commonly encountered debates when discussing end of life care within health economics circles relates to the use of QALYs, and whether they’re appropriate. This paper aimed to map out the pros and cons of using the QALY framework to inform health economic decisions in the palliative care context. Being a review, there were no ground-breaking findings, more a refresher on what the issues are with the QALY at end of life: i) restrictions in life years gained, ii) conceptualisation of quality of life and its measurement, and iii) valuation and additivity of time. The review acknowledges the criticisms of the QALY but concludes that it is still of use for informing decision making. A key finding, and one which should be common sense, is that the EQ-5D should not be relied on as the sole measure within this context: the dimensions important to those at end of life are not adequately captured by the EQ-5D, and other measures should be considered. A limitation for me was that the review did not include Round’s (2016) book Care at the End of Life: An Economic Perspective (disclaimer: I’m a co-author on a chapter), which has significant overlap and builds on a number of the issues relevant to the paper. That aside, this is a useful paper for those new to the pitfalls of economic evaluation at the end of life and provides an excellent summary of many of the key issues.

The causal effect of retirement on mortality: evidence from targeted incentives to retire early. Health Economics [PubMed] [RePEc] Published 23rd February 2017

It’s been said that those who retire earlier die earlier, and a quick google search suggests there are many statistics supporting this. However, I’m unsure how robust the causality is in such studies. For example, the sick may choose to leave the workforce early. Previous academic literature had been inconclusive regarding the effects, and in which direction they occurred. This paper sought to elucidate this by taking advantage of pension reforms within the Netherlands which meant certain cohorts of Dutch civil servants could qualify for early retirement at a younger age. This change led to a steep increase in retirement and provided an opportunity to examine causal impacts by instrumenting retirement with the early retirement window. Administrative data from the entire population was used to examine the probability of dying resulting from earlier retirement. Contrary to preconceptions, the probability of men dying within five years dropped by 2.6% in those who took early retirement: a large and significant impact. The biggest impact was found within the first year of retirement. An explanation for this is that the reduction of stress and lifestyle change upon retiring may postpone death for the civil servants which were in poor health. The paper is an excellent example of harnessing a natural experiment for research purposes. It provides a valuable contribution to the evidence base whilst also being reassuring for those of us who plan to retire in the next few years (lottery win pending).

Mapping to estimate health-state utility from non–preference-based outcome measures: an ISPOR Good Practices for Outcomes Research Task Force report. Value in Health [PubMed] Published 16th February 2017

Finally, I just wanted to signpost this new good practice guide. If you ever attend HESG, ISPOR, or IHEA, you’ll nearly always encounter a paper on mapping (cross-walking). Given the ethical issues surrounding research waste and the increasing pressure to publish, mapping provides an excellent opportunity to maximise the value of your data. Of course, mapping also serves a purpose for the health economics community: it facilitates the estimation of QALYs in studies where no preference based measure exists. There are many iffy mapping functions out there so it’s good to see ISPOR have taken action by producing a report on best practice for mapping. As with most ISPOR guidelines the paper covers all the main areas you’d expect and guides you through the key considerations to undertaking a mapping exercise, this includes: pre-modelling considerations, data requirements, selection of statistical models, selection of covariates, reporting of results, and validation. Additionally there is also a short section for those who are keen to use a mapping function to generate QALYs but are unsure which to pick. As with any set of guidelines, it’s not exactly a thriller, it is however extremely useful for anyone seeking to conduct mapping.


Thesis Thursday: Edward Webb

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Edward Webb who graduated with a PhD from the University of Copenhagen. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Attention and perception in decision-making and interactions
Alexander Sebald, Peter Norman Sørensen
Repository link

Attention and perception aren’t things we often talk about in health economics. Why are they important?

There’s been a lot of work done on attention and perception in economics recently, which I think is a great development. They are really vital topics since unless you know how people perceive the information available to them, and what aspects of their environment are most likely to command their attention, it’s difficult to forecast their behaviour.

I think attention and perception will become more widely talked about in health in future, as there’s many cases in which they have a lot of relevance. For example, you might want to know whether rare symptoms grab doctors’ attention because they’re unusual, or whether they don’t notice them because they’re not expecting them. (There’s a great study by Drew, Vo and Wolfe where radiologists looking at CT scans of the chest failed to notice a picture of a gorilla embedded in them by the experimenters.)

Or if you’re planning some dietary intervention, you might want to take into account how unhealthy food such as pizza and chips attracts people’s attention much more than healthy food, and to look at why this is the case.

What can the new theoretical frameworks described in your thesis tell us about individual behaviour?

Most of the literature in psychology is about how individuals behave. I tried a lot in my thesis to move beyond studying individual decision making to look at how the effects of attention and perception change in different economic environments, as this can often be counter-intuitive.

As an example, in one of the chapters of my thesis I explore the effects of individuals having limited ability to tell the quality of different products apart. It turns out that the effects on a market can be radically different depending on whether there are fixed or marginal costs of quality.

I was also very interested in looking at how individuals with limited or biased attention interact with profit maximising firms. There’s an expectation that companies will rip people off and exploit them, and certainly, that can happen, but I was able to show that it’s not necessarily the case. The case of individuals having limited ability to tell products’ quality apart which I mentioned above is a good example. When firms rely on product differentiation to earn profits, they’re actually harmed by people with this limitation, rather than exploiting them.

Did you find yourself reaching beyond the economics literature for guidance, either in the subject matter or the techniques that you used?

Yes, I read quite a lot outside the standard economics literature during my thesis. Behavioural and experimental economics more or less sits on the boundary between economics and psychology, so it felt very natural to seek guidance from other disciplines. This was especially the case for the eye-tracking experiment that I carried out with the help of my co-authors Andreas Gotfredsen, Carsten S. Nielsen and Alexander Sebald. I needed to learn quite a bit about psychological work on visual attention.

I like that economics is as much a set of analytic tools as a subject area, which gives it the advantage of being able to take on nontraditional topics.

You studied in Denmark, yet your thesis is written in English. Did this raise any additional challenges in completing your PhD?

Danish people speak better English than what I can! Language really wasn’t a problem at all at work, since English is very much the language of academia. Seminars were in English, PhD students and a lot of masters students wrote their theses in English and nearly all postgraduate and some undergraduate teaching was in English. I did feel quite privileged to have the advantage of being a native speaker of the language, and appreciative that most of my colleagues were fine with working in a second language. That’s why I was always very willing to help people out with proofreading English. I only hope I didn’t make too many mistakes!

On the social side, you can get away with living in Denmark without speaking Danish, and many people do. Indeed, I probably wouldn’t have made the effort of becoming a (moderate) Danish speaker if my partner wasn’t Danish.

Copenhagen, and Denmark in general, is a fantastic place to live and work, and I’d urge anyone who is thinking about moving there not to be put off by the language barrier.

How did your experiences during your PhD contribute to your decision to work in the field of health economics?

The question makes it sound like I had a coherent plan! In reality, I’m terrible about thinking about the long term. (I must be a natural Keynesian.) I ended up moving back to the UK after I graduated ironically because of my Danish partner, as she had found a job here. She also works in health, as a medical physicist and cancer researcher at Leeds. I applied for economics jobs in the area and was over the moon to secure a place at the Academic Unit of Health Economics at Leeds.

It’s a little more applied and hands-on than what I was working on before, which is great. I came into economics because I was interested in finding out how people act and interact, and so it’s fantastic to have the opportunity now to work principally with discrete choice experiments, trying to work out patients’ and clinicians’ preferences.

Since I started at Leeds a few months ago I’ve really enjoyed my time. The environment is very stimulating and all my colleagues are extremely friendly and easy going and are always willing to help out or discuss an interesting new idea.