Chris Sampson’s journal round-up for 19th 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.

Health-related resource-use measurement instruments for intersectoral costs and benefits in the education and criminal justice sectors. PharmacoEconomics [PubMed] Published 8th June 2017

Increasingly, people are embracing a societal perspective for economic evaluation. This often requires the identification of costs (and benefits) in non-health sectors such as education and criminal justice. But it feels as if we aren’t as well-versed in capturing these as we are in the health sector. This study reviews the measures that are available to support a broader perspective. The authors search the Database of Instruments for Resource Use Measurement (DIRUM) as well as the usual electronic journal databases. The review also sought to identify the validity and reliability of the instruments. From 167 papers assessed in the review, 26 different measures were identified (half of which were in DIRUM). 21 of the instruments were only used in one study. Half of the measures included items relating to the criminal justice sector, while 21 included education-related items. Common specifics for education included time missed at school, tutoring needs, classroom assistance and attendance at a special school. Criminal justice sector items tended to include legal assistance, prison detainment, court appearances, probation and police contacts. Assessments of the psychometric properties was found for only 7 of the 26 measures, with specific details on the non-health items available for just 2: test-retest reliability for the Child and Adolescent Services Assessment (CASA) and validity for the WPAI+CIQ:SHP,V2 (there isn’t room on the Internet for the full name). So there isn’t much evidence of any validity for any of these measures in the context of intersectoral (non-health) costs and benefits. It’s no doubt the case that health-specific resource use measures aren’t subject to adequate testing, but this study has identified that the problem may be even greater when it comes to intersectoral costs and benefits. Most worrying, perhaps, is the fact that 1 in 5 of the articles identified in the review reported using some unspecified instrument, presumably developed specifically for the study or adapted from an off-the-shelf instrument. The authors propose that a new resource use measure for intersectoral costs and benefits (RUM ICB) be developed from scratch, with reference to existing measures and guidance from experts in education and criminal justice.

Use of large-scale HRQoL datasets to generate individualised predictions and inform patients about the likely benefit of surgery. Quality of Life Research [PubMed] Published 31st May 2017

In the NHS, EQ-5D data are now routinely collected from patients before and after undergoing one of four common procedures. These data can be used to see how much patients’ health improves (or deteriorates) following the operations. However, at the individual level, for a person deciding whether or not to undergo the procedure, aggregate outcomes might not be all that useful. This study relates to the development of a nifty online tool that a prospective patient can use to find out the expected likelihood that they will feel better, the same or worse following the procedure. The data used include EQ-5D-3L responses associated with almost half a million unilateral hip or knee replacements or groin hernia repairs between April 2009 and March 2016. Other variables are also included, and central to this analysis is a Likert scale about improvement or worsening of hip/knee/hernia problems compared to before the operation. The purpose of the study is to group people – based on their pre-operation characteristics – according to their expected postoperative utility scores. The authors employed a recursive Classification and Regression Tree (CART) algorithm to split the datasets into strata according to the risk factors. The final set of risk variables were age, gender, pre-operative EQ-5D-3L profile and symptom duration. The CART analysis grouped people into between 55 and 60 different groups for each of the procedures, with the groupings explaining 14-27% of the variation in postoperative utility scores. Minimally important (positive and negative) differences in the EQ-5D utility score were estimated with reference to changes in the Likert scale for each of the procedures. These ranged in magnitude from 0.041 to 0.106. The resulting algorithms are what drive the results delivered by the online interface (you can go and have a play with it). There are a few limitations to the study, such as the reliance on complete case analysis and the fact that the CART analysis might lack predictive ability. And there’s an interesting problem inherent in all of this, that the more people use the tool, the less representative it will become as it influences selection into treatment. The validity of the tool as a precise risk calculator is quite limited. But that isn’t really the point. The point is that it unlocks some of the potential value of PROMs to provide meaningful guidance in the process of shared decision-making.

Can present biasedness explain early onset of diabetes and subsequent disease progression? Exploring causal inference by linking survey and register data. Social Science & Medicine [PubMed] Published 26th May 2017

The term ‘irrational’ is overused by economists. But one situation in which I am willing to accept it is with respect to excessive present bias. That people don’t pay enough attention to future outcomes seems to be a fundamental limitation of the human brain in the 21st century. When it comes to diabetes and its complications, there are lots of treatments available, but there is only so much that doctors can do. A lot depends on the patient managing their own disease, and it stands to reason that present bias might cause people to manage their diabetes poorly, as the value of not going blind or losing a foot 20 years in the future seems less salient than the joy of eating your own weight in carbs right now. But there’s a question of causality here; does the kind of behaviour associated with time-inconsistent preferences lead to poorer health or vice versa? This study provides some insight on that front. The authors outline an expected utility model with quasi-hyperbolic discounting and probability weighting, and incorporate a present bias coefficient attached to payoffs occurring in the future. Postal questionnaires were collected from 1031 type 2 diabetes patients in Denmark with an online discrete choice experiment as a follow-up. These data were combined with data from a registry of around 9000 diabetes patients, from which the postal/online participants were identified. BMI, HbA1c, age and year of diabetes onset were all available in the registry and the postal survey included physical activity, smoking, EQ-5D, diabetes literacy and education. The DCE was designed to elicit time preferences using the offer of (monetary) lottery wins, with 12 different choice sets presented to all participants. Unfortunately, despite the offer of a real-life lottery award for taking part in the research, only 79 of 1031 completed the online DCE survey. Regression analyses showed that individuals with diabetes since 1999 or earlier, or who were 48 or younger at the time of onset, exhibited present bias. And the present bias seems to be causal. Being inactive, obese, diabetes illiterate and having lower quality of life or poorer glycaemic control were associated with being present biased. These relationships hold when subject to a number of control measures. So it looks as if present bias explains at least part of the variation in self-management and health outcomes for people with diabetes. Clearly, the selection of the small sample is a bit of a concern. It may have meant that people with particular risk preferences (given that the reward was a lottery) were excluded, and so the sample might not be representative. Nevertheless, it seems that at least some people with diabetes could benefit from interventions that increase the salience of future health-related payoffs associated with self-management.

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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.

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Thesis Thursday: Sara Machado

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 Sara Machado who graduated with a PhD from Boston University. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Essays on the economics of blood donations
Supervisors
Daniele Paserman, Johannes Schmieder, Albert Ma
Repository link
https://open.bu.edu/pdfpreview/bitstream/handle/2144/19216/Machado_bu_0017E_12059.pdf

What makes blood donation an interesting context for economic research?

I’m generally interested in markets in which there is no price mechanism to help supply and demand meet. There are several examples of such markets in the health field, such as organ, bone marrow, and blood donations. In general, all altruistic markets share this feature. I define altruistic markets as markets with a volunteer supply and no market price, therefore mainly driven by social preferences.

In a way, the absence of a price leads to a very traditional coordination problem. However, it requires not-so-traditional solutions, such as market design, registries, and different types of incentives, due to many historical, political, and ethical constraints (which leads us to the concept of repugnant markets, by Roth (2007)). The specific constraints for blood donations are outlined in Slonim et al’s The Market for Blood, which also outlines the main experimental findings regarding the effects of incentives on blood donations. The blood donations market is the perfect setup to study altruistic markets, not only because of its volunteer supply but also due to the fact that it is a potentially repeated behaviour. Moreover, the donation is not to a specific patient, but to the supply of blood in general. Social preferences, as well as risk and time preferences, play a key role in minimizing market imbalances.

How did you come to identify the specific research questions for your PhD?

I was quite fortunate, due to an unfortunate situation… There was a notorious blood shortage, in Portugal, when I started thinking about possible topics for my dissertation. It got a lot of media coverage, possibly due to political factors, since the shortage happened shortly after a change in the incentives for blood donors. My first question, which eventually became the main chapter of my dissertation, was whether there was a causal relationship.

The second chapter is the outcome of spending many hours cleaning the data, to tell you the truth. I started to realize that there are many other factors determining blood donation behaviour. All non-monetary aspects of the donation process are very relevant in determining future donation behaviour (also highlighted by Slonim et al (2014) and Lacetera et al (2010)). I show that time can be a far more important currency than other forms of incentives.

Finally, I realized how important it would be for me to be able to measure social preferences to continue my research on altruistic markets and joined a team lead by Matteo Galizzi, who is working on measuring preferences of a representative sample of the UK population. My third chapter is the first installment of our work in this domain.

Your research looked at people’s behaviour. How does it relate to the growing recognition that people make ‘irrational’ choices?

The more I look into this, the more I think that we have to be careful about a generalization of irrationality. There is nothing “irrational” in blood donors’ behaviour, for the most part. So far, I have only resorted to very neoclassical models to explain donors’ behaviour – and it worked just fine.

The way I see it, there are two separate aspects to take into account. First, the market response. It is worrisome if we find market responses that are only possible if the majority of agents are making “irrational choices”. Those markets need tailored interventions to inform the decision-making process.

The second aspect zooms in into individual decision-making. In this case, it is important to determine whether there are psychological biases leading to suboptimal, or irrational, choices.

One might argue that a blood donation due to an emotional response to some stimuli is “irrational”. I strongly disagree with that categorization. For example, there is nothing suboptimal in donating blood as a sign of gratitude to previous blood donors.

The main message is that it is important to identify behavioural biases that lead to inefficient market outcomes, but “irrational choices” is too wide an umbrella term and should be used with caution.

Are any of your key findings generalisable to settings other than blood donation?

I think two key findings are quite general. The first one is the fact that it is possible to design incentive schemes that bypass the question of the crowding out of intrinsic motivation. This is a fairly general issue, that ranges from motivating employees at the workplace in general to the design of incentive schemes for physicians, to the elicitation of charitable giving, just to name a few examples. As long as it is a repeated behaviour, the result holds. This highlights a different aspect, the importance of placing lab and isolated field experimental evidence into perspective when informing policy making. There is extensive experimental literature on the crowding out of intrinsic motivation, but very little has been done at the market level and with a longitudinal component. This has limited the ability to take into account the advantages of focusing on repeated blood donation, on the one hand, and of incorporating demand side responses, on the other hand (namely by increasing the number of blood drives).

The second key aspect is the advantage of using time as the main opportunity cost faced by a volunteer supply, in the context of prosocial behaviour.

Based on your research, what might an optimal blood donation policy look like?

I believe there are two key ingredients in the design of the optimal blood donation policy: 1) promoting blood donation as a repeated behaviour; and 2) increasing the responsiveness of blood donation services in order to minimize demand and supply imbalances.

The first aspect can be addressed by designing incentive schemes targeted at repeated donors, with no rewards for non-regular behaviour. The second would greatly benefit from the existence of a blood donor registry, similar to the one already in place for bone marrow donation. This registry would allow for regular blood donors to be called to donate when their blood is needed, minimizing waste in the system. The organization of blood drives would also be more efficient if such a system was in place.

These two components contribute to the development of the blood donor identity, which guarantees a steady supply of blood, whenever necessary.