Thesis Thursday: Alastair Irvine

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 Alastair Irvine who has a PhD from the University of Aberdeen. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Time preferences and the patient-doctor interaction
Supervisors
Marjon van der Pol, Euan Phimister
Repository link
http://digitool.abdn.ac.uk/webclient/DeliveryManager?pid=238373

How can people’s time preferences affect the way they use health care?

Time preferences are a way of thinking about how people choose between things that happen over time. Some people prefer a treatment with large side effects and a long chain of future benefits; others prefer smaller benefits but less side effects. These influence a wide range of health outcomes and decisions. One of the most interesting questions I had coming into the PhD was around non-adherence.

Non-adherence can’t be captured by ‘standard’ exponential time preferences because there is no way for something you prefer now to be ‘less preferred’ in the future if everything is held constant. Instead, present-bias preferences can capture non-adherent behaviour. With these preferences, people place a higher weight on the ‘current period’ relative to all future periods but weight all future periods consistently. What that means is you can have a situation where you plan to do something – eat healthily, take your medication – but end up not doing it. When planning, you placed less relative weight on the near term ‘cost’ (like medication side effects) than you do when the decision arrives.

In what way might the patient-doctor interaction affect a patient’s adherence to treatment?

There’s asymmetric information between doctors and patient, leading to an agency relationship. Doctors in general know more about treatment options than patients, and don’t know their patient’s preferences. So if doctors are making recommendations to patients, this asymmetry can lead to recommendations that are accepted by the patient but not adhered to. For example, present-biased patients accept the same treatments as exponential discounters. Depending on the treatment parameters, present-biased people will not adhere to some treatments. If the doctor doesn’t anticipate this when making recommendations, it leads to non-adherence.

One of the issues from a contracting perspective is that naive present-bias people don’t anticipate their own non-adherence, so we can’t write traditional ‘separating contracts’ that lead present-bias people to one treatment and exponential discounters to another. However, if the doctor can offer a lower level of treatment to all patients – one that has less side effects and a concomitantly lower benefit – then everyone sticks to that treatment. This clearly comes at the expense of the exponential discounters’ health, but if the proportion of present-bias is high enough it can be an efficient outcome.

Were you able to compare the time preferences of patients and of doctors?

Not this time! It had been the ‘grand plan’ at the start of the PhD to compare matched doctor and patient time preferences then link it to treatment choices but that was far too ambitious for the time, and there had been very little work establishing how time preferences work in the patient-doctor interaction so I felt we had a lot to do.

One interesting question we did ask was whether doctors’ time preferences for themselves were the same as for their patients. A lot of the existing evidence asks doctors for their own time preferences, but surely the important time preference is the one they apply to their patients?

We found that while there was little difference between these professional and private time preferences, a lot of the responses displayed increasing impatience. This means that as the start of treatment gets pushed further into the future, doctors started to prefer shorter-but-sooner benefits for themselves and their patients. We’re still thinking about whether this reflects that in the real world (outside the survey) doctors already account for the time patients have spent with symptoms when assessing how quickly a treatment benefit should arrive.

How could doctors alter their practice to reduce non-adherence?

We really only have two options – to make ‘the right thing’ easier or the ‘wrong thing’ more costly. The implication of present-bias is you need to use less intense treatments because the problem is the (relative) over-weighting of the side effects. The important thing we need for that is good information on adherence.

We could pay people to adhere to treatment. However, my gut feeling is that payments are hard to implement on the patient side without being coercive (e.g making non-adherence costly with charges) or expensive for the implementer when identification of completion is tricky (giving bonuses to doctors based on patient health outcomes). So doctors can reduce non-adherence by anticipating it, and offering less ‘painful’ treatments.

It’s important to say I was only looking at one kind of non-adherence. If patients have bad experiences then whatever we do shouldn’t keep them taking a treatment they don’t want. However, the fact that stopping treatment is always an option for the patient makes non-adherence hard to address because as an economist you would like to separate different reasons for stopping. This is a difficulty for analysing non-adherence as a problem of temptation. In temptation preferences we would like to change the outcome set so that ‘no treatment’ is not a tempting choice, but there are real ethical and practical difficulties with that.

To what extent did the evidence generated by your research support theoretical predictions?

I designed a lab experiment that put students in the role of the doctor with patients that may or may not be present-biased. The participants had to recommend treatments to a series of hypothetical patients and was set up so that adapting to non-adherence with less intense treatments was best. Participants got feedback on their previous patients, to learn about which treatments patients stuck to over the rounds.

We paid one arm a salary, and another a ‘performance payment’. The latter only got paid when patients stuck to treatment and the pay correlated with the patient outcomes. In both arms, patients’ outcomes were reflected with a charity donation.

The main result is that there was a lot of adaptation to non-adherence in both arms. The adaptation was stronger under the performance payment, reflecting the upper limit of the adaptation we can expect because it perfectly aligns patient and doctor preferences.

In the experimental setting, even when there is no direct financial benefit of doing so, participants adapted to non-adherence in the way I predicted.

Chris Sampson’s journal round-up for 14th May 2018

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.

A practical guide to conducting a systematic review and meta-analysis of health state utility values. PharmacoEconomics [PubMed] Published 10th May 2018

I love articles that outline the practical application of a particular method to solve a particular problem, especially when the article shares analysis code that can be copied and adapted. This paper does just that for the case of synthesising health state utility values. Decision modellers use utility values as parameters. Most of the time these are drawn from a single source which almost certainly introduces some kind of bias to the resulting cost-effectiveness estimates. So it’s better to combine all of the relevant available information. But that’s easier said than done, as numerous researchers (myself included) have discovered. This paper outlines the various approaches and some of the merits and limitations of each. There are some standard stages, for which advice is provided, relating to the identification, selection, and extraction of data. Those are by no means simple tasks, but the really tricky bit comes when you try and pool the utility values that you’ve found. The authors outline three strategies: i) fixed effect meta-analysis, ii) random effects meta-analysis, and iii) mixed effects meta-regression. Each is illustrated with a hypothetical example, with Stata and R commands provided. Broadly speaking, the authors favour mixed effects meta-regression because of its ability to identify the extent of similarity between sources and to help explain heterogeneity. The authors insist that comparability between sources is a precondition for pooling. But the thing about health state utility values is that they are – almost by definition – never comparable. Different population? Not comparable. Different treatment pathway? No chance. Different utility measure? Ha! They may or may not appear to be similar statistically, but that’s totally irrelevant. What matters is whether the decision-maker ‘believes’ the values. If they believe them then they should be included and pooled. If decision-makers have reason to believe one source more or less than another then this should be accounted for in the weighting. If they don’t believe them at all then they should be excluded. Comparability is framed as a statistical question, when in reality it is a conceptual one. For now, researchers will have to tackle that themselves. This paper doesn’t solve all of the problems around meta-analysis of health state utility values, but it does a good job of outlining methodological developments to date and provides recommendations in accordance with them.

Unemployment, unemployment duration, and health: selection or causation? The European Journal of Health Economics [PubMed] Published 3rd May 2018

One of the major socioeconomic correlates of poor health is unemployment. It appears not to be very good for you. But there’s an obvious challenge here – does unemployment cause ill-health, or are unhealthy people just more likely to be unemployed? Both, probably, but that answer doesn’t make for clear policy solutions. This paper – following a large body of literature – attempts to explain what’s going on. Its novelty comes in the way the author considers timing and distinguishes between mental and physical health. The basis for the analysis is that selection into unemployment by the unhealthy ought to imply time-constant effects of unemployment on health. On the other hand, the negative effect of unemployment on health ought to grow over time. Using seven waves of data from the German Socio-economic Panel, a sample of 17,000 people (chopped from 48,000) is analysed, of which around 3,000 experienced unemployment. The basis for measuring mental and physical health is summary scores from the SF-12. A fixed-effects model is constructed based on the dependence of health on the duration and timing of unemployment, rather than just the occurrence of unemployment per se. The author finds a cumulative effect of unemployment on physical ill-health over time, implying causation. This is particularly pronounced for people unemployed in later life, and there was essentially no impact on physical health for younger people. The longer people spent unemployed, the more their health deteriorated. This was accompanied by a strong long-term selection effect of less physically healthy people being more likely to become unemployed. In contrast, for mental health, the findings suggest a short-term selection effect of people who experience a decline in mental health being more likely to become unemployed. But then, following unemployment, mental health declines further, so the balance of selection and causation effects is less clear. In contrast to physical health, people’s mental health is more badly affected by unemployment at younger ages. By no means does this study prove the balance between selection and causality. It can’t account for people’s anticipation of unemployment or future ill-health. But it does provide inspiration for better-targeted policies to limit the impact of unemployment on health.

Different domains – different time preferences? Social Science & Medicine [PubMed] Published 30th April 2018

Economists are often criticised by non-economists. Usually, the criticisms are unfounded, but one of the ways in which I think some (micro)economists can have tunnel vision is in thinking that preferences elicited with respect to money exhibit the same characteristics as preferences about things other than money. My instinct tells me that – for most people – that isn’t true. This study looks at one of those characteristics of preferences – namely, time preferences. Unfortunately for me, it suggests that my instincts aren’t correct. The authors outline a quasi-hyperbolic discounting model, incorporating both short-term present bias and long-term impatience, to explain gym members’ time preferences in the health and monetary domains. A survey was conducted with members of a chain of fitness centres in Denmark, of which 1,687 responded. Half were allocated to money-related questions and half to health-related questions. Respondents were asked to match an amount of future gains with an amount of immediate gains to provide a point of indifference. Health problems were formulated as back pain, with an EQ-5D-3L level 2 for usual activities and a level 2 for pain or discomfort. The findings were that estimates for discount rates and present bias in the two domains are different, but not by very much. On average, discount rates are slightly higher in the health domain – a finding driven by female respondents and people with more education. Present bias is the same – on average – in each domain, though retired people are more present biased for health. The authors conclude by focussing on the similarity between health and monetary time preferences, suggesting that time preferences in the monetary domain can safely be applied in the health domain. But I’d still be wary of this. For starters, one would expect a group of gym members – who have all decided to join the gym – to be relatively homogenous in their time preferences. Findings are similar on average, and there are only small differences in subgroups, but when it comes to health care (even public health) we’re never dealing with average people. Targeted interventions are increasingly needed, which means that differential discount rates in the health domain – of the kind identified in this study – should be brought into focus.

Credits

 

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.