Chris Sampson’s journal round-up for 19th August 2019

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.

Paying for kidneys? A randomized survey and choice experiment. American Economic Review [RePEc] Published August 2019

This paper starts with a quote from Alvin Roth about ‘repugnant transactions’, of which markets for organs provide a prime example. This idea of ‘repugnant transactions’ has been hijacked by some pop economists to represent the stupid opinions of non-economists. If you ask me, markets for organs aren’t repugnant, they just seem like a very bad idea in terms of both efficiency and equity. But it doesn’t matter what I think; it matters what the people of the United States think.

The authors of this study conducted an online survey with a representative sample of 2,666 Americans. Each respondent was randomised to evaluate one of eight systems compared with the current system. The eight systems differed with respect to i) cash or non-cash compensation of ii) different sizes ($30,000 or $100,000), iii) paid by either a public agency or the organ recipient. Participants made five binary choices that differed according to the gain – in transplants generated – associated with the new system. Half of the participants were also asked to express moral judgements.

Both the system features (e.g. who pays) and the outcomes of the new system influenced people’s choices. Broadly speaking, the results suggest that people aren’t opposed to donors being paid, but are opposed to patients paying. (Remember, we’re talking about the US here!). Around 21% of respondents opposed payment no matter what, 46% were in favour no matter what, and 18% were sensitive to the gain in the number of transplants. A 10% point increase in transplants resulted in a 2.6% point increase in support. Unsurprisingly, individuals’ moral judgements were predictive of the attitudes they expressed, particularly with respect to fairness. The authors describe their results as exhibiting ‘strong polarisation’, which is surely inevitable for questions that involve moral judgement.

Being in AER, this is a long meandering paper with extensive analyses and thoroughly reported results. There’s lots of information and findings that I can’t share here. It’s a valuable study with plenty of food for thought, but I can’t help but think that it is, methodologically, a bit weak. If we want to understand the different views in society, surely some Q methodology would be more useful than a basic online survey. And if we want to elicit stated preferences, surely a discrete choice experiment with a well-thought-out efficient design would give us more meaningful results.

Estimating local need for mental healthcare to inform fair resource allocation in the NHS in England: cross-sectional analysis of national administrative data linked at person level. The British Journal of Psychiatry [PubMed] Published 8th August 2019

The need to fairly (and efficiently) allocate NHS resources across the country played an important part in the birth of health economics in the UK, and resulted in resource allocation formulas. Since 1996 there has been a separate formula for mental health services, which is periodically updated. This study describes the work undertaken for the latest update.

The model is based on predicting service use and total mental health care costs observed in 2015 from predictors in the years 2013-2014, to inform allocations in 2019-2024. Various individual-level data sources available to the NHS were used for 43.7 million people registered with a GP practice and over the age of 20. The cost per patient who used mental health services ranged from £94 to over one million, averaging around £2,000. The predictor variables included individual indicators such as age, sex, ethnicity, physical diagnoses, and household type (e.g. number of adults and kids). The model also used variables observed at the local or GP practice level, such as the proportion of people receiving out-of-work benefits and the distance from the mental health trust. All of this got plugged into a good old OLS regression. From individual-level predictions, the researchers created aggregated indices of need for each clinical commission group (CCG).

A lot went into the model, which explained 99% of the variation in costs between CCGs. A key way in which this model differs from previous versions is that it relies on individual-level indicators rather than those observed at the level of GP practice or CCG. There was a lot of variation in the CCG need indices, ranging from 0.65 for Surrey Heath to 1.62 for Southwark, where 1.00 is the average. You’ll need to check the online appendices for your own CCG’s level of need (Lewisham: 1.52). As one might expect, the researchers observed a strong correlation between a CCG’s need index and the CCG’s area’s level of deprivation. Compared with previous models, this new model indicates a greater allocation of resources to more deprived and older populations.

Measuring, valuing and including forgone childhood education and leisure time costs in economic evaluation: methods, challenges and the way forward. Social Science & Medicine [PubMed] Published 7th August 2019

I’m a ‘societal perspective’ sceptic, not because I don’t care about non-health outcomes (though I do care less) but because I think it’s impossible to capture everything that is of value to society, and that capturing just a few things will introduce a lot of bias and noise. I would also deny that time has any intrinsic value. But I do think we need to do a better job of evaluating interventions for children. So I expected this paper to provide me with a good mix of satisfaction and exasperation.

Health care often involves a loss of leisure or work time, which can constitute an opportunity cost and is regularly included in economic evaluations – usually proxied by wages – for adults. The authors outline the rationale for considering ‘time-related’ opportunity costs in economic evaluations and describe the nature of lost time for children. For adults, the distinction is generally between paid or unpaid work and leisure time. Arguably, this distinction is not applicable to children. Two literature reviews are described. One looked at economic evaluations in the context of children’s health, to see how researchers have valued lost time. The other sought to identify ideas about the value of lost time for children from a broader literature.

The authors do a nice job of outlining how difficult it is to capture non-health-related costs and outcomes in the context of childhood. There is a handful of economic evaluations that have tried to measure and value children’s foregone time. The valuations generally focussed on the costs of childcare rather than the costs to the child, though one looked at the rate of return to education. There wasn’t a lot to go off in the non-health literature, which mostly relates to adults. From what there is, the recommendation is to capture absence from formal education and foregone leisure time. Of course, consideration needs to be given to the importance of lost time and thus the value of capturing it in research. We also need to think about the risk of double counting. When it comes to measurement, we can probably use similar methods as we would for adults, such as diaries. But we need very different approaches to valuation. On this, the authors found very little in the way of good examples to follow. More research needed.

Credits

Thesis Thursday: David Mott

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 David Mott who has a PhD from Newcastle University. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
How do preferences for public health interventions differ? A case study using a weight loss maintenance intervention
Supervisors
Luke Vale, Laura Ternent
Repository link
http://hdl.handle.net/10443/4197

Why is it important to understand variation in people’s preferences?

It’s not all that surprising that people’s preferences for health care interventions vary, but we don’t have a great understanding of what might drive these differences. Increasingly, preference information is being used to support regulatory decisions and, to a lesser but increasing extent, health technology assessments. It could be the case that certain subgroups of individuals would not accept the risks associated with a particular health care intervention, whereas others would. Therefore, identifying differences in preferences is important. However, it’s also useful to try to understand why this heterogeneity might occur in the first place.

The debate on whose preferences to elicit for health state valuation has traditionally focused on those with experience (e.g. patients) and those without (e.g. the general population). Though this dichotomy is problematic; it has been shown that health state utilities systematically differ between these two groups, presumably due to the difference in relative experience. My project aimed to explore whether experience also affects people’s preferences for health care interventions.

How did you identify different groups of people, whose preferences might differ?

The initial plan for the project was to elicit preferences for a health care intervention from general population and patient samples. However, after reviewing the literature, it seemed highly unlikely that anyone would advocate for preferences for treatments to be elicited from general population samples. It has long been suggested that discrete choice experiments (DCEs) could be used to incorporate patient preferences into decision-making, and it turned out that patients were the focus of the majority of the DCE studies that I reviewed. Given this, I took a more granular approach in my empirical work.

We recruited a very experienced group of ‘service users’ from a randomised controlled trial (RCT). In this case, it was a novel weight loss maintenance intervention aimed at helping obese adults that had lost at least 5% of their overall weight to maintain their weight loss. We also recruited an additional three groups from an online panel. The first group were ‘potential service users’ – those that met the trial criteria but could not have experienced the intervention. The second group were ‘potential beneficiaries’ – those that were obese or overweight and did not meet the trial criteria. The final group were ‘non-users’ – those with a normal BMI.

What can your study tell us about preferences in the context of a weight loss maintenance intervention?

The empirical part of my study involved a DCE and an open-ended contingent valuation (CV) task. The DCE was focused on the delivery of the trial intervention, which was a technology-assisted behavioural intervention. It had a number of different components but, briefly, it involved participants weighing themselves regularly on a set of ‘smart scales’, which enabled the trial team to access and monitor the data. Participants received text messages from the trial team with feedback, reminders to weigh themselves (if necessary), and links to online tools and content to support the maintenance of their weight loss.

The DCE results suggested that preferences for the various components of the intervention varied significantly between individuals and between the different groups – and not all were important. In contrast, the efficacy and cost attributes were important across the board. The CV results suggested that a very significant proportion of individuals would be willing to pay for an effective intervention (i.e. that avoided weight regain), with very few respondents expressing a willingness to pay for an intervention that led to more than 10-20% weight regain.

Do alternative methods for preference elicitation provide a consistent picture of variation in preferences?

Existing evidence suggests that willingness to pay (WTP) estimates from CV tasks might differ from those derived from DCE data, but there aren’t a lot of empirical studies on this in health. Comparisons were planned in my study, but the approach taken in the end was suboptimal and ultimately inconclusive. The original plan was to obtain WTP estimates for an entire WLM intervention using the DCE and to compare this with the estimates from the CV task. Due to data limitations, it wasn’t possible to make this comparison. However, the CV task was a bit unusual because we asked for respondents’ WTP at various different efficacy levels. So instead the comparison made was between average WTP values for a percentage point of weight re-gain. The differences were statistically insignificant.

Are some people’s preferences ‘better defined’ than others’?

We hypothesised that those with experience of the trial intervention would have ‘better defined’ preferences. To explore this, we compared the data quality across the different user groups. From a quick glance at the DCE results, it is pretty clear that the data were much better for the most experienced group; the coefficients were larger, and a much higher proportion was statistically significant. However, more interestingly, we found that the most experienced group were 23% more likely to have passed all of the rationality tests that were embedded in the DCE. Therefore, if you accept that better quality data is an indicator of ‘better defined’ preferences, then the data do seem reasonably supportive of the hypothesis. That being said, there were no significant differences between the other three groups, begging the question: was it the difference in experience, or some other difference between RCT participants and online panel respondents?

What does your research imply for the use of preferences in resource allocation decisions?

While there are still many unanswered questions, and there is always a need for further research, the results from my PhD project suggest that preferences for health care interventions can differ significantly between respondents with differing levels of experience. Had my project been applied to a more clinical intervention that is harder for an average person to imagine experiencing, I would expect the differences to have been much larger. I’d love to see more research in this area in future, especially in the context of benefit-risk trade-offs.

The key message is that the level of experience of the participants matters. It is quite reasonable to believe that a preference study focusing on a particular subgroup of patients will not be generalisable to the broader patient population. As preference data, typically elicited from patients, is increasingly being used in decision-making – which is great – it is becoming increasingly important for researchers to make sure that their respondent samples are appropriate to support the decisions that are being made.

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.