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.

Sam Watson’s journal round-up for 25th June 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.

The efficiency of slacking off: evidence from the emergency department. Econometrica [RePEc] Published May 2018

Scheduling workers is a complex task, especially in large organisations such as hospitals. Not only should one consider when different shifts start throughout the day, but also how work is divided up over the course of each shift. Physicians, like anyone else, value their leisure time and want to go home at the end of a shift. Given how they value this leisure time, as the end of a shift approaches physicians may behave differently. This paper explores how doctors in an emergency department behave at ‘end of shift’, in particular looking at whether doctors ‘slack off’ by accepting fewer patients or tasks and also whether they rush to finish those tasks they have. Both cases can introduce inefficiency by either under-using their labour time or using resources too intensively to complete something. Immediately, from the plots of the raw data, it is possible to see a drop in patients ‘accepted’ both close to end of shift and close to the next shift beginning (if there is shift overlap). Most interestingly, after controlling for patient characteristics, time of day, and day of week, there is a decrease in the length of stay of patients accepted closer to the end of shift, which is ‘dose-dependent’ on time to end of shift. There are also marked increases in patient costs, orders, and inpatient admissions in the final hour of the shift. Assuming that only the number of patients assigned and not the type of patient changes over the course of a shift (a somewhat strong assumption despite the additional tests), then this would suggest that doctors are rushing care and potentially providing sub-optimal or inefficient care closer to the end of their shift. The paper goes on to explore optimal scheduling on the basis of the results, among other things, but ultimately shows an interesting, if not unexpected, pattern of physician behaviour. The results relate mainly to efficiency, but it’d be interesting to see how they relate to quality in the form of preventable errors.

Semiparametric estimation of longitudinal medical cost trajectory. Journal of the American Statistical Association Published 19th June 2018

Modern computational and statistical methods have opened up a range of statistical models to estimation hitherto inestimable. This includes complex latent variable structures, non-linear models, and non- and semi-parametric models. Recently we covered the use of splines for semi-parametric modelling in our Method of the Month series. Not that complexity is everything of course, but given this rich toolbox to more faithfully replicate the data generating process, one does wonder why the humble linear model estimated with OLS remains so common. Nevertheless, I digress. This paper addresses the problem of estimating the medical cost trajectory for a given disease from diagnosis to death. There are two key issues: (i) the trajectory is likely to be non-linear with costs probably increasing near death and possibly also be higher immediately after diagnosis (a U-shape), and (ii) we don’t observe the costs of those who die, i.e. there is right-censoring. Such a set-up is also applicable in other cases, for example looking at health outcomes in panel data with informative dropout. The authors model medical costs for each month post-diagnosis and time of censoring (death) by factorising their joint distribution into a marginal model for censoring and a conditional model for medical costs given the censoring time. The likelihood then has contributions from the observed medical costs and their times, and the times of the censored outcomes. We then just need to specify the individual models. For medical costs, they use a multivariate normal with mean function consisting of a bivariate spline of time and time of censoring. The time of censoring is modelled non-parametrically. This setup of the missing data problem is sometimes referred to as a pattern mixing model, in that the outcome is modelled as a mixture density over different populations dying at different times. The authors note another possibility for informative missing data, which was considered not to be estimable for complex non-linear structures, was the shared parameter model (to soon appear in another Method of the Month) that assumes outcomes and dropout are independent conditional on an underlying latent variable. This approach can be more flexible, especially in cases with varying treatment effects. One wonders if the mixed model representation of penalised splines wouldn’t fit nicely in a shared parameter framework and provide at least as good inferences. An idea for a future paper perhaps… Nevertheless, the authors illustrate their method by replicating the well-documented U-shaped costs from the time of diagnosis in patients with stage IV breast cancer.

Do environmental factors drive obesity? Evidence from international graduate students. Health Economics [PubMedPublished 21st June 2018

‘The environment’ can encompass any number of things including social interactions and networks, politics, green space, and pollution. Sometimes referred to as ‘neighbourhood effects’, the impact of the shared environment above and beyond the effect of individual risk factors is of great interest to researchers and policymakers alike. But there are a number of substantive issues that hinder estimation of neighbourhood effects. For example, social stratification into neighbourhoods likely means people living together are similar so it is difficult to compare like with like across neighbourhoods; trying to model neighbourhood choice will also, therefore, remove most of the variation in the data. Similarly, this lack of common support, i.e. overlap, between people from different neighbourhoods means estimated effects are not generalisable across the population. One way of getting around these problems is simply to randomise people to neighbourhoods. As odd as that sounds, that is what occurred in the Moving to Opportunity experiments and others. This paper takes a similar approach in trying to look at neighbourhood effects on the risk of obesity by looking at the effects of international students moving to different locales with different local obesity rates. The key identifying assumption is that the choice to move to different places is conditionally independent of the local obesity rate. This doesn’t seem a strong assumption – I’ve never heard a prospective student ask about the weight of our student body. Some analysis supports this claim. The raw data and some further modelling show a pretty strong and robust relationship between local obesity rates and weight gain of the international students. Given the complexity of the causes and correlates of obesity (see the crazy diagram in this post) it is hard to discern why certain environments contribute to obesity. The paper presents some weak evidence of differences in unhealthy behaviours between high and low obesity places – but this doesn’t quite get at the environmental link, such as whether these behaviours are shared through social networks or perhaps the structure and layout of the urban area, for example. Nevertheless, here is some strong evidence that living in an area where there are obese people means you’re more likely to become obese yourself.

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Chris Sampson’s journal round-up for 4th December 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.

Funding breakthrough therapies: a systematic review and recommendation. Health Policy Published 2nd December 2017

One of the (numerous) financial pressures on health care funders in the West is the introduction of innovative (and generally very expensive) new therapies. Some of these can be considered curative, which isn’t necessarily the best way for manufacturers to create a steady income. New funding arrangements have been proposed to facilitate patient access while maintaining financial sustainability. This article focuses on a specific group of innovative therapies known as ‘Advanced Therapy Medicinal Products’ (ATMPs), which includes gene therapies. The authors conducted a systematic review of papers proposing funding models and considered their appropriateness for ATMPs. There were 48 papers included in the review that proposed payment mechanisms for high-cost therapies. Three top-level groups were identified: i) financial agreements, ii) performance-based agreements, and iii) healthcoin (a tradable currency representing the value of outcomes). The different mechanisms are compared in terms of their feasibility, acceptability, burden, ‘financial attractiveness’ and their appeal to payers and manufacturers. Annuity payments are identified as relatively attractive compared to other options, but each mechanism is summarily shown to be imperfect in the ATMP context. So, instead, the authors propose an ATMP-specific fund. For UK readers, this will likely smell a bit too much like the disastrous Cancer Drugs Fund. It isn’t clear why such a programme would be superior to annuity payments or more inventive mechanisms, or even whether it would be theoretically sound. Thus, the proposal is not convincing.

Supply-side effects from public insurance expansions: evidence from physician labor markets. Health Economics [PubMed] Published 1st December 2017

Crazy though American health care may be, its inconsistency in coverage can make for good research fodder. The Child Health Insurance Program (CHIP) was set up in 1997 and then, when the initial money ran out 10 years later, the program was (eventually) expanded. In this study, the authors use the changes in CHIP to examine the impact of expanded public coverage on provider behaviour, namely; subspecialty training (which could become more attractive with a well-insured customer base), practice setting and prevailing wage offers. The data for the study relate to the physician labour market for New York state for 2002-2013, as collected in the Graduate Medical Education survey. A simple difference-in-differences analysis is conducted with reference to the 2009 CHIP expansion, controlling for physician demographics. Paediatricians are the treatment group and the control group is adult physician generalists (mostly internal medicine). 2009 seems to be associated with a step-change in the proportion of paediatricians choosing to subspecialise – an increased probability of about 8 percentage points. There is also an upward shift in the proportion of paediatricians entering private practice, with some (weak) evidence that there is an increased preference for rural areas. These changes don’t seem to be driven by relative wage increases, with no major change in trends. So it seems that the expanded coverage did have important supply-side effects. But the waters are muddy here. In particular, we have the Great Recession and Obamacare as possible alternative explanations. Though it’s difficult to come up with good reasons for why these might better explain the observed changes.

Reflections on the NICE decision to reject patient production losses. International Journal of Technology Assessment in Health Care [PubMedPublished 20th November 2017

When people conduct economic evaluations ‘from a societal perspective’, this often just means a health service perspective with productivity losses added. NICE explicitly exclude the inclusion of these production losses in health technology appraisals. This paper reviews the issues at play, focussing on the normative question of why they should (or should not) be included. Findings from a literature review are summarised with reference to the ethical, theoretical and policy questions. Unethical discrimination potentially occurs if people are denied health care on the basis of non-health-related characteristics, such as the ability to work. All else equal, should health care for men be prioritised over health care for women because men have higher wages? Are the unemployed less of a priority because they’re unemployed? The only basis on which to defend the efficiency of an approach that includes productivity losses seems to be a neoclassical welfarist one, which is hardly tenable in the context of health care. If we adopt the extra-welfarist understanding of opportunity cost as foregone health then there is really no place for production losses. The authors also argue that including production losses may be at odds with policy objectives, at least in the context of the NHS in the UK. Health systems based on privately-funded care or social insurance may have different priorities. The article concludes that taking account of production losses is at odds with the goal of health maximisation and therefore the purpose of the NHS in the UK. Personally, I think priority setting in health care should take a narrow health perspective. So I agree with the authors that production losses shouldn’t be included. I’m not sure this article will convince those who disagree, but it’s good to have a reference to vindicate NICE’s position.

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