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

Using HTA and guideline development as a tool for research priority setting the NICE way: reducing research waste by identifying the right research to fund. BMJ Open [PubMed] Published 8th March 2018

As well as the cost-effectiveness of health care, economists are increasingly concerned with the cost-effectiveness of health research. This makes sense, given that both are usually publicly funded and so spending on one (in principle) limits spending on the other. NICE exists in part to prevent waste in the provision of health care – seeking to maximise benefit. In this paper, the authors (all current or ex-employees of NICE) consider the extent to which NICE processes are also be used to prevent waste in health research. The study focuses on the processes underlying NICE guideline development and HTA, and the work by NICE’s Science Policy and Research (SP&R) programme. Through systematic review and (sometimes) economic modelling, NICE guidelines identify research needs, and NICE works with the National Institute for Health Research to get their recommended research commissioned, with some research fast-tracked as ‘NICE Key Priorities’. Sometimes, it’s also necessary to prioritise research into methodological development, and NICE have conducted reviews to address this, with the Internal Research Advisory Group established to ensure that methodological research is commissioned. The paper also highlights the roles of other groups such as the Decision Support Unit, Technical Support Unit and External Assessment Centres. This paper is useful for two reasons. First, it gives a clear and concise explanation of NICE’s processes with respect to research prioritisation, and maps out the working groups involved. This will provide researchers with an understanding of how their work fits into this process. Second, the paper highlights NICE’s current research priorities and provides insight into how these develop. This could be helpful to researchers looking to develop new ideas and proposals that will align with NICE’s priorities.

The impact of the minimum wage on health. International Journal of Health Economics and Management [PubMed] Published 7th March 2018

The minimum wage is one of those policies that is so far-reaching, and with such ambiguous implications for different people, that research into its impact can deliver dramatically different conclusions. This study uses American data and takes advantage of the fact that different states have different minimum wage levels. The authors try to look at a broad range of mechanisms by which minimum wage can affect health. A major focus is on risky health behaviours. The study uses data from the Behavioral Risk Factor Surveillance System, which includes around 300,000 respondents per year across all states. Relevant variables from these data characterise smoking, drinking, and fruit and vegetable consumption, as well as obesity. There are also indicators of health care access and self-reported health. The authors cut their sample to include 21-64-year-olds with no more than a high school degree. Difference-in-differences are estimated by OLS according to individual states’ minimum wage changes. As is often the case for minimum wage studies, the authors find several non-significant effects: smoking and drinking don’t seem to be affected. Similarly, there isn’t much of an impact on health care access. There seems to be a small positive impact of minimum wage on the likelihood of being obese, but no impact on BMI. I’m not sure how to interpret that, but there is also evidence that a minimum wage increase leads to a reduction in fruit and vegetable consumption, which adds credence to the obesity finding. The results also demonstrate that a minimum wage increase can reduce the number of days that people report to be in poor health. But generally – on aggregate – there isn’t much going on at all. So the authors look at subgroups. Smoking is found to increase (and BMI decrease) with minimum wage for younger non-married white males. Obesity is more likely to be increased by minimum wage hikes for people who are white or married, and especially for those in older age groups. Women seem to benefit from fewer days with mental health problems. The main concerns identified in this paper are that minimum wage increases could increase smoking in young men and could reduce fruit and veg consumption. But I don’t think we should overstate it. There’s a lot going on in the data, and though the authors do a good job of trying to identify the effects, other explanations can’t be excluded. Minimum wage increases probably don’t have a major direct impact on health behaviours – positive or negative – but policymakers should take note of the potential value in providing public health interventions to those groups of people who are likely to be affected by the minimum wage.

Aligning policy objectives and payment design in palliative care. BMC Palliative Care [PubMed] Published 7th March 2018

Health care at the end of life – including palliative care – presents challenges in evaluation. The focus is on improving patients’ quality of life, but it’s also about satisfying preferences for processes of care, the experiences of carers, and providing a ‘good death’. And partly because these things can be difficult to measure, it can be difficult to design payment mechanisms to achieve desirable outcomes. Perhaps that’s why there is no current standard approach to funding for palliative care, with a lot of variation between countries, despite the common aspiration for universality. This paper tackles the question of payment design with a discussion of the literature. Traditionally, palliative care has been funded by block payments, per diems, or fee-for-service. The author starts with the acknowledgement that there are two challenges to ensuring value for money in palliative care: moral hazard and adverse selection. Providers may over-supply because of fee-for-service funding arrangements, or they may ‘cream-skim’ patients. Adverse selection may arise in an insurance-based system, with demand from high-risk people causing the market to fail. These problems could potentially be solved by capitation-based payments and risk adjustment. The market could also be warped by blunt eligibility restrictions and funding caps. Another difficulty is the challenge of achieving allocative efficiency between home-based and hospital-based services, made plain by the fact that, in many countries, a majority of people die in hospital despite a preference for dying at home. The author describes developments (particularly in Australia) in activity-based funding for palliative care. An interesting proposal – though not discussed in enough detail – is that payments could be made for each death (per mortems?). Capitation-based payment models are considered and the extent to which pay-for-performance could be incorporated is also discussed – the latter being potentially important in achieving those process outcomes that matter so much in palliative care. Yet another challenge is the question of when palliative care should come into play, because, in some cases, it’s a matter of sooner being better, because the provision of palliative care can give rise to less costly and more preferred treatment pathways. Thus, palliative care funding models will have implications for the funding of acute care. Throughout, the paper includes examples from different countries, along with a wealth of references to dig into. Helpfully, the author explicitly states in a table the models that different settings ought to adopt, given their prevailing model. As our population ages and the purse strings tighten, this is a discussion we can expect to be having more and more.

Credits

 

A taxonomy of behavioural interventions

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Back in March I made a note to myself to write a paper – or, more likely, a blog post – presenting a taxonomy of behavioural interventions. I had gotten tired of everything being called a ‘nudge’ and with debates about whether nudges are ethical. I even bought a copy of Nudge so that I could use it to populate the taxonomy with examples.

Thankfully, someone else was already working on this and has beaten me to it – producing almost exactly what I had in mind. Mira Fischer from the University of Cologne and Sebastian Lotz from Stanford have written a working paper titled ‘Is soft paternalism ethically legitimate? – the relevance of psychological processes for the assessment of nudge-based policies‘. They differentiate between 4 types of behavioural intervention – or ‘nudge’ – and discuss the ethical implications associated with each by considering the psychological processes at play. It’s far better than any blog post I could have written, and I recommend reading it.

Fischer and Lotz’s taxonomy

Consider a utility-maximising individual with 2 choices (A or B), each with 2 possible outcomes (1 and 2), such that the utility associated with choice A would be U_{A} = \pi_{A1}(u_{A1M}+u_{A1N})+\pi_{A2}(u_{A2M}+u_{A2N}), where ‘π’ is the probability and ‘u’ the utility of the outcome and the ‘M’ and ‘N’ refers to monetary and non-monetary utility. Based on this, the authors then discuss the ways in which various types of nudge might influence the individual’s choice. The table below is not from the paper and is my interpretation of the taxonomy.

Type Name Point of influence Means of impact on expected utility of choice Examples
1 ‘discomfort nudge’ choice evaluation non-monetary utility default settings on electronic devices; communication of social norms
2 ‘probability nudge’ choice evaluation subjective probability of realisation informational campaigns
3 ‘indifference nudge’ preference formation monetary or non-monetary utility positioning of healthy/unhealthy products
4 ‘automatism nudges’ ? ? changes in road markings

Is the taxonomy complete and well-defined?

In my opinion, it is not.

I do not believe that Type 4 nudges exist in the way described. The authors use the example of changing road markings to make drivers think they are travelling faster than they actually are and thus reduce their speed. It seems clear to me that this is an example of Type 2; the driver has been made to believe that the probability of them crashing at their current speed is greater than they would otherwise have believed. The idea that there is an ethical difference between nudges to our ‘automatic’ behaviour and nudges to our considered behaviour – given that so much of our behaviour is automatic – I believe is unfounded.

When I was considering writing my own taxonomy of behavioural interventions, I was approaching it from a decision analysis perspective. Simply imagining the structure of an individual’s decision process and considering the different points at which an individual could be influenced. Based on the Thaler/Sunstein definition, a nudge can affect any part of a person’s decision process.

Based on this I believe there are 3 points of influence: i) before an individual’s preferences are defined ii) after the definition of preferences but before the observation of the choice set and iii) once the choice set has been recognised. Once preferences are defined and the choice set has been recognised there are 2 means of influencing choice; utility or probability.

As such, I think the taxonomy should look like this:

Type Point of influence Means of impact on expected utility of choice Examples
A preference formation values/priorities education; positioning of food
B choice set observation choice set expansion/compression positioning of food; introduction of cycle lanes
C choice evaluation subjective probability informational campaigns
D choice evaluation utility defaults; communication of social norms

As the authors outline in their paper, particular nudges will cross type boundaries. I have included the ‘positioning of food’ nudge under 2 types to highlight this. If positioning causes an individual to choose a healthy item – where they otherwise would have chosen a less healthy one – this could either be because they saw the healthy item first or because they simply didn’t see and fully consider the unhealthy option. In the former case Type A is at work, while in the latter case Type B is at work. I believe that educational interventions could fall into any of the above types because they can improve an individual’s ability to satisfy their own preferences. Type D could, of course, include a tax or a subsidy.

Furthermore, the ethical implications may be different depending on whether the impact on types B, C or D is positive or negative, and also whether the impact on utility is monetary or non-monetary, which would increase the total number of types to 9.

I don’t know whether I, the authors or both of us are right, but there’s one thing we can agree on. One nudge isn’t necessarily as ethical as the next, so we need better ways of defining behavioural interventions.

DOI: 10.6084/m9.figshare.1232109

#HEJC for 06/05/2013

This month’s meeting will take place Monday 6th May, at 5pm London time. That’ll be 11am in New Orleans and 7pm in Athens. Join the Facebook event here. We’ll also hold an antipodal meeting 12 hours later on Tuesday 7th May, at 5am London time. That’ll be midday in Kuala Lumpur and 1pm in Tokyo. Join the Facebook event here. For more information about the Health Economics Twitter Journal Club and how to take part, click here.

The paper for discussion this month is a working paper published in the Munich Personal RePEc Archive. The authors are Lydia Lawless, Rodolfo Nayga and Andreas DrichoutisThe title of the paper is:

“Time preference and health behaviour: A review”

Following the meeting, a transcript of the discussion can be downloaded here.

Links to the article

Direct: http://mpra.ub.uni-muenchen.de/45382

RePEc: http://ideas.repec.org/p/pra/mprapa/45382.html

Other: tbc

Summary of the paper

Time preferences affect individuals’ consumption decisions. Our understanding of time preferences can inform public policy, particularly in the area of health behaviours. Furthermore, in economic evaluation in health care, assumptions about time preferences play a crucial role in determining the cost-effectiveness of an intervention. The authors carry out a literature review; focussing on papers published post-2002 so as to avoid repeating previous reviews. In this review the authors sought to:

  1. examine the influence of time preferences on health behaviours
  2. explain how the societal time discount rate differs from the private time discount rate
  3. determine how time discount rates affect the decisions of governments in the developing world
  4. assess how time discount rates affect individuals’ decision making in regard to risky behaviours such as smoking, diet and sexual behaviour
  5. discuss the repercussions of time preferences for the prevention of poor health.

The authors identified 3 main strategies that are used to capture time preferences; observed behaviour, experimental settings and the use of time preference proxies. The authors conclude that context plays a key role in determining the nature of time preferences; developing countries may exhibit different trends to developed countries. Furthermore, time preferences from a societal perspective do no necessarily match those of the individual.

Discussion points

  • Do the authors succeed in reviewing all relevant literature?
  • Is the authors’ review strategy sufficient?
  • Does the study successfully address the 5 aims set out in the introduction?
  • How might this study inform future research?

Missed the meeting? Add your thoughts on the paper in the comments below.