Chris Sampson’s journal round-up for 13th March 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.

The effects of exercise and relaxation on health and wellbeing. Health Economics [PubMedPublished 9th Month 2017

Encouraging self-management of health sounds like a good idea, but the evidence is pretty weak. As economists, we know that something must be displaced in order to do it. This study considers the opportunity cost of time and how it might affect self-management activity and any associated benefits. Employment and education are likely to increase income and thus facilitate more expenditure on exercise. But the time cost of exercise is also likely to increase, meaning that the impact on demand is ambiguous. The study uses data from a trial of self-management support that included people with diabetes, COPD or IBS. EQ-5D, self-assessed health and the amount of time spent ‘being happy’ were all collected. Information was available for 12 different self-management activities, including ‘do exercises’ and ‘rest and relax’, and the extent to which individuals did these. Outcomes for 3,472 people at 12-month follow-up are estimated, controlling for outcomes at baseline and 6 months. The study assumes that employment and education affect health via their influence on exercise and relaxation. That seems a bit questionable and the other 10 self-management indicators could have been looked at to test this. People in full-time employment were 11 percentage points less likely to use relaxation to manage their condition, suggesting that the substitution effect on time dominates as the opportunity cost of self-management increases. Having a degree or professional qualification increased the probability of using exercise by 5 percentage points, suggesting that the income effect dominates. Those who are more likely to use either exercise or relaxation are also more likely to do the other. An interesting suggestion is that time preference might explain things here. Those with more education may prefer to exercise (as an investment) than to get the instant gratification of rest and relaxation. It’s important that policy recommendations take into consideration the fact that different groups will respond differently to incentives for self-management, at least partly due to their differing time constraints. The thing I find most interesting is the analysis of the different outcomes (something I’ve worked on). Exercise is found to improve self-assessed health, while relaxation increases happiness. Neither exercise or relaxation had a (statistically significant) effect on EQ-5D. Depending on your perspective, this either suggests that the EQ-5D is failing to identify important changes in broad health-related domains or it means that self-management does not achieve the goals (QALYs to the max) of the health service.

New findings from the time trade-off for income approach to elicit willingness to pay for a quality adjusted life year. The European Journal of Health Economics [PubMedPublished 8th March 2017

The question ‘what is a QALY worth’ could invoke any number of reactions in a health economist, from chin scratching to eye rolling. The perspective that we’re probably most familiar with in the UK is that the value of a QALY is the value of health foregone in order to achieve it (i.e. opportunity cost within the health care perspective). An alternative perspective is that the value of a QALY is the consumption value of health; how much consumption would individuals be willing to give up in order to obtain an additional QALY? This second perspective facilitates a broader societal perspective. It can tell us whether or not the budget is set at an appropriate level, while the health care perspective can only take the budget as given. This study relates mainly to decisions made with the ‘consumption value’ perspective. One approach that has been proposed is to assess willingness to pay for a QALY using a time trade-off exercise that incorporates trade-offs between length and quality of life and income. This study builds on the original work by using a multiplicative utility function to estimate willingness to pay and also bringing in prospect theory to allow for reference dependence and loss aversion. 550 participants were asked to choose between living 10 years in their current health state with their current salary or to live a reduced number of years in their current health state with a luxury income (pre-specified by the participant). Respondents were also asked to make a similar choice, but framed as a loss of income, between living 10 years at a subsistence income or fewer years with their current income. A quality of life trade-off exercise was also conducted, in which people traded reduced health and a lower income. The findings support the predictions of prospect theory. Loss aversion is found to be stronger for duration than for quality of life. Individuals were more willing to sacrifice life years to move from subsistence income to current income than to move from current income to luxury income. The results imply that quality of life and income are closer substitutes than longevity and income. That makes sense, given the all-or-nothing nature of being alive. Crucially, the findings highlight the need to better understand the shape of the underlying lifetime utility function. In all tasks, more than half of respondents were either non-traders or over-traded, indicating a negative willingness to pay. That should give pause for thought when it comes to any aggregation of the results. Willingness to pay studies often throw up more questions than answers. This one does so more than most, particularly about sources of bias in people’s responses. The authors identify plenty of opportunities for future research.

Beyond QALYs: multi-criteria based estimation of maximum willingness to pay for health technologies. The European Journal of Health Economics [PubMed] Published 3rd March 2017

Life is messy. Evaluating things in terms of a single outcome, whether that be QALYs, £££s or whatever, is necessarily simplifying and restrictive. That’s not necessarily a bad thing, but we’d do well to bear it in mind. In this paper, Erik Nord sets out a kind of cost value analysis that does away with QALYs (gasp!). The author starts by outlining some familiar criticisms of the QALY approach, such as its failure to consider the inherent value of life and people’s differing reference points. Generally, I see these as features rather than bugs, and it isn’t QALYs themselves in the crosshairs here so much as cost-per-QALY analysis. The proposed method flips current practice by putting societal preferences about fair and efficient resource allocation before attaching values to the outcomes. As such, it acknowledges the fact that society’s preferences for gains in quality of life differ from those for gains in length of life. For example, society may prefer treating the more severely ill (independent of age) but also exhibit a ‘fair innings’ preference that is related to age. Thus, quality and quantity of life are disaggregated and the QALY is no more. A set of tables is presented that can be read to assess ‘value’ in alternative scenarios, given the assumptions set out in the paper. There is merit in the approach and a lot that I like about the possibilities of its use. But for me, the whole thing was made less attractive by the way it is presented in the paper. The author touts willingness to pay – for quality of life gains and for longevity gains – as the basis for value. Anything that makes resource allocation more dependent on willingness to pay values for things without a price (health, life) is a big no-no for me. But the method doesn’t depend on that. Furthermore, as is so often the case, most of the criticisms within relate to ways of using QALYs, rather than the fundamental basis for their estimation. This only weakens the argument for an alternative. But I can think of plenty of problems with QALYs, some of which might be addressed by this alternative approach. It’s unfortunate that the paper doesn’t outline how these more fundamental problems might be addressed. There may come a day when we do away with QALYs, and we may end up doing something similar to what’s outlined here, but we need to think harder about how this alternative is really better.

Credits

Thesis Thursday: Ayesha Ali

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

Title
Essays on health economics: trans fat policies, commuting, physical activity, and body mass index in the US
Supervisors
Colin Green, Bruce Hollingsworth
Repository link
http://www.research.lancs.ac.uk/portal/en/publications/-(182eadeb-a873-4d45-93bc-a987899c6587).html

What drew you to this particular topic and made you want to dedicate your PhD to it?

I’ve always been very fascinated about how people make decisions when it comes to health-related behaviors. My Mom was a doctor, so health has always been a main driver in what our family ate and what sort of values my parents emphasized. Growing up, I think we were always kind of “the weird family” in the neighborhood and in our extended family, because we often put health before cultural norms (i.e. changing traditional recipes to be healthier, avoiding all of those colorful and fun children’s cereals and candies, etc.), so I’ve been very aware and very curious about what drives health-related choices for different people. I’ve also lived in a number of very different communities, where norms about behaviors related to health varied significantly from place to place, and that variation has always been something that I’ve sort of observed and wondered about as well. So, I think these observations are what drew me to economics, as a way of understanding choices. My thesis work was somewhat of an attempt to crack the surface.

I don’t think I got the chance in my thesis to dig as deeply into the issue where health behaviors and culture meet as I had wanted, but I was lucky to get to use some of the types of data and some of the econometric tools that will come in handy as I continue to explore this area.

Your study focussed on US data, but you studied at a UK university. Was this a help or a hindrance?

I think it was definitely a challenge to work with non-UK data while studying at a UK University; for example, I didn’t find certain types of support (in terms of local workshops or having other colleagues using the same data) or familiarity that was often easily available to someone using UK data. However, my supervisors were very supportive of me and there were students in my cohort using other non-UK data so I was not alone in that regard.

One challenge that I had was that I couldn’t just ask someone who uses the data, but instead had to spend a bit of time trying to understand how other researchers in the literature used the data and then try to figure out whether or not their assumptions and ways of using the data were applicable to my work. I spent a lot of time reading data documentation files — both of my datasets (NHANES and ATUS) have really good online resources that I’m now very familiar with! I think that a lot of this being “on my own” with the data helped me to develop a feeling of confidence that I may not have had otherwise. I’m somewhat prone to second-guessing myself, so being able to learn to have confidence in my work was really valuable.

So, although challenging, overall I would say it was definitely more of a help than a hindrance.

Methodologically, what was the most challenging aspect of the research?

In my third chapter I use time use data, a two-part model, and a recursive bivariate probit model to estimate physical activity participation and duration decisions given an individual’s commuting time, with an instrument for commuting. There are some conflicting ideas on how best to use time-use data in the literature (see: Franzis and Stewart, 2010; Gershuny, 2012; Stewart, 2013) and few examples of instruments for commuting (see: Baum-Snow, 2007; Gimenez-Nadal and Molina, 2011).

I received a lot of different feedback on the best estimation approach to use. I wanted to estimate participation and intensity elasticities for individuals who do physical activity on a given day. There are a number of ways to deal with these two questions separately or simultaneously and also to deal with the large number of zeros in the data (i.e. individuals who did not participate in physical activity). At one point, I remember that I thought I had everything figured out with this chapter; and then following a conference presentation of this work, one commenter was dead set that I should be using a switching model. I hadn’t considered that approach and wasn’t familiar with it at all, so I had to go look it up, figure out what it was and whether or not it worked with what I was doing. So, just figuring out the best way to deal with my data and with the questions I was asking in this third chapter were probably the most challenging part of the thesis for me.

If you could have a decision maker implement one policy change supported by your work, what would it be?

If I could ideally have policy makers do one thing that is supported by my work, I think it would actually be a rather general thing, not related to one specific policy, but sort of related to the entire approach of policy-making. I would want policy makers to consider the groups they are targeting with more care. For example, my third chapter looks at time use among obese individuals and healthier-weight individuals and finds that many decisions, such as the decision of where to live and work, are often driven by different factors in these different groups. In general, my work suggests that different groups may be driven by different motivating factors and if we don’t understand what these are, policies might not successfully reach those who could most benefit. That being said, this probably isn’t as easy as it sounds, as there are a lot of political influences on how policy decisions are made.

If you had to do it all again (perish the thought), is there anything you would have done differently?

I think overall, I’m really happy with my experience; I had some good resources and Lancaster was a great environment for me. My PhD cohort was close-knit and faculty in my department were very approachable and supportive. If anyone is interested, I also found McCloskey’s Economical Writing and Thomson’s Guide for the Young Economist to be really helpful at various stages of the PhD.

If I could have changed one thing though, it was how I dealt with insecurity. Even in such a supportive environment, the competitive nature of academia can contribute to feelings of insecurity; I worked hard to recognize my own insecurities and fight through them, but I didn’t always succeed. So, if I could do something differently, I would like to have been less afraid to own up to not knowing something and to just keep asking questions until I understood.

I really enjoyed having the chance to talk with you about my experiences and motivation. And I hope that if anyone can find my experiences useful to them at their stage of the process, they do. Thanks again for inviting me.