Thesis Thursday: Edward Webb

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 Edward Webb who graduated with a PhD from the University of Copenhagen. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Attention and perception in decision-making and interactions
Supervisors
Alexander Sebald, Peter Norman Sørensen
Repository link
http://www.econ.ku.dk/forskning/Publikationer/ph.d_serie_2007-/Ph.D.181.pdf

Attention and perception aren’t things we often talk about in health economics. Why are they important?

There’s been a lot of work done on attention and perception in economics recently, which I think is a great development. They are really vital topics since unless you know how people perceive the information available to them, and what aspects of their environment are most likely to command their attention, it’s difficult to forecast their behaviour.

I think attention and perception will become more widely talked about in health in future, as there’s many cases in which they have a lot of relevance. For example, you might want to know whether rare symptoms grab doctors’ attention because they’re unusual, or whether they don’t notice them because they’re not expecting them. (There’s a great study by Drew, Vo and Wolfe where radiologists looking at CT scans of the chest failed to notice a picture of a gorilla embedded in them by the experimenters.)

Or if you’re planning some dietary intervention, you might want to take into account how unhealthy food such as pizza and chips attracts people’s attention much more than healthy food, and to look at why this is the case.

What can the new theoretical frameworks described in your thesis tell us about individual behaviour?

Most of the literature in psychology is about how individuals behave. I tried a lot in my thesis to move beyond studying individual decision making to look at how the effects of attention and perception change in different economic environments, as this can often be counter-intuitive.

As an example, in one of the chapters of my thesis I explore the effects of individuals having limited ability to tell the quality of different products apart. It turns out that the effects on a market can be radically different depending on whether there are fixed or marginal costs of quality.

I was also very interested in looking at how individuals with limited or biased attention interact with profit maximising firms. There’s an expectation that companies will rip people off and exploit them, and certainly, that can happen, but I was able to show that it’s not necessarily the case. The case of individuals having limited ability to tell products’ quality apart which I mentioned above is a good example. When firms rely on product differentiation to earn profits, they’re actually harmed by people with this limitation, rather than exploiting them.

Did you find yourself reaching beyond the economics literature for guidance, either in the subject matter or the techniques that you used?

Yes, I read quite a lot outside the standard economics literature during my thesis. Behavioural and experimental economics more or less sits on the boundary between economics and psychology, so it felt very natural to seek guidance from other disciplines. This was especially the case for the eye-tracking experiment that I carried out with the help of my co-authors Andreas Gotfredsen, Carsten S. Nielsen and Alexander Sebald. I needed to learn quite a bit about psychological work on visual attention.

I like that economics is as much a set of analytic tools as a subject area, which gives it the advantage of being able to take on nontraditional topics.

You studied in Denmark, yet your thesis is written in English. Did this raise any additional challenges in completing your PhD?

Danish people speak better English than what I can! Language really wasn’t a problem at all at work, since English is very much the language of academia. Seminars were in English, PhD students and a lot of masters students wrote their theses in English and nearly all postgraduate and some undergraduate teaching was in English. I did feel quite privileged to have the advantage of being a native speaker of the language, and appreciative that most of my colleagues were fine with working in a second language. That’s why I was always very willing to help people out with proofreading English. I only hope I didn’t make too many mistakes!

On the social side, you can get away with living in Denmark without speaking Danish, and many people do. Indeed, I probably wouldn’t have made the effort of becoming a (moderate) Danish speaker if my partner wasn’t Danish.

Copenhagen, and Denmark in general, is a fantastic place to live and work, and I’d urge anyone who is thinking about moving there not to be put off by the language barrier.

How did your experiences during your PhD contribute to your decision to work in the field of health economics?

The question makes it sound like I had a coherent plan! In reality, I’m terrible about thinking about the long term. (I must be a natural Keynesian.) I ended up moving back to the UK after I graduated ironically because of my Danish partner, as she had found a job here. She also works in health, as a medical physicist and cancer researcher at Leeds. I applied for economics jobs in the area and was over the moon to secure a place at the Academic Unit of Health Economics at Leeds.

It’s a little more applied and hands-on than what I was working on before, which is great. I came into economics because I was interested in finding out how people act and interact, and so it’s fantastic to have the opportunity now to work principally with discrete choice experiments, trying to work out patients’ and clinicians’ preferences.

Since I started at Leeds a few months ago I’ve really enjoyed my time. The environment is very stimulating and all my colleagues are extremely friendly and easy going and are always willing to help out or discuss an interesting new idea.

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Kenneth Arrow on healthcare economics: a 21st century appreciation

Nobel laureate Kenneth Arrow passed away on February 21, 2017. In a classic, fifty-year-old paper entitled Uncertainty and the Welfare Economics of Medical Care, Arrow discussed how:

“the operation of the medical-care industry and the efficacy with which it satisfies the needs of society differs from… a competitive model… If a competitive equilibrium exists at all, and if all commodities relevant to costs or utilities are in fact priced in the market, then the equilibrium is necessarily [Pareto] optimal” (emphasis added)

Note the implicit assumption that price reflects value, to which I’ll return. As Arrow elegantly explained, there are vast differences between the actual healthcare market and the competitive model, and, moreover, these differences arise from important features of the actual healthcare market.

Identifying the lack of realism of the competitive model in health care may lead to deeper understanding of the actual system. In essence this is what Arrow does. Although both medical care and our expectations have changed greatly, Arrow ’63 is still valid and worth reading today.

Here is Arrow’s summary of the differences between the healthcare market and typical competitive markets.

The nature of demand

Demand for medical services is irregular and unpredictable:

“Medical services, apart from preventive services, afford satisfaction only in the event of illness, a departure from the normal state of affairs… Illness is, thus, not only risky but a costly risk in itself, apart from the cost of medical care.”

Expected behavior of the physician

“It is at least claimed that treatment is dictated by objective needs of the case and not limited by financial considerations… Charity treatment in one form or another does exist because of this tradition about human rights to adequate medical care.”

Product uncertainty

“Recovery from disease is as unpredictable as its incidence…  Because medical knowledge is so complicated, the information possessed by the physician as to the consequences and possibilities of treatment is necessarily very much greater than that of the patient, or at least so it is believed by both parties.”

Supply conditions

Barriers to entry include licensing and other controls on quality (accreditation) and costs.

“One striking consequence of the control of quality is the restriction on the range offered… The declining ratio of physicians to total employees in the medical-care industry shows that substitution of less trained personnel, technicians and the like, is not prevented completely, but the central role of the highly trained physician is not affected at all.”

Pricing practices

There are no fixed prices:

“extensive price discrimination by income (with an extreme of zero prices for sufficiently indigent patients)… the apparent rigidity of so-called administered prices considerably understates the actual flexibility.”

Avik Roy observes in a critical National Review article that “Because patients don’t see the bill until after the non-refundable service has been consumed, and because patients are given little information about price and cost, patients and payors are rarely able to shop around for a medical service based on price and value.”

Medicine has seen major changes since Arrow’s 1963 paper. For example, the treatment of blocked coronary arteries has evolved from coronary bypass to angioplasty to early stents and finally drug-eluting stents. We have seen the advent of minimally invasive surgery, robotic surgery and catheter-based cardiac valve repair and replacement. We have seen drugs to treat hepatitis C and biologicals to treat arthritis and cancer. Many conditions have been transformed from acute to chronic but (at least temporarily) manageable. There are also divergent trends, such as increases in both natural childbirth and Caesarean sections.

In the last 50 years, medicine has become more powerful, but also significantly more complex and overall, more expensive. Intensive care units are a good example, both valuable therapeutically, but expensive to provide. At the same time, many treatments are both better (more valuable to the patient) and less expensive to provide; these range from root canal (frequently two visits to the dentist instead of four) to the significantly less invasive treatments for many cardiac rhythm abnormalities (radio-frequency ablation) and stents for coronary artery disease. The advent of epinephrine auto-injectors has been a lifesaver, but the cost of the Epi-Pen has increased significantly.

Can a competitive economic system appropriately and reasonably price such treatments and devices? Arrow argues that, if not, non-market social institutions will arise and address these challenges. Here is a deeper look.

Arrow’s first two points are still virtually axiomatic today: demand for medical services has become even more unpredictable with the continued growth of advanced, effective interventions and corresponding, appropriately increasing (in my opinion), patient expectations. Similarly, as medical care advances, we increasingly see medical care as a human right and in many cases, a societal obligation. We have come to expect treatment dictated by objective needs and not limited by financial considerations, not only from physicians but from a growing number of key players including pharmaceutical companies. To their credit, in many cases (AIDS comes to mind) pharmaceutical companies have responded by sharply reducing prices in the developing world.

Powerful chemotherapeutic and biologic drugs may have increased the uncertainty and asymmetry of information observed by Arrow, both in their effectiveness and in their side effects. In many cases one needs the language and mathematics of probability and statistics to evaluate, assess and describe their efficacy and utility. One needs an understanding of probability to determine when and how to use common preventive techniques, such as mammograms and PSA screening. Here is an example, paraphrased from Gigerenzer and Edwards (see also Strogatz). Women 40 to 50 years old, with no family history of breast cancer, are a low-risk population; the overall probability of breast cancer in this population is 0.8%. Assume that mammography has a sensitivity of 90% and a false positive rate of 7%.  A woman has a positive mammogram. What is the probability that she has breast cancer? Among 25 German doctors surveyed, 36% said 90% or more, 32% said 50-80%, and 32% said 10% or less. Most (95%) of United States doctors thought the probability was approximately 75%.  (See the links above for the answer, or see my next blog on the challenge of communicating probability).

Arrow’s information asymmetry remains, despite the growing availability of accessible medical information on the web, perhaps for good reasons such as the ability to effectively address the needs of sicker patients.

I would amend Arrow’s discussion of supply conditions to include a wide variety of cost barriers ranging from large fixed costs of ICUs to the costs of medical research. The high cost of basic medical services relative to per capita GDP in the the developing world represents a barrier as high as any faced in the developed world.  As Arrow notes, society has addressed this challenge through a variety of pricing mechanisms outside traditional competitive models. This may not, and in general will not achieve a Pareto optimum, but their wide endorsement by society does indeed suggest that these approaches achieve a more general optimum.

“I propose here the view that, when the market fails to achieve an optimal state, society will, to some extent at least, recognize the gap, and nonmarket social institutions will arise attempting to bridge it… But it is contended here that the special structural characteristics of the medical-care market are largely attempts to overcome the lack of optimality due to the nonmarketability of the bearing of suitable risks and the imperfect marketability of information. These compensatory institutional changes, with some reinforcement from usual profit motives, largely explain the observed noncompetitive behavior of the medical-care market, behavior which, in itself, interferes with optimality. The social adjustment towards optimality thus puts obstacles in its own path.”

It is this view which I find too limiting. I would suggest that society has at least implicitly concluded that price alone does not define value, and thus formed a broader definition of optimality, not simply Pareto optimality in a competitive market. Society is finding and supporting ways to overcome obstacles toward this broader sense of optimality.

The Bill & Melinda Gates Foundation vaccination project aims to reduce the number of children that die each year from preventable disease (currently around 1.5 million). The lifebox project, founded by Dr Atul Gawande, provides affordable, high quality pulse oximeters to the developing world and now seeks to address basic surgical safety in the developing world. Important advances also arise in the developing world; most recently, an easy to deliver, more effective oral cholera vaccine developed in Vietnam.

Arrow himself recognizes the limits of a traditional economic description of the medical care market in his concluding Postscript, arguing that “The logic and limitations of ideal competitive behavior under uncertainty force us to recognize the incomplete description of reality supplied by the impersonal price system.” I conclude more generally that prices not only do not necessarily represent value in medical care (as Arrow observed), but that the combination of uncertainty, externalities, high costs, divergent economies, and technological advance means that price alone cannot describe value in medical care. A broader more general theory of healthcare economics with a foundation standing on the shoulders of giants such as Kenneth Arrow, with perhaps a more general multi-dimensional Pareto optimum, might help us all better understand where we are and where we might go.

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Sam Watson’s journal round-up for 6th 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.

It’s good to be first: order bias in reading and citing NBER working papers. The Review of Economics and Statistics [RePEcPublished 23rd February 2017

Each week one of the authors at this blog choose three or four recently published studies to summarise and briefly discuss. Making this choice from the many thousands of articles published every week can be difficult. I browse those journals that publish in my area and search recently published economics papers on PubMed and Econlit for titles that pique my interest. But this strategy is not without its own flaws as this study aptly demonstrates. When making a choice among many alternatives, people aren’t typically presented with a set of choices, rather a list. This arises in healthcare as well. In an effort to promote competition, at least in the UK, patients are presented with a list of possible of providers and some basic information about those providers. We recently covered a paper that explored this expansion of choice ‘sets’ and investigated its effects on quality. We have previously criticised the use of such lists. People often skim these lists relying on simple heuristics to make choices. This article shows that for the weekly email of new papers published by the National Bureau of Economic Research (NBER), being listed first leads to an increase of approximately 30% in downloads and citations, despite the essentially random ordering of the list. This is certainly not the first study to illustrate the biases in human decision making, but it shows that this journal round-up may not be a fair reflection of the literature, and providing more information about healthcare providers may not have the impact on quality that might be hypothesised.

Economic conditions, illicit drug use, and substance use disorders in the United States. Journal of Health Economics [PubMed] Published March 2017

We have featured a large number of papers about the relationship between macroeconomic conditions and health and health-related behaviours on this blog. It is certainly one of the health economic issues du jour and one we have discussed in detail. Generally speaking, when looking at an aggregate level, such as countries or states, all-cause mortality appears to be pro-cyclical: it declines in economic downturns. Whereas an examination at individual or household levels suggest unemployment and reduced income is generally bad for health. It is certainly possible to reconcile these two effects as any discussion of Simpson’s paradox will reveal. This study takes the aggregate approach to looking at US state-level unemployment rates and their relationship with drug use. It’s relevant to the discussion around economic conditions and health; the US has seen soaring rates of opiate-related deaths recently, although whether this is linked to the prevailing economic conditions remains to be seen. Unfortunately, this paper predicates a lot of its discussion about whether there is an effect on whether there was statistical significance, a gripe we’ve contended with previously. And there are no corrections for multiple comparisons, despite the well over 100 hypothesis tests that are conducted. That aside, the authors conclude that the evidence suggests that use of ecstasy and heroin is procyclical with respect to unemployment (i.e increase with greater unemployment) and LSD, crack cocaine, and cocaine use is counter-cyclical. The results appear robust to the model specifications they compare, but I find it hard to reconcile some of the findings with the prior information about how people actually consume drugs. Many drugs are substitutes and/or compliments for one another. For example, many heroin users began using opiates through abuse of prescription drugs such as oxycodone but made the switch as heroin is generally much cheaper. Alcohol and marijuana have been shown to be substitutes for one another. All of this suggesting a lack of independence between the different outcomes considered. People may also lose their job because of drug use. Taken all together I remain a little sceptical of the conclusions from the study, but it is nevertheless an interesting and timely piece of research.

Child-to-adult neurodevelopmental and mental health trajectories after early life deprivation: the young adult follow-up of the longitudinal English and Romanian Adoptees study. The Lancet [PubMedPublished 22nd February 2017

Does early life deprivation lead to later life mental health issues? A question that is difficult to answer with observational data. Children from deprived backgrounds may be predisposed to mental health issues, perhaps through familial inheritance. To attempt to discern whether deprivation in early life is a cause of mental health issues this paper uses data derived from a cohort of Romanian children who spent time in one of the terribly deprived institutions of Ceaușescu’s Romania and who were later adopted by British families. These institutions were characterised by poor hygiene, inadequate food, and lack of social or educational stimulation. A cohort of British adoptees was used for comparison. For children who spent more than six months in one of the deprived institutions, there was a large increase in cognitive and social problems in later life compared with either British adoptees or those who spent less than six months in an institution. The evidence is convincing, with differences being displayed across multiple dimensions of mental health, and a clear causal mechanism by which deprivation acts. However, for this and many other studies that I write about on this blog, a disclaimer might be needed when there is significant (pun intended) abuse and misuse of p-values. Ziliak and McClosky’s damning diatribe on p-values, The Cult of Statistical Significance, presents examples of lists of p-values being given completely out of context, with no reference to the model or hypothesis test they are derived from, and with the implication that they represent whether an effect exists or not. This study does just that. I’ll leave you with this extract from the abstract:

Cognitive impairment in the group who spent more than 6 months in an institution remitted from markedly higher rates at ages 6 years (p=0·0001) and 11 years (p=0·0016) compared with UK controls, to normal rates at young adulthood (p=0·76). By contrast, self-rated emotional symptoms showed a late onset pattern with minimal differences versus UK controls at ages 11 years (p=0·0449) and 15 years (p=0·17), and then marked increases by young adulthood (p=0·0005), with similar effects seen for parent ratings. The high deprivation group also had a higher proportion of people with low educational achievement (p=0·0195), unemployment (p=0·0124), and mental health service use (p=0·0120, p=0·0032, and p=0·0003 for use when aged <11 years, 11–14 years, and 15–23 years, respectively) than the UK control group.

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