Thesis Thursday: Mathilde Péron

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 Mathilde Péron who graduated with a PhD from Université Paris Dauphine. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Three essays on supplementary health insurance
Supervisors
Brigitte Dormont
Repository link
https://basepub.dauphine.fr/handle/123456789/16695

How important is supplementary health insurance in France, compared with other countries?

In France in 2016, Supplementary Health Insurance (SHI) financed 13.3% of total health care expenditure. SHI supplements a partial mandatory coverage by covering co-payments as well as medical goods and services outside the public benefit package, such as dental and optical care or balance billing. SHI is not a French singularity. Canada, Austria, Switzerland, the US (with Medicare / Medigap) or the UK do offer voluntary SHI contracts. A remarkable fact, however, is that 95% of the French population is covered by a SHI contract. In comparison, although the extent of public coverage is very similar in France and in the UK, the percentage of British patients enrolled in a private medical insurance is below 15%.

The large SHI enrolment and the subsequent limited out-of-pocket payments – €230 per year on average, the lowest among EU countries – should not hide important inequalities in the extent of coverage and premiums paid. SHI coverage is now mandatory for employees of the private sector. They benefit from subsidized contracts and uniform premiums. Individuals with an annual income below €8,700 benefit from free basic SHI coverage which covers copayments, essentially. However, the rest of the population (students, temporary workers, unemployed, retirees, independent, and civil servants) buy SHI in a competitive market where premiums generally increase with age.

Can supplementary health insurance markets lead to an adverse selection death spiral?

Competitive health insurance markets are subject to asymmetric information that prevent the existence of pooling contracts (Rothschild and Stiglitz, 1976Cutler and Zeckhauser, 1998). The US market is a good example; in the 1950s not-for-profit insurance companies (Blue Cross, Blue Shields) – which offered pooled contracts – almost all disappeared (Thomasson, 2002). And, despite a notably higher public coverage that could limit adverse selection effects, the French SHI market is not an exception.

Historically, SHI coverage was provided by not-for-profit insurers, the Mutuelles, who relied on solidarity principles. But as the competition becomes more intense, the Mutuelles experience the adverse selection death spiral; they lose their low-risk clients attracted by lower premiums. To survive, they have to give up on uniform premiums and standardized coverage. Today 90% of SHI contracts in the individual market have premiums that increase with age. It is worth noting that in France insurers have strong fiscal incentives to avoid medical underwriting, so age remains the only predictor for individual risk. Still, premiums can vary with a ratio of 1 to 3, which raises legitimate concerns about the affordability of insurance and access to health care for patients with increasing medical needs.

How does supplementary health insurance influence prices in health care, and how did you measure this in your research?

A real policy concern is that SHI might have an inflationary effect by allowing patients to consume more at higher prices. Access to specialists who balance bill (i.e. charge more than the regulated fee) – a signal for higher quality and reduced waiting times – is a good example (Dormont and Peron, 2016).

To measure the causal impact of SHI on balance billing consumption we use original individual-level data, collected from the administrative claims of a French insurer. We observe balance billing consumption and both mandatory and SHI reimbursements for 43,111 individuals from 2010 to 2012. In 2010, the whole sample was covered by the same SHI contract, which does not cover balance billing. We observe the sample again in 2012 after that 3,819 among them decided to switch to other supplementary insurers, which we assume covers balance billing. We deal with the endogeneity of the decision to switch by introducing individual effects into the specifications and by using instrumental variables for the estimation.

We find that individuals respond to better coverage by increasing their proportion of visits to a specialist who balance bills by 9%, resulting in a 32% increase in the amount of balance billing per visit. This substitution to more expensive care is likely to encourage the rise in medical prices.

Does the effect of supplementary insurance on health care consumption differ according to people’s characteristics?

An important result is that the magnitude of the impact of SHI on balance billing strongly depends on the availability of specialists. We find no evidence of moral hazard in areas where specialists who do not charge balance billing are readily accessible. On the contrary, in areas where they are scarce, better coverage is associated with a 47% increase in the average amount of balance billing per consultation. This result suggests that the most appropriate policy to contain medical prices is not necessarily to limit SHI coverage but to monitor the supply of care in order to guarantee patients a genuine choice of their physicians.

We further investigate the heterogeneous impact of SHI in a model where we specify individual heterogeneity in moral hazard and consider its possible correlation with coverage choices (Peron and Dormont, 2017 [PDF]). We find evidence of selection on moral hazard: individuals with unobserved characteristics that make them more likely to ask for comprehensive SHI show a larger increase in balance billing per visit. This selection effect is likely to worsen the inflationary impact of SHI. On the other hand, we also find that the impact of a better coverage is larger for low-income people, suggesting that SHI plays a role in access to care.

Have the findings from your PhD research influenced your own decision to buy supplementary health insurance?

As an economist, it’s interesting to reflect on your own decisions, isn’t it? Well, I master cost-benefit analysis, I have a good understanding of expected utility and definitely more information than the average consumer in the health insurance market. Still, my choice of SHI might appear quite irrational. I’m (reasonably) young and healthy, I could have easily switched to a contract with lower premiums and higher benefits, but I did not. I stayed with a contract where premiums mainly depend on income and benefits are standardized, an increasingly rare feature in the market. I guess that stresses out the importance of other factors in my decision to buy SHI, my inertia as a consumer, probably, but also my willingness to pay for solidarity.

Ambulance and economics

I have recently been watching the BBC series AmbulanceIt is a fly-on-the-wall documentary following the West Midlands Ambulance Service interspersed with candid interviews with ambulance staff, much in the same vein as other health care documentaries like 24 Hours in A&EAs much as anything it provides a (stylised) look at the conditions on the ground for staff and illustrates how health care institutions are as much social institutions as essential services. In a recent episode, the cost of a hoax call was noted as some thousands of pounds. Indeed, the media and health services often talk about the cost of hoax calls in this way:

Warning for parents as one hoax call costs public £2,465 and diverts ambulance from real emergency call.

Frequent 999 callers cost NHS millions of pounds a year.

Nuisance caller cost the taxpayer £78,000 by making 408 calls to the ambulance service in two years.

But these are accounting costs, not the full economic cost. The first headline almost captures this by suggesting the opportunity cost was attendance at a real emergency call. However, given the way that ambulance resources are deployed and triaged across calls, it is very difficult to say what the opportunity cost is: what would be the marginal benefit of having an additional ambulance crew for the duration of a hoax call? What is the shadow price of an ambulance unit?

Few studies have looked at this question. The widely discussed study by Claxton et al. in the UK, looked at shadow prices of health care across different types of care, but noted that:

Expenditure on, for example, community care, A&E, ambulance services, and outpatients can be difficult to attribute to a particular [program budget category].

One review identified a small number of studies examining the cost-benefit and cost-effectiveness of emergency response services. Estimates of the marginal cost per life saved ranged from approximately $5,000 to $50,000. However, this doesn’t really tell us the impact of an additional crew, nor were many of these studies comparable in terms of the types of services they looked at, and these were all US-based.

There does exist the appropriately titled paper Ambulance EconomicsThis paper approaches the question we’re interested in, in the following way:

The centrepiece of our analysis is what we call the Ambulance Response Curve (ARC). This shows the relationship between the response time for an individual call (r) and the number of ambulances available and not in use (n) at the time the call was made. For example, let us suppose that 35 ambulances are on duty and 10 of them are being used. Then n has the value of 25 when the next call is taken. Ceteris paribus, as increases, we expect that r will fall.

On this basis, one can look at how an additional ambulance affects response times, on average. One might then be able to extrapolate the health effects of that delay. This paper suggests that an additional ambulance would reduce response times by around nine seconds on average for the service they looked at – not actually very much. However, the data are 20 years old, and significant changes to demand and supply over that period are likely to have a large effect on the ARC. Nevertheless, changes in response time of the order of minutes are required in order to have a clinically significant impact on survival, which are unlikely to occur with one additional ambulance.

Taken altogether, the opportunity cost of a hoax call is not likely to be large. This is not to downplay the stupidity of such calls, but it is perhaps reassuring that lives are not likely to be in the balance and is a testament to the ability of the service to appropriately deploy their limited resources.

Credits

Journal Club Briefing: Dolan and Kahneman (2008)

Today’s Journal Club Briefing comes from the Academic Unit of Health Economics at the University of Leeds. At their journal club on 2nd August 2017, they discussed Dolan and Kahneman’s 2008 article from The Economic Journal: ‘Interpretations of utility and their implications for the valuation of health‘. If you’ve discussed an article at a recent journal club meeting at your own institution and would like to write a briefing for the blog, get in touch.

Why this paper?

Dolan and Kahneman (2008) is a paper which was published nearly ten years ago, was written several years before that, and was not published in a health-related journal. It’s hence, at first sight, a slightly curious choice for a health economics journal club. However, it raises issues which are at the heart of health economics practice. The questions raised by this article have not as yet been answered, and don’t look likely to be answered anytime soon.

Summary

Experienced vs. decision utility

The article’s point of departure is the distinction between experienced utility and decision utility, often a source of fruitful research in behavioural economics. Experienced utility is utility in the Benthamite sense, meaning the hedonic experience in the current moment: the pleasure and/or pain felt by a person at any given point in time. Decision utility is utility as taught in undergraduate economics textbooks: an objective function which the individual dispassionately acts to maximise. In the neoclassical framework of said undergraduate textbooks, this is a distinction without a difference. The individual correctly forecasts the expected flow of experienced utility given the available information and her actions, forms a decision utility function from it and acts to maximise it.

However, Thaler and Sunstein wouldn’t have sold as many books if things were so simple. Many systematic and significant instances of divergences between experienced and decision utility have been well documented, and several people (including one of the authors of this paper) have won Nobel prizes for it. The one which this article focuses on is adaptation.

Adaptation

The authors summarise a large body of evidence that shows that individuals suffer a large loss of utility after a traumatic event (e.g. the loss of a limb or loss of function), but that for many conditions they will adapt to their new situation and recover much of their utility loss. After as little as a year, their valuation of their health is very similar to that of the general population. Furthermore, the authors precis various studies which show that individuals routinely underestimate drastically the amount of adaptation that would occur should such a traumatic event befall them.

This improvement over time in the health-related utility experienced by people with many conditions is partly due to hedonic adaptation – the internal scale of pleasure/pain re-calibrates to their new situation – and partly due to behavioural change, such as finding new pastimes to replace those ruled out by their condition. While the causes of adaptation are fascinating, the focus here is not on the mechanisms behind it, but rather on the consequences for measuring utility and the implications for resource allocation.

Health valuation and adaptation

The methods health economists use to evaluate the utility of being in a given health state, such as time trade-off, standard gamble or discrete choice experiments, will tend to elicit decision utility. They are based on choices between hypothetical states and so will not capture the changes in experienced utility due to adaptation. Thus valuations of health states from the general public will tend to be lower than the valuations from people actually living in the health state.

At first glance, the consequences for resource allocation may not appear to be particularly severe. It may lead to more resources being devoted to healthcare as a whole (at least for life-improving treatments – life-extending treatments are a different case), but the overall healthcare budget is in practice largely a political decision. However, it will not lead to distortions between treatments for alternative conditions.

Yet adaptation is not a universal phenomenon. There are conditions for which little or no adaptation is seen (for example unexplained pain), and when it occurs, it occurs at different speeds and to differing extents for different conditions. The authors show that valuations of conditions with a greater initial utility loss are lower than conditions with a lesser initial loss but a lower degree of adaptation, and thus will receive a greater level of resources, despite the sum of experienced utility being the same for both. The authors argue that this is unfair, and that health economists should update their practices to better capture experienced utility.

Public vs. patient preference

A common argument in favour of the status quo is that (in many countries at least) it is public resources which are being allocated, and thus it is public preferences which should be respected. It appears legitimate to allocate resources to assuage public fears of health states, even if those health states are worse in their imagination than in reality. The authors consider this argument and reply that, in this case, the instruments of health economists are still not fit for purpose. General measures of health states, such as EQ-5D, go out of their way to describe states in abstract terms and to separate them from causes, such as cancer, which may carry an emotional affect. It cannot be argued that public valuations are justified because resources should be allocated according to public fears if the measurement of valuation deliberately tries not to elicit those fears.

The argument that adaptation causes serious problems for valuing health and for allocation of health resources is a persuasive one. It is undoubtedly true that changes in utility over time, and other violations of the neoclassical economic paradigm such as reference dependence, do not presently receive sufficient attention in health economics and policy decisions in general.

Discussion

Which yardstick?

Despite the stimulating discussion and the overall brilliance of the paper, there are some elements which can be challenged. One of them is that throughout, the authors’ arguments and recommendations are made from the standpoint that the sum over time of the flow of experienced utility from a health state is to be used as the sole measure of value. This would consist in what one of the authors calls the day reconstruction method (DRM) which consists in rating a range of feelings including happiness, worry, and frustration.

Despite the acknowledgement of some philosophical difficulties, the sum of the flow of experienced utility is treated as if it is the only true yardstick with which to measure health, without a convincing justification and no discussion on the qualitative aspect of the measurement as opposed to a truly cardinal measure of health allowing ranking of individuals’ health states.

Public vs. private preferences revisited

The authors raise the question of whether current practice can be justified by a desire to soothe public fears, and dismiss it since the elicitation tools are not suitable. However, they do not address the question of whether allocating public resources according to the public’s (incorrect) fears of given diseases or health states could be a legitimate health policy aim. One could imagine, for example, a discrete choice experiment eliciting how much the general public dreads cancer over other diseases, and make an argument that the welfare of the public is improved by allocating resources based on these results. There are myriad problems with such an approach, of course, but there seem to be no fewer problems with alternative approaches.

Intertemporal welfare

Intertemporal welfare judgements are notoriously difficult once the exponential discounting framework is left. It seems just as legitimate to base valuations on the ex post judgement of individuals who have fully adjusted to a health state as on an integration of past feelings, most of which are now distant memories. Most people would agree that the time to value their experience of a marathon is after completing it, not during the twenty-fifth mile or at the start line.

Indeed, this appears to be the position tacitly taken elsewhere by Kahneman in his work on the peak-end rule. In Redelmeier et al. (2003), it was found that the retrospective rating of the pain of a colonoscopy was based almost exclusively on the peak intensity of pain and on the pain felt at the end. Thus procedures which were extended by an extra three minutes were remembered as less painful than standard procedures, even though the total pain experienced was greater. Furthermore, those who underwent the extended procedure were more likely to state they would undergo it again. It would seem strange, in this case, to judge them as worse off.

Schelling (1984) ends his superlative discussion of the problems of intertemporal decision making with the following thought experiment. Just as with valuing health, there are no easy answers.

[S]ome anesthetics block transmission of the nervous impulses that constitute pain; others have the characteristic that the patient responds to the pain as if feeling it fully but has utterly no recollection afterwards. One of these is sodium pentothal. In my imaginary experiment we wish to distinguish the effects of the drug from the effects of the unremembered pain, and we want a healthy control subject in parallel with some painful operations that will be performed with the help of this drug. For a handsome fee you will be knocked out for an hour or two, allowed to sleep it off, then tested before you go home. You do this regularly, and one afternoon you walk into the lab a little early and find the experimenters viewing some videotape. On the screen is an experimental subject writhing, and though the audio is turned down the shrieks are unmistakably those of a person in pain. When the pain stops the victim pleads, “Don’t ever do that again. Please.”

The person is you.

Do you care?

Do you walk into your booth, lie on the couch, and hold out your arm for today’s injection?

Should I let you?

Credits