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.

Chris Sampson’s journal round-up for 9th October 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.

Evaluating the relationship between visual acuity and utilities in patients with diabetic macular edema enrolled in intravitreal aflibercept studies. Investigative Ophthalmology & Visual Science [PubMed] Published October 2017

Part of my day job involves the evaluation of a new type of screening programme for diabetic eye disease, including the use of a decision analytic model. Cost-effectiveness models usually need health state utility values for parameters in order to estimate QALYs. There are some interesting challenges in evaluating health-related quality of life in the context of vision loss; does vision in the best eye or worst eye affect quality of life most; do different eye diseases have different impacts independent of sight loss; do generic preference-based measures even work in this context? This study explores some of these questions. It combines baseline and follow-up EQ-5D and VFQ-UI (a condition-specific preference-based measure) responses from 1,320 patients from 4 different studies, along with visual acuity data. OLS and random effects panel models are used to predict utility values dependent on visual acuity and other individual characteristics. Best-seeing eye seems to be a more important determinant than worst-seeing eye, which supports previous studies. But worst-seeing eye is still important, with about a third of the impact of best-seeing eye. So economic evaluations shouldn’t ignore the bilateral nature of eye disease. Visual acuity – in both best- and worst-seeing eye – was more strongly associated with the condition-specific VFQ-UI than with the EQ-5D index, leading to better predictive power, which is not a big surprise. One way to look at this is that the EQ-5D underestimates the impact of visual acuity on utility. An alternative view could be that the VFQ-UI valuation process overestimates the impact of visual acuity on utility. This study is a nice demonstration of the fact that selecting health state utility values for a model-based economic evaluation is not straightforward. Attention needs to be given to the choice of measure (e.g. generic or condition-specific), but also to the way states are defined to allow for accurate utility values to be attached.

Do capability and functioning differ? A study of U.K. survey responses. Health Economics [PubMed] Published 24th September 2017

I like the capability approach in theory, but not in practice. I’ve written before about some of my concerns. One of them is that we don’t know whether capability measures (such as the ICECAP) offer anything beyond semantic nuance. This study sought to address that. A ‘functioning and capability’ instrument was devised, which reworded the ICECAP-A by changing phrases like “I am able to be” to phrases like “I am”, so that each question could have a ‘functioning’ version as well as a ‘capability’ version. Then, both the functioning and capability versions of the domains were presented in tandem. Questionnaires were sent to 1,627 individuals who had participated in another study about spillover effects in meningitis. Respondents (n=1,022) were family members of people experiencing after-effects of meningitis. The analysis focusses on the instances where capabilities and functionings diverge. Across the sample, 34% of respondents reported a difference between capability and functioning on at least one domain. For all domain-level responses, 12% were associated with higher capability than functioning, while 2% reported higher functioning. Some differences were observed between different groups of people. Older people tended to be less likely to report excess capabilities, while those with degree-level education reported greater capabilities. Informal care providers had lower functionings and capabilities but were more likely to report a difference between the two. Women were more likely to report excess capabilities in the ‘attachment’ domain. These differences lead the author to conclude that the wording of the ICECAP measure enables researchers to capture something beyond functioning, and that the choice of a capability measure could lead to different resource allocation decisions. I’m not convinced. The study makes an error that is common in this field; it presupposes that the changes in wording successfully distinguish between capabilities and functionings. This is implemented analytically by dropping from the primary analysis the cases where capabilities exceeded functionings, which are presumed to be illogical. If we don’t accept this presupposition (and we shouldn’t) then the meaning of the findings becomes questionable. The paper does outline most of the limitations of the study, but it doesn’t dedicate much space to alternative explanations. One is to do with the distinction between ‘can’ and ‘could’. If people answer ‘capability’ questions with reference to future possibilities, then the difference could simply be driven by optimism about future functionings. This future-reference problem is most obvious in the ‘achievement and progress’ domain, which incidentally, in this study, was the domain with the greatest probability of showing a discrepancy between capabilities and functionings. Another alternative explanation is that showing someone two slightly different questions coaxes them into making an artificial distinction that they wouldn’t otherwise make. In my previous writing on this, I suggested that two things needed to be identified. The first was to see whether people give different responses with the different wording. This study goes some way towards that, which is a good start. The second was to see whether people value states defined in these ways any differently. Until we have answers to both these questions I will remain sceptical about the implications of the ICECAP’s semantic nuance.

Estimating a constant WTP for a QALY—a mission impossible? The European Journal of Health Economics [PubMed] Published 21st September 2017

The idea of estimating willingness to pay (WTP) for a QALY has fallen out of fashion. It’s a nice idea in principle but, as the title of this paper suggests, it’s not easy to come up with a meaningful answer. One key problem has been that WTP for a QALY is not constant in the number of QALYs being gained – that is, people are willing to pay less (at the margin) for greater QALY gains. But maybe that’s OK. NICE and their counterparts tend not to use a fixed threshold but rather a range: £20,000-£30,000 per QALY, say. So maybe the variability in WTP for a QALY can be reflected in this range. This study explores some of the reasons – including uncertainty – for differences in elicited WTP values for a QALY. A contingent valuation exercise was conducted using a 2014 Internet panel survey of 1,400 Swedish citizens. The survey consisted 21 questions about respondents’ own health, sociodemographics, prioritisation attitudes, WTP for health improvements, and a societal decision-making task. Respondents were randomly assigned to one of five scenarios with different magnitudes and probabilities of health gain, with yes/no responses for five different WTP ‘bids’. The estimated WTP for a QALY – using the UK EQ-5D-3L tariff – was €17,000. But across the different scenarios, the WTP ranged from €10,600 to over a million. Wide confidence intervals abound. The authors’ findings only partially support an assumption of weak scope sensitivity – that more QALYs are worth paying more for – and do not at all support a strong assumption of scope sensitivity that WTP is proportional to QALY gain. This is what is known as scope bias, and this insensitivity to scope also applied to the variability in uncertainty. The authors also found that using different EQ-5D or VAS tariffs to estimate health state values resulted in variable differences in WTP estimates. Consistent relationships between individuals’ characteristics and their WTP were not found, though income and education seemed to be associated with higher willingness to pay across the sample. It isn’t clear what the implications of these findings are, except for the reinforcement of any scepticism you might have about the sociomathematical validity (yes, I’m sticking with that) of the QALY.

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Chris Sampson’s journal round-up for 25th September 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.

Good practices for real‐world data studies of treatment and/or comparative effectiveness: recommendations from the Joint ISPOR‐ISPE Special Task Force on Real‐World Evidence in Health Care Decision Making. Value in Health Published 15th September 2017

I have an instinctive mistrust of buzzwords. They’re often used to avoid properly defining something, either because it’s too complicated or – worse – because it isn’t worth defining in the first place. For me, ‘real-world evidence’ falls foul. If your evidence isn’t from the real world, then it isn’t evidence at all. But I do like a good old ISPOR Task Force report, so let’s see where this takes us. Real-world evidence (RWE) and its sibling buzzword real-world data (RWD) relate to observational studies and other data not collected in an experimental setting. The purpose of this ISPOR task force (joint with the International Society for Pharmacoepidemiology) was to prepare some guidelines about the conduct of RWE/RWD studies, with a view to improving decision-makers’ confidence in them. Essentially, the hope is to try and create for RWE the kind of ecosystem that exists around RCTs, with procedures for study registration, protocols, and publication: a noble aim. The authors distinguish between 2 types of RWD: ‘Exploratory Treatment Effectiveness Studies’ and ‘Hypothesis Evaluating Treatment Effectiveness Studies’. The idea is that the latter test a priori hypotheses, and these are the focus of this report. Seven recommendations are presented: i) pre-specify the hypotheses, ii) publish a study protocol, iii) publish the study with reference to the protocol, iv) enable replication, v) test hypotheses on a separate dataset than the one used to generate the hypotheses, vi) publically address methodological criticisms, and vii) involve key stakeholders. Fair enough. But these are just good practices for research generally. It isn’t clear how they are in any way specific to RWE. Of course, that was always going to be the case. RWE-specific recommendations would be entirely contingent on whether or not one chose to define a study as using ‘real-world evidence’ (which you shouldn’t, because it’s meaningless). The authors are trying to fit a bag of square pegs into a hole of undefined shape. It isn’t clear to me why retrospective observational studies, prospective observational studies, registry studies, or analyses of routinely collected clinical data should all be treated the same, yet differently to randomised trials. Maybe someone can explain why I’m mistaken, but this report didn’t do it.

Are children rational decision makers when they are asked to value their own health? A contingent valuation study conducted with children and their parents. Health Economics [PubMed] [RePEc] Published 13th September 2017

Obtaining health state utility values for children presents all sorts of interesting practical and theoretical problems, especially if we want to use them in decisions about trade-offs with adults. For this study, the researchers conducted a contingent valuation exercise to elicit children’s (aged 7-19) preferences for reduced risk of asthma attacks in terms of willingness to pay. The study was informed by two preceding studies that sought to identify the best way in which to present health risk and financial information to children. The participating children (n=370) completed questionnaires at school, which asked about socio-demographics, experience of asthma, risk behaviours and altruism. They were reminded (in child-friendly language) about the idea of opportunity cost, and to consider their own budget constraint. Baseline asthma attack risk and 3 risk-reduction scenarios were presented graphically. Two weeks later, the parents completed similar questionnaires. Only 9% of children were unwilling to pay for risk reduction, and most of those said that it was the mayor’s problem! In some senses, the children did a better job than their parents. The authors conducted 3 tests for ‘incorrect’ responses – 14% of adults failed at least one, while only 4% of children did so. Older children demonstrated better scope sensitivity. Of course, children’s willingness to pay was much lower in absolute terms than their parents’, because children have a much smaller budget. As a percentage of the budget, parents were – on average – willing to pay more than children. That seems reassuringly predictable. Boys and fathers were willing to pay more than girls and mothers. Having experience of frequent asthma attacks increased willingness to pay. Interestingly, teenagers were willing to pay less (as a proportion of their budget) than younger children… and so were the teenagers’ parents! Children’s willingness to pay was correlated with that of their own parent’s at the higher risk reductions but not the lowest. This study reports lots of interesting findings and opens up plenty of avenues for future research. But the take-home message is obvious. Kids are smart. We should spend more time asking them what they think.

Journal of Patient-Reported Outcomes: aims and scope. Journal of Patient-Reported Outcomes Published 12th September 2017

Here we have a new journal that warrants a mention. The journal is sponsored by the International Society for Quality of Life Research (ISOQOL), making it a sister journal of Quality of Life Research. One of its Co-Editors-in-Chief is the venerable David Feeny, of HUI fame. They’ll be looking to publish research using PRO(M) data from trials or routine settings, studies of the determinants of PROs, qualitative studies in the development of PROs; anything PRO-related, really. This could be a good journal for more thorough reporting of PRO data that can get squeezed out of a study’s primary outcome paper. Also, “JPRO” is fun to say. The editors don’t mention that the journal is open access, but the website states that it is, so APCs at the ready. ISOQOL members get a discount.

Research and development spending to bring a single cancer drug to market and revenues after approval. JAMA Internal Medicine [PubMed] Published 11th September 2017

We often hear that new drugs are expensive because they’re really expensive to develop. Then we hear about how much money pharmaceutical companies spend on marketing, and we baulk. The problem is, pharmaceutical companies aren’t forthcoming with their accounts, so researchers have to come up with more creative ways to estimate R&D spending. Previous studies have reported divergent estimates. Whether R&D costs ‘justify’ high prices remains an open question. For this study, the authors looked at public data from the US for 10 companies that had only one cancer drug approved by the FDA between 2007 and 2016. Not very representative, perhaps, but useful because it allows for the isolation of the development costs associated with a single drug reaching the market. The median time for drug development was 7.3 years. The most generous estimate of the mean cost of development came in at under a billion dollars; substantially less than some previous estimates. This looks like a bargain; the mean revenue for the 10 companies up to December 2016 was over $6.5 billion. This study may seem a bit back-of-the-envelope in nature. But that doesn’t mean it isn’t accurate. If anything, it begs more confidence than some previous studies because the methods are entirely transparent.

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