Public or patient preferences: ex ante, ex post… extraneous?

As alluded to in yesterday’s journal round-up, on reading a recent article by Versteegh and Brouwer, I have had some thoughts about the way we think about the the debate between the use of either patient or public preferences for health state valuation.

When it comes to valuing health states, NICE (and some of their counterparts) advise the use of preferences from the general public. An alternative argument is that we might use patient preferences, because the public probably do not have an accurate understanding of what it’s like to live in a particular health state. In their new paper, Versteegh and Brouwer outline the key arguments in favour of using public preferences but highlight the limited nature of these arguments. One thing they discuss is the notion that public preferences are ex ante while patient preferences are ex post. It’s analogous to preferences vs satisfaction, or decision utility vs experienced utility. The authors outline some limitations to this interpretation. In this blog post I’d like to build on this discussion. My main focus is on defining what we actually mean when we talk about ‘patient preferences’.

Before and after what?

Ex ante means ‘before the event’, and ex post after it. But when we are valuing health states there is no event before or after which utility can be estimated. We are trying to value a state, not preferences regarding an event. We may contrive an event – such as the onset of a particular health state – but that is theoretically quite a different thing to value. Indeed, this contrivance of an event taking place may be a problem.

We should probably do away with these terms and just speak in English, but let’s be realistic. At the very least, we need to be clear about what ex ante and ex post mean in this context; the ‘event’ in question is experience of the given health state.

But then, health state valuation isn’t about just one health state – it’s only possible to value health states in relation to one another and in particular ‘full health’ and a state equivalent to being dead. Furthermore, there is little doubt that a person’s valuation of past or future health states relates to their current health state. Chances are that any individual completing a health state valuation will be valuing some states from an ex ante position and some from an ex post position, both of which are influenced by their current health status.

Ultimately, whether preferences being elicited are ex ante or ex post has nothing to do with whom is being asked, and everything to do with what they are being asked about.


But that isn’t the crux of the matter anyway. What we really want to do here is differentiate between ‘patient preferences’ and ‘public preferences’. ‘The public’ is easy to define. It’s everyone. We usually try to get a representative sample because we cannot ask everyone to do a TTO exercise. But we need to be clearer about how we define patients. Patients are not ex ante – that we can agree on. Or can we? What if we ask an individual about an inevitable future health state associated with disease progression, of which they have a good understanding? What’s worse, patients might also not be ex post, depending on our definition of these terms.

It seems far more intuitive and accurate to describe patients as ex tempore: essentially meaning ‘at the time’. Patients’ health state preferences are neither retrospective nor prospective, but explicitly in relation to their current health state. Crucially, it is that current health state that we are trying to value.

So, a person valuing their own health state is doing so ex tempore, and that’s usually what we mean by ‘patient preferences’. But I hope it’s clear by this point that an individual patient’s preferences need not necessarily be ex tempore either.

People who have never experienced a given health state are necessarily stating their preferences ex ante, but they could still be a patient or not. Meanwhile, somebody who does have experience of a health state could be valuing it from any of the alternative temporal positions. They may, for example, be valuing a future in a health state that they have previously experienced. Versteegh and Brouwer provide a nice taxonomy of the arguments for the use of public preferences. I’d like to provide my own taxonomy here, of the different types of preferences we might elicit. I see it as follows:

Experience of health state No experience of health state
  ex ante ex tempore ex post ex ante ex tempore ex post
Patient A1 A2 A3 B
Non-patient C1 C2 C3 D

There are 4 types of responder (A, B, C and D), determined by whether they are a patient and whether they have previously experienced the health state currently being valued. Similarly, there are 3 different types of health state valuation, depending on whether the state being valued is a past, present or future state. For any given person valuing any given health state, the elicited preferences will be one of the labelled boxes. Ask that same person to value a different health state, or ask a different person to value the same health state, and the elicited preference may well differ.

There may of course be other ways in which individuals differ, such as the extent to which they have adapted to their current health state. But while that’s an important consideration in determining from whom we ought to elicit preferences, I don’t think it’s a key question in identifying patient preferences as opposed to public preferences.

Patient vs patients

One implication of this is that we have (at least) two types of patient preferences. Patient preferences could be A+B. That is, we value a particular health state in all patients, regardless of their current health state. That might be done in a sample representative of the current population of people considered to be a patient, however that might be defined. It strikes me that this is the true definition of patienthood as might be used in other contexts.

The kind of patient we talk about when we discuss ‘patient preferences’ is, I think, just those people falling into ‘A2’; patients valuing their own current health state.

Versteegh and Brouwer seem to suggest that any valuation of current health – that is, ex tempore – represents patient preferences. In practice this will likely be ‘A2’ through the identification of participants, but it’s important to consider the existence of ‘C2’. Just because a person is experiencing the health state of interest does not necessarily make them a patient in any practical sense of the word.

For what it’s worth, I think that public preferences are the least bad option for now. But Versteegh and Brouwer’s suggestion that we should report both is a good one, which could lead to more research that may very well change my mind. I think it will also force this issue of clearer definition of ‘patient preferences’.

Photo credit: Tori Cat (CC BY-NC-ND 2.0)

Chris Sampson’s journal round-up for 22nd August 2016

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.

Simulation as an ethical imperative and epistemic responsibility for the implementation of medical guidelines in health care. Medicine, Health Care and Philosophy [PubMed] Published 6th August 2016

Some people describe RCTs as a ‘gold standard’ for evidence. But if more than one RCT exists, or we have useful data from outside the RCT, that probably isn’t true. Decision modelling has value over and above RCT data, as well as in lieu of it. One crucial thing that cannot – or at least not usually – be captured in an RCT is how well the evidence might be implemented. Medical guidelines will be developed, but there will be a process of adjustments and no doubt errors; all of which might impact on the quality of life of patients. Here we stray into the realms of implementation science. This paper argues that health care providers have a responsibility to acquire knowledge about implementation and the learning curve of medical guidelines. To this end, there is an epistemic and ethical imperative to simulate the possible impacts on patients’ health of the implementation learning curve. The authors provide some examples of guideline implementation that might have benefited from simulation. However, it’s very easy in hindsight to identify what went wrong and none of the examples set out realistic scenarios for simulation analyses that could have been carried out in advance. It isn’t clear to me how or why we should differentiate – in ethical or epistemic terms – implementation from effectiveness evaluation. It is clear, however, that health economists could engage more with implementation science, and that there is an ethical imperative to do so.

Estimating marginal healthcare costs using genetic variants as instrumental variables: Mendelian randomization in economic evaluation. PharmacoEconomics [PubMedPublished 2nd August 2016

To assert that obesity is associated with greater use of health care resources is uncontroversial. However, to assert that all of the additional cost associated with obesity is because of obesity is a step too far. There are many other determinants of health care costs (and outcomes) that might be independently associated with obesity. One way of dealing with this problem of identifying causality is to use instrumental variables in econometric analysis, but appropriate IVs can be tricky to identify. Enter, Mendelian randomisation. This is a method that can be used to adopt genetic variants as IVs. This paper describes the basis for Mendelian randomisation and outlines the suitability of genetic traits as IVs. En route, the authors provide a nice accessible summary of the IV approach more generally. The focus throughout the paper is upon estimating costs, with obesity used as an example. The article outlines a lot of the potential challenges and pitfalls associated with the approach, such as the use of weak instruments and non-linear exposure-outcome relationships. On the whole, the approach is intuitive and fits easily within existing methodologies. Its main value may lie in the estimation of more accurate parameters for model-based economic evaluation. Of course, we need data. Ideally, longitudinal medical records linked to genotypic information for a large number of people. That may seem like wishful thinking, but the UK Biobank project (and others) can fit the bill.

Patient and general public preferences for health states: A call to reconsider current guidelines. Social Science & Medicine [PubMed] Published 31st July 2016

One major ongoing debate in health economics is the question of whether public or patient preferences should be used to value health states and thus to estimate QALYs. Here in the UK NICE recommends public preferences, and I’d hazard a guess that most people agree. But why? After providing some useful theoretical background, this article reviews the arguments made in favour of the use of public preferences. It focuses on three that have been identified in Dutch guidelines. First, that cost-effectiveness analysis should adopt a societal perspective. The Gold Panel invoked a Rawlsian veil of ignorance argument to support the use of decision (ex ante) utility rather than experiences (ex post). The authors highlight that this is limited, as the public are not behind a veil of ignorance. Second, that the use of patient preferences might (wrongfully) ignore adaptation. This is not a complete argument as there may be elements of adaptation that decision makers wish not to take into account, and public preferences may still underestimate the benefits of treatment due to adaptation. Third, the insurance principle highlights that the obligation to be insured is made ex ante and therefore the benefits of insurance (i.e. health care) should also be valued as such. The authors set out a useful taxonomy of the arguments, their reasoning and the counter arguments. The key message is that current arguments in favour of public preferences are incomplete. As a way forward, the authors suggest that both patient and public preferences should be used alongside each other and propose that HTA guidelines require this. The paper got my cogs whirring, so expect a follow-up blog post tomorrow.

What, who and when? Incorporating a discrete choice experiment into an economic evaluation. Health Economics Review [PubMed] Published 29th July 2016

This study claims to be the first to carry out a discrete choice experiment on clinical trial participants, and to compare willingness to pay results with standard QALY-based net benefit estimates; thus comparing a CBA and a CUA. The trial in question evaluates extending the role of community pharmacists in the management of coronary heart disease. The study focusses on the questions of what, who and when: what factors should be evaluated (i.e. beyond QALYs)? whose preferences (i.e. patients with experience of the service or all participants)? and when should preferences be evaluated (i.e. during or after the intervention)? Comparisons are made along these lines. The DCE asked participants to choose between their current situation and two alternative scenarios involving either the new service or the control. The trial found no significant difference in EQ-5D scores, SF-6D scores or costs between the groups, but it did identify a higher level of satisfaction with the intervention. The intervention group (through the DCE) reported a greater willingness to pay for the intervention than the control group, and this appeared to increase with prolonged use of the service. I’m not sure what the take-home message is from this study. The paper doesn’t answer the questions in the title – at least, not in any general sense. Nevertheless, it’s an interesting discussion about how we might carry out cost-benefit analysis using DCEs.

Photo credit: Antony Theobald (CC BY-NC-ND 2.0)

Sam Watson’s journal round-up for August 15th

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 association between asymmetric information, hospital competition and quality of healthcare: evidence from Italy. Journal of the Royal Statistical Society A. Published August 2016.

The measurement and analysis of factors that affect hospital quality is a popular topic in both the economic and statistical literature. We recently discussed a paper by Martin Gaynor and colleagues that examined the effects that increased patient choice and quality information had on hospital quality. This new paper by Paola Berta et al considers the same question in Italian hospitals in Lombardy. Analyses are considered at the ward level, rather than the hospital level since this, it is argued, is more likely to be the level of choice that the patient makes given a specific condition. Furthermore, patients do not have access to information such as adjusted hospital mortality rates, so it is assumed that patients may be getting information via their social network – a coefficient for the proportion of people in the local municipality who used the same ward is added to the patient choice model. The predicted probabilities from this choice model are used to generate a Herfindal index for hospital competition, which is used as a covariate in a multi-level model of 30-day hospital mortality.

They find that firstly patients are more likely to go to a ward if more people in the local area go to that ward and secondly that hospital competition does not appear to have an effect on the mortality rate. Given these two findings it is concluded that in the absence of decent quality information – an information asymmetry – patients are choosing based on information from their social networks, which is why increased competition does not lead to improved outcomes. This may explain the difference in findings between the Gaynor paper, where patients did have information, and this paper. This paper present a thorough and clear analysis but questions surrounding the measurement of quality remain. The dynamic equilibrium between patient choice over hospital and hospital choice over quality is complex; and hospital choice about quality may be further constrained by their budgets. Putting all this together is no simple task.

Alcohol Availability, Prenatal Conditions, and Long-Term Economic Outcomes. Journal of Political Economy. Forthcoming.

The fetal origins hypothesis has certainly generated no end of new papers, even in top ranked journals, for this journal round up. So this week in “infant health corner” we have a new study that examines the effect of the availability of alcohol to expectant parents on the health of the infant. While maternal alcohol consumption has been the subject of innumerable previous studies, this is the first to consider its effects on long term outcomes such school attendance and wages. In 1967-8, in an attempt to encourage substitution from high alcohol content spirits to lower content beer, the Swedish government allowed grocery stores in some regions to sell beer. Previously all alcohol was sold in state owned off-license stores. The policy inadvertently led to an increase in alcohol consumption and was ended soon thereafter. This policy experiment provides the foundation for this analysis. Expectant mothers also increased their alcohol consumption during this period. People who were exposed in utero to the policy were found to have lower school completion rates, lower cognitive and non-congitive ability, and lower wages.

Health Insurance Mandates, Mammography, and Breast Cancer Diagnoses. American Economic Review: Economic Policy. [RePEcPublished August 2016.

Recent federal healthcare reform in the United States mandates insurance companies to provide access to preventative health services and prohibits cost-sharing for these schemes. One such service is breast cancer screening using mammograms. The aim of the reform is to increase utilization of such screening programs, which a number of US bodies have determined is currently too low. This paper investigates the effects the healthcare reform had on mammogram uptake and finds significant increases in the numbers of screened women and diagnoses of breast cancer. The authors do attempt to address one of the key questions about breast cancer screening programs – that they may cause more harm than good. They find that in most cases the increases in screening were not consistent with guidelines from the American Cancer Society, suggesting harms were being caused by the program. Perhaps another case of policy not following the evidence.