IVF and the evaluation of policies that don’t affect particular persons

Over at the CLAHRC West Midlands blog, Richard Lilford (my boss, I should hasten to add!) writes about the difficulties with the economic evaluation of IVF. The post notes that there are a number of issues that “are not generally considered in the standard canon for health economic assessment” including the problems with measuring benefits, choosing an appropriate discount rate, indirect beneficiaries, and valuing the life of the as yet unborn child. Au contraire! These issues are the very bread and butter of health economics and economic evaluation research. But I would concede that their impact on estimates of cost-effectiveness are not nearly well enough integrated into standard assessments.

We’ve covered the issue of choosing a social discount rate on this blog before with regards to treatments with inter-generational effects. I want instead to consider the last point about how we should, in the most normative of senses, consider the life of the child born as a result of IVF.

It puts me in mind of the work of the late, great Derek Parfit. He could be said to have single-handedly developed the field of ethics about future people. He identified a number of ethical problems that still often don’t have satisfactory answers. Decisions like funding IVF have an impact on the very existence of persons. But these decisions do not affect the well-being or rights of any particular persons, rather, as Parfit terms them, general persons. Few would deny that we have moral obligations not to cause material harm to future generations. Most would reject the narrow view that the only relevant outcomes are those that affect actual, particular persons, the narrow person-centred view. For example, in considering the problem of global warming, we do not reject its consequences on future generations as being irrelevant. But there remains the question about how we morally treat these general, future persons. Parfit calls this the non-identity problem and it applies neatly to the issue of IVF.

To illustrate the problem of IVF consider the choice:

If we choose A Adam and Barbara will not have children Charles will not exist
If we choose B Adam and Barbara will have a child Charles will live to 70

If we ignore evidence that suggests quality of life actually declines after one has children, we will assume that Adam and Barbara having children will in fact raise their quality of life since they are fulfilling their preferences. It would then seem to be clear that the fact of Charles existing and living a healthy life would be better than him not existing at all and the net benefit of Choice B is greater. But then consider the next choice:

If we choose A Adam and Barbara will not have children Charles will not exist Dianne will not exist
If we choose B Adam and Barbara will have a child Charles will live to 70 Dianne will not exist
If we choose C Adam and Barbara will have children Charles will live to 40 Dianne will live to 40

Now, Choice C would still seem to be preferable to Choice B if all life years have the same quality of life. But we could continue adding children with shorter and shorter life expectancies until we have a large population that lives a very short life, which is certainly not a morally superior position. This is a version of Parfit’s repugnant conclusion, in which general utilitarian principles leads us to prefer a situation with a very large, very low quality of life population to a smaller, better off one. No satisfying solution has yet been proposed. For IVF this might imply increasing the probability of multiple births!

We can also consider the “opposite” of IVF, contraception. In providing contraception we are superficially choosing Choice A above, which by the same utilitarian reasoning would be a worse situation than one in which those children are born. However, contraception is often used to be able to delay fertility decisions, so the choice actually becomes between a child being born earlier and living a worse life than a child being born later in better circumstances. So for a couple, things would go worse for the general person who is their first child, if things are worse for the particular person who is actually their first child. So it clearly matters how we frame the question as well.

We have a choice about how to weigh up the different situations if we reject the ‘narrow person-centred view’. On a no difference view, the effects on general and particular persons are weighted the same. On a two-tier view, the effects on general persons only matter a fraction of those on particular persons. For IVF this relates to how we weight Charles’s (and Diane’s) life in an evaluation. But current practice is ambiguous about how we weigh up these lives, and if we have a ‘two-tier view’, how we weight the lives of general persons.

From an economic perspective, we often consider that the values we place on benefits resulting from decisions as being determined by societal preferences. Generally, we ignore the fact that for many treatments the actual beneficiaries do not yet exist, which would suggest a ‘no difference view’. For example, when assessing the benefits of providing a treatment for childhood leukaemia, we don’t value the benefits to those particular children who have the disease differently to those general persons who may have the disease in the future. Perhaps we do not consider this since the provision of the treatment does not cause a difference in who will exist in the future. But equally when assessing the effects of interventions that may cause, in a counterfactual sense, changes in fertility decisions and the existence of persons, like social welfare payments or a lifesaving treatment for a woman of childbearing age, we do not think about the effects on the general persons that may be a child of that person or household. This would then suggest a ‘narrow person-centred view’.

There is clearly some inconsistency in how we treat general persons. For IVF evaluations, in particular, many avoid this question altogether and just estimate the cost per successful pregnancy, leaving the weighing up of benefits to later decision makers. While the arguments clearly don’t point to a particular conclusion, my tentative conclusion would be a ‘no difference view’. At any rate, it is an open question. In my rare lectures, I often remark that we spend a lot more time on empirical questions than questions of normative economics. This example shows how this can result in inconsistencies in how we choose to analyse and report our findings.

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Chris Sampson’s journal round-up for 19th March 2018

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.

Using HTA and guideline development as a tool for research priority setting the NICE way: reducing research waste by identifying the right research to fund. BMJ Open [PubMed] Published 8th March 2018

As well as the cost-effectiveness of health care, economists are increasingly concerned with the cost-effectiveness of health research. This makes sense, given that both are usually publicly funded and so spending on one (in principle) limits spending on the other. NICE exists in part to prevent waste in the provision of health care – seeking to maximise benefit. In this paper, the authors (all current or ex-employees of NICE) consider the extent to which NICE processes are also be used to prevent waste in health research. The study focuses on the processes underlying NICE guideline development and HTA, and the work by NICE’s Science Policy and Research (SP&R) programme. Through systematic review and (sometimes) economic modelling, NICE guidelines identify research needs, and NICE works with the National Institute for Health Research to get their recommended research commissioned, with some research fast-tracked as ‘NICE Key Priorities’. Sometimes, it’s also necessary to prioritise research into methodological development, and NICE have conducted reviews to address this, with the Internal Research Advisory Group established to ensure that methodological research is commissioned. The paper also highlights the roles of other groups such as the Decision Support Unit, Technical Support Unit and External Assessment Centres. This paper is useful for two reasons. First, it gives a clear and concise explanation of NICE’s processes with respect to research prioritisation, and maps out the working groups involved. This will provide researchers with an understanding of how their work fits into this process. Second, the paper highlights NICE’s current research priorities and provides insight into how these develop. This could be helpful to researchers looking to develop new ideas and proposals that will align with NICE’s priorities.

The impact of the minimum wage on health. International Journal of Health Economics and Management [PubMed] Published 7th March 2018

The minimum wage is one of those policies that is so far-reaching, and with such ambiguous implications for different people, that research into its impact can deliver dramatically different conclusions. This study uses American data and takes advantage of the fact that different states have different minimum wage levels. The authors try to look at a broad range of mechanisms by which minimum wage can affect health. A major focus is on risky health behaviours. The study uses data from the Behavioral Risk Factor Surveillance System, which includes around 300,000 respondents per year across all states. Relevant variables from these data characterise smoking, drinking, and fruit and vegetable consumption, as well as obesity. There are also indicators of health care access and self-reported health. The authors cut their sample to include 21-64-year-olds with no more than a high school degree. Difference-in-differences are estimated by OLS according to individual states’ minimum wage changes. As is often the case for minimum wage studies, the authors find several non-significant effects: smoking and drinking don’t seem to be affected. Similarly, there isn’t much of an impact on health care access. There seems to be a small positive impact of minimum wage on the likelihood of being obese, but no impact on BMI. I’m not sure how to interpret that, but there is also evidence that a minimum wage increase leads to a reduction in fruit and vegetable consumption, which adds credence to the obesity finding. The results also demonstrate that a minimum wage increase can reduce the number of days that people report to be in poor health. But generally – on aggregate – there isn’t much going on at all. So the authors look at subgroups. Smoking is found to increase (and BMI decrease) with minimum wage for younger non-married white males. Obesity is more likely to be increased by minimum wage hikes for people who are white or married, and especially for those in older age groups. Women seem to benefit from fewer days with mental health problems. The main concerns identified in this paper are that minimum wage increases could increase smoking in young men and could reduce fruit and veg consumption. But I don’t think we should overstate it. There’s a lot going on in the data, and though the authors do a good job of trying to identify the effects, other explanations can’t be excluded. Minimum wage increases probably don’t have a major direct impact on health behaviours – positive or negative – but policymakers should take note of the potential value in providing public health interventions to those groups of people who are likely to be affected by the minimum wage.

Aligning policy objectives and payment design in palliative care. BMC Palliative Care [PubMed] Published 7th March 2018

Health care at the end of life – including palliative care – presents challenges in evaluation. The focus is on improving patients’ quality of life, but it’s also about satisfying preferences for processes of care, the experiences of carers, and providing a ‘good death’. And partly because these things can be difficult to measure, it can be difficult to design payment mechanisms to achieve desirable outcomes. Perhaps that’s why there is no current standard approach to funding for palliative care, with a lot of variation between countries, despite the common aspiration for universality. This paper tackles the question of payment design with a discussion of the literature. Traditionally, palliative care has been funded by block payments, per diems, or fee-for-service. The author starts with the acknowledgement that there are two challenges to ensuring value for money in palliative care: moral hazard and adverse selection. Providers may over-supply because of fee-for-service funding arrangements, or they may ‘cream-skim’ patients. Adverse selection may arise in an insurance-based system, with demand from high-risk people causing the market to fail. These problems could potentially be solved by capitation-based payments and risk adjustment. The market could also be warped by blunt eligibility restrictions and funding caps. Another difficulty is the challenge of achieving allocative efficiency between home-based and hospital-based services, made plain by the fact that, in many countries, a majority of people die in hospital despite a preference for dying at home. The author describes developments (particularly in Australia) in activity-based funding for palliative care. An interesting proposal – though not discussed in enough detail – is that payments could be made for each death (per mortems?). Capitation-based payment models are considered and the extent to which pay-for-performance could be incorporated is also discussed – the latter being potentially important in achieving those process outcomes that matter so much in palliative care. Yet another challenge is the question of when palliative care should come into play, because, in some cases, it’s a matter of sooner being better, because the provision of palliative care can give rise to less costly and more preferred treatment pathways. Thus, palliative care funding models will have implications for the funding of acute care. Throughout, the paper includes examples from different countries, along with a wealth of references to dig into. Helpfully, the author explicitly states in a table the models that different settings ought to adopt, given their prevailing model. As our population ages and the purse strings tighten, this is a discussion we can expect to be having more and more.

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Chris Sampson’s journal round-up for 5th March 2018

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.

Healthy working days: the (positive) effect of work effort on occupational health from a human capital approach. Social Science & Medicine Published 28th February 2018

If you look at the literature on the determinants of subjective well-being (or happiness), you’ll see that unemployment is often cited as having a big negative impact. The same sometimes applies for its impact on health, but here – of course – the causality is difficult to tease apart. Then, in research that digs deeper, looking at hours worked and different types of jobs, we see less conclusive results. In this paper, the authors start by asserting that the standard approach in labour economics (on which I’m not qualified to comment) is to assume that there is a negative association between work effort and health. This study extends the framework by allowing for positive effects of work that are related to individuals’ characteristics and working conditions, and where health is determined in a Grossman-style model of health capital that accounts for work effort in the rate of health depreciation. This model is used to examine health as a function of work effort (as indicated by hours worked) in a single wave of the European Working Conditions Survey (EWCS) from 2010 for 15 EU member states. Key items from the EWCS included in this study are questions such as “does your work affect your health or not?”, “how is your health in general?”, and “how many hours do you usually work per week?”. Working conditions are taken into account by looking at data on shift working and the need to wear protective equipment. One of the main findings of the study is that – with good working conditions – greater work effort can improve health. The Marxist in me is not very satisfied with this. We need to ask the question, compared to what? Working fewer hours? For most people, that simply isn’t an option. Aren’t the people who work fewer hours the people who can afford to work fewer hours? No attention is given to the sociological aspects of employment, which are clearly important. The study also shows that overworking or having poorer working conditions reduces health. We also see that, for many groups, longer hours do not negatively impact on health until we reach around 120 hours a week. This fails a good sense check. Who are these people?! I’d be very interested to see if these findings hold for academics. That the key variables are self-reported undermines the conclusions somewhat, as we can expect people to adjust their expectations about work effort and health in accordance with their colleagues. It would be very difficult to avoid a type 2 error (with respect to the negative impact of effort on health) using these variables to represent health and the role of work effort.

Agreement between retrospectively and contemporaneously collected patient-reported outcome measures (PROMs) in hip and knee replacement patients. Quality of Life Research [PubMed] Published 26th February 2018

The use of patient-reported outcomes (PROMs) in elective care in the NHS has been a boon for researchers in our field, providing before-and-after measurement of health-related quality of life so that we can look at the impact of these interventions. But we can’t do this in emergency care because the ‘before’ is never observed – people only show up when they’re in the middle of the emergency. But what if people could accurately recall their pre-emergency health state? There’s some evidence to suggest that people can, so long as the recall period is short. This study looks at NHS PROMs data (n=443), with generic and condition-specific outcomes collected from patients having hip or knee replacements. Patients included in the study were additionally asked to recall their health state 4 weeks prior to surgery. The authors assess the extent to which the contemporary PROM measurements agree with the retrospective measurements, and the extent to which any disagreement relates to age, socioeconomic status, or the length of time to recall. There wasn’t much difference between contemporary and retrospective measurements, though patients reported slightly lower health on the retrospective questionnaires. And there weren’t any compelling differences associated with age or socioeconomic status or the length of recall. These findings are promising, suggesting that we might be able to rely on retrospective PROMs. But the elective surgery context is very different to the emergency context, and I don’t think we can expect the two types of health care to impact recollection in the same way. In this study, responses may also have been influenced by participants’ memories of completing the contemporary questionnaire, and the recall period was very short. But the only way to find out more about the validity of retrospective PROM collection is to do more of it, so hopefully we’ll see more studies asking this question.

Adaptation or recovery after health shocks? Evidence using subjective and objective health measures. Health Economics [PubMed] Published 26th February 2018

People’s expectations about their health can influence their behaviour and determine their future health, so it’s important that we understand people’s expectations and any ways in which they diverge from reality. This paper considers the effect of a health shock on people’s expectations about how long they will live. The authors focus on survival probability, measured objectively (i.e. what actually happens to these patients) and subjectively (i.e. what the patients expect), and the extent to which the latter corresponds to the former. The arguments presented are couched within the concept of hedonic adaptation. So the question is – if post-shock expectations return to pre-shock expectations after a period of time – whether this is because people are recovering from the disease or because they are moving their reference point. Data are drawn from the Health and Retirement Study. Subjective survival probability is scaled to whether individuals expect to survive for 2 years. Cancer, stroke, and myocardial infarction are the health shocks used. The analysis uses some lagged regression models, separate for each of the three diagnoses, with objective and subjective survival probability as the dependent variable. There’s a bit of a jumble of things going on in this paper, with discussions of adaptation, survival, self-assessed health, optimism, and health behaviours. So it’s a bit difficult to see the wood for the trees. But the authors find the effect they’re looking for. Objective survival probability is negatively affected by a health shock, as is subjective survival probability. But then subjective survival starts to return to pre-shock trends whereas objective survival does not. The authors use this finding to suggest that there is adaptation. I’m not sure about this interpretation. To me it seems as if subjective life expectancy is only weakly responsive to changes in objective life expectancy. The findings seem to have more to do with how people process information about their probability of survival than with how they adapt to a situation. So while this is an interesting study about how people process changes in survival probability, I’m not sure what it has to do with adaptation.

3L, 5L, what the L? A NICE conundrum. PharmacoEconomics [PubMed] Published 26th February 2018

In my last round-up, I said I was going to write a follow-up blog post to an editorial on the EQ-5D-5L. I didn’t get round to it, but that’s probably best as there has since been a flurry of other editorials and commentaries on the subject. Here’s one of them. This commentary considers the perspective of NICE in deciding whether to support the use of the EQ-5D-5L and its English value set. The authors point out the differences between the 3L and 5L, namely the descriptive systems and the value sets. Examples of the 5L descriptive system’s advantages are provided: a reduced ceiling effect, reduced clustering, better discriminative ability, and the benefits of doing away with the ‘confined to bed’ level of the mobility domain. Great! On to the value set. There are lots of differences here, with 3 main causes: the data, the preference elicitation methods, and the modelling methods. We can’t immediately determine whether these differences are improvements or not. The authors stress the point that any differences observed will be in large part due to quirks in the original 3L value set rather than in the 5L value set. Nevertheless, the commentary is broadly supportive of a cautionary approach to 5L adoption. I’m not. Time for that follow-up blog post.

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