Tag Archives: Quality of life

The Health Economics of Infertility Treatment

Recently I found myself holding forth on the above topic in a plenary talk at the International Federation of Fertility Societies combined meeting with the African Fertility Society in Kampala. I made the point that the health economics of infertility raises a number of issues that are not generally considered in the standard canon for health economic assessment of health technology assessments (HTA).[1] Four issues stand out:

  1. The benefits of infertility treatment are more difficult to capture on a single quality of life (QoL) scale than in the case for standard HTA.
  2. The standard practice of discounting benefits can be questioned.
  3. The beneficiaries are more diverse, potentially extending to many family members.
  4. The issue of whether the lifelong utility of the potential child should be include is controversial.

I shall briefly consider these in turn.

  1. Benefit – generic quality of life (QoL) scales do not seem up to the job. First, it is very difficult to capture the benefits over a lifetime. The ‘area under the curve’ is the important relevant quantity and this is not well captured in cross-sectional studies. Second, QoL deteriorates when a sub-fertile couple have a baby, as it does for fertile people. I discovered this many years ago in a collaborative study with the Health Economics department at the University of York (unpublished). This finding reinforces the importance of a lifetime perspective. Third, it is doubtful that maximisation of the dimensions captured in a generic QoL scale are the things that people wish to maximise when they decide to have children – there is a deeper purpose in play. So, a utility function based on a direct trade-off would be preferable to a standard generic QoL scale, such as the SF-12 or EQ-5D. This way, the respondent can take a lifetime perspective and factor in all the valued benefits and disbenefits of treatment. Torrance used a standard gamble on a large study of US citizens and measured a disutility of 0.07 (utility 0.93).[2] That is to say, the average respondent would run up to a 7% risk of death to enable them to have a first child. Such a standard gamble method would likely underestimate the utility loss for those who actually experience infertility for reasons David Arnold and I explicated elsewhere.[3] A perhaps better method to capture the benefit over a lifetime would be willingness-to-pay studies and here, in addition to studies at the population level (say using discrete choice methods), studies of revealed preferences are possible. This is because much IVF takes place in an entirely private market. This enables the ‘market clearing’ price for infertility services to be observed (ideally in relation to disposable income). The high proportions of disposable incomes infertile people allocate to infertility treatment, sometimes amounting to catastrophic losses,[4] provides some evidence that Torrance’s study underestimates the trade-offs people will make in order to have children.
  2. Choice of discount rate – The fact that benefits continue to accrue, and may increase, over time, suggests that discounting may not be normative. Given that disbenefits generally precede benefits, it makes little sense to discount from the point of intervention. That said, it is important to factor disbenefits of treatment and downstream costs into the analysis. Disbenefits include the cost and discomfort of treatment and knock-on costs, for example, resulting from an increased risk of prematurity. Conversely, there may be hidden benefits beyond the joys of parenthood – for example, in reduced Intimate Partner Violence.[5]
  3. Diverse beneficiaries – In ‘normal’ health economics, benefits are hypotheticated on the affected person, even though loved ones also stand to benefit. Loved ones benefit through the improved health of the affected person. Ignoring third party benefits can be condoned on the ‘level playing field’ principle – in a comparison across diseases of middle-age, beneficiaries of various alternative treatments are in a roughly similar position – they have similar numbers of loved ones on average. On this basis, the decision tree can be ‘pruned’. It could be argued that this argument breaks down when comparisons are made across generational lines. In the particular case of infertility, mothers and fathers get direct benefits, as do grandparents and others, not only through the ‘affected’ person, but directly from the child that results from the treatment. For instance, the father is just as much a beneficiary as the mother. Grandparents are not far behind – I can attest to that. On the other hand, factoring these beneficiaries into the equation seems to tilt the playing field too far the other way, i.e. towards infertility services. Factoring the benefits that accrue to all these people would weight services for children in general, and infertility services in particular, very strongly. This is a topic requiring more philosophical analysis and, perhaps, empirical investigation.
  4. What about the child who would otherwise not have existed – the question of the utility of the hypothetical lives is vexed. Certainty, no-one counts the utility loss from contraception, even when no later ‘replacement child’ is envisaged. On the other hand, the utility of neonatal survival is included in standard economic practice. My preference is not to include this utility, but I am hard-pressed to defend this on a bottom-up, philosophical basis. Richard Hare, the famous Oxford philosopher, did attempt such an analysis and his conclusions support my instinct. Certainly, including the lifetime utility of the child massively improves the cost-benefit ratio of infertility services,[6-8] and if the tax return from the child is included, then a treatment such as IVF becomes a ‘no-brainer’ since it ‘dominates’ – it saves money and yields benefit down to a very low success rate (<6% of so).[9]

What would happen if we:

  1. Accepted a utility function of 0.9 (close to that of Torrance).
  2. Ignored other beneficiaries, including the child?

We present such an analysis below. Even under these relatively conservative constraints, IVF is cost-effective in most countries, and could be cost-effective in LMICs if some new idea, such as incubation within the vagina, were used.

Say we gave infertility a disutility of 0.1 over 50 years, undiscounted.
Then, the utility in an infertile couple successfully treated = 5 QALYs undiscounted and 1.1 discounted at 3%.
Let’s say society will pay $100 per QALY.
Then a treatment with a 25% success rate can have a net cost of up to $125 undiscounted, but less than $28 discounted.

— Richard Lilford, CLAHRC WM Director

I thank Sheryl van der Poel for sending some of the references quoted in this article.

References:

  1. Drummond MF. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press. 2005.
  2. Torrance GW. Measurement of Health State Utilities for Economic Appraisal. J Health Econ. 1986; 5: 1-30.
  3. Arnold D, Girling A, Stevens A, Lilford R. Comparison of direct and indirect methods of estimating health state utilities for resource allocation: review and empirical analysis. BMJ; 2009; 339: b2688.
  4. Wu AK, Odisho AY, Washington III SL, Katz PP, Smith JF. Out-of-Pocket Fertility Patient Expense: Data from a Multicenter Prospective Infertility Cohort. J Urology. 2014; 191(2): 427-32.
  5. Stellar C, Garcia-Moreno C, Temmerman M, van der Poel S. A systematic review and narrative report of the relationship between infertility, subfertility, and intimate partner violence. Int J Gynecol Obstet, 2016; 133: 3-8.
  6. Connolly MP, Pollard MS, Hoorens S, Kaplan BR, Oskowitz SP, Silber SJ. Long-term Economic Benefits Attributed to IVF-conceived Children: A Lifetime Tax Calculation. Am J Manag Care. 2008; 14(9): 598-604.
  7. Svensson A, Connolly M, Gallo F, Hägglund L. Long-term fiscal implications of subsidizing in-vitro fertilization in Sweden: A lifetime tax perspective. Scand J Pub Health. 2008; 36: 841-9.
  8. Fragoulakis V & Maniadakis N. Estimating the long-term effects of in vitro fertilization in Greece: an analysis based on a lifetime-investment model. Clinicoecon Outcomes Res. 2013; 5: 247-55.
  9. Baird DT, Collins J, Egozcue J, et al. Fertility and Ageing. Hum Reprod Update. 2005; 11(3): 261-76.
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Measuring the Quality of Life: Holy Grail

The standard method to measure quality of life (QoL) is to convert a generic quality of life score to a utility value, thus:

Score on a generic quality of life questionnaire X Conversion factor (tariff) = Utility

The QoL score must be generic because it has to cover all ailments, from deafness to paraplegia to depression.

The conversion factor converts this score to a utility that provides a common 1-0 (best health to death) scale, but also allows for negative values (worse than death). The most commonly used QoL questionnaire is the EQ-5D, which has only five dimensions (mobility, ability to care for oneself, ability to perform usual activities, pain/discomfort, and anxiety/depression). There is reason to question whether this is sufficiently broad for health (narrowly defined) use. For instance, it might not fully capture the utility loss from blindness. If it does not fully capture health narrowly defined, then it may be assumed that it falls shorter still for health more broadly defined to include effects of social care, economic independence, and overall happiness. Scales such as the WALY (Wellbeing-Adjusted Life Years) scale try to capture these outcomes. However, it is cumbersome to have two separate scales; ideally we need one, covering the same dimensions, but without introducing distortions by double counting some of them, but not others. Work is ongoing to sort this all out by collating information on many dimensions and eliminating those that largely duplicate information that others capture more specifically.

Enter Amartya Sen, an economist who won the Economics Nobel Prize in 1998 (alright, technically the Svergies Riksbank Prize in Economic Sciences). He emphasised human capabilities and argued that happiness was not enough – it was more important to have the capacity to understand and appreciate what the world has to offer and to be involved politically, than to simply have a hedonic life. Professor Jo Coast, collaborator of CLAHRC WM, has produced a score called ICECAP-A (ICEpop CAPability measure for Adults) for the purpose of measuring capabilities.[1]

Capability-based measures and patient perceived quality of daily life are fundamentally different constructs and pin-point the fundamental philosophical distinctions that lie at the heart of the quality of care debate. Like Candide in Voltaire’s play, I would gladly sacrifice happiness for an intellectual appreciation of the world and what lies beyond.

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Al-Janabi H, Flynn T, Coast J. Development of a self-report measure of capability wellbeing for adults: the ICECAP-A. Qual Life Res. 2012; 21(1): 167-76.

Measuring Quality of Care

McGlynn and Adams [1] repeat a point frequently made by the CLAHRC WM Director – before using outcomes to judge the quality of care, first model plausible effects.[2] [3] Only a small fraction of an outcome may be amenable to improved care.

The rate of hospital deaths in the UK is about 3%. Allowing a generous 20% of those to be preventable sets an upper headroom for improvement of 0.6%. So don’t expect quality of care to show up in mortality statistics. Or, to take another example, about 1% of hospital patients suffer a preventable medication related adverse event.[4] So don’t expect improved medicine management to show up in quality of life scores among the hospital population.

— Richard Lilford, CLAHRC WM Director

References:

  1. McGlynn EA, Adams JL. What makes a good quality measure? JAMA. 2014; 312(15): 1517-8.
  2. Yao GL, Novielli N, Manaseki-Holland S,Chen YF, van der Klink M, Barach P, Chilton PJ, Lilford RJ. Evaluation of a predevelopment service delivery intervention: an application to improve clinical handovers. BMJ Qual Saf. 2012; 21(s1): i29-38.
  3. Girling AJ, Hofer TP, Wu J, Chilton PJ, Nicholl JP, Mohammed MA, Lilford RJ. Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study. BMJ Quality & Safety. 2012; 21: 1052-6.
  4. de Vries EN, Ramrattan MA, Smorenburg SM, Gouma DJ, Boermeester MA. The incidence and nature of in-hospital adverse events: a systematic review. Qual Saf Health Care. 2008; 17(3): 216-23.