Thesis Thursday: Matthew Quaife

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 Matthew Quaife who has a PhD from the London School of Hygiene and Tropical Medicine. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Using stated preferences to estimate the impact and cost-effectiveness of new HIV prevention products in South Africa
Supervisors
Fern Terris-Prestholt, Peter Vickerman
Repository link
http://researchonline.lshtm.ac.uk/4646708

Stated preferences for what?

Our main study looked at preferences for new HIV prevention products in South Africa – estimating the uptake and cost-effectiveness of multi-purpose prevention products, which protect against HIV, pregnancy and STIs. You’ll notice that condoms do this, so why even bother? Condom use needs both partners to agree (for the duration of a given activity) and, whilst female partners tend to prefer condom-protected sex, there is lots of evidence that male partners – who also have greater bargaining power in many contexts – do not.

Oral pre-exposure prophylaxis (PrEP), microbicide gels, and vaginal rings are new products which prevent HIV infection. More importantly, they are female-initiated and can generally be used without a male partner’s knowledge. But trials and demonstration projects among women at high risk of HIV in sub-Saharan Africa have shown low levels of uptake and adherence. We used a DCE to inform the development of attractive and usable profiles for these products, and also estimate how much additional demand – and therefore protection – would be gained from adding contraceptive or STI-protective attributes.

We also elicited the stated preferences of female sex workers for client risk, condom use, and payments for sex. Sex workers can earn more for risky unprotected sex, and we used a repeated DCE to predict risk compensation (i.e. how much condom use would change) if they were to use HIV prevention products.

What did you find most influenced people’s preferences in your research?

Unsurprisingly for products, HIV protection was most important to people, followed by STI and then pregnancy protection. But digging below these averages with a latent class analysis, we found some interesting variation within female respondents: over a third were not concerned with HIV protection at all, instead strongly caring about pregnancy and STI protection. Worryingly, these were more likely to be respondents from high-incidence adolescent and sex worker groups. The remainder of the sample overwhelmingly chose based on HIV protection.

In the second sex worker DCE, we found that using a new HIV prevention product made condoms become less important and price more important. We predict that the price premium for unprotected sex would reduce by two thirds, and the amount of condomless sex would double. This is an interesting labour market/economic finding, but – if true – also has real public health implications. Since economic changes mean sex workers move from multi-purpose condoms to single-purpose products which need high levels of adherence, we thought this would be interesting to model.

How did you use information about people’s preferences to inform estimates of cost-effectiveness?

In two ways. First, we used simple uptake predictions from DCEs to parameterise an HIV transmission model, allowing for condom substitution uptake to vary by condom users and non-users (it was double in the latter). We were also able to model the potential uptake of multipurpose products which don’t exist yet – e.g. a pill protecting from HIV and pregnancy. We predict that this combination, in particular, would double uptake among high-risk young women.

Second, we predicted risk compensation among sex workers who chose new products instead of condoms. We were also able to calculate the price elasticity of supply of unprotected sex, which we built into a dynamic transmission model as a determinant of behaviour.

Can discrete choice experiments accurately predict the kinds of behaviours that you were looking at?

To be honest, when I started the PhD I was really sceptical – and I still am to an extent. But two things make me think DCEs can be useful in predicting behaviours.

First is the data. We published a meta-analysis of how well DCEs predict real-world health choices at an individual level. We only found six studies with individual-level data, but these showed DCEs predict with an 88% sensitivity but just a 34% specificity. If a DCE says you’ll do something, you more than likely will – which is important for modelling heterogeneity in uptake. We desperately need more studies following up DCE participants making real-world choices.

Second is the lack of alternative inputs. Where products are new and potential users are inexperienced, modellers pick an uptake number/range and hope for the best. Where we don’t know efficacy, we may assume that uptake and efficacy are linearly related – but they may not be (e.g. if proportionately more people use a 95% effective product than a 45% effective one). Instead, we might assume uptake and efficacy are independent, but that might sound even less realistic. I think that DCEs can tell us something about these behaviours that are useful for the parameters and structures of models, even if they are not perfect predictors.

Your tread the waters of infectious disease modelling in your research – was the incorporation of economic factors a challenge?

It was pretty tricky, though not as challenging as building the simple dynamic transmission model as a first exposure to R. In general, behaviours are pretty crudely modelled in transmission models, largely due to assumptions like random mixing and other population-level dynamics. We made a simple mechanistic model of sex work based on the supply elasticities estimated in the DCE, and ran a few scenarios, each time estimating the impact of prevention products. We simulated the price of unprotected sex falling and quantity rising as above, but also overlaid a few behavioural rules (e.g. Camerer’s constant income hypothesis) to simulate behavioural responses to a fall in overall income. Finally, we thought about competition between product users and non-users, and how much the latter may be affected by the market behaviours of the former. Look out for the paper at Bristol HESG!

How would you like to see research build on your work to improve HIV prevention?

I did a public engagement event last year based on one statistic: if you are a 16-year old girl living in Durban, you have an 80% lifetime risk of acquiring HIV. I find it unbelievable that, in 2018, when millions have been spent on HIV prevention and we have a range of interventions that can prevent HIV, incidence among some groups is still so dramatically and persistently high.

I think research has a really important role in understanding how people want to protect themselves from HIV, STIs, and pregnancy. In addition to highlighting the populations where interventions will be most cost-effective, we show that variation in preferences drives impact. I hope we can keep banging the drum to make attractive and effective options available to those at high risk.

Chris Sampson’s journal round-up for 2nd April 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.

Quality-adjusted life-years without constant proportionality. Value in Health Published 27th March 2018

The assumption of constant proportional trade-offs (CPTO) is at the heart of everything we do with QALYs. It assumes that duration has no impact on the value of a given health state, and so the value of a health state is constant regardless of its duration. This assumption has been repeatedly demonstrated to fail. This study looks for a non-constant alternative, which hasn’t been done before. The authors consider a quality-adjusted lifespan and four functional forms for the relationship between time and the value of life: constant, discount, logarithmic, and power. These relationships were tested in an online survey with more than 5,000 people, which involved the completion of 30-40 time trade-off pairs based on the EQ-5D-5L. Respondents traded off health states of varying severities and durations. Initially, a saturated model (making no assumptions about functional form) was estimated. This demonstrated that the marginal value of lifespan is decreasing. The authors provide a set of values attached to different health states at different durations. Then, the econometric model is adjusted to suit a power model, with the power estimated for duration expressed in days, weeks, months, or years. The power value for time is 0.415, but different expressions of time could introduce bias; time expressed in days (power=0.403) loses value faster than time expressed in years (power=0.654). There are also some anomalies that arise from the data that don’t fit the power function. For example, a single day of moderate problems can be worse than death, whereas 7 days or more is not. Using ‘power QALYs’ could be the future. But the big remaining question is whether decisionmakers ought to respond to people’s time preferences in this way.

A systematic review of studies comparing the measurement properties of the three-level and five-level versions of the EQ-5D. PharmacoEconomics [PubMed] Published 23rd March 2018

The debate about the EQ-5D-5L continues (on Twitter, at least). Conveniently, this paper addresses a concern held by some people – that we don’t understand the implications of using the 5L descriptive system. The authors systematically review papers comparing the measurement properties of the 3L and 5L, written in English or German. The review ended up including 24 studies. The measurement properties that were considered by the authors were: i) distributional properties, ii) informativity, iii) inconsistencies, iv) responsiveness, and v) test-retest reliability. The last property involves consideration of index values. Each study was also quality-assessed, with all being considered of good to excellent quality. The studies covered numerous countries and different respondent groups, with sample sizes from the tens to the thousands. For most measurement properties, the findings for the 3L and 5L were very similar. Floor effects were generally below 5% and tended to be slightly reduced for the 5L. In some cases, the 5L was associated with major reductions in the proportion of people responding as 11111 – a well-recognised ceiling effect associated with the 3L. Just over half of the studies reported on informativity using Shannon’s H’ and Shannon’s J’. The 5L provided consistently better results. Only three studies looked at responsiveness, with two slightly favouring the 5L and one favouring the 3L. The latter could be explained by the use of the 3L-5L crosswalk, which is inherently less responsive because it is a crosswalk. The overarching message is consistency. Business as usual. This is important because it means that the 3L and 5L descriptive systems provide comparable results (which is the basis for the argument I recently made that they are measuring the same thing). In some respects, this could be disappointing for 5L proponents because it suggests that the 5L descriptive system is not a lot better than the 3L. But it is a little better. This study demonstrates that there are still uncertainties about the differences between 3L and 5L assessments of health-related quality of life. More comparative studies, of the kind included in this review, should be conducted so that we can better understand the differences in results that are likely to arise now that we have moved (relatively assuredly) towards using the 5L instead of the 3L.

Preference-based measures to obtain health state utility values for use in economic evaluations with child-based populations: a review and UK-based focus group assessment of patient and parent choices. Quality of Life Research [PubMed] Published 21st March 2018

Calculating QALYs for kids continues to be a challenge. One of the challenges is the choice of which preference-based measure to use. Part of the problem here is that the EuroQol group – on which we rely for measuring adult health preferences – has been a bit slow. There’s the EQ-5D-Y, which has been around for a while, but it wasn’t developed with any serious thought about what kids value and there still isn’t a value set for the UK. So, if we use anything, we use a variety of measures. In this study, the authors review the use of generic preference-based measures. 45 papers are identified, including 5 different measures: HUI2, HUI3, CHU-9D, EQ-5D-Y, and AQOL-6D. No prizes for guessing that the EQ-5D (adult version) was the most commonly used measure for child-based populations. Unfortunately, the review is a bit of a disappointment. And I’m not just saying that because at least one study on which I’ve worked isn’t cited. The search strategy is likely to miss many (perhaps most) trial-based economic evaluations with children, for which cost-utility analyses don’t usually get a lot of airtime. It’s hard to see how a review of this kind is useful if it isn’t comprehensive. But the goal of the paper isn’t just to summarise the use of measures to date. The focus is on understanding when researchers should use self- or proxy-response, and when a parent-child dyad might be most useful. The literature review can’t do much to guide that question except to assert that the identified studies tended to use parent–proxy respondents. But the study also reports on some focus groups, which are potentially more useful. These were conducted as part of a wider study relating to the design of an RCT. In five focus groups, participants were presented with the EQ-5D-Y and the CHU-9D. It isn’t clear why these two measures were selected. The focus groups included parents and some children over the age of 11. Unfortunately, there’s no real (qualitative) analysis conducted, so the findings are limited. Parents expressed concern about a lack of sensitivity. Naturally, they thought that they knew best and should be the respondents. Of the young people reviewing the measures themselves, the EQ-5D-Y was perceived as more straightforward in referring to tangible experiences, whereas the CHU-9D’s severity levels were seen as more representative. Older adolescents tended to prefer the CHU-9D. The youths weren’t so sure of themselves as the adults and, though they expressed concern about their parents not understanding how they feel, they were generally neutral to who ought to respond. The older kids wanted to speak for themselves. The paper provides a good overview of the different measures, which could be useful for researchers planning data collection for child health utility measurement. But due to the limitations of the review and the lack of analysis of the focus groups, the paper isn’t able to provide any real guidance.

Credits

 

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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