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.

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.

Credit

 

Chris Sampson’s journal round-up for 5th February 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.

Cost-effectiveness analysis of germ-line BRCA testing in women with breast cancer and cascade testing in family members of mutation carriers. Genetics in Medicine [PubMed] Published 4th January 2018

The idea of testing women for BRCA mutations – faulty genes that can increase the probability and severity of breast and ovarian cancers – periodically makes it into the headlines. That’s not just because of Angelina Jolie. It’s also because it’s a challenging and active area of research with many uncertainties. This new cost-effectiveness analysis evaluates a programme that incorporates cascade testing; testing relatives of mutation carriers. The idea is that this could increase the effectiveness of the programme with a reduced cost-per-identification, as relatives of mutation carriers are more likely to also carry a mutation. The researchers use a cohort-based Markov-style decision analytic model. A programme with three test cohorts – i) women with unilateral breast cancer and a risk prediction score >10%, ii) first-degree relatives, and iii) second-degree relatives – was compared against no testing. A positive result in the original high-risk individual leads to testing in the first- and second-degree relatives, with the number of subsequent tests occurring in the model determined by assumptions about family size. Women who test positive can receive risk-reducing mastectomy and/or bilateral salpingo-oophorectomy (removal of the ovaries). The results are favourable to the BRCA testing programme, at $19,000 (Australian) per QALY for testing affected women only and $15,000 when the cascade testing of family members was included, with high probabilities of cost-effectiveness at $50,000 per QALY. I’m a little confused by the model. The model includes the states ‘BRCA positive’ and ‘Breast cancer’, which clearly are not mutually exclusive. And It isn’t clear how women entering the model with breast cancer go on to enjoy QALY benefits compared to the no-test group. I’m definitely not comfortable with the assumption that there is no disutility associated with risk-reducing surgery. I also can’t see where the cost of identifying the high-risk women in the first place was accounted for. But this is a model, after all. The findings appear to be robust to a variety of sensitivity analyses. Part of the value of testing lies in the information it provides about people beyond the individual patient. Clearly, if we want to evaluate the true value of testing then this needs to be taken into account.

Economic evaluation of direct-acting antivirals for hepatitis C in Norway. PharmacoEconomics Published 2nd February 2018

Direct-acting antivirals (DAAs) are those new drugs that gave NICE a headache a few years back because they were – despite being very effective and high-value – unaffordable. DAAs are essentially curative, which means that they can reduce resource use over a long time horizon. This makes cost-effectiveness analysis in this context challenging. In this new study, the authors conduct an economic evaluation of DAAs compared with the previous class of treatment, in the Norwegian context. Importantly, the researchers sought to take into account the rebates that have been agreed in Norway, which mean that the prices are effectively reduced by up to 50%. There are now lots of different DAAs available. Furthermore, hepatitis C infection corresponds to several different genotypes. This means that there is a need to identify which treatments are most (cost-)effective for which groups of patients; this isn’t simply a matter of A vs B. The authors use a previously developed model that incorporates projections of the disease up to 2030, though the authors extrapolate to a 100-year time horizon. The paper presents cost-effectiveness acceptability frontiers for each of genotypes 1, 2, and 3, clearly demonstrating which medicines are the most likely to be cost-effective at given willingness-to-pay thresholds. For all three genotypes, at least one of the DAA options is most likely to be cost-effective above a threshold of €70,000 per QALY (which is apparently recommended in Norway). The model predicts that if everyone received the most cost-effective strategy then Norway would expect to see around 180 hepatitis C patients in 2030 instead of the 300-400 seen in the last six years. The study also presents the price rebates that would be necessary to make currently sub-optimal medicines cost-effective. The model isn’t that generalisable. It’s very much Norway-specific as it reflects the country’s treatment guidelines. It also only looks at people who inject drugs – a sub-population whose importance can vary a lot from one country to the next. I expect this will be a valuable piece of work for Norway, but it strikes me as odd that “affordability” or “budget impact” aren’t even mentioned in the paper.

Cost-effectiveness of prostate cancer screening: a systematic review of decision-analytical models. BMC Cancer [PubMed] Published 18th January 2018

You may have seen prostate cancer in the headlines last week. Despite the number of people in the UK dying each year from prostate cancer now being greater than the number of people dying from breast cancer, prostate cancer screening remains controversial. This is because over-detection and over-treatment are common and harmful. Plenty of cost-effectiveness studies have been conducted in the context of detecting and treating prostate cancer. But there are various ways of modelling the problem and various specifications of screening programme that can be evaluated. So here we have a systematic review of cost-effectiveness models evaluating prostate-specific antigen (PSA) blood tests as a basis for screening. From a haul of 1010 studies, 10 made it into the review. The studies modelled lots of different scenarios, with alternative screening strategies, PSA thresholds, and treatment pathways. The results are not consistent. Many of the scenarios evaluated in the studies were more costly and less effective than current practice (which tended to be the lack of any formal screening programme). None of the UK-based cost-per-QALY estimates favoured screening. The authors summarise the methodological choices made in each study and consider the extent to which this relates to the pathways being modelled. They also specify the health state utility values used in the models. This will be a very useful reference point for anyone trying their hand at a prostate cancer screening model. Of the ten studies included in the review, four of them found at least one screening programme to be potentially cost-effective. ‘Adaptive screening’ – whereby individuals’ recall to screening was based on their risk – was considered in two studies using patient-level simulations. The authors suggest that cohort-level modelling could be sufficient where screening is not determined by individual risk level. There are also warnings against inappropriate definition of the comparator, which is likely to be opportunistic screening rather than a complete absence of screening. Generally speaking, a lack of good data seems to be part of the explanation for the inconsistency in the findings. It could be some time before we have a clearer understanding of how to implement a cost-effective screening programme for prostate cancer.

Credits