Infectious Disease Modelling and Health Economic Evaluation of Vaccines

Who should attend?
People who have an interest in quantitative research, and
who want to learn about infectious disease modelling and
health economic evaluation e.g., health economists,
(bio)statisticians and mathematicians who want to expand
their toolbox, as well as health science professionals and
policy advisors who want to have a deeper understanding
of cost-effectiveness analysis when it is applied to
vaccines.

Programme

  • DAY 1 Introduction to mathematical models for infectious diseases (using R)
  • DAY 2 Inferring model parameters from data (using R)
  • DAY 3 Meta-population and individual-based models (using R)
  • DAY 4 Introduction to health economic evaluation and dealing with uncertainty (using R and MS Excel)
  • DAY 5 Economic evaluation of vaccination programmes, specific issues (using R and MS Excel)

Participants can attend all days or select DAYS 1-3 or 4-5.

Instructors
Prof. Philippe Beutels, Prof. Niel Hens, Prof. Joke Bilcke,
Dr. Pietro Coletti & Dr. Lander Willem
All instructors are researchers of the SIMID group, i.e.
members of the Centre for Health Economics Research
& Modelling Infectious Diseases (CHERMID), Vaccine &
Infectious Disease Institute at the University of Antwerp
and/or of the Center for Statistics, Interuniversity Institute of
Biostatistics and statistical Bioinformatics at Hasselt
University and K.U.Leuven.

Chris Sampson’s journal round-up for 23rd 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.

What should we know about the person behind a TTO? The European Journal of Health Economics [PubMed] Published 18th April 2018

The time trade-off (TTO) is a staple of health state valuation. Ask someone to value a health state with respect to time and – hey presto! – you have QALYs. This editorial suggests that completing a TTO can be a difficult task for respondents and that, more importantly, individuals’ characteristics may determine the way that they respond and therefore the nature of the results. One of the most commonly demonstrated differences, in this respect, is the fact that valuations of people’s own health states tend to be higher than health states valued hypothetically. But this paper focuses on indirect (hypothetical) valuations. The authors highlight mixed evidence for the influence of age, gender, marital status, having children, education, income, expectations about the future, and of one’s own health state. But why should we try and find out more about respondents when conducting TTOs? The authors offer 3 reasons: i) to inform sampling, ii) to inform the design and standardisation of TTO exercises, and iii) to inform the analysis. I agree – we need to better understand these sources of heterogeneity. Not to over-engineer responses, but to aid our interpretation, even if we want societally-representative valuations that include all of these variations in response behaviour. TTO valuation studies should collect data relating to the individual respondents. Unfortunately, what those data should be aren’t listed in this study, so the research question in the title isn’t really answered. But maybe that’s something the authors have in hand.

Computer modeling of diabetes and its transparency: a report on the eighth Mount Hood Challenge. Value in Health Published 9th April 2018

The Mount Hood Challenge is a get-together for people working on the (economic) modelling of diabetes. The subject of the 2016 meeting was transparency, with two specific goals: i) to evaluate the transparency of two published studies, and ii) to develop a diabetes-specific checklist for transparent reporting of modelling studies. Participants were tasked (in advance of the meeting) with replicating the two published studies and using the replicated models to evaluate some pre-specified scenarios. Both of the studies had some serious shortcomings in the reporting of the necessary data for replication, including the baseline characteristics of the population. Five modelling groups replicated the first model and seven groups replicated the second model. Naturally, the different groups made different assumptions about what should be used in place of missing data. For the first paper, none of the models provided results that matched the original. Not even close. And the differences between the results of the replications – in terms of costs incurred and complications avoided – were huge. The performance was a bit better on the second paper, but hardly worth celebrating. In general, the findings were fear-confirming. Informed by these findings, the Diabetes Modeling Input Checklist was created, designed to complement existing checklists with more general applications. It includes specific data requirements for the reporting of modelling studies, relating to the simulation cohort, treatments, costs, utilities, and model characteristics. If you’re doing some modelling in diabetes, you should have this paper to hand.

Setting dead at zero: applying scale properties to the QALY model. Medical Decision Making [PubMed] Published 9th April 2018

In health state valuation, whether or not a state is considered ‘worse than dead’ is heavily dependent on methodological choices. This paper reviews the literature to answer two questions: i) what are the reasons for anchoring at dead=0, and ii) how does the position of ‘dead’ on the utility-scale impact on decision making? The authors took a standard systematic approach to identify literature from databases, with 7 papers included. Then the authors discuss scale properties and the idea that there are interval scales (such as temperature) and ratio scales (such as distance). The difference between these is the meaningfulness of the reference point (or origin). This means that you can talk about distance doubling, but you can’t talk about temperature doubling, because 0 metres is not arbitrary, whereas 0 degrees Celsius is. The paper summarises some of the arguments put forward for using dead=0. They aren’t compelling. The authors argue that the duration part of the QALY (i.e. time) needs to have ratio properties for the QALY model to function. Time obviously holds this property and it’s clear that duration can be anchored at zero. The authors then demonstrate that, for the QALY model to work, the health-utility scale must also exhibit ratio scale properties. The basis for this is the assumption that zero duration nullifies health states and that ‘dead’ nullifies duration. But the paper doesn’t challenge the conceptual basis for using dead in health state valuation exercises. Rather, it considers the mathematical properties that must hold to allow for dead=0, and asserts them. The authors’ conclusion that dead “needs to have the value of 0 in a QALY model” is correct, but only within the existing restrictions and assumptions underlying current practice. Nevertheless, this is a very useful study for understanding the challenge of anchoring and explicating the assumptions underlying the QALY model.

Credits

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.