Thesis Thursday: Koh Jun Ong

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 Koh Jun Ong who has a PhD from the University of Groningen. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Economic aspects of public health programmes for infectious disease control: studies on human immunodeficiency virus & human papillomavirus
Supervisors
Maarten Postma, Mark Jit
Repository link
http://hdl.handle.net/11370/0edbcfae-2a0c-4103-9722-fb8086d75cff

Which public health programmes did you consider in your research?

Three public health programmes were considered in the thesis: 1) HIV Pre-Exposure Prophylaxis (PrEP), 2) Human Papillomavirus (HPV) vaccination, and 3) HIV screening to reduce undiagnosed infections in the population.

The first two of the three involved primary infectious disease prevention among men who have sex with men (MSM), and both of these programmes were to be delivered via sexual health clinics in England (commonly known as genitourinary medicine, GUM, clinics).

The third public health infectious disease control programme involved secondary prevention of onward HIV transmission in the general population by encouraging routine HIV screening to reduce undiagnosed HIV, with a view of earlier diagnosis leading to antiretroviral treatment initiation, which will stop HIV transmission with viral suppression.

Was it necessary to develop complex mathematical models?

It depends on the policy research question. A dynamic model was used for the HPV vaccination research question, which captures the ecological externality that vaccination provides by reducing transmission to non-vaccinees. A dynamic model was used because this programme would likely reach a high proportion of MSM who attend GUM clinics in England, and therefore the subsequent knock-on impact of disease transmission in the population was likely to be substantial.

The policy research question was different for PrEP and a static model was more suitable since the objective was to advise NHS England on whether and how such a programme, with relatively small numbers of patients over an initial time-limited period, may represent value for money in England. We first considered a public health control programme, with promising new efficacy data from the 500-person PrEP pilot study (the UK-based PROUD trial) and additional information from per protocol participants in the earlier iPrEx study. The initial consideration was to maintain the preventative effect of a drug that needs to be taken on a daily basis (compared with near one-off HPV vaccination – three doses in total delivered within a year’s time). Regular monitoring of STI and patient’s renal function meant there were clinical service capacity issue to consider, which was likely to limit access initially. Thus, a static model that did not take into account transmission was used.

However, dynamic modelling would be useful to inform policy decisions as PrEP usage expands. Firstly, because it would then be important to capture the indirect effect on infection transmission. Secondly, because when the force of infection begins to fall as incidence declines, dynamic modelling will inform future delivery of a programme that maintains its value. These represent important areas for future research.

Finally, the model designed for the research question on HIV screening was quite straightforward as its aim is primarily to advise local commissioners on financial implications of offering routine screening in their local area, which is dependent on local clinical resources and local disease prevalence.

Did you draw any important conclusions from your literature reviews?

Two literature reviews were conducted: 1) a review on economic parameters i.e. cost and utility estimates for HPV-related outcomes, and 2) a review on published MSM HPV vaccination economic evaluations.

In relation to the first review, most economic models of HPV-related interventions selected economic parameters in a pretty ad hoc way, without reviewing the entirety of the literature. We found substantial variations in cost and utility estimates for all diseases considered in our systematic review, wherever there were more than one publication. These variations in value estimates could result from the differences in cancer site, disease stages, study population, treatment pathway/settings, treatment country and utility elicitation methods used. It would be important for future models to be transparent about parameter sources and assumptions, and to recognise that as patient disease management changes over time, there will be corresponding effects on both cost and utility, necessitating future updates to the estimates. These must be considered when applied to future economic evaluations, to ensure that assumptions are up-to-date and closely reflect the case mix of patients being evaluated.

In relation to the second review, despite limited models, different modelling approaches and assumptions, a general theme from these studies reveal modelling outcomes to be most sensitive to assumptions around vaccine efficacy and price. Future studies could consider synchronising parameter assumptions to test outputs generated by different models.

What can your research tell us about the ‘cost-effective but unaffordable’ paradox?

A key finding and concluding remark of this thesis was that “findings around cost-effectiveness should not be considered independently of budget impact and affordability considerations, as the two are interlinked”. Ultimately, cost-effectiveness is linked to the budget and, in an ideal world, a cost-effectiveness threshold should correspond to the opportunity cost of replacing least cost-effective care at the margin of the whole healthcare budget spend. This willingness to pay threshold should be linked to the amount of budgetary resources an intervention displaces. After all, the concept of opportunity cost in a fixed budget setting means that decisions to invest in something translates to funding being displaced elsewhere.

Since most health economies do not have unlimited resources, even if investment in a new intervention gives high returns and therefore is worthwhile from a value for money perspective, without the necessary resources it cannot always be afforded despite its high return on investment. Having a limited budget means that funding an expensive new intervention may mean moving funding away from existing services, which may be more cost-effective than the new intervention. Hence, the services from which funds are moved from will lose out, and this may leave society worse-off.

A simple analogy may be that buying a property that guarantees return over a defined period is worthwhile, but if I cannot afford it in the first place, is this still an option?

This was clearly demonstrated in the PrEP example, where despite potential to be cost-effective, the high cost of the intervention at list price carried with it a very high budget impact. The size of the population needed to be given PrEP to achieve substantial public health benefits is large, which meant that a public health programme could pose an affordability challenge to the national health care system.

Based on your findings, how might HIV and HPV prevention strategies be made more cost-effective?

Two strategies could influence cost effectiveness: optimizing the population covered and using an appropriate comparator price.

The most obvious way to improve cost-effectiveness is to optimise the population covered. For example, we know that HIV risk, as measured by HIV incidence, is higher among GUM-attending MSM. Therefore, delivering a PrEP programme to this population (at least in the initial phase until the intervention becomes more affordable) will likely result in a higher number of new HIV infections prevented. Similarly, HIV screening offered to areas with high local prevalence would likely give a higher number of new diagnoses.

The other important factor to consider around cost-effectiveness is the comparator price on which the technology appraisal is based. In the chapter on estimating HIV care cost in England, we demonstrated that with imminent availability of generic antiretrovirals, the lifetime care cost for a person living with HIV will reduce substantially. This reduced cost, representing cost of care with existing intervention, should be used as comparator for newer HIV interventions, as they would represent what society will be paying in the absence of the new interventions, allowing corresponding reduced price expectations for new interventions to ensure cost-effectiveness is maintained.

How did you find the experience of completing your thesis by publication?

It was brilliant! I must acknowledge all the contributions from my supervisors and co-authors in making this possible and for the very positive experience of this process. A major advantage of doing a PhD by publication is that the work conducted was regularly peer-reviewed, hence providing an extra check of the robustness of the analyses. And also the fact that these works are out for public consumption almost immediately, making the science available for other researchers to consider and to move the science to the next stage.

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