Essen Economics of Mental Health Workshop

The third Essen Economics of Mental Health Workshop will take place on August 3 – 4, 2020 in Essen, Germany.

Half of those with a lifetime mental health problem first experience symptoms by the age of 14, and 75% before they reach their mid-twenties (Kessler et al., 2007). The conditions are often persistent and recurrent meaning they influence the entire work-life of the affected individuals (OECD, 2012).

This workshop aims to gather (junior) researchers with an interest in applying the tools of economics to problems surrounding mental health. This includes, but is not limited to, mental health economics studies looking at informal care, loneliness, social exclusion, access to health care, insurance coverage, declines in physical health, age of onset, dementia, suicide, etc. Empirical analyses in this field are especially encouraged for Submission.

Ezra Golberstein (University of Minnesota) and Martin Knapp (London School of Economics and Political Science) will deliver the keynotes for this workshop.

For further details, please see the Flyer (you may also use this for further distribution) or consult the application page.

Rita Faria’s journal round-up for 20th January 2020

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.

Opportunity cost neglect in public policy. Journal of Economic Behavior & Organization Published 10th January 2020

Opportunity cost is a key concept in economics, and health economics is no exception. We all agree that policy-makers should consider the opportunity cost alongside the benefits of the various policy options. The question is… do they? This fascinating paper by Emil Persson and Gustav Tinghög suggests that they may not.

The paper reports two studies: one in the general population, and the other in a sample of experts on priority setting in health. In both studies, the participants were asked to choose between making a purchase or not, and were randomised to choices with and without a reminder about the opportunity cost. The reminder consisted of the “no” option having the comment “saving the money for other purchases“. There were choices about private consumption (e.g. buying a new mobile phone) and health care policy (e.g. funding a new cancer screening programme).

In the study in the general population, the participants were 6% less likely to invest in public policies if they were reminded of the opportunity cost. There was no effect in private consumption decisions. In the study with experts on health care priority setting, the participants were 10% less likely to invest in a health programme when reminded about opportunity costs, although the result was “marginally significant“. There was a numerical difference of -6% regarding private consumption, but non-significant. The authors concluded that both lay people and experts neglect opportunity cost in public policy but much less so in their own private consumption decisions.

It struck me that this effect is driven by quite a small difference between the scenarios – simply stating that choosing to reject the policy means that the money will be saved for future purchases. I wonder about how this information affects the decision. After all, the scenarios only quantify the costs of the policy, without information about the benefits or the opportunity cost. For example, the benefits of the cancer screening programme were that “cancer treatment will be more effective, lives will be saved and human suffering will be avoided” and the cost was 48 million SEK per year. Whether this policy is good or bad value for money all depends on how much suffering it avoids and how much would be avoided by investing the money in something else. It would be interesting to have coupled the survey with interviews to understand how the participants interpreted the information and their decision making process.

On a wider note, this paper agrees with health economists’ anecdotal experience that policy-makers find it hard to think about opportunity cost. This is not helped by settings where they hear about the experience of people who would benefit from a positive recommendation and from doctors who would like to have the new drug in their medical arsenal, but not much about the people who will bear the opportunity cost. The message is clear: we need to do better at communicating the opportunity cost of public policies!

Assessment of progression-free survival as a surrogate end point of overall survival in first-line treatment of ovarian cancer. JAMA Network Open [PubMed] Published 10th January 2020

A study about the relationship between progression-free survival and overall survival may seem an odd choice for a health economics journal round-up, but it is actually quite relevant. In cost-effectiveness analysis of new cancer drugs, the trial primary endpoint may be progression-free survival (PFS). Data on overall survival (OS) may be too immature to assess the treatment effect or for extrapolation to the longer term. To predict QALYs and lifetime costs with and without the new drug, the cost-effectiveness model may need to assume a surrogate relationship between PFS and OS. That is, that an effect on PFS is reflected, to some extent, in an effect on OS. The question is, how strong is that surrogate relationship? This study tries to answer this question in advanced ovarian cancer.

Xavier Paoletti and colleagues conducted a systematic review and meta-analysis using individual patient data from 11,029 people who took part in 17 RCTs of first-line therapy in advanced ovarian cancer. They assessed the surrogate relationship at the individual and at the trial-level. The individual-level surrogate relationship refers to the correlation between PFS and OS for the individual patient. As the authors note, this may only reflect that people who have longer life expectancy also take longer to progress. At the trial-level, they looked at the correlation between the hazard ratio (HR) on OS and the HR on PFS. This reflects how much of the effect on OS could be predicted by the effect on PFS. They used the surrogate criteria proposed by the Follicular Lymphoma Analysis of Surrogacy Hypothesis initiative. As this is outside my area of expertise, I won’t comment on the methodology.

One of their results is quite striking: in 16/17 RCTs, the experimental drug did not have HRs for PFS and OS statistically different from the control. This means that there have not been any new drugs with statistically significant benefits! In terms of the surrogate relationship, they found that there is an individual-level association – that is, people who take longer to progress also survive for longer. In contrast, they did not find a surrogate relationship between PFS and OS at the trial-level. Given that the HRs were centred around 1, the poor correlation may be partly due to the lack of variation in HRs rather than a poor surrogate relationship.

Now the challenge remains in cost-effectiveness modelling when OS is immature. Extrapolate OS with high uncertainty? Use a poor surrogate relationship with PFS? Or formal expert elicitation? Hopefully methodologists are looking into this! In the meantime, regulators may wish to think again about licensing drugs with evidence only on PFS.

After 20 years of using economic evaluation, should NICE be considered a methods innovator? PharmacoEconomics [PubMed] Published 13th January 2020

NICE is currently starting a review of the methods and process for health technology assessment. Mark Sculpher and Steve Palmer take this opportunity to reflect on how NICE’s methods have evolved over time and to propose areas ripe for an update.

It was very enjoyable to read about the history of the Methods Guide and how NICE has responded to its changing context, responsibilities, and new challenges. For example, the cost-effectiveness threshold of £20k-£30k/QALY was introduced by the 2004 Methods Guide. This threshold was reinforced by the 2019 Voluntary Scheme for Branded Medicines Pricing and Access. The funny thing is, although NICE is constrained to the £20k-£30k/QALY threshold, the Department of Health and Social Care routinely uses Claxton et al’s £13k/QALY benchmark.

Mark and Steve go through five key topics in health technology assessment to pick out the areas that should be considered for an update. The topics are: health measurement and valuation, broader benefits, perspective, modelling, and uncertainty.  For example, whether/how to consider caregiver burden, and benefits (and opportunity costs) on caregivers, guidance on model validation, and formal incorporation of value of information methods. These are all sorely needed and would definitely cement NICE’s position as the international standard-setter for health technology assessment.

Beyond NICE and the UK, I found that this paper provides a good overview on hot topics in cost-effectiveness for the next few years. Must read for cost-effectiveness analysts!

Credits

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.