Author Chris Sampson

Chris Sampson’s journal round-up for 20th May 2019

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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.

A new method to determine the optimal willingness to pay in cost-effectiveness analysis. Value in Health Published 17th May 2019

Efforts to identify a robust estimate of the willingness to pay for a QALY have floundered. Mostly, these efforts have relied on asking people about their willingness to pay. In the UK, we have moved away from using such estimates as a basis for setting cost-effectiveness thresholds in the context of resource allocation decisions. Instead, we have attempted to identify the opportunity cost of a QALY, which is perhaps even more difficult, but more easy to justify in the context of a fixed budget. This paper seeks to inject new life into the willingness-to-pay approach by developing a method based on relative risk aversion.

The author outlines the relationship between relative risk aversion and the rate at which willingness-to-pay changes with income. Various candidate utility functions are described with respect to risk preferences, with a Weibull function being adopted for this framework. Estimates of relative risk aversion have been derived from numerous data sources, including labour supply, lottery experiments, and happiness surveys. These estimates from the literature are used to demonstrate the relationship between relative risk aversion and the ‘optimal’ willingness to pay (K), calibrated using the Weibull utility function. For an individual with ‘representative’ parameters plugged into their utility function, K is around twice the income level. K always increases with relative risk aversion.

Various normative questions are raised, including whether a uniform K should be adopted for everybody within the population, and whether individuals should be able to spend on health care on top of public provision. This approach certainly appears to be more straightforward than other approaches to estimating willingness-to-pay in health care, and may be well-suited to decentralised (US-style) resource allocation decision-making. It’s difficult to see how this framework could gain traction in the UK, but it’s good to see alternative approaches being proposed and I hope to see this work developed further.

Striving for a societal perspective: a framework for economic evaluations when costs and effects fall on multiple sectors and decision makers. Applied Health Economics and Health Policy [PubMed] Published 16th May 2019

I’ve always been sceptical of a ‘societal perspective’ in economic evaluation, and I have written in favour of a limited health care perspective. This is mostly for practical reasons. Being sufficiently exhaustive to identify a truly ‘societal’ perspective is so difficult that, in attempting to do so, there is a very high chance that you will produce estimates that are so inaccurate and imprecise that they are more dangerous than useful. But the fact is that there is no single decision-maker when it comes to public expenditure. Governments are made up of various departments, within which there are many levels and divisions. Not everybody will care about the health care perspective, so other objectives ought to be taken into account.

The purpose of this paper is to build on the idea of the ‘impact inventory’, described by the Second Panel on Cost-Effectiveness in Health and Medicine, which sought to address the challenge of multiple objectives. The extended framework described in this paper captures effects and opportunity costs associated with an intervention within various dimensions. These dimensions could (or should) align with decision-makers’ objectives. Trade-offs invariably require aggregation, and this aggregation could take place either within individuals or within dimensions – something not addressed by the Second Panel. The authors describe the implications of each approach to aggregation, providing visual representations of the impact inventory in each case. Aggregating within individuals requires a normative judgement about how each dimension is valued to the individual and then a judgement about how to aggregate for overall population net benefit. Aggregating across individuals within dimensions requires similar normative judgements. Where the chosen aggregation functions are linear and additive, both approaches will give the same results. But as soon as we start to consider equity concerns or more complex aggregation, we’ll see different decisions being indicated.

The authors adopt an example used by the Second Panel to demonstrate the decisions that would be made within a health-only perspective and then decisions that consider other dimensions. There could be a simple extension beyond health, such as including the impact on individuals’ consumption of other goods. Or it could be more complex, incorporating multiple dimensions, sectors, and decision-makers. For the more complex situation, the authors consider the inclusion of the criminal justice sector, introducing the number of crimes averted as an object of value.

It’s useful to think about the limitations of the Second Panel’s framing of the impact inventory and to make explicit the normative judgements involved. What this paper seems to be saying is that cross-sector decision-making is too complex to be adequately addressed by the Second Panel’s impact inventory. The framework described in this paper may be too abstract to be practically useful, and too vague to be foundational. But the complexities and challenges in multi-sector economic evaluation need to be spelt out – there is no simple solution.

Advanced data visualisation in health economics and outcomes research: opportunities and challenges. Applied Health Economics and Health Policy [PubMed] Published 4th May 2019

Computers can make your research findings look cool, which can help make people pay attention. But data visualisation can also be used as part of the research process and provide a means of more intuitively (and accurately) communicating research findings. The data sets used by health economists are getting bigger, which provides more opportunity and need for effective visualisation. The authors of this paper suggest that data visualisation techniques could be more widely adopted in our field, but that there are challenges and potential pitfalls to consider.

Decision modelling is an obvious context in which to use data visualisation, because models tend to involve large numbers of simulations. Dynamic visualisations can provide a means by which to better understand what is going on in these simulations, particularly with respect to uncertainty in estimates associated with alternative model structures or parameters. If paired with interactive models and customised dashboards, visualisation can make complex models accessible to non-expert users. Communicating patient outcomes data is also highlighted as a potential application, aiding the characterisation of differences between groups of individuals and alternative outcome measures.

Yet, there are barriers to wider use of visualisation. There is some scepticism about bias in underlying analyses, and end users don’t want to be bamboozled by snazzy graphics. The fact that journal articles are still the primary mode of communicating research findings is a problem, as you can’t have dynamic visualisations in a PDF. There’s also a learning curve for analysts wishing to develop complex visualisations. Hopefully, opportunities will be identified for two-way learning between the health economics world and data scientists more accustomed to data visualisation.

The authors provide several examples (static in the publication, but with links to live tools), to demonstrate the types of visualisations that can be created. Generally speaking, complex visualisations are proposed as complements to our traditional presentations of results, such as cost-effectiveness acceptability curves, rather than as alternatives. The key thing is to maintain credibility by ensuring that data visualisation is used to describe data in a more accurate and meaningful way, and to avoid exaggeration of research findings. It probably won’t be long until we see a set of good practice guidelines being developed for our field.

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Thesis Thursday: Kevin Momanyi

Chris Sampson 3 Comments

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 Kevin Momanyi who has a PhD from the University of Aberdeen. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Enhancing quality in social care through economic analysis
Supervisors
Paul McNamee
Repository link
http://digitool.abdn.ac.uk/webclient/DeliveryManager?pid=240815

What are reablement and telecare services and why should economists study them?

Reablement and telecare are two types of services within homecare that enable individuals to live independently in their own homes with little or no assistance from other people. Reablement focuses on helping individuals relearn the skills needed for independent living after an illness or injury. It is a short term intervention that lasts for about 6 to 12 weeks and usually involves several health care professionals and social care workers working together to meet some set objectives. Telecare, on the other hand, entails the use of devices (e.g. community alarms and linked pill dispensers) to facilitate communication between homecare clients and their care providers in the event of an accident or negative health shock. Economists should study reablement and telecare so as to determine whether or not the services have value for money and also develop policies that would reduce social care costs without compromising the welfare of the populace.

In what ways did your study reach beyond the scope of previous research?

My study extended the previous studies in three main ways. Firstly, I estimated the treatment effects in a non-experimental setting unlike the previous studies that used either randomised controlled trials or quasi-experiments. Secondly, I used linked administrative health and social care data in Scotland for the 2010/2011 financial year. The data covered the administrative records for the entire Scottish population and was larger and more robust than the data used by the previous studies. Thirdly, the previous studies were simply concerned with quantifying the treatment effects and thus did not provide a rationale as to how the interventions affect the outcomes of interest. My thesis addressed this knowledge gap by formulating an econometric model that links the demand for reablement/telecare to several outcomes.

How did you go about trying to estimate treatment effects from observational data?

I used a theory driven approach combined with specialised econometric techniques in order to estimate the treatment effects. The theoretical model drew from the Almost Ideal Demand System (AIDS), Andersen’s Behavioural Model of Health Services Use, the Grossman Model of the demand for health capital, and Samuelson’s Revealed Preference Theory; whereas the estimation strategy simultaneously controlled for unexplained trend variations, potential endogeneity of key variables, potential sample selection bias and potential unobserved heterogeneity. For a more substantive discussion of the theoretical model and estimation strategy, see Momanyi, 2018. Although the majority of the studies in the econometric literature advocate for the use of quasi-experimental study designs in estimating treatment effects using observational data, I provided several proofs in my thesis showing that these designs do not always yield consistent results, and that estimating the econometric models in the way that I did is preferable since it nests several study designs and estimation strategies as special cases.

Are there key groups of people that could benefit from greater use of reablement and telecare services?

According to the empirical results of my thesis, there is sufficient evidence to conclude that there are certain groups within the population that could benefit from greater use of telecare. For instance, one empirical study investigating the effect of telecare use on the expected length of stay in hospital showed that the community alarm users with physical disabilities are more likely than the other community alarm users to have a shorter length of stay in hospital, holding other factors constant. Correspondingly, the results also showed that the individuals who use more advanced telecare devices than the community alarm and who are also considered to be frail elderly are expected to have a relatively shorter length of stay in hospital as compared to the other telecare users in the population, all else equal. A discussion of various econometric models that can be used to link telecare use to the length of stay in hospital can be found in Momanyi, 2017.

What would be your main recommendation for policymakers in Scotland?

The main recommendation for policymakers is that they ought to subsidise the cost of telecare services, especially in regions that currently have relatively low utilisation levels, so as to increase the uptake of telecare in Scotland. This was informed by a decomposition analysis that I conducted in the first empirical study to shed light on what could be driving the observed direct relationship between telecare use and independent living at home. The analysis showed that the treatment effect was in part due to the underlying differences (both observable and unobservable) between telecare users and non-users, and thus policymakers could stimulate telecare use in the population by addressing these differences. In addition to that, policymakers should advise the local authorities to target telecare services at the groups of people that are most likely to benefit from them as well as sensitise the population on the benefits of using community alarms. This is because the econometric analyses in my thesis showed that the treatment effects are not homogenous across the population, and that the use of a community alarm is expected to reduce the likelihood of unplanned hospitalisation, whereas the use of the other telecare devices has the opposite effect all else equal.

Can you name one thing that you wish you could have done as part of your PhD, which you weren’t able to do?

I would have liked to include in my thesis an empirical study on the effects of reablement services. My analyses focused only on telecare use as the treatment variable due to data limitations. This additional study would have been vital in validating the econometric model that I developed in the first chapter of the thesis as well as addressing the gaps in knowledge that were identified by the literature review. In particular, it would have been worthwhile to determine whether reablement services should be offered to individuals discharged from hospital or to individuals who have been selected into the intervention directly from the community.

Chris Sampson’s journal round-up for 29th April 2019

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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.

Here comes the SUN: self‐assessed unmet need, worsening health outcomes, and health care inequity. Health Economics [PubMed] Published 24th April 2019

How should we measure inequity in health care? Often, it is measured on the basis of health care use, and the extent to which people with different socioeconomic circumstances – conditional on their level of need – access services. One problem with this approach is that differences might not only reflect barriers to access but also heterogeneity in preferences. If people of lower socioeconomic status prefer to access services less (conditional on need), then this is arguably an artificial signal of inequities in the system. Instead, we could just ask people. But can self-assessed unmet need provide a valid and meaningful measure of inequity?

In this study, the researchers looked at whether self-reported unmet need can predict deterioration in health. The idea here is that we would expect there to be negative health consequences if people genuinely need health care but cannot access it. The Canadian National Population Health Survey asks whether, during the preceding 12 months, the individual needed health care but did not receive it, with around 10% reporting unmet need. General health outcomes are captured by self-assessed health and by the HUI3, and there are also variables for specific chronic conditions. A few model specifications, controlling for a variety of health-related and demographic variables, are implemented. For the continuous variables, the authors use a fixed effects model with lagged health, and for the categorical outcomes they used a random effects probit.

The findings are consistent across models and outcomes. People who report self-assessed unmet need are more likely to have poorer health outcomes in subsequent periods, in terms of both general health and the number of self-reported chronic conditions. This suggests that self-assessed unmet need is probably a meaningful indicator of barriers to access in health care. I’m not aware of any UK-based surveys that include self-assessed unmet need, but this study provides some reason to think that they should.

Cost effectiveness of treatments for diabetic retinopathy: a systematic literature review. PharmacoEconomics [PubMed] Published 22nd April 2019

I’ve spent a good chunk of the last 8 years doing research in the context of diabetic eye disease. Over that time, treatment has changed, and there have been some interesting controversies relating to the costs of new treatments. So this review is timely.

There are four groups of treatments that the authors consider – laser, anti-VEGF eye injections, corticosteroids, and surgery. The usual databases were searched, turning up 1915 abstracts, and 17 articles were included in the review. That’s not a lot of studies, which is why I’d like to call the authors out for excluding one HTA report, which I assume was Royle et al 2015 and which probably should have been included. The results are summarised according to whether the evaluations were of treatments for diabetic macular oedema (DMO) or proliferative diabetic retinopathy (PDR), which are the two main forms of sight-threatening diabetic eye disease. The majority of studies focussed on DMO. As ever, in reviews of this sort, the studies and their findings are difficult to compare. Different methods were employed, for different purposes. The reason that there are so few economic evaluations in the context of PDR is probably that treatments have been so decisively shown to be effective. Yet there is evidence to suggest that, for PDR, the additional benefits of injections do not justify the much higher cost compared with laser. However, this depends on the choice of drug that is being injected, because prices vary dramaticly. For DMO, injections are cost-effective whether combined with laser or not. The evidence on corticosteroids is mixed and limited, but there is promise in recently-developed fluocinolone implants.

Laser might still be king in PDR, and early surgical intervention is also still cost-effective where indicated. For DMO, the strongest evidence is in favour of using an injection (bevacizumab) that can only be used off-label. You can blame Novartis for that, or you can blame UK regulators. Either way, there’s good reason to be angry about it. The authors of this paper clearly have a good understanding of the available treatments, which is not always the case for reviews of economic evaluations. The main value of this study is as a reference point for people developing research in this area, to identify the remaining gaps in the evidence and appropriately align (or not) with prevailing methods.

Exploring the impacts of the 2012 Health and Social Care Act reforms to commissioning on clinical activity in the English NHS: a mixed methods study of cervical screening. BMJ Open [PubMed] Published 14th April 2019

Not everybody loves the Health and Social Care Act of 2012. But both praise and criticism of far-reaching policies like this are usually confined to political arguments. It’s nice to see – and not too long after the fact – some evidence of its impact. In this paper, we learn about the impact of the Act on cervical screening activity.

The researchers used both qualitative and quantitative methods in their study in an attempt to identify whether the introduction of the Act influenced rates of screening coverage. With the arrival of the Act, responsibility for commissioning screening services shifted from primary care trusts to regional NHS England teams, while sexual health services were picked up by local authorities. The researchers conducted 143 (!) interviews with commissioners, clinicians, managers, and administrators from various organisations. Of these, 93 related to the commissioning of sexual health services, with questions regarding the commissioning system before and after the introduction of the Act. How did participants characterise the impact of the Act? Confusion, complexity, variability, uncertainty, and the idea that these characteristics could result in a drop in screening rates.

The quantitative research plan, and in particular the focus on cervical screening, arose from the qualitative findings. The quantitative analysis sought to validate the qualitative findings. But everyone had the Act dropped on them at the same time (those wily politicians know how to evade blame), so the challenge for the researchers was to identify some source of variation that could represent exposure to the effects of the Act. Informed by the interviewees, the authors differentiated between areas based on the number of local authorities that the clinical commissioning group (CCG) had to work with. Boundaries don’t align, so while some CCGs only have to engage with one local authority, some have to do so with as many as three, increasing the complexity created by the Act. As a kind of control, the researchers looked at the rate of unassisted births, which we wouldn’t expect to have been affected by the introduction of the Act. From this, they estimated the triple difference in cervical screening rates before and after the introduction of the Act, between CCGs with one or more than one local authority, minus the difference in the unassisted birth rate. Screening rates (and unassisted delivery rates) were both declining before the introduction of the Act. Without any adjustment, screening rates before and after the introduction of the act decreased by 0.39% more for GP practices in those CCGs that had to work with multiple local authorities. Conversely, unassisted delivery rates actually increased by a similar amount. The adjusted impact of the Act on screening rates was a drop of around 0.62%.

Clearly, there are big disclaimers attached to findings from a study of this sort, though the main finding seems to be robust to a variety of specifications. Any number of other things could explain the change in screening rates over the period, which the researchers couldn’t capture. But the quantitative findings are backed-up by the qualitative reports, making this a far more convincing piece of work. There’s little doubt that NHS redisorganisations of this kind create challenges in the short term, and we can now see the impact that this has on the provision of care.

Public involvement in health outcomes research: lessons learnt from the development of the recovering quality of life (ReQoL) measures. Health and Quality of Life Outcomes [PubMed] Published 11th April 2019

We’ve featured a few papers from the ReQoL project on this blog. The researchers developed several outcome measures to be used in the context of mental health. A couple of weeks ago, we also featured a paper turning a sceptical eye to the idea of co-production, whereby service users or members of the public are not simply research participants but research partners. This paper describes the experience of coproduction in the context of the ReQoL study. The authors are decidedly positive about co-production.

The logic behind the involvement of service users in the development of patient-reported outcome measures is obvious; measures need to be meaningful and understandable to patients, and enabling service users to inform research decisions could facilitate that. But there is little guidance on co-production in the context of developing patient-reported outcomes. Key decisions in the development of ReQoL were made by a ‘scientific group’, which included academics, clinicians, and seven expert service users. An overlapping ‘expert service user group’ also supported the study. In these roles, service users contributed to all stages of the research, confirming themes and items, supporting recruitment, collecting and analysing data, agreeing the final items for the measures, and engaging in dissemination activities. It seems that the involvement was in large part attendance at meetings, discussing data and findings to achieve an interpretation that includes the perspectives of services users. This resulted in decisions – about which items to take forward – that probably would not have been made if the academics and clinicians were left to their own devices. Service users were also involved in the development of research materials, such as the interview topic guide. In some examples, however, it seems like the line between research partner and research participant was blurred. If an expert service user group is voting on candidate items and editing them according to their experience, this is surely a data collection process and the services users become research subjects.

The authors describe the benefits as they saw them, in terms of the expert service users’ positive influence on the research. The costs and challenges are also outlined, including the need to manage disagreements and make additional preparations for meetings. We’re even provided with the resource implications in terms of the additional days of work. The comprehensive description of the researchers’ experiences in this context and the recommendations that they provide make this paper an important companion for anybody designing a research study to develop a new patient-reported outcome measure.

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