Chris Sampson’s journal round-up for 18th November 2019

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 conceptual map of health-related quality of life dimensions: key lessons for a new instrument. Quality of Life Research [PubMed] Published 1st November 2019

EQ-5D, SF-6D, HUI3, AQoL, 15D; they’re all used to describe health states for the purpose of estimating health state utility values, to get the ‘Q’ in the QALY. But it’s widely recognised (and evidenced) that they measure different things. This study sought to better understand the challenge by doing two things: i) ‘mapping’ the domains of the different instruments and ii) advising on the domains to be included in a new measure.

The conceptual model described in this paper builds on two standard models of health – the ICF (International Classification of Functioning, Disability, and Health), which is endorsed by the WHO, and the Wilson and Cleary model. The new model is built around four distinctions, which can be used to define the dimensions included in health state utility instruments: cause vs effect, specific vs broad, physical vs psychological, and subjective vs objective. The idea is that each possible dimension of health can relate, with varying levels of precision, to one or the other of these alternatives.

The authors argue that, conveniently, cause/effect and specific/broad map to one another, as do physical/psychological and objective/subjective. The framework is presented visually, which makes it easy to interpret – I recommend you take a look. Each of the five instruments previously mentioned is mapped to the framework, with the HUI and 15D coming out as ‘symptom’ oriented, EQ-5D and SF-6D as ‘functioning’ oriented, and the AQoL as a hybrid of a health and well-being instrument. Based (it seems) on the Personal Wellbeing Index, the authors also include two social dimensions in the framework, which interact with the health domains. Based on the frequency with which dimensions are included in existing instruments, the authors recommend that a new measure should include three physical dimensions (mobility, self-care, pain), three mental health dimensions (depression, vitality, sleep), and two social domains (personal relationships, social isolation).

This framework makes no sense to me. The main problem is that none of the four distinctions hold water, let alone stand up to being mapped linearly to one another. Take pain as an example. It could be measured subjectively or objectively. It’s usually considered a physical matter, but psychological pain is no less meaningful. It may be a ‘causal’ symptom, but there is little doubt that it matters in and of itself as an ‘effect’. The authors themselves even offer up a series of examples of where the distinctions fall down.

It would be nice if this stuff could be drawn-up on a two-dimensional plane, but it isn’t that simple. In addition to oversimplifying complex ideas, I don’t think the authors have fully recognised the level of complexity. For instance, the work seems to be inspired – at least in part – by a desire to describe health state utility instruments in relation to subjective well-being (SWB). But the distinction between health state utility instruments and SWB isn’t simply a matter of scope. Health state utility instruments (as we use them) are about valuing states in relation to preferences, whereas SWB is about experienced utility. That’s a far more important and meaningful distinction than the distinction between symptoms and functioning.

Careless costs related to inefficient technology used within NHS England. Clinical Medicine Journal [PubMed] Published 8th November 2019

This little paper – barely even a single page – was doing the rounds on Twitter. The author was inspired by some frustration in his day job, waiting for the IT to work. We can all relate to that. This brief analysis sums the potential costs of what the author calls ‘careless costs’, which is vaguely defined as time spent by an NHS employee on activity that does not relate to patient care. Supposing that all doctors in the English NHS wasted an average of 10 minutes per day on such activities, it would cost over £143 million (per year, I assume) based on current salaries. The implication is that a little bit of investment could result in massive savings.

This really bugs me, for at least two reasons. First, it is normal for anybody in any profession to have a bit of downtime. Nobody operates at maximum productivity for every minute of every day. If the doctor didn’t have their downtime waiting for a PC to boot, it would be spent queuing in Costa, or having a nice relaxed wee. Probably both. Those 10 minutes that are displaced cannot be considered equivalent in value to 10 minutes of patient contact time. The second reason is that there is no intervention that can fix this problem at little or no cost. Investments cost money. And if perfect IT systems existed, we wouldn’t all find these ‘careless costs’ so familiar. No doubt, the NHS lags behind, but the potential savings of improvement may very well be closer to zero than to the estimates in this paper.

When it comes to clinical impacts, people insist on being able to identify causal improvements from clearly defined interventions or changes. But when it comes to costs, too many people are confident in throwing around huge numbers of speculative origin.

Socioeconomic disparities in unmet need for student mental health services in higher education. Applied Health Economics and Health Policy [PubMed] Published 5th November 2019

In many countries, the size of the student population is growing, and this population seems to have a high level of need for mental health services. There are a variety of challenges in this context that make it an interesting subject for health economists to study (which is why I do), including the fact that universities are often the main providers of services. If universities are going to provide the right services and reach the right people, a better understanding of who needs what is required. This study contributes to this challenge.

The study is set in the context of higher education in Ireland. If you have no idea how higher education is organised in Ireland, and have an interest in mental health, then the Institutional Context section of this paper is worth reading in its own right. The study reports on findings from a national survey of students. This analysis is a secondary analysis of data collected for the primary purpose of eliciting students’ preferences for counselling services, which has been described elsewhere. In this paper, the authors report on supplementary questions, including measures of psychological distress and use of mental health services. Responses from 5,031 individuals, broadly representative of the population, were analysed.

Around 23% of respondents were classified as having unmet need for mental health services based on them reporting both a) severe distress and b) not using services. Arguably, it’s a sketchy definition of unmet need, but it seems reasonable for the purpose of this analysis. The authors regress this binary indicator of unmet need on a selection of sociodemographic and individual characteristics. The model is also run for the binary indicator of need only (rather than unmet need).

The main finding is that people from lower social classes are more likely to have unmet need, but that this is only because these people have a higher level of need. That is, people from less well-off backgrounds are more likely to have mental health problems but are no less likely to have their need met. So this is partly good news and partly bad news. It seems that there are no additional barriers to services in Ireland for students from a lower social class. But unmet need is still high and – with more inclusive university admissions – likely to grow. Based on the analyses, the authors recommend that universities could reach out to male students, who have greater unmet need.

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Brendan Collins’s journal round-up for 14th January 2019

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.

Income distribution and health: can polarization explain health outcomes better than inequality? The European Journal of Health Economics [PubMed] Published 4th December 2018

One of my main interests is health inequalities. I thought polarisation was intuitive; I had seen it in the context of the UK and the US employment market; an increase in poorly-paid ‘McJobs’ and an increase in well-paid ‘MacJobs’, with fewer jobs in the middle. But I hadn’t seen polarisation measured in a statistical way.

Traditional measures of population inequalities like Gini or Atkinson index measure the share of income or the ratio of richest to poorest. But polarisation goes a step further and looks whether there are discrete clusters or groups who have similar incomes. The theory goes that having discrete groups increases social alienation, conflict and socioeconomic comparison and increases health inequalities. Now, I get how you can test statistically for discrete income clusters, and there is an evidence base for the relationship between polarisation and social tension. But groups will cluster based on other factors besides income. I feel like it may be taking a leap to assume a statistical finding (income polarisation) will always represent a sociological construct (alienation) but I confess I don’t know the literature behind this.

China is a country with an increasing degree of polarisation as measured by the Duclos, Esteban and Ray (DER) polarisation indices, and this study suggests that it is related to health status. This study looked at trends in BMI and systolic blood pressure from 1991 to 2011 and found both to increase with increased polarisation. I imagine a lot of other social change went on in this time period in China. I think BMI might not be a good candidate for measuring the effect of polarisation, as being poor is associated with malnourishment and low weight as well as obesity. The authors found that social capital (based on increasing family size, community size, and living in the same community for a long time) had a protective effect against the effects of polarisation on health. Whether this study provides more evidence for the socioeconomic comparison or status anxiety theories of health inequalities, I am not sure; it could equally provide evidence for the neo-materialist (i.e. simply not having enough resources for a healthy life) theories – the relative importance will likely differ by country anyway.

Maybe we don’t need to add more measures of inequality to the mix but I am intrigued. I am just starting my journey with polarisation but I think it has promise.

Two-year evaluation of mandatory bundled payments for joint replacement. The New England Journal of Medicine [PubMed] Published 2nd January 2019

Joint replacements are a big cost to western healthcare systems and often delayed or rationed (partly because replacement joints may only have a 10-20 year lifespan on average). In the UK, for instance, joint replacements have been rationed based on factors like BMI or pain levels (in my opinion, often in an arbitrary way to save money).

This paper found that having a bundled payments and penalties model (Comprehensive Care for Joint Replacement; CJR) for optimal care around hip and knee replacements reduced Medicare spending per episode compared to areas that did not pilot the programme. The overall difference was small in absolute terms at $812 against a total cost of around $24,000 per episode. The programme involves the hospital meeting a set of performance measures, and if they can do so at a lower cost, any savings are shared between the hospital and the payer. Cost savings were mainly driven by a reduction in patients being discharged to post-acute care facilities. Rates of complex patients were similar between pilot and control areas – this is important because a lower rate of complex cases in the CJR trial areas might indicate hospitals ‘cherry picking’ easier to treat, less expensive cases. Also, rates of complications were not significantly different between the CJR pilot areas and controls.
This paper suggests that having this kind of bundled payment programme can save money while maintaining quality.

Association of the Hospital Readmissions Reduction Program with mortality among Medicare beneficiaries hospitalized for heart failure, acute myocardial infarction, and pneumonia. JAMA [PubMed] Published 25th December 2018

Nobody likes being in hospital. But sometimes hospitals are the best places for people. This paper looks at possible unintended consequences of a US programme; the Hospital Readmissions Reduction Program (HRRP) where the Centers for Medicare & Medicaid Services (CMS) impose financial penalties (almost $2billion dollars’ worth since 2012) on hospitals with elevated 30-day readmission rates for patients with heart failure, acute myocardial infarction, and pneumonia. This study compared four time periods (no control group) and found that, after the programme was implemented, death rates for people who had been admitted with pneumonia and heart failure increased, with these increased deaths occurring more in people who had not been readmitted to hospital. The analysis controlled for differences in demographics, comorbidities, and calendar month using propensity scores and inverse probability weighting.

The authors are clear that their results do not establish cause and effect but are concerning nonetheless and worthy of more analysis. Incidentally, there is another paper this week in Health Affairs which suggests that the benefits of the programme in reducing readmissions was overstated.

There has been a similar financial incentive in the English NHS where hospitals are subject to the 30-day readmission rule, meaning they are not paid for people who are readmitted as an emergency within 30 days of being discharged. This is shortly to be abolished for 2019/20. I wonder if there has been similar research on whether this also led to unintended consequences in the NHS. Maybe there is a general lesson here about thinking a bit deeper about the potential outcomes of incentives in healthcare markets?

In these last two papers, we have had two examples of financial incentive programmes from Medicare. The CJR, which seems to have worked, has been dampened down from a mandatory to a voluntary programme, while the HRRP, which may not have worked, has been extended.

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Simon McNamara’s journal round-up for 6th August 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.

Euthanasia, religiosity and the valuation of health states: results from an Irish EQ5D5L valuation study and their implications for anchor values. Health and Quality of Life Outcomes [PubMed] Published 31st July 2018

Do you support euthanasia? Do you think there are health states worse than death? Are you religious? Don’t worry – I am not commandeering this week’s AHE journal round-up just to bombard you with a series of difficult questions. These three questions form the foundation of the first article selected for this week’s round-up.

The paper is based upon the hypothesis that your religiosity (“adherence to religious beliefs”) is likely to impact your support for euthanasia and, subsequently, the likelihood of you valuing severe health states as worse than death. This seems like a logical hypothesis. Religions tend to be anti-euthanasia, and so it appears likely that religious people will have lower levels of support for euthanasia than non-religious people. Equally, if you don’t support the principle of euthanasia, it stands to reason that you are likely to be less willing to choose immediate death over living in a severe health state – something you would need to do for a health state to be considered as being worse than death in a time trade-off (TTO) study.

The authors test this hypothesis using a sub-sample of data (n=160) collected as part of the Irish EQ-5D-5L TTO valuation study. Perhaps unsurprisingly, the authors find evidence in support of the above hypotheses. Those that attend a religious service weekly were more likely to oppose euthanasia than those who attend a few times a year or less, and those who oppose euthanasia were less likely to give “worse than death” responses in the TTO than those that support it.

I found this paper really interesting, as it raises a number of challenging questions. If a society is made up of people with heterogeneous beliefs regarding religion, how should we balance these in the valuation of health? If a society is primarily non-religious is it fair to apply this valuation tariff to the lives of the religious, and vice versa? These certainly aren’t easy questions to answer, but may be worth reflecting on.

E-learning and health inequality aversion: A questionnaire experiment. Health Economics [PubMed] [RePEc] Published 22nd July 2018

Moving on from the cheery topic of euthanasia, what do you think about socioeconomic inequalities in health? In my home country, England, if you are from the poorest quintile of society, you can expect to experience 62 years in full health in your lifetime, whilst if you are from the richest quintile, you can expect to experience 74 years – a gap of 12 years.

In the second paper to be featured in this round-up, Cookson et al. explore the public’s willingness to sacrifice incremental population health gains in order to reduce these inequalities in health – their level of “health inequality aversion”. This is a potentially important area of research, as the vast majority of economic evaluation in health is distributionally-naïve and effectively assumes that members of the public aren’t at all concerned with inequalities in health.

The paper builds on prior work conducted by the authors in this area, in which they noted a high proportion of respondents in health inequality aversion elicitation studies appear to be so averse to inequalities that they violate monotonicity – they choose scenarios that reduce inequalities in health even if these scenarios reduce the health of the rich at no gain to the poor, or they reduce the health of the poor, or they may reduce the health of both groups. The authors hypothesise that these monotonicity violations may be due to incomplete thinking from participants, and suggest that the quality of their thinking could be improved by two e-learning educational interventions. The primary aim of the paper is to test the impact of these interventions in a sample of the UK public (n=60).

The first e-learning intervention was an animated video that described a range of potential positions that a respondent could take (e.g. health maximisation, or maximising the health of the worst off). The second was an interactive spreadsheet-based questionnaire that presented the consequences of the participant’s choices, prior to them confirming their selection. Both interventions are available online.

The authors found that the interactive tool significantly reduced the amount of extreme egalitarian (monotonicity-violating) responses, compared to a non-interactive, paper-based version of the study. Similarly, when the video was watched before completing the paper-based exercise, the number of extreme egalitarian responses reduced. However, when the video was watched before the interactive tool there was no further decrease in extreme egalitarianism. Despite this reduction in extreme egalitarianism, the median levels of inequality aversion remained high, with implied weights of 2.6 and 7.0 for QALY gains granted to someone from the poorest fifth of society, compared to the richest fifth of society for the interactive questionnaire and video groups respectively.

This is an interesting study that provides further evidence of inequality aversion, and raises further concern about the practical dominance of distributionally-naïve approaches to economic evaluation. The public does seem to care about distribution. Furthermore, the paper demonstrates that participant responses to inequality aversion exercises are shaped by the information given to them, and the way that information is presented. I look forward to seeing more studies like this in the future.

A new method for valuing health: directly eliciting personal utility functions. The European Journal of Health Economics [PubMed] [RePEc] Published 20th July 2018

Last, but not least, for this round-up, is a paper by Devlin et al. on a new method for valuing health.

The relative valuation of health states is a pretty important topic for health economists. If we are to quantify the effectiveness, and subsequently cost-effectiveness, of an intervention, we need to understand which health states are better than others, and how much better they are. Traditionally, this is done by asking members of the public to choose between different health profiles featuring differing levels of fulfilment of a range of domains of health, in order to ‘uncover’ the relative importance the respondent places on these domains, and levels. These can then be used in order to generate social tariffs that assign a utility value to a given health state for use in economic evaluation.

The authors point out that, in the modern day, valuation studies can be conducted rapidly, and at scale, online, but at the potential cost of deliberation from participants, and the resultant risk of heuristic dominated decision making. In response to this, the authors propose a new method – direct elicitation of personal utility functions, and pilot its use for the valuation of EQ-5D in a sample of the English public (n=76).

The proposed approach differs from traditional approaches in three key ways. Firstly, instead of simply attempting to infer the relative importance that participants place on differing domains based upon choices between health profiles, the respondents are asked directly about the relative importance they place on differing domains of health, prior to validating these with profile choices. Secondly, the authors place a heavy emphasis on deliberation, and the construction, rather than uncovering, of preferences during the elicitation exercises. Thirdly, a “personal utility function” for each individual is constructed (in effect a personal EQ-5D tariff), and these individual utility functions are subsequently aggregated into a social utility function.

In the pilot, the authors find that the method appears feasible for wider use, albeit with some teething troubles associated with the computer-based tool developed to implement it, and the skills of the interviewers.

This direct method raises an interesting question for health economics – should we be inferring preferences based upon choices that differ in terms of certain attributes, or should we just ask directly about the attributes? This is a tricky question. It is possible that the preferences elicited via these different approaches could result in different preferences – if they do, on what grounds should we choose one or other? This requires a normative judgment, and at present, it appears both are (potentially) as legitimate as each other.

Whilst the authors apply this direct method to the valuation of health, I don’t see why similar approaches couldn’t be applied to any multi-attribute choice experiment. Keep your eyes out for future uses of it in valuation, and perhaps beyond? It will be interesting to see how it develops.

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