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 18th March 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.

Evaluation of intervention impact on health inequality for resource allocation. Medical Decision Making [PubMed] Published 28th February 2019

How should decision-makers factor equity impacts into economic decisions? Can we trade off an intervention’s cost-effectiveness with its impact on unfair health inequalities? Is a QALY just a QALY or should we weight it more if it is gained by someone from a disadvantaged group? Can we assume that, because people of lower socioeconomic position lose more QALYs through ill health, that most interventions should, by default, reduce inequalities?

I really like the health equity plane. This is where you show health impacts (usually including a summary measure of cost-effectiveness like net health benefit or net monetary benefit) and equity impacts (which might be a change in slope index of inequality [SII] or relative index of inequality) on the same plane. This enables decision-makers to identify potential trade-offs between interventions that produce a greater benefit, but have less impact on inequalities, and those that produce a smaller benefit, but increase equity. I think there has been a debate over whether the ‘win-win’ quadrant should be south-east (which would be consistent with the dominant quadrant of the cost-effectiveness plane) or north-east, which is what seems to have been adopted as the consensus and is used here.

This paper showcases a reproducible method to estimate the equity impact of interventions. It considers public health interventions recommended by NICE from 2006-2016, with equity impacts estimated based on whether they targeted specific diseases, risk factors or populations. The disease distributions were based on hospital episode statistics data by deprivation (IMD). The study used equity weights to convert QALYs gained to different social groups into net social welfare. In this case, valuing the most disadvantaged fifth of people’s health at around 6-7 times that of the least disadvantaged fifth. I think there might still be work to be done around reaching consensus for equity weights.

The total expected effect on inequalities is small – full implementation of all recommendations would produce a reduction of the quality-adjusted life expectancy gap between the healthiest and least healthy from 13.78 to 13.34 QALYs. But maybe this is to be expected; NICE does not typically look at vaccinations or screening and has not looked at large scale public health programmes like the Healthy Child Programme in the whole. Reassuringly, where recommended interventions were likely to increase inequality, the trade-off between efficiency and equity was within the social welfare function they had used. The increase in inequality might be acceptable because the interventions were cost-effective – producing 5.6million QALYs while increasing the SII by 0.005. If these interventions are buying health at a good price, then you would hope this might then release money for other interventions that would reduce inequalities.

I suspect that public health folks might not like equity trade-offs at all – trading off equity and cost-effectiveness might be the moral equivalent of trading off human rights – you can’t choose between them. But the reality is that these kinds of trade-offs do happen, and like a lot of economic methods, it is about revealing these implicit trade-offs so that they become explicit, and having ‘accountability for reasonableness‘.

Future unrelated medical costs need to be considered in cost effectiveness analysis. The European Journal of Health Economics [PubMed] [RePEc] Published February 2019

This editorial says that NICE should include unrelated future medical costs in its decision making. At the moment, if NICE looks at a cardiovascular disease (CVD) drug, it might look at future costs related to CVD but it won’t include changes in future costs of cancer, or dementia, which may occur because individuals live longer. But usually unrelated QALY gains will be implicitly included; so there is an inconsistency. If you are a health economic modeller, you know that including unrelated costs properly is technically difficult. You might weight average population costs by disease prevalence so you get a cost estimate for people with coronary heart disease, diabetes, and people without either disease. Or you might have a general healthcare running cost that you can apply to future years. But accounting for a full matrix of competing causes of morbidity and mortality is very tricky if not impossible. To help with this, this group of authors produced the excellent PAID tool, which helps with doing this for the Netherlands (can we have one for the UK please?).

To me, including unrelated future costs means that in some cases ICERs might be driven more by the ratio of future costs to QALYs gained. Whereas currently, ICERs are often driven by the ratio of the intervention costs to QALYs gained. So it might be that a lot of treatments that are currently cost-effective no longer are, or we need to judge all interventions with a higher ICER willingness to pay threshold or value of a QALY. The authors suggest that, although including unrelated medical costs usually pushes up the ICER, it should ultimately result in better decisions that increase health.

There are real ethical issues here. I worry that including future unrelated costs might be used for an integrated care agenda in the NHS, moving towards a capitation system where the total healthcare spend on any one individual is capped, which I don’t necessarily think should happen in a health insurance system. Future developments around big data mean we will be able to segment the population a lot better and estimate who will benefit from treatments. But I think if someone is unlucky enough to need a lot of healthcare spending, maybe they should have it. This is risk sharing and, without it, you may get the ‘double jeopardy‘ problem.

For health economic modellers and decision-makers, a compromise might be to present analyses with related and unrelated medical costs and to consider both for investment decisions.

Overview of cost-effectiveness analysis. JAMA [PubMed] Published 11th March 2019

This paper probably won’t offer anything new to academic health economists in terms of methods, but I think it might be a useful teaching resource. It gives an interesting example of a model of ovarian cancer screening in the US that was published in February 2018. There has been a large-scale trial of ovarian cancer screening in the UK (the UKCTOCS), which has been extended because the results have been promising but mortality reductions were not statistically significant. The model gives a central ICER estimate of $106,187/QALY (based on $100 per screen) which would probably not be considered cost-effective in the UK.

I would like to explore one statement that I found particularly interesting, around the willingness to pay threshold; “This willingness to pay is often represented by the largest ICER among all the interventions that were adopted before current resources were exhausted, because adoption of any new intervention would require removal of an existing intervention to free up resources.”

The Culyer bookshelf model is similar to this, although as well as the ICER you also need to consider the burden of disease or size of the investment. Displacing a $110,000/QALY intervention for 1000 people with a $109,000/QALY intervention for a million people will bust your budget.

This idea works intuitively – if Liverpool FC are signing a new player then I might hope they are better than all of the other players, or at least better than the average player. But actually, as long as they are better than the worst player then the team will be improved (leaving aside issues around different positions, how they play together, etc.).

However, I think that saying that the reference ICER should be the largest current ICER might be a bit dangerous. Leaving aside inefficient legacy interventions (like unnecessary tonsillectomies etc), it is likely that the intervention being considered for investment and the current maximum ICER intervention to be displaced may both be new, expensive immunotherapies. It might be last in, first out. But I can’t see this happening; people are loss averse, so decision-makers and patients might not accept what is seen as a fantastic new drug for pancreatic cancer being approved then quickly usurped by a fantastic new leukaemia drug.

There has been a lot of debate around what the threshold should be in the UK; in England NICE currently use £20,000 – £30,000, up to a hypothetical maximum £300,000/QALY in very specific circumstances. UK Treasury value QALYs at £60,000. Work by Karl Claxton and colleagues suggests that marginal productivity (the ‘shadow price’) in the NHS is nearer to £5,000 – £15,000 per QALY.

I don’t know what the answer to this is. I don’t think the willingness-to-pay threshold for a new treatment should be the maximum ICER of a current portfolio of interventions; maybe it should be the marginal health production cost in a health system, as might be inferred from the Claxton work. Of course, investment decisions are made on other factors, like impact on health inequalities, not just on the ICER.

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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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