Tag health care costs

Chris Sampson’s journal round-up for 18th November 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 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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Thesis Thursday: Elizabeth Lemmon

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

Title
Essays on the provision of long term care to older adults in Scotland
Supervisors
David Bell, Alasdair Rutherford
Repository link
http://hdl.handle.net/1893/29369

What long term care provision is available to older people in Scotland?

Long term care (LTC) in Scotland comprises both formal care and unpaid care. Formal care encompasses care provided by professionals in a person’s own home as well as care in a residential care setting. Unpaid care is care that is provided by family members, friends, or neighbours. Long term care is provided to older people who need help because they are ill, frail or have a disability. It might mean help with more administrative tasks such as filling in forms, paying bills, shopping, and housework, but can also mean help with things of a more personal nature such as washing and dressing. Since 2002, individuals living in Scotland aged 65 or over are entitled to free personal care (FPC) at home, subject to a needs assessment. This makes Scotland quite different to England, where personal care costs are borne by the service user and their families, and provides a unique opportunity to conduct research.

What were the pros and cons of your chosen data sources?

I used three data sources in my PhD. Those included the Family Resources Survey (FRS), the Scottish Government’s administrative Social Care Survey (SCS) and publically available data zone level data. The key benefit of using survey data like the FRS was that they captured information about care recipients and their caregivers. I used these data in my third paper to look at unpaid carers’ Standard of Living (SoL). The down side of the FRS is that it only captures a subset of the population, which might be systematically different from the population at large. At the same time, although there is information on carers and the person they are caring for, this information is very limited for those who are not living with the care recipient. On the other hand, the benefit of using the SCS, which I used in my first two papers, is that it captures population level information about the provision of LTC services. However, unlike the FRS, the SCS was designed for administrative purposes meaning that it lacks the richness of information on client circumstances and characteristics. One solution to this is to use data zone level information as a proxy for those characteristics, but often this is not enough. Overall, the PhD highlighted both the strengths and weaknesses of working with these different data sources, pointing to the potential of using linked administrative and survey data in future research.

How did you identify sources of inequity in the provision of long term care?

Inequity in the provision of LTC exists if there are differences in LTC provision after accounting for differences in need. I investigated this issue of inequity in my first paper. In particular, we observed from the raw data that there are big differences in FPC provision between the 32 Scottish local authorities. As I mentioned, FPC is available to anyone in Scotland aged 65+ who needs it. Perhaps those differences are due to differences in need. But I didn’t find that this was the case. It seemed that, even after accounting for the need of local authority populations, via the proportion of disability benefit claims, there were still large differences in provision of FPC. I modelled this inequity using a simple regression framework. The results from the regressions found that there is evidence of geographic inequity in the provision of FPC in Scotland. In particular, the analysis suggests that the differences between the FPC rate and the rate of disability are not consistent across local authorities, suggesting that a needy individual might be more or less likely to receive care depending on where they live. One explanation for this is that local authorities differ in terms of their practices for managing the demand for FPC. However, this is an area that would require more detailed investigation with individual local authorities to understand their practices.

What is the role of unpaid care, and how did you model that?

Unpaid care is defined as care that is provided by family members, partners, or friends to those who require help because they are ill, frail, or have a disability. The care that they provide is unpaid and often considered as having a zero cost in economic evaluations. This might lead to inefficient resource allocation and poor policy decisions. In my second paper, I tried to model the effect that unpaid carers have on the FPC use of the cared for. This was difficult due to the potential reverse causality that occurs between the two. I compared different models to try to estimate this effect. Overall, my findings suggest that unpaid carers likely complement FPC services in Scotland. This relationship might be due to unpaid carers advocating on behalf of the cared for, and demanding services from the local authority for them. They might do this because they require more support to enable them to remain in the labour force. It could also be that the type of care unpaid carers provide is different to that provided by formal carers.

Why did you use a ‘standard of living’ approach and what can it tell us about the cost of unpaid care?

The motivation for using the SoL approach, as implemented by Morciano et al (2015), was really that we felt it might capture more of the trade-offs that are involved in providing care. Specifically, it is expected that unpaid carers have to divert resources in order to pay for goods and services for the person they are caring for. Compared to the conventional costing methods which have focused on attaching a monetary value to the time a carer gives up in order to provide care, we argue that the SoL approach may capture a wider array of the trade-offs that are involved in providing unpaid care. For example, are unpaid carers less able to afford to go on holiday or to take part in a regular leisure activity? If it is the case that unpaid carers have to invest resources into providing care then they might have fewer resources to devote to their own needs and wants, resulting in unpaid carers having a lower SoL compared to non-carers. The results suggest that unpaid carers who are living with the person they are caring for would require compensation of £229 per week in order for them to reach the same SoL as a non-carer.

What are the key steps necessary to identify and address unmet need in this context?

My research highlighted that there is possibly unmet need for FPC in Scotland and that this could potentially be more likely for older people who don’t have an unpaid carer helping them to access FPC services. Understanding this unmet need is a key area which requires further research. Unfortunately, it is difficult to measure and we only ever observe the met need for care, i.e. those who end up receiving formal care services. Thus, prior to addressing unmet need, it is important that we can measure it. One step necessary in doing so would be to carry out detailed investigations with individual local authorities. This would help us understand more about the needs of those individuals who apply for FPC but who are turned down. But this is only part of the picture. Understanding where individuals need FPC but don’t apply, either due to transaction costs, a lack of information on how to access those services, or other reasons, is far more difficult. One approach to gaining insight on these individuals could be to conduct qualitative interviews with them and their families.

Chris Sampson’s journal round-up for 5th August 2019

Chris Sampson Leave a comment

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.

The barriers and facilitators to model replication within health economics. Value in Health Published 16th July 2019

Replication is a valuable part of the scientific process, especially if there are uncertainties about the validity of research methods. When it comes to cost-effectiveness modelling, there are endless opportunities for researchers to do things badly, even with the best intentions. Attempting to replicate modelling studies can therefore support health care decision-making. But replication studies are rarely conducted, or, at least, rarely reported. The authors of this study sought to understand the factors that can make replication easy or difficult, with a view to informing reporting standards.

The authors attempted to replicate five published cost-effectiveness modelling studies, with the aim of recreating the key results. Each replication attempt was conducted by a different author and we’re even given a rating of the replicator’s experience level. The characteristics of the models were recorded and each replicator detailed – anecdotally – the things that helped or hindered their attempt. Some replications were a resounding failure. In one case, the replicated cost per patient was more than double the original, at more than £1,000 wide of the mark. Replicators reported that having a clear diagram of the model structure was a big help, as was the provision of example calculations and explicit listing of the key assumptions. Various shortcomings made replication difficult, all relating to a lack of clarity or completeness in reporting. The impact of this on the validation attempt was exacerbated if the model either involved lots of scenarios that weren’t clearly described or if the model had a long time horizon.

The quality of each study was assessed using the Philips checklist, and all did pretty well, suggesting that the checklist is not sufficient for ensuring replicability. If you develop and report cost-effectiveness models, this paper could help you better understand how end-users will interpret your reporting and make your work more replicable. This study focusses on Markov models. They’re definitely the most common approach, so perhaps that’s OK. It might be useful to produce prescriptive guidance specific to Markov models, informed by the findings of this study.

US integrated delivery networks perspective on economic burden of patients with treatment-resistant depression: a retrospective matched-cohort study. PharmacoEconomics – Open [PubMed] Published 28th June 2019

Treatment-resistant depression can be associated high health care costs, as multiple lines of treatment are tried, with patients experiencing little or no benefit. New treatments and models of care can go some way to addressing these challenges. In the US, there’s some reason to believe that integrated delivery networks (IDNs) could be associated with lower care costs, because IDNs are based on collaborative care models and constitute a single point of accountability for patient costs. They might be particularly useful in the case of treatment-resistant depression, but evidence is lacking. The authors of this study investigated the difference in health care resource use and costs for patients with and without treatment-resistant depression, in the context of IDNs.

The researchers conducted a retrospective cohort study using claims data for people receiving care from IDNs, with up to two years follow-up from first antidepressant use. 1,582 people with treatment-resistant depression were propensity score matched to two other groups – patients without depression and patients with depression that was not classified as treatment-resistant. Various regression models were used to compare the key outcomes of all-cause and specific categories of resource use and costs. Unfortunately, there is no assessment of whether the selected models are actually any good at estimating differences in costs.

The average costs and resource use levels in the three groups ranked as you would expect: $25,807 per person per year for the treatment-resistant group versus $13,701 in the non-resistant group and $8,500 in the non-depression group. People with treatment-resistant depression used a wider range of antidepressants and for a longer duration. They also had twice as many inpatient visits as people with depression that wasn’t treatment-resistant, which seems to have been the main driver of the adjusted differences in costs.

We don’t know (from this study) whether or not IDNs provide a higher quality of care. And the study isn’t able to compare IDN and non-IDN models of care. But it does show that IDNs probably aren’t a full solution to the high costs of treatment-resistant depression.

Rabin’s paradox for health outcomes. Health Economics [PubMed] [RePEc] Published 19th June 2019

Rabin’s paradox arises from the theoretical demonstration that a risk-averse individual who turns down a 50:50 gamble of gaining £110 or losing £100 would, if expected utility theory is correct, turn down a 50:50 gamble of losing £1,000 or gaining millions. This is because of the assumed concave utility function over wealth that is used to model risk aversion and it is probably not realistic. But we don’t know about the relevance of this paradox in the health domain… until now.

A key contribution of this paper is that it considers both decision-making about one’s own health and decision-making from a societal perspective. Three different scenarios are set-up in each case, relating to gains and losses in life expectancy with different levels of health functioning. 201 students were recruited as part of a larger study on preferences and each completed all six gamble-pairs (three individual, three societal). To test for Rabin’s paradox, the participants were asked whether they would accept each gamble involving a moderate stake and a large stake.

In short, the authors observe Rabin’s proposed failure of expected utility theory. Many participants rejected small gambles but did not reject the larger gambles. The effect was more pronounced for societal preferences. Though there was a large minority for whom expected utility theory was not violated. The upshot of all this is that our models of health preferences that are based on expected utility may be flawed where uncertain outcomes are involved – as they often are in health. This study adds to a growing body of literature supporting the relevance of alternative utility theories, such as prospect theory, to health and health care.

My only problem here is that life expectancy is not health. Life expectancy is everything. It incorporates the monetary domain, which this study did not want to consider, as well as every other domain of life. When you die, your stock of cash is as useful to you as your stock of health. I think it would have been more useful if the study focussed only on health status and outcomes and excluded all considerations of death.

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