from The Academic Health Economists’ Blo… at http://bit.ly/2QArvlT on November 18, 2019 at 12:11PM
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.