Alastair Canaway’s journal round-up for 18th September 2017

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.

Selection of key health domains from PROMIS® for a generic preference-based scoring system. Quality of Life Research [PubMedPublished 19th August 2017

The US Panel on Cost-Effectiveness recommends the use of QALYs. It doesn’t, however, instruct (unlike the UK) as to what measure should be used. This leaves the door ajar for both new and established measures. This paper sets about developing a new preference-based measure from the Patient-Reported Outcomes Measurement System (PROMIS). PROMIS is a US National Institutes of Health funded suite of person-centred measures of physical, mental, and social health. Across all the PROMIS measures there exist over 70 domains of health relevant to adult health. For all its promise, the PROMIS system does not produce a summary score amenable to the calculation of QALYs, nor for general descriptive purposes such as measuring HRQL over time. This study aimed to reduce the 70 items down to a number suitable for valuation. To do this, Delphi methods were used. The Delphi approach is something that seems to be increasing in popularity in the health economics world. For those unfamiliar, it essentially involves obtaining the opinions of experts independently and iteratively conducting rounds of questioning to reach a consensus (over two or more rounds). In this case nine health outcomes experts were recruited, they were presented with ‘all 37 domains’ (no mention is made of how they got from 70 to 37!) and asked to remove any domains that were not appropriate for inclusion in a general health utility measure or were redundant due to another PROMIS domain. If more than seven experts agreed, then the domain was removed. Responses were combined and presented until consensus was reached. This left 10 domains. They then used a community sample of 50 participants to test for independence of domains using a pairwise independence evaluation test. They were given the option of removing a domain they felt was not important to overall HRQL and asked to rate the importance of remaining domains using a VAS. These findings were used by the research team to whittle down from nine domains to seven. The final domains were: Cognitive function- abilities; Depression; Fatigue; Pain Interference; Physical Function; Ability to participate in social roles and activities; and Sleep disturbance. Many of these are common to existing measures but I did rather like the inclusion of cognitive function and fatigue – something that is missing in many, and to me appear important. The next step is valuation. Upon valuation, this is a promising candidate for use in economic evaluation – particularly in the US where the PROMIS measurement suite is already established.

Predictive validation and the re-analysis of cost-effectiveness: do we dare to tread? PharmacoEconomics [PubMedPublished 22nd August 2017

PharmacoEconomics treated us to a provocative editorial regarding predictive validation and re-analysis of cost-effectiveness models – a call to arms of sorts. For those (like me) who are not modelling experts, predictive validation (aka 4th order validation) refers to the comparison of model outputs with data that are collected after the initial analysis of the model. So essentially you’re comparing what you modelled would happen with what actually happened. The literature suggests that predictive validation is widely ignored. The importance of predictive validity is highlighted with a case study where predictive-validity was examined three years after the end of a trial – upon reanalysis the model was poor. This was then revised, which led to a much better fit of the prospective data. Predictive validation can, therefore, be used to identify sources of inaccuracies in models. If predictive validity was examined more commonly, improvements in model quality more generally are possible. Furthermore, it might be possible to identify specific contexts where poor predictive validity is prevalent and thus require further research. The authors highlight the field of advanced cancers as a particularly relevant context where uncertainty around survival curves is prevalent. By actively scheduling further data collection and updating the survival curves we can reduce the uncertainty surrounding the value of high-cost drugs. Predictive validation can also inform other aspects of the modelling process, such as the best choice of time point from which to extrapolate, or credible rates of change in predicted hazards. The authors suggest using expected value of information analysis to identify technologies with the largest costs of uncertainty to prioritise where predictive validity could be assessed. NICE and other reimbursement bodies require continued data collection for ‘some’ new technologies, the processes are therefore in place for future studies to be designed and implemented in a way to capture such data which allows later re-analysis. Assessing predictive validity seems eminently sensible, there are however barriers. Money is the obvious issue, extended prospective data collection and re-analysis of models requires resources. It does, however, have the potential to save money and improve health in the long run. The authors note how in a recent study they demonstrated that a drug for osteoporosis that had been recommended by Australia’s Pharmaceutical Benefits Advisory Committee was not actually cost-effective when further data were examined. There is clearly value to be achieved in predictive validation and re-analysis – it’s hard to disagree with the authors and we should probably be campaigning for longer term follow-ups, re-analysis and increased acknowledgement of the desirability of predictive validity.

How should cost-of-illness studies be interpreted? The Lancet Psychiatry [PubMed] Published 7th September 2017

It’s a good question – cost of illness studies are commonplace, but are they useful from a health economics perspective? A comment piece in The Lancet Psychiatry examines this issue using the case study of self-harm and suicide. It focuses on a recent publication by Tsiachristas et al, which examines the hospital resource use and care costs for all presentations of self-harm in a UK hospital. Each episode of self-harm cost £809, and when extrapolated to the UK cost £162 million. Over 30% of these costs were psychological assessments which despite being recommended by NICE only 75% of self-harming patients received. If all self-harming patients received assessments as recommended by NICE then another £51 million would be added to the bill. The author raises the question of how much use is this information for health economists. Nearly all cost of illness studies end up concluding that i) they cost a lot, and ii) money could be saved by reducing or ameliorating the underlying factors that cause the illness. Is this helpful? Well, not particularly, by focusing only on one illness there is no consideration of the opportunity cost: if you spend money preventing one condition then that money will be displacing resources elsewhere, likewise, resources spent reducing one illness will likely be balanced by increased spending on another illness. The author highlights this with a thought experiment: “imagine a world where a cost of illness study has been done for every possible diseases and that the total cost of illness was aggregated. The counterfactual from such an exercise is a world where nobody gets sick and everybody dies suddenly at some pre-determined age”. Another issue is that more often than not, cost of illness studies identify that more, not less should be spent on a problem, in the self-harm example it was that an extra £51 million should be spent on psychological assessments. Similarly, it highlights the extra cost of psychological assessments, rather than the glaring issue that 25% who attend hospital for self-harm are not getting the required psychological assessments. This very much links into the final point that cost of illness studies neglect the benefits being achieved. Now all the negatives are out the way, there are at least a couple of positives I can think of off the top of my head i) identification of key cost drivers, and ii) information for use in economic models. The take home message is that although there is some use to cost of illness studies, from a health economics perspective we (as a field) would be better off spending our time steering clear.


Alastair Canaway’s journal round-up for 28th August 2017

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.

Valuing health-related quality of life: an EQ-5D-5L value set for England. Health Economics [PubMed] Published 22nd August 2017

With much anticipation, the new EQ-5D-5L value set was officially published. For over 18 months we’ve had access to values via the OHE’s discussion paper but the formal peer-reviewed paper has (I imagine) been in publication purgatory. This paper presents the results of the value-set for the new (ish) EQ-5D-5L measure. The study used the internationally agreed hybrid model combining TTO and DCE data to generate the values for the 3125 health states. It’s worth noting that the official values are marginally different to those in the discussion paper, although in practice this is likely to have little impact on results. Important results of the new value set include fewer health states worse than death (5.1% vs over 33%), and a higher minimum value (-0.285 vs -0.594). I’d always been a bit suspect of the values for worse than death states for the 3L measure, so this if anything is encouraging. This does, however, have important implications, primarily for interventions seeking to improve those in the worst health, where potential gains may be reduced. Many of us are actively using the EQ-5D-5L within trials and have been eagerly awaiting this value set. Perhaps naively, I always anticipated that with more levels and an improved algorithm it would naturally supersede the 3L and the outdated 3L value set upon publication. Unfortunately, to mark the release of the new value set, NICE released a ‘position statement’ [PDF] regarding the choice of measure and value sets for the NICE reference case. NICE specifies that i) the 5L value set is not recommended for use, ii) the EQ-5D-3L with the original UK TTO value set is recommended and if both measures are included then the 3L should be preferred, iii) if the 5L measure is included, then scores should be mapped to the EQ-5D-3L using the van Hout et al algorithm, iv) NICE supports the use of the EQ-5D-5L generally to collect data on quality of life, and v) NICE will review this decision in August 2018 in light of future evidence. So, unfortunately, for the next year at least, we will be either sticking to the original 3L measure or mapping from the 5L. I suspect NICE is buying some time as transitioning to the 5L is going to raise lots of interesting issues e.g. if a measure is cost-effective according to the 3L, but not the 5L, or vice-versa, and comparability of 5L results to old 3L results. Interesting times lie ahead. As a final note, it’s worth reading the OHE blog post outlining the position statement and OHE’s plans to satisfy NICE.

Long-term QALY-weights among spouses of dependent and independent midlife stroke survivors. Quality of Life Research [PubMed] Published 29th June 2017

For many years, spillover impacts were largely being ignored within economic evaluation. There is increased interest in capturing wider impacts, indeed, the NICE reference case recommends including carer impacts where relevant, whilst the US Panel on Cost-Effectiveness in Health and Medicine now advocates the inclusion of other affected parties. This study sought to examine whether the dependency of midlife stroke survivors impacted on their spouses’ HRQL as measured using the SF-6D. An OLS approach was used whilst controlling for covariates (age, sex and education, amongst others). Spouses of dependent stroke survivors had a lower utility (0.69) than those whose spouses were independent (0.77). This has interesting implications for economic evaluation. For example, if a treatment were to prevent dependence, then there could potentially be large QALY gains to spouses. Spillover impacts are clearly important. If we are to broaden the evaluative scope as suggested by NICE and the US Panel to include spillover impacts, then work is vital in terms of identifying relevant contexts, measuring spillover impacts, and understanding the implications of spillover impacts within economic evaluation. This remains an important area for future research.

Conducting a discrete choice experiment study following recommendations for good research practices: an application for eliciting patient preferences for diabetes treatments. Value in Health Published 7th August 2017

To finish this week’s round-up I thought it’d be helpful to signpost this article on conducting DCEs, which I feel may be helpful for researchers embarking on their first DCE. The article hasn’t done anything particularly radical or made ground-breaking discoveries. What it does however do is give you a practical guide to walk you through each step of the DCE process following the ISPOR guidelines/checklist. Furthermore, it expands upon the ISPOR checklist to provide researchers with a further resource to consider when conducting DCEs. The case study used relates to measuring patient preferences for type 2 diabetes mellitus medications. For every item on the ISPOR checklist, it explains how they made the choices that they did, and what influenced them. The paper goes through the entire process from identifying the research question all the way through to presenting results and discussion (for those interested in diabetes – it turns out people have a preference for immediate consequences and have a high discount rate for future benefits). For people who are keen to conduct a DCE and find a worked example easier to follow, this paper alongside the ISPOR guidelines is definitely one to add to your reference manager.


Alastair Canaway’s journal round-up for 10th July 2017

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.

Use-of-time and health-related quality of life in 10- to 13-year-old children: not all screen time or physical activity minutes are the same. Quality of life Research [PubMedPublished 3rd July 2017

“If you watch too much TV, it’ll make your eyes square” – something I heard a lot as a child. This first paper explores whether this is true (sort of) by examining associations between aspects of time use and HRQL in children aged 10-13 (disclaimer: I peer reviewed it and was pleased to see them incorporate my views). This paper aims to examine how different types of time use are linked to HRQL. Time use was examined by the Multimedia Activity Recall for Children and Adolescents (MARCA) which separates out time into physical activity (sport, active transport, and play), screen time (TV, videogames, computer use), and sleep. The PedsQL was used to assess HRQL, whilst dual x-ray absorptiometry was used to accurately assess fatness. There were a couple of novel aspects to this study, first, the use of absorptiometry to accurately measure body fat percentage rather than the problematic BMI/skin folds in children; second, separating time out into specific components rather than just treating physical activity or screen time as homogeneous components. The primary findings were that for both genders, fatness (negative), sport (positive) and development stage (negative) were associated with HRQL. For boys, the most important other predictor of HRQL was videogames (negative) whilst predictors for girls included television (negative), active transport (negative) and household income (positive). With the exception of ‘active travel’ for girls, I don’t think any of these findings are particularly surprising. As with all cross-sectional studies of this nature, the authors give caution to the results: inability to demonstrate causality. Despite this, it opens the door for various possibilities for future research, and ideas for shaping future interventions in children this age.

Raise the bar, not the threshold value: meeting patient preferences for palliative and end-of-life care. PharmacoEconomics – Open Published 27th June 2017

Health care ≠ end of life care. Whilst health care seeks to maximise health, can the same be said for end of life care? Probably not. This June saw an editorial elaborating on this issue. Health is an important facet of end of life care. However, there are other substantial objects of value in this context e.g. preferences for place of care, preparedness, reducing family burdens etc. Evidence suggests that people at end of life can value these ‘other’ objects more than health status or life extension. Thus there is value beyond that captured by health. This is an issue for the QALY framework where health and length of life are the sole indicators of benefit. The editorial highlights that this is not people wishing for higher cost-per-QALY thresholds at end of life, instead, it is supporting the valuation of key elements of palliative care within the end of life context. It argues that palliative care interventions often are not amenable to integration with survival time in a QALY framework, this effectively implies that end of life care interventions should be evaluated in a separate framework to health care interventions altogether. The editorial discusses the ICECAP-Supportive Care Measure (designed for economic evaluation of end of life measures) as progress within this research context. An issue with this approach is that it doesn’t address allocative efficiency issues (and comparability) with ‘normal’ health care interventions. However, if end of life care is evaluated separately to regular healthcare, it will lead to better decisions within the EoL context. There is merit to this justification, after all, end of life care is often funded via third parties and arguments could, therefore, be made for adopting a separate framework. This, however, is a contentious area with lots of ongoing interest. For balance, it’s probably worth pointing out Chris’s (he did not ask me to put this in!) book chapter which debates many of these issues, specifically in relation to defining objects of value at end of life and whether the QALY should be altogether abandoned at EoL.

Investigating the relationship between costs and outcomes for English mental health providers: a bi-variate multi-level regression analysis. European Journal of Health Economics [PubMedPublished 24th June 2017

Payment systems that incentivise cost control and quality improvements are increasingly used. In England, until recently, mental health services have been funded via block contracts that do not necessarily incentivise cost control and payment has not been linked to outcomes. The National Tariff Payment System for reimbursement has now been introduced to mental health care. This paper harnesses the MHMDS (now called MHSDS) using multi-level bivariate regression to investigate whether it is possible to control costs without negatively affecting outcomes. It does this by examining the relationship between costs and outcomes for mental health providers. Due to the nature of the data, an appropriate instrumental variable was not available, and so it is important to note that the results do not imply causality. The primary results found that after controlling for key variables (demographics, need, social and treatment) there was a minuscule negative correlation between residual costs and outcomes with little evidence of a meaningful relationship. That is, the data suggest that outcome improvements could be made without incurring a lot more cost. This implies that cost-containment efforts by providers should not undermine outcome-improving efforts under the new payment systems. Something to bear in mind when interpreting the results is that there was a rather large list of limitations associated with the analysis, most notably that the analysis was conducted at a provider level. Although it’s continually improving, there still remain issues with the MHMDS data: poor diagnosis coding, missing outcome data, and poor quality of cost data. As somebody who is yet to use MHMDS data, but plans to in the future, this was a useful paper for generating ideas regarding what is possible and the associated limitations.