Alastair Canaway’s journal round-up for 30th July 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.

Is there an association between early weight status and utility-based health-related quality of life in young children? Quality of Life Research [PubMed] Published 10th July 2018

Childhood obesity is an issue which has risen to prominence in recent years. Concurrently, there has been an increased interest in measuring utility values in children for use in economic evaluation. In the obesity context, there are relatively few studies that have examined whether childhood weight status is associated with preference-based utility and, following, whether such measures are useful for the economic evaluation of childhood obesity interventions. This study sought to tackle this issue using the proxy version of the Health Utilities Index Mark 3 (HUI-3) and weight status data in 368 children aged five years. Associations between weight status and HUI-3 score were assessed using various regression techniques. No statistically significant differences were found between weight status and preference-based health-related quality of life (HRQL). This adds to several recent studies with similar findings which imply that young children may not experience any decrements in HRQL associated with weight status, or that the measures we have cannot capture these decrements. When considering trial-based economic evaluation of childhood obesity interventions, this highlights that we should not be solely relying on preference-based instruments.

Time is money: investigating the value of leisure time and unpaid work. Value in Health Published 14th July 2018

For those of us who work on trials, we almost always attempt to do some sort of ‘societal’ perspective incorporating benefits beyond health. When it comes to valuing leisure time and unpaid work there is a dearth of literature and numerous methodological challenges which has led to a bit of a scatter-gun approach to measuring and valuing (usually by ignoring) this time. The authors in the paper sought to value unpaid work (e.g. household chores and voluntary work) and leisure time (“non-productive” time to be spent on one’s likings, nb. this includes lunch breaks). They did this using online questionnaires which included contingent valuation exercises (WTP and WTA) in a sample of representative adults in the Netherlands. Regression techniques following best practice were used (two-part models with transformed data). Using WTA they found an additional hour of unpaid work and leisure time was valued at €16 Euros, whilst the WTP value was €9.50. These values fall into similar ranges to those used in other studies. There are many issues with stated preference studies, which the authors thoroughly acknowledge and address. These costs, so often omitted in economic evaluation, have the potential to be substantial and there remains a need to accurately value this time. Capturing and valuing these time costs remains an important issue, specifically, for those researchers working in countries where national guidelines for economic evaluation prefer a societal perspective.

The impact of depression on health-related quality of life and wellbeing: identifying important dimensions and assessing their inclusion in multi-attribute utility instruments. Quality of Life Research [PubMed] Published 13th July 2018

At the start of every trial, we ask “so what measures should we include?” In the UK, the EQ-5D is the default option, though this decision is not often straightforward. Mental health disorders have a huge burden of impact in terms of both costs (economic and healthcare) and health-related quality of life. How we currently measure the impact of such disorders in economic evaluation often receives scrutiny and there has been recent interest in broadening the evaluative space beyond health to include wellbeing, both subjective wellbeing (SWB) and capability wellbeing (CWB). This study sought to identify which dimensions of HRQL, SWB and CWB were most affected by depression (the most common mental health disorder) and to examine the sensitivity of existing multi-attribute utility instruments (MAUIs) to these dimensions. The study used data from the “Multi-Instrument Comparison” study – this includes lots of measures, including depression measures (Depression Anxiety Stress Scale, Kessler Psychological Distress Scale); SWB measures (Personal Wellbeing Index, Satisfaction with Life Scale, Integrated Household Survey); CWB (ICECAP-A); and multi-attribute utility instruments (15D, AQoL-4D, AQoL-8D, EQ-5D-5L, HUI-3, QWB-SA, and SF-6D). To identify dimensions that were important, the authors used the ‘Glass’s Delta effect size’ (the difference between the mean scores of healthy and self-reported groups divided by the standard deviation of the healthy group). To investigate the extent to which current MAUIs capture these dimensions, each MAUI was regressed on each dimension of HRQL, CWB and SWB. There were lots of interesting findings. Unsurprisingly, the most important dimensions were in the psychosocial dimensions of HRQL (e.g. the ‘coping’, ‘happiness’, and ‘self-worth’ dimensions of the AQoL-8D). Interestingly, the ICECAP-A proved to be the best measure for distinguishing between healthy individuals and those with depression. The SWB measures, on the other hand, were less impacted by depression. Of the MAUIs, the AQoL-8D was the most sensitive, whilst our beloved EQ-5D-5L and SF-6D were the least sensitive at distinguishing dimensions. There is a huge amount to unpack within this study, but it does raise interesting questions regarding measurement issues and the impact of broadening the evaluative space for decision makers. Finally, it’s worth noting that a new MAUI (ReQoL) for mental health has been recently developed – although further testing is needed, this is something to consider in future.

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Chris Sampson’s journal round-up for 2nd July 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.

Choice in the presence of experts: the role of general practitioners in patients’ hospital choice. Journal of Health Economics [PubMed] [RePEc] Published 26th June 2018

In the UK, patients are in principle free to choose which hospital they use for elective procedures. However, as these choices operate through a GP referral, the extent to which the choice is ‘free’ is limited. The choice set is provided by the GP and thus there are two decision-makers. It’s a classic example of the principal-agent relationship. What’s best for the patient and what’s best for the local health care budget might not align. The focus of this study is on the applied importance of this dynamic and the idea that econometric studies that ignore it – by looking only at patient decision-making or only at GP decision-making – may give bias estimates. The author outlines a two-stage model for the choice process that takes place. Hospital characteristics can affect choices in three ways: i) by only influencing the choice set that the GP presents to the patient, e.g. hospital quality, ii) by only influencing the patient’s choice from the set, e.g. hospital amenities, and iii) by influencing both, e.g. waiting times. The study uses Hospital Episode Statistics for 30,000 hip replacements that took place in 2011/12, referred by 4,721 GPs to 168 hospitals, to examine revealed preferences. The choice set for each patient is not observed, so a key assumption is that all hospitals to which a GP made referrals in the period are included in the choice set presented to patients. The main findings are that both GPs and patients are influenced primarily by distance. GPs are influenced by hospital quality and the budget impact of referrals, while distance and waiting times explain patient choices. For patients, parking spaces seem to be more important than mortality ratios. The results support the notion that patients defer to GPs in assessing quality. In places, it’s difficult to follow what the author did and why they did it. But in essence, the author is looking for (and in most cases finding) reasons not to ignore GPs’ preselection of choice sets when conducting econometric analyses involving patient choice. Econometricians should take note. And policymakers should be asking whether freedom of choice is sensible when patients prioritise parking and when variable GP incentives could give rise to heterogeneous standards of care.

Using evidence from randomised controlled trials in economic models: what information is relevant and is there a minimum amount of sample data required to make decisions? PharmacoEconomics [PubMed] Published 20th June 2018

You’re probably aware of the classic ‘irrelevance of inference’ argument. Statistical significance is irrelevant in deciding whether or not to fund a health technology, because we ought to do whatever we expect to be best on average. This new paper argues the case for irrelevance in other domains, namely multiplicity (e.g. multiple testing) and sample size. With a primer on hypothesis testing, the author sets out the regulatory perspective. Multiplicity inflates the chance of a type I error, so regulators worry about it. That’s why triallists often obsess over primary outcomes (and avoiding multiplicity). But when we build decision models, we rely on all sorts of outcomes from all sorts of studies, and QALYs are never the primary outcome. So what does this mean for reimbursement decision-making? Reimbursement is based on expected net benefit as derived using decision models, which are Bayesian by definition. Within a Bayesian framework of probabilistic sensitivity analysis, data for relevant parameters should never be disregarded on the basis of the status of their collection in a trial, and it is up to the analyst to properly specify a model that properly accounts for the effects of multiplicity and other sources of uncertainty. The author outlines how this operates in three settings: i) estimating treatment effects for rare events, ii) the number of trials available for a meta-analysis, and iii) the estimation of population mean overall survival. It isn’t so much that multiplicity and sample size are irrelevant, as they could inform the analysis, but rather that no data is too weak for a Bayesian analyst.

Life satisfaction, QALYs, and the monetary value of health. Social Science & Medicine [PubMed] Published 18th June 2018

One of this blog’s first ever posts was on the subject of ‘the well-being valuation approach‘ but, to date, I don’t think we’ve ever covered a study in the round-up that uses this method. In essence, the method is about estimating trade-offs between (for example) income and some measure of subjective well-being, or some health condition, in order to estimate the income equivalence for that state. This study attempts to estimate the (Australian) dollar value of QALYs, as measured using the SF-6D. Thus, the study is a rival cousin to the Claxton-esque opportunity cost approach, and a rival sibling to stated preference ‘social value of a QALY’ approaches. The authors are trying to identify a threshold value on the basis of revealed preferences. The analysis is conducted using 14 waves of the Australian HILDA panel, with more than 200,000 person-year responses. A regression model estimates the impact on life satisfaction of income, SF-6D index scores, and the presence of long-term conditions. The authors adopt an instrumental variable approach to try and address the endogeneity of life satisfaction and income, using an indicator of ‘financial worsening’ to approximate an income shock. The estimated value of a QALY is found to be around A$42,000 (~£23,500) over a 2-year period. Over the long-term, it’s higher, at around A$67,000 (~£37,500), because individuals are found to discount money differently to health. The results also demonstrate that individuals are willing to pay around A$2,000 to avoid a long-term condition on top of the value of a QALY. The authors apply their approach to a few examples from the literature to demonstrate the implications of using well-being valuation in the economic evaluation of health care. As with all uses of experienced utility in the health domain, adaptation is a big concern. But a key advantage is that this approach can be easily applied to large sets of survey data, giving powerful results. However, I haven’t quite got my head around how meaningful the results are. SF-6D index values – as used in this study – are generated on the basis of stated preferences. So to what extent are we measuring revealed preferences? And if it’s some combination of stated and revealed preference, how should we interpret willingness to pay values?

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Chris Sampson’s journal round-up for 11th June 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.

End-of-life healthcare expenditure: testing economic explanations using a discrete choice experiment. Journal of Health Economics Published 7th June 2018

People incur a lot of health care costs at the end of life, despite the fact that – by definition – they aren’t going to get much value from it (so long as we’re using QALYs, anyway). In a 2007 paper, Gary Becker and colleagues put forward a theory for the high value of life and high expenditure on health care at the end of life. This article sets out to test a set of hypotheses derived from this theory, namely: i) higher willingness-to-pay (WTP) for health care with proximity to death, ii) higher WTP with greater chance of survival, iii) societal WTP exceeds individual WTP due to altruism, and iv) societal WTP may exceed individual WTP due to an aversion to restricting access to new end-of-life care. A further set of hypotheses relating to the ‘pain of risk-bearing’ is also tested. The authors conducted an online discrete choice experiment (DCE) with 1,529 Swiss residents, which asked respondents to suppose that they had terminal cancer and was designed to elicit WTP for a life-prolonging novel cancer drug. Attributes in the DCE included survival, quality of life, and ‘hope’ (chance of being cured). Individual WTP – using out-of-pocket costs – and societal WTP – based on social health insurance – were both estimated. The overall finding is that the hypotheses are on the whole true, at least in part. But the fact is that different people have different preferences – the authors note that “preferences with regard to end-of-life treatment are very heterogeneous”. The findings provide evidence to explain the prevailing high level of expenditure in end of life (cancer) care. But the questions remain of what we can or should do about it, if anything.

Valuation of preference-based measures: can existing preference data be used to generate better estimates? Health and Quality of Life Outcomes [PubMed] Published 5th June 2018

The EuroQol website lists EQ-5D-3L valuation studies for 27 countries. As the EQ-5D-5L comes into use, we’re going to see a lot of new valuation studies in the pipeline. But what if we could use data from one country’s valuation to inform another’s? The idea is that a valuation study in one country may be able to ‘borrow strength’ from another country’s valuation data. The author of this article has developed a Bayesian non-parametric model to achieve this and has previously applied it to UK and US EQ-5D valuations. But what about situations in which few data are available in the country of interest, and where the country’s cultural characteristics are substantially different. This study reports on an analysis to generate an SF-6D value set for Hong Kong, firstly using the Hong Kong values only, and secondly using the UK value set as a prior. As expected, the model which uses the UK data provided better predictions. And some of the differences in the valuation of health states are quite substantial (i.e. more than 0.1). Clearly, this could be a useful methodology, especially for small countries. But more research is needed into the implications of adopting the approach more widely.

Can a smoking ban save your heart? Health Economics [PubMed] Published 4th June 2018

Here we have another Swiss study, relating to the country’s public-place smoking bans. Exposure to tobacco smoke can have an acute and rapid impact on health to the extent that we would expect an immediate reduction in the risk of acute myocardial infarction (AMI) if a smoking ban reduces the number of people exposed. Studies have already looked at this effect, and found it to be large, but mostly with simple pre-/post- designs that don’t consider important confounding factors or prevailing trends. This study tests the hypothesis in a quasi-experimental setting, taking advantage of the fact that the 26 Swiss cantons implemented smoking bans at different times between 2007 and 2010. The authors analyse individual-level data from Swiss hospitals, estimating the impact of the smoking ban on AMI incidence, with area and time fixed effects, area-specific time trends, and unemployment. The findings show a large and robust effect of the smoking ban(s) for men, with a reduction in AMI incidence of about 11%. For women, the effect is weaker, with an average reduction of around 2%. The evidence also shows that men in low-education regions experienced the greatest benefit. What makes this an especially nice paper is that the authors bring in other data sources to help explain their findings. Panel survey data are used to demonstrate that non-smokers are likely to be the group benefitting most from smoking bans and that people working in public places and people with less education are most exposed to environmental tobacco smoke. These findings might not be generalisable to other settings. Other countries implemented more gradual policy changes and Switzerland had a particularly high baseline smoking rate. But the findings suggest that smoking bans are associated with population health benefits (and the associated cost savings) and could also help tackle health inequalities.

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