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 4th 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.

A qualitative investigation of the health economic impacts of bariatric surgery for obesity and implications for improved practice in health economics. Health Economics [PubMed] Published 1st June 2018

Few would question the ‘economic’ nature of the challenge of obesity. Bariatric surgery is widely recommended for severe cases but, in many countries, the supply is not sufficient to satisfy the demand. In this context, this study explores the value of qualitative research in informing economic evaluation. The authors assert that previous economic evaluations have adopted a relatively narrow focus and thus might underestimate the expected value of bariatric surgery. But rather than going and finding data on what they think might be additional dimensions of value, the authors ask patients. Emotional capital, ‘societal’ (i.e. non-health) impacts, and externalities are identified as theories for the types of value that might be derived from bariatric surgery. These theories were used to guide the development of questions and prompts that were used in a series of 10 semi-structured focus groups. Thematic analysis identified the importance of emotional costs and benefits as part of the ‘socioemotional personal journey’ associated with bariatric surgery. Out-of-pocket costs were also identified as being important, with self-funding being a challenge for some respondents. The information seems useful in a variety of ways. It helps us understand the value of bariatric surgery and how individuals make decisions in this context. This information could be used to determine the structure of economic evaluations or the data that are collected and used. The authors suggest that an EQ-5D bolt-on should be developed for ’emotional capital’ but, given that this ‘theory’ was predefined by the authors and does not arise from the qualitative research as being an important dimension of value alongside the existing EQ-5D dimensions, that’s a stretch.

Developing accessible, pictorial versions of health-related quality-of-life instruments suitable for economic evaluation: a report of preliminary studies conducted in Canada and the United Kingdom. PharmacoEconomics – Open [PubMed] Published 25th May 2018

I’ve been telling people about this study for ages (apologies, authors, if that isn’t something you wanted to read!). In my experience, the need for more (cognitively / communicatively) accessible outcome measures is widely recognised by health researchers working in contexts where this is relevant, such as stroke. If people can’t read or understand the text-based descriptors that make up (for example) the EQ-5D, then we need some alternative format. You could develop an entirely new measure. Or, as the work described in this paper set out to do, you could modify existing measures. There are three descriptive systems described in this study: i) a pictorial EQ-5D-3L by the Canadian team, ii) a pictorial EQ-5D-3L by the UK team, and iii) a pictorial EQ-5D-5L by the UK team. Each uses images to represent the different levels of the different dimensions. For example, the mobility dimension might show somebody walking around unaided, walking with aids, or in bed. I’m not going to try and describe what they all look like, so I’ll just encourage you to take a look at the Supplementary Material (click here to download it). All are described as ‘pilot’ instruments and shouldn’t be picked up and used at this stage. Different approaches were used in the development of the measures, and there are differences between the measures in terms of the images selected and the ways in which they’re presented. But each process referred to conventions in aphasia research, used input from clinicians, and consulted people with aphasia and/or their carers. The authors set out several remaining questions and avenues for future research. The most interesting possibility to most readers will be the notion that we could have a ‘generic’ pictorial format for the EQ-5D, which isn’t aphasia-specific. This will require continued development of the pictorial descriptive systems, and ultimately their validation.

QALYs in 2018—advantages and concerns. JAMA [PubMed] Published 24th May 2018

It’s difficult not to feel sorry for the authors of this article – and indeed all US-based purveyors of economic evaluation in health care. With respect to social judgments about the value of health technologies, the US’s proverbial head remains well and truly buried in the sand. This article serves as a primer and an enticement for the use of QALYs. The ‘concerns’ cited relate almost exclusively to decision rules applied to QALYs, rather than the underlying principles of QALYs, presumably because the authors didn’t feel they could ignore the points made by QALY opponents (even if those arguments are vacuous). What it boils down to is this: trade-offs are necessary, and QALYs can be used to promote value in those trade-offs, so unless you offer some meaningful alternative then QALYs are here to stay. Thankfully, the Institute for Clinical and Economic Review (ICER) has recently added some clout to the undeniable good sense of QALYs, so the future is looking a little brighter. Suck it up, America!

The impact of hospital costing methods on cost-effectiveness analysis: a case study. PharmacoEconomics [PubMed] Published 22nd May 2018

Plugging different cost estimates into your cost-effectiveness model could alter the headline results of your evaluation. That might seems obvious, but there are a variety of ways in which the selection of unit costs might be somewhat arbitrary or taken for granted. This study considers three alternative sources of information for hospital-based unit costs for hip fractures in England: (a) spell-level tariffs, (b) finished consultant episode (FCE) reference costs, and (c) spell-level reference costs. Source (b) provides, in theory, a more granular version of (a), describing individual episodes within a person’s hospital stay. Reference costs are estimated on the basis of hospital activity, while tariffs are prices estimated on the basis of historic reference costs. The authors use a previously reported cohort state transition model to evaluate different models of care for hip fracture and explore how the use of the different cost figures affects their results. FCE-level reference costs produced the highest total first-year hospital care costs (£14,440), and spell-level tariffs the lowest (£10,749). The more FCEs within a spell, the greater the discrepancy. This difference in costs affected ICERs, such that the net-benefit-optimising decision would change. The study makes an important point – that selection of unit costs matters. But it isn’t clear why the difference exists. It could just be due to a lack of precision in reference costs in this context (rather than a lack of accuracy, per se), or it could be that reference costs misestimate the true cost of care across the board. Without clear guidance on how to select the most appropriate source of unit costs, these different costing methodologies represent another source of uncertainty in modelling, which analysts should consider and explore.

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Chris Sampson’s journal round-up for 14th May 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.

A practical guide to conducting a systematic review and meta-analysis of health state utility values. PharmacoEconomics [PubMed] Published 10th May 2018

I love articles that outline the practical application of a particular method to solve a particular problem, especially when the article shares analysis code that can be copied and adapted. This paper does just that for the case of synthesising health state utility values. Decision modellers use utility values as parameters. Most of the time these are drawn from a single source which almost certainly introduces some kind of bias to the resulting cost-effectiveness estimates. So it’s better to combine all of the relevant available information. But that’s easier said than done, as numerous researchers (myself included) have discovered. This paper outlines the various approaches and some of the merits and limitations of each. There are some standard stages, for which advice is provided, relating to the identification, selection, and extraction of data. Those are by no means simple tasks, but the really tricky bit comes when you try and pool the utility values that you’ve found. The authors outline three strategies: i) fixed effect meta-analysis, ii) random effects meta-analysis, and iii) mixed effects meta-regression. Each is illustrated with a hypothetical example, with Stata and R commands provided. Broadly speaking, the authors favour mixed effects meta-regression because of its ability to identify the extent of similarity between sources and to help explain heterogeneity. The authors insist that comparability between sources is a precondition for pooling. But the thing about health state utility values is that they are – almost by definition – never comparable. Different population? Not comparable. Different treatment pathway? No chance. Different utility measure? Ha! They may or may not appear to be similar statistically, but that’s totally irrelevant. What matters is whether the decision-maker ‘believes’ the values. If they believe them then they should be included and pooled. If decision-makers have reason to believe one source more or less than another then this should be accounted for in the weighting. If they don’t believe them at all then they should be excluded. Comparability is framed as a statistical question, when in reality it is a conceptual one. For now, researchers will have to tackle that themselves. This paper doesn’t solve all of the problems around meta-analysis of health state utility values, but it does a good job of outlining methodological developments to date and provides recommendations in accordance with them.

Unemployment, unemployment duration, and health: selection or causation? The European Journal of Health Economics [PubMed] Published 3rd May 2018

One of the major socioeconomic correlates of poor health is unemployment. It appears not to be very good for you. But there’s an obvious challenge here – does unemployment cause ill-health, or are unhealthy people just more likely to be unemployed? Both, probably, but that answer doesn’t make for clear policy solutions. This paper – following a large body of literature – attempts to explain what’s going on. Its novelty comes in the way the author considers timing and distinguishes between mental and physical health. The basis for the analysis is that selection into unemployment by the unhealthy ought to imply time-constant effects of unemployment on health. On the other hand, the negative effect of unemployment on health ought to grow over time. Using seven waves of data from the German Socio-economic Panel, a sample of 17,000 people (chopped from 48,000) is analysed, of which around 3,000 experienced unemployment. The basis for measuring mental and physical health is summary scores from the SF-12. A fixed-effects model is constructed based on the dependence of health on the duration and timing of unemployment, rather than just the occurrence of unemployment per se. The author finds a cumulative effect of unemployment on physical ill-health over time, implying causation. This is particularly pronounced for people unemployed in later life, and there was essentially no impact on physical health for younger people. The longer people spent unemployed, the more their health deteriorated. This was accompanied by a strong long-term selection effect of less physically healthy people being more likely to become unemployed. In contrast, for mental health, the findings suggest a short-term selection effect of people who experience a decline in mental health being more likely to become unemployed. But then, following unemployment, mental health declines further, so the balance of selection and causation effects is less clear. In contrast to physical health, people’s mental health is more badly affected by unemployment at younger ages. By no means does this study prove the balance between selection and causality. It can’t account for people’s anticipation of unemployment or future ill-health. But it does provide inspiration for better-targeted policies to limit the impact of unemployment on health.

Different domains – different time preferences? Social Science & Medicine [PubMed] Published 30th April 2018

Economists are often criticised by non-economists. Usually, the criticisms are unfounded, but one of the ways in which I think some (micro)economists can have tunnel vision is in thinking that preferences elicited with respect to money exhibit the same characteristics as preferences about things other than money. My instinct tells me that – for most people – that isn’t true. This study looks at one of those characteristics of preferences – namely, time preferences. Unfortunately for me, it suggests that my instincts aren’t correct. The authors outline a quasi-hyperbolic discounting model, incorporating both short-term present bias and long-term impatience, to explain gym members’ time preferences in the health and monetary domains. A survey was conducted with members of a chain of fitness centres in Denmark, of which 1,687 responded. Half were allocated to money-related questions and half to health-related questions. Respondents were asked to match an amount of future gains with an amount of immediate gains to provide a point of indifference. Health problems were formulated as back pain, with an EQ-5D-3L level 2 for usual activities and a level 2 for pain or discomfort. The findings were that estimates for discount rates and present bias in the two domains are different, but not by very much. On average, discount rates are slightly higher in the health domain – a finding driven by female respondents and people with more education. Present bias is the same – on average – in each domain, though retired people are more present biased for health. The authors conclude by focussing on the similarity between health and monetary time preferences, suggesting that time preferences in the monetary domain can safely be applied in the health domain. But I’d still be wary of this. For starters, one would expect a group of gym members – who have all decided to join the gym – to be relatively homogenous in their time preferences. Findings are similar on average, and there are only small differences in subgroups, but when it comes to health care (even public health) we’re never dealing with average people. Targeted interventions are increasingly needed, which means that differential discount rates in the health domain – of the kind identified in this study – should be brought into focus.

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