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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2 thoughts on “Chris Sampson’s journal round-up for 14th May 2018

  1. Hi Chris et al
    I’d be very keen to have your review and opinion of the following article which is attracting a lot of media attention, particularly in NL:
    https://www.nature.com/articles/s41571-018-0027-x.pdf
    I look forward to hearing from you
    kind regards
    Colette

    Colette Mankowski PhD
    Senior Director HEOR
    Medical Affairs

    Astellas Pharma EMEA
    2000 Hillswood Drive, Chertsey, KT16 0RS, UK

    Mob: +44 (0)7881 940638
    Email: colette.mankowski@astellas.com

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