Chris Sampson’s journal round-up for 24th April 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.

The association between socioeconomic status and adult fast-food consumption in the U.S. Economics & Human Biology Published 19th April 2017

It’s an old stereotype, that people of lower socioeconomic status eat a lot of fast food, and that this contributes to poorer nutritional intake and therefore poorer health. As somebody with a deep affection for Gregg’s pasties and Pot Noodles, I’ve never really bought into the idea. Mainly because a lot of fast food isn’t particularly cheap. And anyway, what about all those cheesy paninis that the middle classes are chowing down on in Starbuck’s? Plus, wouldn’t the more well-off folk have a higher opportunity cost of time that would make fast food more attractive? Happily for me, this paper provides some evidence to support these notions. The study uses 3 recent waves of data from the National Longitudinal Survey of Youth, with 8136 participants born between 1957 and 1964. The authors test for an income gradient in adult fast food consumption, as well as any relationship to wealth. I think that makes it extra interesting because wealth is likely to be more indicative of social class (which is probably what people really think about when it comes to the stereotype). The investigation of wealth also sets it apart from previous studies, which report mixed findings for the income gradient. The number of times people consumed fast food in the preceding 7 days is modelled as a function of price, time requirement, preferences and monetary resources (income and wealth). The models included estimators for these predictors and a number of health behaviour indicators and demographic variables. Logistic models distinguish fast food eaters and OLS and negative binomial models estimate how often fast food is eaten. 79% ate fast food at least once, and 23% were frequent fast food eaters. In short, there isn’t much variation by income and wealth. What there is suggests an inverted U-shape pattern, which is more pronounced when looking at income than wealth. The regression results show that there isn’t much of a relationship between wealth and the number of times a respondent ate fast food. Income is positively related to the number of fast food meals eaten. But other variables were far more important. Living in a central city and being employed were associated with greater fast food consumption, while a tendency to check ingredients was associated with a lower probability of eating fast food. The study has some important policy implications, particularly as our preconceptions may mean that interventions are targeting the wrong groups of people.

Views of the UK general public on important aspects of health not captured by EQ-5D. The Patient [PubMed] Published 13th April 2017

The notion that the EQ-5D might not reflect important aspects of health-related quality of life is a familiar one for those of us working on trial-based analyses. Some of the claims we hear might just be special pleading, but it’s hard to deny at least some truth. What really matters – if we’re trying to elicit societal values – is what the public thinks. This study tries to find out. Face-to-face interviews were conducted in which people completed time trade-off and discrete choice experiment tasks for EQ-5D-5L states. These were followed by a set of questions about the value of alternative upper anchors (e.g. ‘full health’, ‘11111’) and whether respondents believed that relevant health or quality of life domains were missing from the EQ-5D questionnaire. This paper focuses on the aspects of health that people identified as being missing, using a content analysis framework. There were 436 respondents, about half of whom reported being in a 11111 EQ-5D state. 41% of participants considered the EQ-5D questionnaire to be missing some important aspect of health. The authors identified 22 (!) different themes and attached people’s responses to these themes. Sensory deprivation and mental health were the two biggies, with many more responses than other themes. 50 people referred to vision, hearing or other sensory loss. 29 referred to mental health generally while 28 referred to specific mental health problems. This study constitutes a guide for future research and for the development of the EQ-5D and other classification systems. Obviously, the objective of the EQ-5D is not to reflect all domains. And it may be that the public’s suggestions – verbatim, at least – aren’t sensible. 10 people stated ‘cancer’, for example. But the importance of mental health and sensory deprivation in describing the evaluative space does warrant further investigation.

Re-thinking ‘The different perspectives that can be used when eliciting preferences in health’. Health Economics [PubMed] Published 21st March 2017

Pedantry is a virtue when it comes to valuing health states, which is why you’ll often find me banging on about the need for clarity. And why I like this paper. The authors look at a 2003 article by Dolan and co that outlined the different perspectives that health preference researchers ought to be using (though notably aren’t) when presenting elicitation questions to respondents. Dolan and co defined 6 perspectives along two dimensions: preferences (personal, social and socially-inclusive personal) and context (ex ante and ex post). This paper presents the argument that Dolan and co’s framework is incomplete. The authors throw new questions into the mix regarding who the user of treatment is, who the payer is and who is assessing the value, as well as introducing consideration of the timing of illness and the nature of risk. This gives rise to a total of 23 different perspectives along the dimensions of preferences (personal, social, socially-inclusive personal, non-use and proxy) and context (4 ex ante and 1 ex post). This new classification makes important distinctions between different perspectives, and health preference researchers really ought to heed its advice. However, I still think it’s limited. As I described in a recent blog post and discussed at a recent HESG meeting, I think the way we talk about ex ante and ex post in this context is very confused. In fact, this paper demonstrates the problem nicely. The authors first discuss the ex post context, the focus being on the value of ‘treatment’ (an event). Then the paper moves on to the ex ante context, and the discussion relates to ‘illness’ (a state). The problem is that health state valuation exercises aren’t (explicitly) about valuing treatments – or illnesses – but about valuing health states in relation to other health states. ‘Ex ante’ means making judgements about something before an event, and ‘ex post’ means to do so after it. But we’re trying to conduct health state valuation, not health event valuation. May the pedantry continue.

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Alastair Canaway’s journal round-up for 20th March 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.

The use of quality-adjusted life years in cost-effectiveness analyses in palliative care: mapping the debate through an integrative review. Palliative Medicine [PubMed] Published 13th February 2017

February saw a health economics special within the journal Palliative Medicine – the editorials are very much worth a read to get a quick idea of how health economics has (and hasn’t) developed within the end of life care context. One of the most commonly encountered debates when discussing end of life care within health economics circles relates to the use of QALYs, and whether they’re appropriate. This paper aimed to map out the pros and cons of using the QALY framework to inform health economic decisions in the palliative care context. Being a review, there were no ground-breaking findings, more a refresher on what the issues are with the QALY at end of life: i) restrictions in life years gained, ii) conceptualisation of quality of life and its measurement, and iii) valuation and additivity of time. The review acknowledges the criticisms of the QALY but concludes that it is still of use for informing decision making. A key finding, and one which should be common sense, is that the EQ-5D should not be relied on as the sole measure within this context: the dimensions important to those at end of life are not adequately captured by the EQ-5D, and other measures should be considered. A limitation for me was that the review did not include Round’s (2016) book Care at the End of Life: An Economic Perspective (disclaimer: I’m a co-author on a chapter), which has significant overlap and builds on a number of the issues relevant to the paper. That aside, this is a useful paper for those new to the pitfalls of economic evaluation at the end of life and provides an excellent summary of many of the key issues.

The causal effect of retirement on mortality: evidence from targeted incentives to retire early. Health Economics [PubMed] [RePEc] Published 23rd February 2017

It’s been said that those who retire earlier die earlier, and a quick google search suggests there are many statistics supporting this. However, I’m unsure how robust the causality is in such studies. For example, the sick may choose to leave the workforce early. Previous academic literature had been inconclusive regarding the effects, and in which direction they occurred. This paper sought to elucidate this by taking advantage of pension reforms within the Netherlands which meant certain cohorts of Dutch civil servants could qualify for early retirement at a younger age. This change led to a steep increase in retirement and provided an opportunity to examine causal impacts by instrumenting retirement with the early retirement window. Administrative data from the entire population was used to examine the probability of dying resulting from earlier retirement. Contrary to preconceptions, the probability of men dying within five years dropped by 2.6% in those who took early retirement: a large and significant impact. The biggest impact was found within the first year of retirement. An explanation for this is that the reduction of stress and lifestyle change upon retiring may postpone death for the civil servants which were in poor health. The paper is an excellent example of harnessing a natural experiment for research purposes. It provides a valuable contribution to the evidence base whilst also being reassuring for those of us who plan to retire in the next few years (lottery win pending).

Mapping to estimate health-state utility from non–preference-based outcome measures: an ISPOR Good Practices for Outcomes Research Task Force report. Value in Health [PubMed] Published 16th February 2017

Finally, I just wanted to signpost this new good practice guide. If you ever attend HESG, ISPOR, or IHEA, you’ll nearly always encounter a paper on mapping (cross-walking). Given the ethical issues surrounding research waste and the increasing pressure to publish, mapping provides an excellent opportunity to maximise the value of your data. Of course, mapping also serves a purpose for the health economics community: it facilitates the estimation of QALYs in studies where no preference based measure exists. There are many iffy mapping functions out there so it’s good to see ISPOR have taken action by producing a report on best practice for mapping. As with most ISPOR guidelines the paper covers all the main areas you’d expect and guides you through the key considerations to undertaking a mapping exercise, this includes: pre-modelling considerations, data requirements, selection of statistical models, selection of covariates, reporting of results, and validation. Additionally there is also a short section for those who are keen to use a mapping function to generate QALYs but are unsure which to pick. As with any set of guidelines, it’s not exactly a thriller, it is however extremely useful for anyone seeking to conduct mapping.

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Chris Sampson’s journal round-up for 27th February 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.

Does it pay to know prices in health care? American Economic Journal: Economic Policy Published February 2017

In the US, people in need of health care have to pay for it – or for insurance to cover it – without knowing in advance how much said health care actually costs. Weird, right? Instinctively, it feels as if people really ought to be able to find out. However, if knowing prices in advance doesn’t actually affect consumption, maybe we can say it really doesn’t matter. Well, we can’t. As this new study shows, having access to price information affects consumer choices. There’s plenty of price dispersion to make this potentially important: in this study’s dataset, a move from the 90th to the 50th percentile is on average associated with a price drop of 35%. The data relate to 387,774 procedures for 6,208 people working for a corporate client of a price information firm. Access to this service was staggered for different employees, creating the potential for experimental investigation. The principal strategy is difference-in-differences regression analysis. Access to the price information service was associated with prices around 1.6% lower on average. For primary care – which might be less price sensitive – and for complex cases where lots of procedures are taking place, the effect is weakened. The results seem robust to matching and other tests. The author is able to provide further insight by showing that access to price information increases the probability of seeing a new doctor by 14%. And when an instrumental variable approach is used to assess the price reduction specifically for people who searched for price information and then received a procedure within 30 days, the reduction in price reaches a whopping 17%. This suggests that the average impact of a 1.6% reduction could be a lot higher if people searched for price information more frequently. The fact that they don’t is likely due to a particular kind of moral hazard being at play. Moral hazard in search occurs when people have no incentive to search for cheaper services. The author goes on to show that in any given week an individual is around 90% less likely to search if they have already met their deductible, and that this translates into an elasticity of search propensity to the proportion out-of-pocket expense of approximately 1.8. We mustn’t forget the other side of the welfare coin here. What if people are choosing lower quality care in order to save money, or foregoing it altogether? Looking at the rate of follow-through after searches and bringing in hospital quality data seems to show that this isn’t a concern here. This group of people aren’t representative of the general population so it may be that access to prices is only valuable to certain groups. Nevertheless, this paper tells us a lot about the importance of price information and in particular the special kind of moral hazard that can arise in the presence of comprehensive insurance coverage.

Mitigating the consequences of a health condition: The role of intra- and interhousehold assistance. Journal of Health Economics Published 20th February 2017

There’s a lot of research around the effect that an individual’s health problem can have on their immediate family, both in terms of the overspill in quality of life impacts and the costs of satisfying need for health care. However, large panel data research can be limited because the data can’t connect non-coresident family members. This study considers informal insurance and consumption smoothing within families beyond the current household. The data come from the Panel Study of Income Dynamics, with 7,578 individuals and around 33,000 household years from 2001-2011. The panel follows offspring after they leave a household, facilitating the identification of genetically linked families. Participants are asked whether they suffer from 11 different health problems and, if they do, the extent to which it limits their daily activities. The data also include information on different categories of spending, including health. The analysis involves regression that accounts for individual fixed effects and looks at the impact of a change in health status on consumption. If a household is fully insured, changes in health status should not affect non-health expenditures. The analysis focuses on the impact of severe limitations, which are reported at some point by 1,321 people. Such a change in health status was associated with a reduction in annual working hours of around 20%, corresponding to $5000 for men and $2800 for women. Additionally, household health expenditures increased by $479 on average. The notion of complete insurance facilitating consumption smoothing appears to fail, with a decline in consumption of around 10%. Partial insurance smoothes roughly half the loss. Households with formal insurance exhibit a much smaller reduction in consumption. A key finding is that being married may facilitate consumption smoothing to the extent of full insurance, while unmarried couples take a bigger hit. Home equity seems to play an important role in this dynamic, with married couples more likely to remortgage in response to a health shock. Married couples also receive more in social security transfers. Unmarried couples, it seems, have to turn to non-coresident family members instead and are 50% more likely to use this channel than married couples. Male children are more likely to use their own home equity to support their parents, while female children tend to reduce their own consumption. This study identifies a lot of interesting relationships and divergent strategies for consumption smoothing that warrant further investigation.

Handling missing data in within-trial cost-effectiveness analysis: a review with future recommendations. PharmacoEconomics – Open Published 9th February 2017

If you conduct trial-based cost-effectiveness analyses then chances are that at some point you’ve had to go and figure out how to deal with all that missing data. There are a handful of quality papers out there that offer guidance. If we all followed their advice then we’d be doing a decent job of it. This new paper demonstrates that we aren’t all doing a good job of it and offers fresh guidance. The paper starts by outlining the ‘principled’ approach to handling missing data. Essentially it means being sensible with the data, considering the most appropriate statistical model and describing assumptions about the missing data mechanism. Imputation methods that can support this principled approach are briefly discussed. The authors present a quality evaluation scheme, which can be used to assess the appropriateness of methods adopted in a study and the completeness of reporting. It makes recommendations with respect to the description of missing data, the methods used to handle it and the limitations associated with the study. The quality evaluation scheme can be used to score and rank papers from A-E. This is what the authors go on to do, with a systematic review including 81 eligible papers. A previous review found complete case analysis to be the most popular base case method adopted. In 2009-2015, multiple imputation became the most frequently used base case method, though complete case analysis remains common and many studies are still unclear about the methods adopted. Most articles did not describe any robustness analysis, reporting only the base case approach to missing data. Many articles were classified as the lowest quality (E), though this has improved over time. The authors demonstrate that their proposed grading system is associated with the strength of the assumptions in the adopted methods. If you’re engaged in trial-based economic evaluation, you ought to read this paper.

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