Chris Sampson’s journal round-up for 19th March 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.

Using HTA and guideline development as a tool for research priority setting the NICE way: reducing research waste by identifying the right research to fund. BMJ Open [PubMed] Published 8th March 2018

As well as the cost-effectiveness of health care, economists are increasingly concerned with the cost-effectiveness of health research. This makes sense, given that both are usually publicly funded and so spending on one (in principle) limits spending on the other. NICE exists in part to prevent waste in the provision of health care – seeking to maximise benefit. In this paper, the authors (all current or ex-employees of NICE) consider the extent to which NICE processes are also be used to prevent waste in health research. The study focuses on the processes underlying NICE guideline development and HTA, and the work by NICE’s Science Policy and Research (SP&R) programme. Through systematic review and (sometimes) economic modelling, NICE guidelines identify research needs, and NICE works with the National Institute for Health Research to get their recommended research commissioned, with some research fast-tracked as ‘NICE Key Priorities’. Sometimes, it’s also necessary to prioritise research into methodological development, and NICE have conducted reviews to address this, with the Internal Research Advisory Group established to ensure that methodological research is commissioned. The paper also highlights the roles of other groups such as the Decision Support Unit, Technical Support Unit and External Assessment Centres. This paper is useful for two reasons. First, it gives a clear and concise explanation of NICE’s processes with respect to research prioritisation, and maps out the working groups involved. This will provide researchers with an understanding of how their work fits into this process. Second, the paper highlights NICE’s current research priorities and provides insight into how these develop. This could be helpful to researchers looking to develop new ideas and proposals that will align with NICE’s priorities.

The impact of the minimum wage on health. International Journal of Health Economics and Management [PubMed] Published 7th March 2018

The minimum wage is one of those policies that is so far-reaching, and with such ambiguous implications for different people, that research into its impact can deliver dramatically different conclusions. This study uses American data and takes advantage of the fact that different states have different minimum wage levels. The authors try to look at a broad range of mechanisms by which minimum wage can affect health. A major focus is on risky health behaviours. The study uses data from the Behavioral Risk Factor Surveillance System, which includes around 300,000 respondents per year across all states. Relevant variables from these data characterise smoking, drinking, and fruit and vegetable consumption, as well as obesity. There are also indicators of health care access and self-reported health. The authors cut their sample to include 21-64-year-olds with no more than a high school degree. Difference-in-differences are estimated by OLS according to individual states’ minimum wage changes. As is often the case for minimum wage studies, the authors find several non-significant effects: smoking and drinking don’t seem to be affected. Similarly, there isn’t much of an impact on health care access. There seems to be a small positive impact of minimum wage on the likelihood of being obese, but no impact on BMI. I’m not sure how to interpret that, but there is also evidence that a minimum wage increase leads to a reduction in fruit and vegetable consumption, which adds credence to the obesity finding. The results also demonstrate that a minimum wage increase can reduce the number of days that people report to be in poor health. But generally – on aggregate – there isn’t much going on at all. So the authors look at subgroups. Smoking is found to increase (and BMI decrease) with minimum wage for younger non-married white males. Obesity is more likely to be increased by minimum wage hikes for people who are white or married, and especially for those in older age groups. Women seem to benefit from fewer days with mental health problems. The main concerns identified in this paper are that minimum wage increases could increase smoking in young men and could reduce fruit and veg consumption. But I don’t think we should overstate it. There’s a lot going on in the data, and though the authors do a good job of trying to identify the effects, other explanations can’t be excluded. Minimum wage increases probably don’t have a major direct impact on health behaviours – positive or negative – but policymakers should take note of the potential value in providing public health interventions to those groups of people who are likely to be affected by the minimum wage.

Aligning policy objectives and payment design in palliative care. BMC Palliative Care [PubMed] Published 7th March 2018

Health care at the end of life – including palliative care – presents challenges in evaluation. The focus is on improving patients’ quality of life, but it’s also about satisfying preferences for processes of care, the experiences of carers, and providing a ‘good death’. And partly because these things can be difficult to measure, it can be difficult to design payment mechanisms to achieve desirable outcomes. Perhaps that’s why there is no current standard approach to funding for palliative care, with a lot of variation between countries, despite the common aspiration for universality. This paper tackles the question of payment design with a discussion of the literature. Traditionally, palliative care has been funded by block payments, per diems, or fee-for-service. The author starts with the acknowledgement that there are two challenges to ensuring value for money in palliative care: moral hazard and adverse selection. Providers may over-supply because of fee-for-service funding arrangements, or they may ‘cream-skim’ patients. Adverse selection may arise in an insurance-based system, with demand from high-risk people causing the market to fail. These problems could potentially be solved by capitation-based payments and risk adjustment. The market could also be warped by blunt eligibility restrictions and funding caps. Another difficulty is the challenge of achieving allocative efficiency between home-based and hospital-based services, made plain by the fact that, in many countries, a majority of people die in hospital despite a preference for dying at home. The author describes developments (particularly in Australia) in activity-based funding for palliative care. An interesting proposal – though not discussed in enough detail – is that payments could be made for each death (per mortems?). Capitation-based payment models are considered and the extent to which pay-for-performance could be incorporated is also discussed – the latter being potentially important in achieving those process outcomes that matter so much in palliative care. Yet another challenge is the question of when palliative care should come into play, because, in some cases, it’s a matter of sooner being better, because the provision of palliative care can give rise to less costly and more preferred treatment pathways. Thus, palliative care funding models will have implications for the funding of acute care. Throughout, the paper includes examples from different countries, along with a wealth of references to dig into. Helpfully, the author explicitly states in a table the models that different settings ought to adopt, given their prevailing model. As our population ages and the purse strings tighten, this is a discussion we can expect to be having more and more.

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Sam Watson’s journal round-up for 24th 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.

Ten years after the financial crisis: the long reach of austerity and its global impacts on health. Social Science & Medicine [PubMedPublished 22nd June 2017

The subject of austerity and its impact on health has generated its own subgenre in the academic literature. We have covered a number of papers on these journal round-ups on this topic, which, given the nature of economic papers, are generally quantitative in nature. However, while quantitative studies are necessary for generation of knowledge of the social world, they are not sufficient. At aggregate levels, quantitative studies may often rely on a black box approach. We may reasonably conclude a policy caused a change in some population-level indicator on the basis of a causal inference type paper, but we often need other types of evidence to answer why or how this occurred. A realist philosophy of social science may see this as a process of triangulation; at the very least it’s a process of abduction to develop theory that best explains what we observe. In clinical research, Bradford-Hill’s famous criteria can be used as a heuristic for causal inference: a cause can be attributed to an effect if it demonstrates a number of criteria including dose-response and reproducibility. For social science, we can conceive of a similar set of criteria. Effects must follow causes, there has to be a plausible mechanism, and so forth. This article in Social Science & Medicine introduces a themed issue on austerity and its effects on health. The issue contains a number of papers examining experiences of people with respect to austerity and how these may translate into changes in health. One example is a study in a Mozambican hospital and how health outcomes change in response to continued restructuring programs due to budget shortfalls. Another study explores the narrative of austerity in Guyana and it has long been sold as necessary for future benefits which never actually materialise. It is not immediately clear how austerity is being defined here, but it is presumably something like ‘a fiscal contraction that causes a significant increase in aggregate unemployment‘. In any case, it makes for interesting reading and complements economics research on the topic. It is a refreshing change from the bizarre ravings we featured a couple of weeks ago!

Home-to-home time — measuring what matters to patients and payers. New England Journal of Medicine [PubMedPublished 6th July 2017

Length of hospital stay is often used as a metric to evaluate hospital performance: for a given severity of illness, a shorter length of hospital stay may suggest higher quality care. However, hospitals can of course game these metrics, and they are further complicated by survival bias. Hospitals are further incentivised to reduce length of stay. For example, the move from per diem reimbursement to per episode had the effect of dramatically reducing length of stay in hospitals. As a patient recovers, they may no longer need hospital based care, the care they require may be adequately provided in other institutional settings. Although, in the UK there has been a significant issue with many patients convalescing in hospital for extended periods as they wait for a place in residential care homes. Thus from the perspective of the whole health system, length of stay in hospital may no longer be the right metric to evaluate performance. This article makes this argument and provides some interesting statistics. For example, between 2004 and 2011 the average length of stay in hospital among Medicare beneficiaries in the US decreased from 6.3 to 5.7 days; post-acute care stays increased from 4.8 to 6.0 days. Thus, the total time in care actually increased from 11.1 to 11.7 days over this period. In the post-acute care setting, Medicare still reimburses providers on a per diem basis, so total payments adjusted for inflation also increased. This article makes the argument that we need to structure incentives and reimbursement schemes across the whole care system if we want to ensure efficiency and equity.

The population health benefits of a healthy lifestyle: life expectancy increased and onset of disability delayed. Health Affairs [PubMedPublished July 2017

Obesity and tobacco smoking increase the risk of ill health and in so doing reduce life expectancy. The same goes for alcohol, although the relationship between alcohol consumption and risk of illness is less well understood. One goal of public health policy is to mitigate these risks. One successful way of communicating the risks of different behaviours is as changes to life expectancy, or conversely ‘effective age‘. From a different perspective, understanding how different risk factors affect life expectancy and disability-free life expectancy is important for cost-benefit analyses of different public health interventions. This study estimates life expectancy and disability-free life expectancy associated with smoking, obesity, and moderate alcohol consumption using the US-based Health and Retirement Study. However, I struggle to see how this study adds much; while it communicates its results well, it is, in essence, a series of univariate comparisons followed by a multivariate comparison. This has been done widely before, such as here and here. Nevertheless, the results reinforce those previous studies. For example, obesity reduced disability-free life expectancy by 3 years for men and 6 years for women.

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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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