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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On the commensurability of efficiency

In this week’s round-up, I highlighted a recent paper in the journal Cambridge Quarterly of Healthcare Ethics. There are some interesting ideas presented regarding the challenge of decision-making at the individual patient level, and in particular a supposed trade-off between achieving efficiency and satisfying health need.

The gist of the argument is that these two ‘values’ are incommensurable in the sense that the comparative value of two choices is ambiguous where the achievement of efficiency and need satisfaction needs to be traded. In the journal round-up, I highlighted 2 criticisms. First, I suggested that efficiency and health need satisfaction are commensurable. Second, I suggested that the paper did not adequately tackle the special nature of microlevel decision-making. The author – Anders Herlitz – was gracious enough to respond to my comments with several tweets.

Here, I’d like to put forth my reasoning on the subject (albeit with an ignorance of the background literature on incommensurability and other matters of ethics).

Consider a machine gun

A machine gun is far more efficient than a pistol, right? Well, maybe. A machine gun can shoot more bullets than a pistol over a sustained period. Likewise, a doctor who can treat 50 patients per day is more efficient than a doctor who can treat 20 patients per day.

However, the premise of this entire discussion, as established by Herlitz, is values. Herlitz introduces efficiency as a value and not as some dispassionate indicator of return on input. When we are considering values – as we necessarily are when we are discussing decision-making and more generally ‘what matters’ – we cannot take the ‘more bullets’ approach to assessing efficiency.

That’s because ‘more bullets’ is not what we mean when we talk about the value of efficiency. The production function is fundamental to our understanding of efficiency as a value. Once values are introduced, it is plain to see that in the context of war (where value is attached to a greater number of deaths) a machine gun may very well be considered more efficient. However, bearing a machine gun is far less efficient than bearing a pistol in a civilian context because we value a situation that results in fewer deaths.

In this analogy, bullets are health care and deaths are (somewhat confusingly, I admit) health improvement. Treating more people is not better because we want to provide more health care, but because we want to improve people’s health (along with some other basket of values).

Efficiency only has value with respect to the outcome in whose terms it is defined, and is therefore always commensurable with that outcome. That is, the production function is an inherent and necessary component of an efficiency to which we attach value.

I believe that Herlitz’s idea of incommensurability could be a useful one. Different outcomes may well be incommensurable in the way described in the paper. But efficiency has no place in this discussion. The incommensurability Herlitz describes in his paper seems to be a simple conflict between utilitarianism and prioritarianism, though I don’t have the wherewithal to pursue that argument so I’ll leave it there!

Microlevel efficiency trade-offs

Having said all that, I do think there could be a special decision-making challenge regarding efficiency at the microlevel. And that might partly explain Herlitz’s suggestion that efficiency is incommensurable with other outcomes.

There could be an incommensurability between values that can be measured in their achievement at the individual level (e.g. health improvement) and values that aren’t measured with individual-level outcomes (e.g. prioritisation of more severe patients). Those two outcomes are incommensurable in the way Herlitz described, but the simple fact that we tend to think about the former as an efficiency argument and the latter as an equity argument is irrelevant. We could think about both in efficiency terms (for example, treating n patients of severity x is more efficient than treating n-1 patients of severity x, or n patients of severity x-1), we just don’t. The difficulty is that this equity argument is meaningless at the individual level because it relies on information about outcomes outside the microlevel. The real challenge at the microlevel, therefore, is to acknowledge scope for efficiency in all outcomes of value. The incommensurability that matters is between microlevel and higher-level assessments of value.

As an aside, I was surprised that the Rule of Rescue did not get a mention in the paper. This is a perfect example of a situation in which arguments that tend to be made on efficiency grounds are thrown out and another value (the duty to save an immediately endangered life) takes over. One doesn’t need to think very hard about how Rule of Rescue decision-making could be framed as efficient.

In short, efficiency is never incommensurable because it is never an end in itself. If you’re concerned with being more efficient for the sake of being more efficient then you are probably not making very efficient decisions.

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Alastair Canaway’s journal round-up for 27th November 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.

Elevated mortality among weekend hospital admissions is not associated with adoption of seven day clinical standards. Emergency Medicine Journal [PubMedPublished 8th November 2017

Our esteemed colleagues in Manchester brought more evidence to the seven-day NHS debate (debacle?). Patients who are admitted to hospital in an emergency at weekends have higher mortality rates than those during the week. Despite what our Secretary of State will have you believe, there is an increasing body of evidence suggesting that once case-mix is adequately adjusted for, the ‘weekend effect’ becomes negligible. This paper takes a slightly different angle for examining the same phenomenon. It harnesses the introduction of four priority clinical standards in England, which aim to reduce the number of deaths associated with the weekend effect. These are time to first consultant review; access to diagnostics; access to consultant-directed interventions; and on-going consultant review. The study uses publicly available data on the performance of NHS Trusts in relation to these four priority clinical standards. For the latest financial year (2015/16), Trusts’ weekend effect odds ratios were compared to their achievement against the four clinical standards. Data were available for 123 Trusts. The authors found that adoption of the four clinical standards was not associated with the extent to which mortality was elevated for patients admitted at the weekend. Furthermore, they found no association between the Trusts’ performance against any of the four standards and the magnitude of the weekend effect. The authors offer three reasons as to why this may be the case. First, data quality could be poor, second, it could be that the standards themselves are inadequate for reducing mortality, finally, it could be that the weekend effect in terms of mortality may be the wrong metric by which to judge the benefits of a seven-day service. They note that their previous research demonstrated that the weekend effect is driven by admission volumes at the weekend rather than the number of deaths, so it will not be impacted by care provision, and this is consistent with the findings in this study. The spectre of opportunity cost looms over the implementation of these standards; although no direct harm may arise from the introduction of these standards, resources will be diverted away from potentially more beneficial alternatives, this is a serious concern. The seven-day debate continues.

The effect of level overlap and color coding on attribute non-attendance in discrete choice experiments. Value in Health Published 16th November 2017

I think discrete choice experiments (DCE) are difficult to complete. That may be due to me not being the sharpest knife in the drawer, or it could be due to the nature of DCEs, or a bit of both. For this reason, I like best-worst scaling (BWS). BWS aside, DCEs are a common tool used in health economics research to assess and understand preferences. Given the difficulty of DCEs, people often resort to heuristics, that is, respondents often simplify choice tasks by taking shortcuts, e.g. ignoring one or more attribute (attribute non-attendance) or always selecting the option with the highest level of a certain attribute. This has downstream consequences leading to bias within preference estimates. Furthermore, difficulty with comprehension leads to high attrition rates. This RCT sought to examine whether participant dropout and attribute non-attendance could be reduced through two methods: level overlap, and colour coding. Level overlap refers to the DCE design whereby in each choice task a certain number of attributes are presented with the same level; in different choice tasks different attributes are overlapped. The idea of this is to prevent dominant attribute strategies whereby participants always choose the option with the highest level of one specific attribute and forces them to evaluate all attributes. The second method involves colour coding and the provision of other visual cues to reduce task complexity, e.g. colour coding levels to make it easy to see which levels are equal. There were five trial arms. The control arm featured no colour coding and no attribute overlap. The other four arms featured either colour coding (two different types were tested), attribute overlap, or a combination of them both. A nationally (Dutch) representative sample in relation to age, gender, education and geographic region were recruited online. In total 3394 respondents were recruited and each arm contained over 500 respondents. Familiarisation and warm-up questions were followed by 21 pairwise choice tasks in a randomised order. For the control arm (no overlap, no colour coding) 13.9% dropped out whilst only attending to on average 2.1 out of the five attributes. Colour coding reduced this to 9.6% with 2.8 attributes being attended. Combining level overlap with intensity colour coding reduced drop out further to 7.2% whilst increasing attribute attendance to four out of five. Thus, the combination of level overlap and colour coding nearly halved the dropout and doubled the attribute attendance within the DCE task. An additional, and perhaps most important benefit of the improvement in attribute attendance is that it reduces the need to model for potential attribute non-attendance post-hoc. Given the difficult of DCE completion, it seems colour coding in combination with level overlap should be implored for future DCE tasks.

Evidence on the longitudinal construct validity of major generic and utility measures of health-related quality of life in teens with depression. Quality of Life Research [PubMed] Published 17th November 2017

There appears to be increasing recognition of the prevalence and seriousness of youth mental health problems. Nearly 20% of young people will suffer depression during their adolescent years. To facilitate cost-utility analysis it is necessary to have a measure of preference based health-related quality of life (HRQL). However, there are few measures designed for use in adolescents. This study sought to examine various existing HRQL measures in relation to their responsiveness for the evaluation of interventions targeting depression in young people. This builds on previous work conducted by Brazier et al that found the EQ-5D and SF-6D performed adequately for depression in adults. In total 392 adolescents aged between 13 and 17 years joined the study, 376 of these completed follow up assessments. Assessments were taken at baseline and 12 weeks. The justification for 12 weeks is that it represented the modal time to clinical change. The following utility instruments were included: the HUI suite, the EQ-5D-3L, Quality of Well-Being Scale (QWB), and the SF-6D (derived from SF-36). Other non-preference based HRQL measures were also included: disease-specific ratings and scales, and the PedsQL 4.0. All (yes, you read that correctly) measures were found to be responsive to change in depression symptomology over the 12-week follow up period and each of the multi-attribute utility instruments was able to detect clinically meaningful change. In terms of comparing the utility instruments, the HUI-3, the QWB and the SF-6D were the most responsive whilst the EQ-5D-3L was the least responsive. In summary, any of the utility instruments could be used. One area of disappointment for me was that the CHU-9D was not included within this study – it’s one of the few instruments that has been developed by and for children and would have very much been a worthy addition. Regardless, this is an informative study for those of us working within the youth mental health sphere.

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