# 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

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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# Hawking is right, Jeremy Hunt does egregiously cherry pick the evidence

I’m beginning to think Jeremy Hunt doesn’t actually care what the evidence says on the weekend effect. Last week, renowned physicist Stephen Hawking criticized Hunt for ‘cherry picking’ evidence with regard to the ‘weekend effect’: that patients admitted at the weekend are observed to be more likely than their counterparts admitted on a weekday to die. Hunt responded by doubling down on his claims:

Some people have questioned Hawking’s credentials to speak on the topic beyond being a user of the NHS. But it has taken a respected public figure to speak out to elicit a response from the Secretary of State for Health, and that should be welcomed. It remains the case though that a multitude of experts do continue to be ignored. Even the oft-quoted Freemantle paper is partially ignored where it notes of the ‘excess’ weekend deaths, “to assume that [these deaths] are avoidable would be rash and misleading.”

We produced a simple tool to demonstrate how weekend effect studies might estimate an increased risk of mortality associated with weekend admissions even in the case of no difference in care quality. However, the causal model underlying these arguments is not always obvious. So here it is:

A simple model of the effect of the weekend on patient health outcomes. The dashed line represents unobserved effects

So what do we know about the weekend effect?

1. The weekend effect exists. A multitude of studies have observed that patients admitted at the weekend are more likely to die than those admitted on a weekday. This amounts to having shown that $E(Y|W,S) \neq E(Y|W',S)$. As our causal model demonstrates, being admitted is correlated with health and, importantly, the day of the week. So, this is not the same as saying that risk of adverse clinical outcomes differs by day of the week if you take into account propensity for admission, we can’t say $E(Y|W) \neq E(Y|W')$. Nor does this evidence imply care quality differs at the weekend, $E(Q|W) \neq E(Q|W')$. In fact, the evidence only implies differences in care quality if the propensity to be admitted is independent of (unobserved) health status, i.e. $Pr(S|U,X) = Pr(S|X)$ (or if health outcomes are uncorrelated with health status, which is definitely not the case!).
2. Admissions are different at the weekend. Fewer patients are admitted at the weekend and those that are admitted are on average more severely unwell. Evidence suggests that the better patient severity is controlled for, the smaller the estimated weekend effect. Weekend effect estimates also diminish in models that account for the selection mechanism.
3. There is some evidence that care quality may be worse at the weekend (at least in the United States). So $E(Q|W) \neq E(Q|W')$. Although this has not been established in the UK (we’re currently investigating it!)
4. Staffing levels, particularly specialist to patient ratios, are different at the weekend, $E(X|W) \neq E(X|W')$.
5. There is little evidence to suggest how staffing levels and care quality are related. While the relationship seems evident prima facie, its extent is not well understood, for example, we might expect a diminishing return to increased staffing levels.
6. There is a reasonable amount of evidence on the impact of care quality (preventable errors and adverse events) on patient health outcomes.

But what are we actually interested in from a policy perspective? Do we actually care that it is the weekend per se? I would say no, we care that there is potentially a lapse in care quality. So, it’s a two part question: (i) how does care quality (and hence avoidable patient harm) differ at the weekend $E(Q|W) - E(Q|W') = ?$; and (ii) what effect does this have on patient outcomes $E(Y|Q)=?$. The first question answers to what extent policy may affect change and the second gives us a way of valuing that change and yet the vast majority of studies in the area address neither. Despite there being a number of publicly funded research projects looking at these questions right now, it’s the studies that are not useful for policy that keep being quoted by those with the power to make change.

Hawking is right, Jeremy Hunt has egregiously cherry picked and misrepresented the evidence, as has been pointed out again and again and again and again and … One begins to wonder if there isn’t some motive other than ensuring long run efficiency and equity in the health service.

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# Variations in NHS admissions at a glance

Variations in admissions to NHS hospitals are the source of a great deal of consternation. Over the long-run, admissions and the volume of activity required of the NHS have increased, without equivalent increases in funding or productivity. Over the course of the year, there are repeated claims of crises as hospitals are ill-equipped for the increase in demand in the winter. While different patterns of admissions at weekends relative to weekdays may be the foundation of the ‘weekend effect’ as we recently demonstrated. And yet all these different sources of variation produce a singular time series of numbers of daily admissions. But, each of the different sources of variation are important for different planning and research aims. So let’s decompose the daily number of admissions into its various components.

## Data

Daily number of emergency admissions to NHS hospitals between April 2007 and March 2015 from Hospital Episode Statistics.

## Methods

A similar analysis was first conducted on variations in the number of births by day of the year. A full description of the model can be found in Chapter 21 of the textbook Bayesian Data Analysis (indeed the model is shown on the front cover!). The model is a sum of Gaussian processes, each one modelling a different aspect of the data, such as the long-run trend or weekly periodic variation. We have previously used Gaussian processes in a geostatistical model on this blog. Gaussian processes are a flexible class of models for which any finite dimensional marginal distribution is Gaussian. Different covariance functions can be specified for different models, such as the aforementioned periodic or long-run trends. The model was run using the software GPstuff in Octave (basically an open-source version of Matlab) and we have modified code from the GPstuff website.

## Results

The four panels of the figure reveal to us things we may claim to already know. Emergency admissions have been increasing over time and were about 15% higher in 2015 than in 2007 (top panel). The second panel shows us the day of the week effects: there are about 20% fewer admissions on a Saturday or Sunday than on a weekday. The third panel shows a decrease in summer and increase in winter as we often see reported, although perhaps not quite as large as we might have expected. And finally the bottom panel shows the effects of different days of the year. We should note that the large dip at the end of March/beginning of April is an artifact of coding at the end of the financial year in HES and not an actual drop in admissions. But, we do see expected drops for public holidays such as Christmas and the August bank holiday.

While none of this is unexpected it does show that there’s a lot going on underneath the aggregate data. Perhaps the most alarming aspect of the data is the long run increase in emergency admissions when we compare it to the (lack of) change in funding or productivity. It suggests that hospitals will often be running at capacity so other variation, such as over winter, may lead to an excess capacity problem. We might also speculate on other possible ‘weekend effects’, such as admission on a bank holiday.

As a final thought, the method used to model the data is an excellent way of modelling data with an unknown structure without posing assumptions such as linearity that might be too strong. Hence their use in geostatistics. They are widely used in machine learning and artificial intelligence as well. We often encounter data with unknown and potentially complicated structures in health care and public health research so hopefully this will serve as a good advert for some new methods. See this book, or the one referenced in the methods section, for an in depth look.

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