# 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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# Sam Watson’s journal round-up for 21st August 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.

Multidimensional performance assessment of public sector organisations using dominance criteria. Health Economics [RePEcPublished 18th August 2017

The empirical assessment of the performance or quality of public organisations such as health care providers is an interesting and oft-tackled problem. Despite the development of sophisticated methods in a large and growing literature, public bodies continue to use demonstrably inaccurate or misleading statistics such as the standardised mortality ratio (SMR). Apart from the issue that these statistics may not be very well correlated with underlying quality, organisations may improve on a given measure by sacrificing their performance on another outcome valued by different stakeholders. One example from a few years ago showed how hospital rankings based upon SMRs shifted significantly if one took into account readmission rates and their correlation with SMRs. This paper advances this thinking a step further by considering multiple outcomes potentially valued by stakeholders and using dominance criteria to compare hospitals. A hospital dominates another if it performs at least as well or better across all outcomes. Importantly, correlation between these measures is captured in a multilevel model. I am an advocate of this type of approach, that is, the use of multilevel models to combine information across multiple ‘dimensions’ of quality. Indeed, my only real criticism would be that it doesn’t go far enough! The multivariate normal model used in the paper assumes a linear relationship between outcomes in their conditional distributions. Similarly, an instrumental variable model is also used (using the now routine distance-to-health-facility instrumental variable) that also assumes a linear relationship between outcomes and ‘unobserved heterogeneity’. The complex behaviour of health care providers may well suggest these assumptions do not hold – for example, failing institutions may well show poor performance across the board, while other facilities are able to trade-off outcomes with one another. This would suggest a non-linear relationship. I’m also finding it hard to get my head around the IV model: in particular what the covariance matrix for the whole model is and if correlations are permitted in these models at multiple levels as well. Nevertheless, it’s an interesting take on the performance question, but my faith that decent methods like this will be used in practice continues to wane as organisations such as Dr Foster still dominate quality monitoring.

A simultaneous equation approach to estimating HIV prevalence with nonignorable missing responses. Journal of the American Statistical Association [RePEcPublished August 2017

Non-response is a problem encountered more often than not in survey based data collection. For many public health applications though, surveys are the primary way of determining the prevalence and distribution of disease, knowledge of which is required for effective public health policy. Methods such as multiple imputation can be used in the face of missing data, but this requires an assumption that the data are missing at random. For disease surveys this is unlikely to be true. For example, the stigma around HIV may make many people choose not to respond to an HIV survey, thus leading to a situation where data are missing not at random. This paper tackles the question of estimating HIV prevalence in the face of informative non-response. Most economists are familiar with the Heckman selection model, which is a way of correcting for sample selection bias. The Heckman model is typically estimated or viewed as a control function approach in which the residuals from a selection model are used in a model for the outcome of interest to control for unobserved heterogeneity. An alternative way of representing this model is as copula between a survey response variable and the response variable itself. This representation is more flexible and permits a variety of models for both selection and outcomes. This paper includes spatial effects (given the nature of disease transmission) not only in the selection and outcomes models, but also in the model for the mixing parameter between the two marginal distributions, which allows the degree of informative non-response to differ by location and be correlated over space. The instrumental variable used is the identity of the interviewer since different interviewers are expected to be more or less successful at collecting data independent of the status of the individual being interviewed.

Clustered multistate models with observation level random effects, mover–stayer effects and dynamic covariates: modelling transition intensities and sojourn times in a study of psoriatic arthritis. Journal of the Royal Statistical Society: Series C [ArXiv] Published 25th July 2017

Modelling the progression of disease accurately is important for economic evaluation. A delicate balance between bias and variance should be sought: a model too simple will be wrong for most people, a model too complex will be too uncertain. A huge range of models therefore exists from ‘simple’ decision trees to ‘complex’ patient-level simulations. A popular choice are multistate models, such as Markov models, which provide a convenient framework for examining the evolution of stochastic processes and systems. A common feature of such models is the Markov property, which is that the probability of moving to a given state is independent of what has happened previously. This can be relaxed by adding covariates to model transition properties that capture event history or other salient features. This paper provides a neat example of extending this approach further in the case of arthritis. The development of arthritic damage in a hand joint can be described by a multistate model, but there are obviously multiple joints in one hand. What is more, the outcomes in any one joint are not likely to be independent of one another. This paper describes a multilevel model of transition probabilities for multiple correlated processes along with other extensions like dynamic covariates and different mover-stayer probabilities.

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# Thesis Thursday: Estela Capelas Barbosa

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Estela Capelas Barbosa who graduated with a PhD from the University of York. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Overall unfair inequality in health care: an application to Brazil
Supervisor
Richard Cookson
http://etheses.whiterose.ac.uk/16649/

What’s the difference between fair and unfair inequality, and why is it important to distinguish the two?

Not all inequality is the same. Whilst most inequality in health and health care is unwanted, one could argue that some inequality is even desirable. For example, we all agree that women should receive more care than men because they have a higher need for health care. The same argument could be used for children. Therefore, when looking into inequality, from a philosophical point of view, it is important to distinguish between inequality that is deemed fair (as in my women’s example) and that considered unfair. But there is a catch! Because ‘fair’ and ‘unfair’ are normative value judgements, different people may have different views as to what is fair or unfair. That’s why, in the thesis, I worked hard to come up with a framework that was flexible enough to allow for different views of fair and unfair.

Your thesis describes a novel way of thinking about inequality. What led you to believe that other conceptualisations were inadequate?

Previously, inequality in health care was either dealt with in overall terms, using a Gini coefficient type of analysis, or focused on income and socioeconomic inequality (see Wagstaff and Van Doorslaer, 2004). As a field researcher in Brazil, I had first-hand experience that there was more to unfair inequality than income. I remember personally meeting a very wealthy man that had many difficulties in accessing the healthcare system simply because he lived in a very remote rural area of the country. I wanted to better understand this and look beyond income to explain inequality in Brazil. Thus, neither of the well-established methods seemed really appropriate for my analysis. I knew I could adjust my Gini for need, but this type of analysis did not explicitly allow for a distinction between unfair and fair inequality. At the other extreme, income-related inequality was just a very narrow definition of unfairness. Although the established methods were my starting point, I agreed with Fleurbaey and Schokkaert that there could be yet another way of looking at inequality in health care, and I drew inspiration from their proposed method for health and made adjustments and modifications for the application to health care.

What were some of your key findings about the sources of inequality, and how were they measured in your data?

I guess my most important finding is that the sources of unfair inequality have changed between 1998 and 2013. For example, the contribution of income to unfair inequality decreased in this time for physician visits and mammography screening, yet for cervical screening it nearly doubled between 2003 and 2013. I have also found that there are other sources of inequality which are important (sometimes even more than income), as for example having private health insurance, education, living in urban areas and region.

As to my data, it came from Health Supplement of the Brazilian National Household Sample Survey for the years 1998, 2003 and 2008 and the first National Health Survey, conducted in 2013 (see www.ibge.gov.br). The surveys use standardised questionnaires and rely on self-report for most questions, particularly those related to health care coverage and health status.

Your analysis looks at a relatively long period of time. What can you tell us about long-term trends in Brazil?

It is difficult to talk about long-term trends in Brazil at the moment. Our (universal) healthcare system has only been in place since 1988 and, since the last wave of data (in 2013), there has been a strong political movement to dismantle the national system and sell it to the private sector. I guess the movement to reduce and/or privatise the NHS also exists here, but, unlike in the UK, our national system has always been massively under-resourced, so it is not as highly-regarded by the population.

Having said that, it is fair to say that in its first 25 years of existence, Brazil has accomplished a lot in terms of healthcare (I have described – in Portuguese – some of the achievements and challenges). The Brazilian National Health System covers over 200 million people and accounts for nearly 500 thousand hospital beds. In terms of inequality, over time, it has decreased for physician visits and cervical screening, though for mammography there is no clear trend.

What would you like to see policymakers in Brazil prioritise in respect to reducing inequality?

First and foremost, I would like policymakers to understand that over three-quarters of the Brazilian population relies on the national system as their one and only health care provider. Second, I would like to reinforce the idea that social inequality in health care in Brazil is not only and indeed not primarily related to income. In fact, other social variables such as education, region, urban or rural residency and health insurance status are as important or even more important than income. This implies that there are supply side actions that can be taken, which should be much easier to implement. For example, more health care equipment, such as MRIs and CT scanners could be purchased for the North and Northeast regions. This could potentially reduce unfair inequality. Policies can also be directed at improving access to care in rural regions, although this factor is not as important a contributor to inequality as it used to be. I guess the overall message is: there are several things that can be done to reduce unfair inequality in Brazil, but all depend on political will and understanding the importance of the healthcare system for the health of the population.