Thesis Thursday: Miqdad Asaria

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 Miqdad Asaria 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
The economics of health inequality in the English National Health Service
Supervisors
Richard Cookson, Tim Doran
Repository link
http://etheses.whiterose.ac.uk/16189

What types of inequality are relevant in the context of the NHS?

For me the inequalities that really matter are the inequalities in health outcomes, in the English context it is particularly the socioeconomic patterning of these inequalities that is of concern. The focus of health policy in England over the last 200 years has been on improving the average health of the population as well as on providing financial risk protection against catastrophic health expenditure. Whilst great strides have been made in improving average population health through various pioneering interventions including the establishment of the NHS, health inequality has in fact consistently widened over this period. Recent research suggests that in terms of quality-adjusted life expectancy the gap between people living in the most deprived fifth of neighbourhoods in the country as compared to those living in the most affluent fifth is now approximately 11 quality-adjusted life years.

However, these socio-economic inequalities in health typically accumulate across the life course and there is a limited amount that health care on its own can do to prevent these gaps from widening or indeed to close these gaps once they emerge. This is why health systems including the NHS typically focus on measuring and tackling the inequalities that they can influence even though eliminating such inequalities can have at best only modest impacts on reducing health inequality overall. These comprise of inequalities in access to and quality of healthcare as well as inequality of those health outcomes specifically amenable to healthcare.

What were the key methods and data that you used to identify levels of health inequality?

I am currently working on a project with the Ministry of Health and Family Welfare in India and it is really making me appreciate the amazingly detailed and comprehensive administrative datasets available to researchers in England. For the work underpinning my thesis I linked 10 years of data looking at every hospital admission and outpatient visit in the country with the quality and outcomes achieved for patients registered at each primary care practice, the number of doctors working at each primary care practice, general population census data, cause-specific mortality data, hospital cost data and deprivation data all at neighbourhood level. I spent a lot of time assembling, cleaning and linking these data sets and then used this data platform to build a range of health inequality indicators – some of which can be seen in an interactive tool I built to present the data to clinical commissioning groups.

As well as measuring inequality retrospectively in order to provide evidence to evaluate past NHS policies, and building tools to enable the NHS to monitor inequality going forward, another key focus of my thesis was to develop methods to model and incorporate health inequality impacts into cost-effectiveness analysis. These methods allow analysts to evaluate proposed health interventions in terms of their impact on the distribution of health rather than just their impact on the mythical average citizen. The distributional cost-effectiveness analysis framework I developed is based on the idea of using social welfare functions to evaluate the estimated health distributions arising from the rollout of different health care interventions and compute the equity-efficiency trade-offs that would need to be made in order to prefer one intervention over another. A key parameter in this analysis required in order to make equity-efficiency trade-offs is the level of health inequality aversion. This parameter was quite tricky to estimate with methods used to elicit it from the general public being prone to various framing effects. The preliminary estimates that I used in my analysis for this parameter suggested that at the margin the general public thought people living in the most deprived fifth of neighbourhoods in the country deserve approximately 7 times the priority in terms of health care spending as those who live in the most affluent fifth of neighbourhoods.

Does your PhD work enable us to attach a ‘cost’ to inequality, and ‘value’ to policies that reduce it?

As budding economists, we are ever cautious to distinguish association and causation. My thesis starts by estimating the cost associated with inequality to the NHS. That is the additional cost to the NHS spent on treating the excess morbidity in those living in relatively deprived neighbourhoods. I estimated the difference between the actual NHS hospital budget and what the cost would have been if everybody in the country had the morbidity profile of those who live in just the most affluent fifth of neighbourhoods. For inpatient hospital costs this difference came to £4.8 billion per year and widening this to all NHS costs this came to £12.5 billion per year approximately a fifth of the total NHS budget. I looked both cross-sectionally and also modelled lifetime estimated health care use and found that even over their entire lifetimes people living in more deprived neighbourhoods consumed more health care despite their substantially shorter life expectancies.

This cost is of course very different to the value of policies to reduce inequality. This difference arises for two main reasons. First, my estimates were not causal but rather associations so we are unable to conclude that reducing socioeconomic inequality would actually result in everybody in the country gaining the morbidity profile of those living in the most affluent fifth of neighbourhoods. Second and perhaps more significantly, my estimates do not value any of the health benefits that would result from reducing health inequality they just count the costs that could be saved by the NHS due to the excess morbidity avoided. The value of these health benefits forgone in terms of quality adjusted life years gained would have to be converted into monetary terms using an estimate of willingness to pay for health and added to these cost savings (which themselves would need to be converted to consumption values) to get a total value of reducing inequality from a health perspective. There would also, of course, be a range of non-health impacts of reducing inequality that would need to be accounted for if this exercise were to be comprehensively conducted.

In simple terms, if the causal link between socioeconomic inequality and health could be determined then the value to the health sector of policies that could substantially reduce this inequality would likely be far greater than the costs quoted here.

How did you find the PhD-by-publication route? Would you recommend it?

I came to academia relatively late having previously worked in both the government and the private sector for a number of years. The PhD by publication route suited me well as it allowed me to get stuck into a number of projects, work with a wide range of academics and build an academic career whilst simultaneously curating a set of papers to submit as a thesis. However, it is certainly not the fastest way to achieve PhD status, my thesis took 6 years to compile. The publication route is also still relatively uncommon in England and I found both my supervisors and examiners somewhat perplexed about how to approach it. Additionally, my wife who did her PhD by the traditional route assures me that it is not a ‘proper’ PhD!

For those fresh out of an MSc programme the traditional route probably works well, giving you the opportunity to develop research skills and focus on one area in depth with lots of guidance from a dedicated supervisor. However, for people like me who probably would never have got around to doing a traditional PhD, it is nice that there is an alternative way to acquire the ‘Dr’ title which I am finding confers many unanticipated benefits.

What advice would you give to a researcher looking to study health inequality?

The most important thing that I have learnt from my research is that health inequality, particularly in England, has very little to do with health care and everything to do with socioeconomic inequality. I would encourage researchers interested in this area to look at broader interventions tackling the social determinants of health. There is lots of exciting work going on at the moment around basic income and social housing as well as around the intersection between the environment and health which I would love to get stuck into given the chance.

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Sam Watson’s journal round-up for 12th June 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.

Machine learning: an applied econometric approach. Journal of Economic Perspectives [RePEcPublished Spring 2017

Machine learning tools have become ubiquitous in the software we use on a day to day basis. Facebook can identify faces in photos; Google can tell you the traffic for your journey; Netflix can recommend you movies based on what you’ve watched before. Machine learning algorithms provide a way to estimate an unknown function f that predicts an outcome Y given some data x: Y = f(x) + \epsilon. The potential application of these algorithms to many econometric problems is clear. This article outlines the principles of machine learning methods. It divides econometric problems into prediction, \hat{y}, and parameter estimation, \hat{\beta} and suggests machine learning is a useful tool for the former. However, this distinction is a false one, I believe. Parameters are typically estimated because they represent an average treatment effect, say E(y|x=1) - E(y|x=0). But, we can estimate these quantities in ‘\hat{y} problems’ since f(x) = E(y|x). Machine learning algorithms, therefore, represent a non-parametric (or very highly parametric) approach to the estimation of treatment effects. In cases where functional form is unknown, where there may be nonlinearities in the response function, and interactions between variables, this approach can be very useful. They do not represent a panacea to estimation problems of course, since interpretation rests on the assumptions. For example, as Jennifer Hill discusses, additive regression tree methods can be used to estimate conditional average treatment effects if we can assume the treatment is ignorable conditional on the covariates. This article, while providing a good summary of methods, doesn’t quite identify the right niche where these approaches might be useful in econometrics.

Incorporating equity in economic evaluations: a multi-attribute equity state approach. European Journal of Health Economics [PubMedPublished 1st June 2017

Efficiency is a key goal for the health service. Economic evaluation provides evidence to support investment decisions, whether displacing resources from one technology to another can produce greater health benefits. Equity is generally not formally considered except through the final investment decision-making process, which may lead to different decisions by different commissioning groups. One approach to incorporating equity considerations into economic evaluation is the weighting of benefits, such as QALYs, by group. For example, a number of studies have estimated that benefits of end-of-life treatments have a greater social valuation than other treatments. One way of incorporating this into economic evaluation is to raise the cost-effectiveness threshold by an appropriate amount for end-of-life treatments. However, multiple attributes may be relevant for equity considerations, negating a simplistic approach like this. This paper proposed a multi-attribute equity state approach to incorporating equity concerns formally in economic evaluation. The basic premise of this approach is to firstly define a set of morally relevant attributes, to secondly derive a weighting scheme for each set of characteristics (similarly to how QALY weights are derived from the EQ-5D questionnaire), and thirdly to apply these weights to economic evaluation. A key aspect of the last step is to weight both the QALYs gained by a population from a new technology and those displaced from another. Indeed, identifying where resources are displaced from is perhaps the biggest limitation to this approach. This displacement problem has also come up in other discussions revolving around the estimation of the cost-effectiveness threshold. This seems to be an important area for future research.

Financial incentives, hospital care, and health outcomes: evidence from fair pricing laws. American Economic Journal: Economic Policy [RePEcPublished May 2017

There is a not-insubstantial literature on the response of health care providers to financial incentives. Generally, providers behave as expected, which can often lead to adverse outcomes, such as overtreatment in cases where there is potential for revenue to be made. But empirical studies of this behaviour often rely upon the comparison of conditions with different incentive schedules; rarely is there the opportunity to study the effects of relative shifts in incentive within the same condition. This paper studies the effects of fair pricing laws in the US, which limited the amount uninsured patients would have to pay hospitals, thus providing the opportunity to study patients with the same conditions but who represent different levels of revenue for the hospital. The introduction of fair pricing laws was associated with a reduction in total billing costs and length of stay for uninsured patients but little association was seen with changes in quality. A similar effect was not seen in the insured suggesting the price ceiling introduced by the fair pricing laws led to an increase in efficiency.

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Alastair Canaway’s journal round-up for 5th June 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.

Redistribution and redesign in health care: an ebbing tide in England versus growing concerns in the United States. Health Economics [PubMed] Published 4th May 2017

Health Economics included an editorial that will be of interest to a wider readership. It focusses on the similarities and differences between the US and the UK’s health care systems, particularly in terms of (re)design, redistribution, and the challenges facing each. The UK system is characterised by a preference for collectivism in funding and access, and in the US, a pluralism of funding. In both countries, groups seek to reverse their existing approach (the grass is always greener). The editorial outlines recent changes in healthcare design, notably, the impact of the affordable care act (ACA). The main focus of the editorial is twofold: i) a discussion of the efforts in England to limit public spending whilst increasing hospital sector efficiency, ii) discussion of the US’s attempt to reduce the growth in the role of government in financing and delivering healthcare. In respect to the UK, the diagnosis is worrying yet unsurprising: chronic underfunding combined with a plethora of unevidenced reform proposals has left the NHS on a knife-edge; the prognosis is that it is uncertain whether the NHS will survive the next few years. In the US, the picture is more complex and the paper discussed possible repeal components of the ACA. A key point of the discussion relates to the assumption that US healthcare is much more expensive than any OECD country due to American’s using too much medical care. In fact as the authors note, the evidence points to the contrary, and the high expenditure is due to a myriad of factors including high wages, high drug prices, and a system which requires many more lawyers, administrators and consultants. The paper discusses various nuances with both systems in the current political context and is well worth reading for a quick overview of some the key issues facing both countries.

Statistical alchemy: conceptual validity and mapping to generate health state utility values. Pharmacoeconomics – Open Published 15th May 2017

With a passing interest in mapping and counting myself as a bit of a mapping skeptic, this paper discussing mapping in terms of ‘statistical alchemy’ obviously caught my eye. As most will know, mapping is a frequently used technique to obtain utility estimates by predicting utility values from data collected using other measures. The focus of the paper is ‘conceptual validity’: ‘the degree to which the content of two different instruments reflect one another when used for mapping’. There were three aims i) explain the idea of conceptual validity in relation to mapping, ii) consider the implications of poor conceptual validity when mapping for decision making in the context of resource allocation, and iii) provide suggestions to improve conceptual validity. The paper successfully achieves the first goal with an exposition of the (many) issues with mapping in relation to conceptual validity. The paper highlights that poor conceptual validity will result in systematic biases in the preferences for health when mapped estimates are used. This is aptly demonstrated through an example using a multiple sclerosis measure, and the EQ-5D. A number of ways for improving the conceptual validity are also presented, these include: i) response mapping, ii) assessment of ‘conceptual decision validity’ (which draws upon face, construct and criterion validity) to determine whether there is a prima facie case that a mapping function may lead to a valid decision, and iii) the need to examine ‘what is lost’ should mapping be used. I found it to be a thoughtful paper, and echoed some of my concerns with existing mapping functions. For those interested in conducting a mapping exercise this is an essential read as an introduction to some of the pitfalls you will encounter.

Is there additional value attached to health gains at the end of life? A revisit. Health Economics Published 1st June 2017

Following NICE’s (2009) guidance for the acceptability of higher cost-per-QALY thresholds for life extending treatments, the past eight years has seen an increase in research examining whether the general public actually have an appetite for this. That is, do the general public have a preference for an end of life premium? Many studies have sought to answer this, with mixed results. All previous attempts however, have tackled this issue from an ex-post perspective: respondents are asked to choose between providing treatment after the diagnosis when they face a shorter life expectancy without treatment. The issue highlighted in this paper is that by presenting life expectancy as certain and salient (e.g. 2 years, or 10 years), it may be interpreted as a life sentence regardless of length. This paper goes down an alternative route by adopting an ex-ante insurance approach. Additionally a new comparator is used, end of life treatment is compared with a preventative treatment that offers life extension with the same expected health gain. It also explores whether preferences depend on recipient age. The paper found that preventative treatments were prioritised over end of life treatments, and thus a dearth of justification for the end of life premium exists. This is another addition to the mixed literature regarding preferences for end of life treatments. The paper does have its limitations which it readily admits. It is however another useful addition this tricky research area.

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