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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Chris Sampson’s journal round-up for 8th May 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.

Verification of decision-analytic models for health economic evaluations: an overview. PharmacoEconomics [PubMed] Published 29th April 2017

Increasingly, it’s expected that model-based economic evaluations can be validated and shown to be fit-for-purpose. However, up to now, discussions have focussed on scientific questions about conceptualisation and external validity, rather than technical questions, such as whether the model is programmed correctly and behaves as expected. This paper looks at how things are done in the software industry with a view to creating guidance for health economists. Given that Microsoft Excel remains one of the most popular software packages for modelling, there is a discussion of spreadsheet errors. These might be errors in logic, simple copy-paste type mistakes and errors of omission. A variety of tactics is discussed. In particular, the authors describe unit testing, whereby individual parts of the code are demonstrated to be correct. Unit testing frameworks do not exist for application to spreadsheets, so the authors recommend the creation of a ‘Tests’ spreadsheet with tests for parameter assignments, functions, equations and exploratory items. Independent review by another modeller is also recommended. Six recommendations are given for taking model verification forward: i) the use of open source models, ii) standardisation in model storage and communication (anyone for a registry?), iii) style guides for script, iv) agency and journal mandates, v) training and vi) creation of an ISPOR/SMDM task force. This is a worthwhile read for any modeller, with some neat tactics that you can build into your workflow.

How robust are value judgments of health inequality aversion? Testing for framing and cognitive effects. Medical Decision Making [PubMed] Published 25th April 2017

Evidence shows that people are often extremely averse to health inequality. Sometimes these super-egalitarian responses imply such extreme preferences that monotonicity is violated. The starting point for this study is the idea that these findings are probably influenced by framing effects and cognitive biases, and that they may therefore not constitute a reliable basis for policy making. The authors investigate 4 hypotheses that might indicate the presence of bias: i) realistic small health inequality reductions vs larger one, ii) population- vs individual-level descriptions, iii) concrete vs abstract intervention scenarios and iv) online vs face-to-face administration. Two samples were recruited: one with a face-to-face discussion (n=52) and the other online (n=83). The questionnaire introduced respondents to health inequality in England before asking 4 questions in the form of a choice experiment, with 20 paired choices. Responses are grouped according to non-egalitarianism, prioritarianism and strict egalitarianism. The main research question is whether or not the alternative strategies resulted in fewer strict egalitarian responses. Not much of an effect was found with regard to large gains or to population-level descriptions. There was evidence that the abstract scenarios resulted in a greater proportion of people giving strong egalitarian responses. And the face-to-face sample did seem to exhibit some social desirability bias, with more egalitarian responses. But the main take-home message from this study for me is that it is not easy to explain-away people’s extreme aversion to health inequality, which is heartening. Yet, as with all choice experiments, we see that the mode of administration – and cognitive effects induced by the question – can be very important.

Adaptation to health states: sick yet better off? Health Economics [PubMed] Published 20th April 2017

Should patients or the public value health states for the purpose of resource allocation? It’s a question that’s cropped up plenty of times on this blog. One of the trickier challenges is understanding and dealing with adaptation. This paper has a pretty straightforward purpose – to look for signs of adaptation in a longitudinal dataset. The authors’ approach is to see whether there is a positive relationship between the length of time a person has an illness and the likelihood of them reporting better health. I did pretty much the same thing (for SF-6D and satisfaction with life) in my MSc dissertation, and found little evidence of adaptation, so I’m keen to see where this goes! The study uses 4 waves of data from the British Cohort Study, looking at self-assessed health (on a 4-point scale) and self-reported chronic illness and health shocks. Latent self-assessed health is modelled using a dynamic ordered probit model. In short, there is evidence of adaptation. People who have had a long-standing illness for a greater duration are more likely to report a higher level of self-assessed health. An additional 10 years of illness is associated with an 8 percentage point increase in the likelihood of reporting ‘excellent’ health. The study is opaque about sample sizes, but I’d guess that finding is based on not-that-many people. Further analyses are conducted to show that adaptation seems to become important only after a relatively long duration (~20 years) and that better health before diagnosis may not influence adaptation. The authors also look at specific conditions, finding that some (e.g. diabetes, anxiety, back problems) are associated with adaptation, while others (e.g. depression, cancer, Crohn’s disease) are not. I have a bit of a problem with this study though, in that it’s framed as being relevant to health care resource allocation and health technology assessment. But I don’t think it is. Self-assessed health in the ‘how healthy are you’ sense is very far removed from the process by which health state utilities are obtained using the EQ-5D. And they probably don’t reflect adaptation in the same way.

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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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