Thesis Thursday: Lidia Engel

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 Lidia Engel who graduated with a PhD from Simon Fraser University. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Going beyond health-related quality of life for outcome measurement in economic evaluation
David Whitehurst, Scott Lear, Stirling Bryan
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Your thesis explores the potential for expanding the ‘evaluative space’ in economic evaluation. Why is this important?

I think there are two answers to this question. Firstly, methods for economic evaluation of health care interventions have existed for a number of years but these evaluations have mainly been applied to more narrowly defined ‘clinical’ interventions, such as drugs. Interventions nowadays are more complex, where benefits cannot be simply measured in terms of health. You can think of areas such as public health, mental health, social care, and end-of-life care, where interventions may result in broader benefits, such as increased control over daily life, independence, or aspects related to the process of health care delivery. Therefore, I believe there is a need to re-think the way we measure and value outcomes when we conduct an economic evaluation. Secondly, ignoring broader outcomes of health care interventions that go beyond the narrow focus of health-related quality of life can potentially lead to misallocation of scarce health care resources. Evidence has shown that the choice of outcome measure (such as a health outcome or a broader measure of wellbeing) can have a significant influence on the conclusions drawn from an economic evaluation.

You use both qualitative and quantitative approaches. Was this key to answering your research questions?

I mainly applied quantitative methods in my thesis research. However, Chapter 3 draws upon some qualitative methodology. To gain a better understanding of ‘benefits beyond health’, I came across a novel approach, called Critical Interpretive Synthesis. It is similar to meta-ethnography (i.e. a synthesis of qualitative research), with the difference that the synthesis is not of qualitative literature but of methodologically diverse literature. It involves an iterative approach, where searching, sampling, and synthesis go hand in hand. It doesn’t only produce a summary of existing literature but enables the development of new interpretations that go beyond those originally offered in the literature. I really liked this approach because it enabled me to synthesise the evidence in a more effective way compared with a conventional systematic review. Defining and applying codes and themes, as it is traditionally done in qualitative research, allowed me to organize the general idea of non-health benefits into a coherent thematic framework, which in the end provided me with a better understanding of the topic overall.

What data did you analyse and what quantitative methods did you use?

I conducted three empirical analyses in my thesis research, which all made use of data from the ICECAP measures (ICECAP-O and ICECAP-A). In my first paper, I used data from the ‘Walk the Talk (WTT)‘ project to investigate the complementarity of the ICECAP-O and the EQ-5D-5L in a public health context using regression analyses. My second paper used exploratory factor analysis to investigate the extent of overlap between the ICECAP-A and five preference-based health-related quality of life measures, using data from the Multi Instrument Comparison (MIC) project. I am currently finalizing submission of my third empirical analysis, which reports findings from a path analysis using cross-sectional data from a web-based survey. The path analysis explores three outcome measurement approaches (health-related quality of life, subjective wellbeing, and capability wellbeing) through direct and mediated pathways in individuals living with spinal cord injury. Each of the three studies addressed different components of the overall research question, which, collectively, demonstrated the added value of broader outcome measures in economic evaluation when compared with existing preference-based health-related quality of life measures.

Thinking about the different measures that you considered in your analyses, were any of your findings surprising or unexpected?

In my first paper, I found that the ICECAP-O is more sensitive to environmental features (i.e. social cohesion and street connectivity) when compared with the EQ-5D-5L. As my second paper has shown, this was not surprising, as the ICECAP-A (a measure for adults rather than older adults) and the EQ-5D-5L measure different constructs and had only limited overlap in their descriptive classification systems. While a similar observation was made when comparing the ICECAP-A with three other preference-based health-related quality of life measures (15D, HUI-3, and SF-6D), a substantial overlap was observed between the ICECAP-A and the AQoL-8D, which suggests that it is possible for broader benefits to be captured by preference-based health-related measures (although some may not consider the AQoL-8D to be exclusively ‘health-related’, despite the label). The findings from the path analysis confirmed the similarities between the ICECAP-A and the AQoL-8D. However, the findings do not imply that the AQoL-8D and ICECAP-A are interchangeable instruments, as a mediation effect was found that requires further research.

How would you like to see your research inform current practice in economic evaluation? Is the QALY still in good health?

I am aware of the limitations of the QALY and although there are increasing concerns that the QALY framework does not capture all benefits of health care interventions, it is important to understand that the evaluative space of the QALY is determined by the dimensions included in preference-based measures. From a theoretical point of view, the QALY can embrace any characteristics that are important for the allocation of health care resources. However, in practice, it seems that QALYs are currently defined by what is measured (e.g. the dimensions and response options of EQ-5D instruments) rather than the conceptual origin. Therefore, although non-health benefits have been largely ignored when estimating QALYs, one should not dismiss the QALY framework but rather develop appropriate instruments that capture such broader benefits. I believe the findings of my thesis have particular relevance for national HTA bodies that set guidelines for the conduct of economic evaluation. While the need to maintain methodological consistency is important, the assessment of the real benefits of some health care interventions would be more accurate if we were less prescriptive in terms of which outcome measure to use when conducting an economic evaluation. As my thesis has shown, some preference-based measures already adopt a broad evaluative space but are less frequently used.

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.

Overall unfair inequality in health care: an application to Brazil
Richard Cookson
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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 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.

Thesis Thursday: Till Seuring

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 Till Seuring who graduated with a PhD from the University of East Anglia. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

The economics of type 2 diabetes in middle-income countries
Marc Suhrcke, Max Bachmann, Pieter Serneels
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What made you want to study the economics of diabetes?

I was diagnosed with type 1 diabetes when I was 18. So while looking for a topic for my master’s thesis in development economics, I was wondering about how big of a problem diabetes – in particular, type 2 diabetes – would be in low- and middle-income countries (LMICs), because I had never heard about it during my studies. Looking for data I found some on Mexico, where, as it turned out, diabetes was a huge problem and ended up writing my master’s thesis on the labour market effects of diabetes in Mexico. After that, I worked at the International Diabetes Federation as a health economist in a junior position for about a year and a half and at one of their conferences met Prof Marc Suhrcke, who is doing a lot of global health and non-communicable disease related work. We stayed in contact and in the end he offered me the possibility to pursue a PhD on diabetes in LMICs. So this is how I ended up at the University of East Anglia in Norwich studying the economics of diabetes.

Which sources of data did you use for your analyses, and how was your experience of using them?

I exclusively used household survey data that was publicly available. In my master’s thesis, I had already worked with the Mexican Family Life Survey, which is quite an extensive household survey covering many socioeconomic as well as health-related topics. I ended up using it for two of my thesis chapters. The nice thing about it is that it has a panel structure now with three waves, and the last waves also included information on HbA1c levels – a biomarker used to infer on blood glucose levels over the last three months – that I could use to detect people with undiagnosed diabetes in the survey. The second source of data was the China Health and Nutrition Survey, which has many of the same qualities, with even more waves of data. There are more and more surveys with high-quality data coming out so it will be exciting to explore them further in the future.

How did you try to identify the effects of diabetes as separate from other influences?

As in many other fields, there is great worry that diabetes might be endogenous when trying to investigate its relationship with economic outcomes. For example, personal characteristics (such as ambition) could affect your likelihood to be employed or your wage, but maybe also your exercise levels and consequently your risk to develop diabetes. Unfortunately, such things are very difficult to measure so that they often remain unobserved. Similarly, changes in income or job status could affect lifestyles that in turn could change the risk to develop diabetes, making estimates prone to selection biases and reverse causality. To deal with this, I used several strategies. In my first paper on Mexico, I used a commonly used instrumental variable strategy. My instrument was parental diabetes and we argued that, given our control variables, it was unrelated to employment status but predicted diabetes in the children due to the genetic component of diabetes. In the second paper on Mexico, I used fixed effects estimation to control for any time-invariant confounding. This strategy does not need an instrument, however, unobserved time-variant confounding or reverse causality may still be a problem. I tackled the latter in my last paper on the effect of diabetes on employment and behavioural outcomes in China, using a methodology mainly used in epidemiology called marginal structural models, which uses inverse probability weighting to account for the selection into diabetes on previous values of the outcomes of interest, e.g. changes in employment status or weight. Of course, in the absence of a true experiment, it still remains difficult to truly establish causality using observational data, so one still needs to be careful to not over-interpret these findings.

The focus of your PhD was on middle-income countries. Does diabetes present particular economic challenges in this setting?

Well, over the last 30 years many middle-income countries, especially in Asia but also Latin America, have gone from diabetes rates much below high-income countries to surpassing them. China today has about 100 million people with diabetes, sporting the largest diabetes population worldwide. While, as countries become richer, first the economically better-off populations tend to have a higher diabetes prevalence, in many middle-income countries diabetes is now affecting, in particular, the middle class and the poor, who often lack the financial resources to access treatment or to even be diagnosed. Consequently, many remain poorly treated and develop diabetes complications that can lead to amputations, loss of vision and cardiovascular problems. Once these complications appear, the associated medical expenditures can represent a very large economic burden, and as I have shown in this thesis, can also lead to income losses because people lose their jobs.

What advice would you give to policymakers looking to minimise the economic burden of diabetes?

The policy question is always the most difficult one, but I’ll try to give some answers. The results of the thesis suggest that there is a considerable economic burden of diabetes which disproportionately affects the poor, the uninsured and women. Further, many people remain undiagnosed and some of the results of the biomarker analysis I conducted in one of my papers suggest that diagnosis likely often happens too late to prevent adverse health outcomes. Therefore, earlier diagnosis may help to reduce the burden, the problem is that once people are diagnosed they will also need treatment, and it appears that even now many do not receive appropriate treatment. Therefore, simply aiming to diagnose more people will not be sufficient. Policymakers in these countries will need to make sure that they will also be able to offer treatment to everybody, in particular the disadvantaged groups. Otherwise, inequities will likely become even greater and healthcare systems even more overburdened. How this can be achieved is another question and more research will be needed. Promising areas could be a greater integration of diabetes treatment into the existing health care systems specialised in treating communicable diseases such as tuberculosis, which often are related to diabetes. This would both improve treatment and likely limit the amount of additional costs. Of course, investments in early life health, nutrition and education will also help to reduce the burden by improving health and thereby economic possibilities, so that people may never become diabetic or at least have better possibilities to cope with the disease.