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
Repository link

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.

Sam Watson’s journal round-up for 4th July 2016

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 determinants of productivity in medical testing: intensity and allocation of care. American Economic Review [PDF] Forthcoming

The overuse of medical testing is a growing problem for the health system. Many people believe regular check-ups and a frequent use of healthcare services for preventative reasons can help avoid debilitating ailments, yet the evidence does not bear this out. It is costly and can lead to overdiagnosis and other harms. This fascinating study examines this problem but also looks at whether physicians, for a given level of spending, are allocating resources to where they might have the most benefit. This latter problem of misallocation of resources by physicians, as the authors show, has significant effects on efficiency but has remained previously unexamined in the health economics literature. Looking at CT scans for pulmonary embolism, the study demonstrates significant variation between doctors in their threshold for referring a patient for a scan. More cautious doctors who refer lower risk patients have a significantly lower number of patients who return a positive test, as one might expect. But, interestingly they also find that many of the highest risk patients are not being referred, and doctors appear to rely more on the presence of symptoms to determine a referral rather than purpose-built risk scores. Evidence such as this has a significant bearing on the generalisability of cost-effectiveness studies; while a treatment or test may be cost-effective in the idealised setting of a trial, in practice it may well not be being used optimally.

The effect of local area crime on mental health. The Economic Journal [RePEcPublished 8th June 2016

Neighbourhood effects are an oft studied but poorly understood phenomenon where the context of the local environment affects individual health, wealth, and well-being. In a recent journal round-up we featured new results from the Moving to Opportunity experiment that demonstrated that those people who randomly received a voucher allowing them to move to better neighbourhoods had better health outcomes and their children had better health, educational, and labour market outcomes. This study here provides some evidence about one possible pathway that may mediate such effects. European citizens cite crime as one of their top five concerns despite the very low crime rates in European nations; crime’s effect on well-being likely has an effect beyond direct victimisation. Indeed, this fear and anxiety caused by crimes, such as terrorism, may well be the largest source of harm from such acts. Using the British Household Panel Survey and the English Longitudinal Study of Aging, this study examines the effects of local area crime on individual mental health outcomes. The principal finding is that a one standard deviation increase in the overall crime rate leads to a decrease of approximately 0.08 to 0.15 standard deviations in mental well-being. This, the authors write, is two to four times the magnitude of the effect of an equivalent increase in local area unemployment.

Long-term effects of famine on chronic diseases: evidence from China’s Great Leap Forward Famine. Health Economics [PubMedPublished 16th June 2016

And finally, not one, but two papers on the effects of prenatal conditions and mother and child health! In the last post on this blog, we discussed many of the ways in which economic conditions might affect infant health at birth. The first of these two papers contributes to the literature on the consequences of economic conditions and infant health at the aggregate level. Major events, such as the Great Famine of 1959-61 in China that this paper studies, can impact the birth cohort in different and opposing ways: a selection effect means the average health of babies is improved as many of those in the poorest health do not survive, while the health of a fetus is negatively impacted by the conditions faced by the mother lowering infant health – an adverse effect. By comparing the effects of the famine on the outcomes of those who were children and those who were in utero at the time, the authors find evidence of both the selection and adverse effects, although the former appears to predominate among those who were in utero. This finding fits in neatly to the discussion of the last post. But this discussion focused mostly on infant health – what of maternal health?

The effects of prenatal care utilization on maternal health and health behaviors. Health Economics [RePEcPublished 3rd July 2016

The second of these two studies considers adverse maternal health outcomes, such as insufficient or excessive gestational weight gain and smoking, and their relationship to prenatal care. Unsurprisingly, it is found that poor quality of prenatal care, a low frequency of prenatal care visits, or a combination of the two has an adverse impact on maternal health as measured by the aforementioned outcomes. As this area of research continues to grow the benefits of improved care for mothers become more obvious both for mother and child. However, the measurement of such benefits for cost-effectiveness studies is significantly impacted by a choice of social discount rate owing to the long term consequences of interventions in this area (the life of mother and child). Perhaps another reason to revisit the arguments for the appropriate social discount rate.

Photo credit: Antony Theobald (CC BY-NC-ND 2.0)