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.

#HEJC for 26/02/2015

The next #HEJC discussion will take place Thursday 26th February, at 11pm London time on Twitter. To see what this means for your time zone visit or join the Facebook event. For more information about the Health Economics Journal Club and how to take part, click here.

The paper for discussion is a working paper published by the Canadian Centre for Health Economics (CCHE). The authors are Koffi-Ahoto Kpelitse, Rose Anne Devlin and Sisira Sarma. The title of the paper is:

The effect of income on obesity among Canadian adults

Following the meeting, a transcript of the Twitter discussion can be downloaded here.

Links to the article



Summary of the paper

This is the first paper to examine the causal relationship between income and obesity in the Canadian context. To do so, they examined data from five biennial Canadian Community Health Survey (from 2000/01 to 2009/10), a nationally representative survey collecting information on over 100,000 individuals each survey.

Initially, the paper explored the Grossman model, which suggested increasing income would promote healthy lifestyle investments, and thus lead to a negative relationship between income and obesity. Previous studies that examined this link were discussed, some (eg. Lindahl (2005)) demonstrating a negative relationship; some (eg. Schmeiser (2009)) demonstrating a positive relationship; some (eg. Cawley (2010)) finding no evidence of a causal relationship.

Additionally, education and employment were explored. Again, the Grossman model was used as a basis, predicting i) a negative relationship between education level and obesity with a greater income effect amongst educated people and ii) a negative relationship between employment level and obesity. However, regarding education, prior studies discussed have shown “mixed results”, and regarding employment, the authors were not aware of any study to examine this causal relationship, but suggested the relationship was ambiguous.

Finally, the relationship between gender and obesity were discussed. Numerous studies have shown negative association between income and BMI amongst women, but for men, the relationship is unclear (some showing positive relationship, some negative, and some no significant relationship at all). The importance of the effect of obesity on labour market outcomes (outlining the “large” empirical literature showing obese women more likely to suffer discrimination in the labour market) was outlined.

In this study, the authors found that:

  • From 2000/01 to 2009/10, BMI and obesity rates amongst both men and women have risen.
    • For men, the obesity rate rises from 19.48% for those with income below $10k to 26.09% for those with income over $80k.
    • For women obesity falls from 26.71% for those below $10k to 17.38% for those with income over $80k.
  • For men, a 1% rise in household income leads to 0.027 point decrease in BMI (2SLS estimate); 0.084kg reduction and 0.27% point decrease in probability of being obese (linear IV procedure).
  • For women, a 1% rise in household income leads to 0.113 point decrease in BMI (much higher than for men; this used a 2SLS estimate); 0.300kg reduction; and 0.76% point decrease in probability of being obese (linear IV procedure).
  • For men the effect of income on BMI was only demonstrated at higher BMI distribution, while for women the effect of income on BMI was found throughout with a larger effect at higher BMI.
  • Education had a variable relationship amongst both men and women, not consistent with the theoretical prediction that the effect would be larger amongst educated people.
  • The effect of employment for men was mixed, with a negative effect of income on BMI only in employed men and a negative effect of income on obesity probability only in unemployed men.
  • The effect of employment for women was more consistent with theoretical predictions, showing negative effects of income on both BMI and on the probability of being obese across employment status.
  • Higher BMI and probability of obesity was associated with older age, marriage (much greater effect in women), household size (much greater effect in women) and home ownership.
  • Lower BMI and probability of obesity was associated with being widowed/separated/divorced, being an immigrant and living in urban area (in men).

In summary, this study supports the findings of Lindahl, and stands in contrast to Schmeiser, Cawley and other related studies.

Discussion points

  • Why might there be significant variation in findings between the different studies discussed?
  • Are there ways in which unemployment and neighbourhood income might directly influence BMI?
  • Is the set of control variables used in the authors’ models satisfactory?
  • Is it of concern that policies to increase household income could be regarded a pure, explicit public health policy?
  • Are there relevant studies from other countries?
  • To what extent are these findings generalisable?

Can’t join in with the Twitter discussion? Add your thoughts on the paper in the comments below.