Meeting round-up: CINCH Academy 2017

The CINCH Academy took place for the fourth time in Essen, Germany, from August 28th to September 3rd, 2017. We were twelve PhD students participating in the summer school coming not only from Germany but also from the UK, Netherlands, and Russia. On Monday morning, Christoph Kronenberg who organized the CINCH Academy 2017 welcomed us. After some information on the schedule and – most importantly – the social activities planned for the week, Owen O’Donnell started with the first class on health inequality.

Prof O’Donnell held courses during the first half of the summer school. In his lectures, he presented tools to measure inequality in the distribution of health depending on socioeconomic indicators like income or education. With examples in Stata, we were familiarized with the application, advantages, and potential drawbacks of different health inequality indices.

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In the second part of the week, Frank Windmeijer held courses on panel data models. He started with the linear instrumental variables model and successively approached instrumental estimations in linear and dynamic panel models. Needless to mention, the famous Windmeijer correction was included in Frank’s explanations. As in the health inequality courses, we applied our newly gained knowledge immediately to examples in Stata exercises.

Besides productive classes on health inequality and panel models, each participant of the summer school presented their own work. Maksym Obriza chaired the paper presentations, making sure that we stuck to the schedule and providing valuable feedback. Presentations lasted 30 minutes and were followed by 10 minutes for the discussant and 5 minutes for questions from the audience. The presentations covered a broad range of topics, including the interaction of health and labour, the effect of regulatory actions on children’s health, and the impact of hospital environment on physicians’ treatment choices. While most participants analysed datasets for their research projects, I presented a lab experiment.

Of course, we were also able to get to know each other better and to discuss our research more informally during various social activities throughout the week. At the get-together on Monday evening, the group became even bigger when CINCH members Reinhold Schnabel and Daniel Avdic joined for dinner at Leo’s Casa. On Wednesday, the first part of CINCH Academy ended with an excursion to the UNESCO world heritage site Zollverein. The working conditions for workers with low education levels in the coking plant, which were vividly described by our guide, by no doubt led to poor health. We agreed that in this case no tests were needed to convince us of a causality between low education and poor health – a perfect illustration for health inequality. More discussions followed on Thursday during dinner at Ponistra, which is known for its excellent food.

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After a lot of input on health inequality, panel models, and our own research, the summer school ended Sunday noon with the presentation of the Best Paper Award. Juditha Wójcik received the award for her joint work with Sebastian Vollmer on long-term consequences of the 1918 influenza pandemic for which they analysed 117 census datasets.

Although the schedule seemed to be very tough in the beginning, I really enjoyed CINCH Academy. I did not only learn a lot but also got to know a very nice group of junior health economists.

Credits

Chris Sampson’s journal round-up for 11th April 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.

Estimating the medical care costs of obesity in the United States: systematic review, meta-analysis, and empirical analysis. Value in Health Published 6th April 2016

I’m always a little wary of the “[insert disease] costs the economy $[insert big number] per year” studies. There is just too much up for debate: whether a cost can be attributed to the disease; who bears the cost; whether in fact it should be considered a cost at all. A second look through the lense of a critical review is just what these studies need. Obesity is a big deal, but there is wide variation in estimates of its cost to the US economy. This study includes a systematic review and meta-analysis looking at the medical costs of obesity estimated by studies between 2008 and 2012. Twelve studies were included in the review. The annual cost of obesity per person that was reported in the studies ranged from $227 to $7269. Wow! The pooled estimate from the meta-analysis was $1910; around $150 billion for the US as a whole. The authors looked at the methods used in the studies, but due to the variation in methods chosen and data used they weren’t able to learn that much about how this might affect estimates. The studies aren’t entirely comparable to one another. So the authors also carry out an original analysis using data from the Medical Expenditure Panel Survey to explore the impact on estimates of alternative modelling strategies. The analysis was varied by 4 age groups, 5 statistical models and 4 sets of confounders to give 80 estimates in total. The alternative statistical models didn’t make much difference, but the authors found that their extended estimating equation had the best goodness of fit. This analysis found an average cost of $1343 per person. Age groups and confounders were important. Costs were especially high in the over 65s. Older obese people have a lot of obesity-related diseases, while obese children have very few and have relatively low costs. Controlling for obesity-related disease explained away most of the incremental cost. This brings us back to the question of what should and what shouldn’t be considered a cost of the disease. What we really want to know is the counterfactual cost of the presence of obesity; what if these people weren’t obese? It remains unclear how studies might even go about defining this, let alone actually estimating it.

Introduction of a national minimum wage reduced depressive symptoms in low-wage workers: a quasi-natural experiment in the UK. Health Economics [PubMedPublished 4th April 2016

The introduction to this paper states that “no study has investigated the health effects of the UK National Minimum Wage”. That took me by surprise. So – apparently – here is the first, and it’s particularly relevant given the recent introduction of the so-called ‘National Living Wage’. The authors use data from the BHPS to test whether the increase in wages for low earners associated with the introduction of the minimum wage resulted in a positive health effect. A difference-in-differences analysis was performed using data from just before and just after the introduction of the minimum wage. Health effect is measured using the General Health Questionnaire (GHQ), which asks about current mental health problems relative to what the respondent normally feels. The intervention group was those earning less than £3.60 per hour in 1998 and between £3.60 and £4.00 per hour in 1999. There are 2 alternative control groups; one consisting those earning just above the minimum wage in 1998 and another for people whose employer did not comply. Plenty of effort is made to try and isolate the effect by incorporating physical health changes into the model and exploring the role of financial strain as a mediating effect. The results show a (statistically significant) positive impact on the GHQ. But the results aren’t quite as compelling as they might at first seem. There are a lot of exclusions that might not stand up to scrutiny, and the intervention group was made up of just 63 people. It would be good to see the analysis adapted into an economic evaluation of the policy.

An econometric model of healthcare demand with nonlinear pricing. Health Economics [PubMedPublished 4th April 2016

In Germany, health insurance is mandatory and most people receive their coverage through a public system. Between 2004 and 2013 it operated an interesting policy: the first visit to a doctor in each calendar quarter was subject to a co-payment of €10, with no copayment for subsequent visits. That’s not a lot of money for most people, but instinct would tell me that at least some people would probably avoid a single visit within a quarter and perhaps bunch-up visits if possible. This study tests that instinct. The authors develop a model of health care demand based on health shocks arriving as a Poisson process. It assumes that the co-payment increases the probability of no visit taking place and that if one does take place then this is more likely to be later in the quarter. A joint analysis of two difference-in-differences experiments is used, based on both the introduction and the repeal of the policy. The control group consists the people with private health insurance who were not affected by the policy changes. Data come from the German Socio-Economic Panel and the main analysis included over 30,000 observations. This was in part thanks to the development and successful implementation of a method to address mismatching between observation date and calendar quarters. None of the various model specifications identified a statistically significant effect of the policy on the number of doctor visits, so I suspect it won’t be reintroduced any time soon.

Diagnosing the causes of rising health-care expenditure in Canada: does Baumol’s cost disease loom large? American Journal of Health Economics Published 31st March 2016

Baumol’s cost disease is a neat idea: health care costs will rise faster than most others because health care is labour intensive and – while wages will grow in line with other industries – productivity growth cannot keep up. There’s some evidence that Baumol’s cost disease does exist, but there is less evidence about how big a deal it is compared to other non-observable drivers of rising health care expenditure. As for many other countries, Canada’s health care spending has grown at a much faster rate than the consumer price index. This new study looks at national and provincial data from Canada for 1982-2011 and decomposes the growth rate into that driven by the cost disease, technological progress and observable factors. Observable variables include population ageing, per capita income growth, economic recession and social determinants of health. The analysis uses a recently developed method, referred to as the Hartwig-Colombier test, to evaluate the impact of Baumol’s cost disease. In line with previous research, growth in per capita income is shown to be the most important driver of health care spending growth. For all provinces, the analysis finds that the cost disease is relatively unimportant. Technological progress appears to have a far greater influence, accounting for at least 31% of spending increases. Furthermore, the authors find that population ageing is not such a big concern and that the spending increases resulting from it are manageable. The implication is that if Canada wants to control spending growth then it should focus on managing the adoption of new technologies.