Sam Watson’s journal round up for 16th October 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.

Effect of forced displacement on health. Journal of the Royal Statistical Society: Series A [RePEcPublished October 2017

History, as they say, is doomed to repeat itself. Despite repeated cries of ‘never again’, war and conflict continue to harm and displace people around the world. The mass displacement of the Rohingya in Myanmar is leading to the formation of the world’s largest refugee camp in Bangladesh. Aside from the obvious harm from conflict itself, displacement is likely to have pernicious effects on health. Livelihoods and well-being is lost as well as access to basic amenities and facilities. The conflict in Croatia and wider Eastern Europe created a mass displacement of people, however, many went to relatively wealthy neighbouring countries across Europe. Thus, the health effects of displacement in this conflict should provide an understanding of the lower bound of what happens. This paper looks into this question using a health survey of Croatians from 2003. An empirical issue the authors spend a substantial amount of time addressing is that displacement status is likely to be endogenous: beyond choices about protecting their household and possessions, health and the ability to travel may play a large role in decisions to move. The mortality rate from conflict is used as an instrument for displacement, being a proxy for the intensity of war. However, conflict intensity is obviously likely to have a effect itself on health status. A method of relaxing the exclusion restriction is used, which tempers the estimates somewhat. Nevertheless, there is evidence that displacement impacts upon hypertension, self-assessed health, and emotional and physical dimensions of the SF-36. However, it seems to me that there may be another empirical issue not dealt with – the sample selection problem. While the number of casualties was low relative to the size of the population and numbers of displaced people, those who died obviously don’t feature in the sample. And those who died may have also been more likely or not to be displaced and be in worse health. Maybe only a bias of second order but a point it seems is left unconsidered.

Can variation in subgroups’ average treatment effects explain treatment effect heterogeneity? Evidence from a social experiment. Review of Economics and Statistics [RePEcPublished October 2017

A common approach to explore treatment effect heterogeneity is to estimate mean impacts by subgroups. In applied health economics studies I have most often seen this done by pooling data and adding interactions of the treatment with subgroups of interest to a regression model. For example, there is a large interest in differences in access to care across socioeconomic groups – in the UK we often use quintiles, or other division, of the Index of Multiple Deprivation, which is estimated at small area level, to look at this. However, this paper looks at the question of whether this approach to estimating heterogeneity is any good. Using data from a large jobs treatment program, they compare estimates of quantile treatment effects, which are considered to fully capture treatment effect heterogeneity, to results from various specifications of models that assume constant treatment effects within subgroups. If they found there was little difference in the two methods, I doubt the paper would have been published in such a good journal, so it’s no surprise that their conclusions are that the subgroup models perform poorly. Even allowing for more flexibility, such as by allowing effects to vary over time, and adding submodels for a point mass at zero, they still don’t do that well. Interestingly, subgroups defined according to different variables, e.g. education or pre-treatment earnings, fare differently – so comparisons across types of subgroups is important when the analyst is looking at heterogeneity. The takeaway message though is that constant effects subgroups models aren’t that good – more flexible semi or nonparametric methods may be preferred.

The hidden costs of terrorism: The effects on health at birth. Journal of Health Economics [PubMedPublished October 2017

We here at the blog have covered a long series of papers on the effects of in utero stressors on birth and later life health and economic outcomes. The so-called fetal-origins hypothesis posits that the nine months in the womb are some of the most important in predicting later life health outcomes. This may be one of the main mechanisms explaining intergenerational transmission of health. Some of these previous studies have covered reduced maternal nutrition, exposure to conditions of famine, or unemployment shocks in the household. This study examines the effect of the mother being pregnant in a province in Spain during which a terrorist attack by ETA occurred. At first glance, one might be forgiven for being sceptical at first, given (i) terrorist attacks were rare, (ii) the chances of actually being affected by an attack in a province if an attack occurred is low, so (iii) the chances are that the effect of feeling stressed on birth weight is small and likely to be swamped by a multitude of other factors (see all the literature we’ve covered on the topic!) All credit to the authors for doing a thorough job of trying to address all these concerns, but I’ll admit I remain sceptical. The effect sizes are very small indeed, as we suspected, and unfortunately there is not enough evidence to examine whether those women who had low birth weight live births were stressed or demonstrating adverse health behaviours.

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Review: Health Econometrics Using Stata (Partha Deb et al)

Health Econometrics Using Stata

Partha Deb, Edward C. Norton, Willard G. Manning

Paperback, 264 pages, ISBN: 978-1-59718-228-7, published 31 August 2017

Amazon / Google Books / Stata Press

This book is the perfect guide to understanding the various econometric methods available for modelling of costs and counts data for the individual who understands econometrics best after applying it to a dataset (like myself). Pre-requisites include a decent knowledge of Stata and a desire to apply econometric methods to a cost or count outcome variable

It’s important to say that this book does not cover all aspects of econometrics within health economics, but instead focuses on ‘modelling health care costs and counts’ (the title of the short course from which the book evolved). As expected from this range of texts, the vast majority of the book comes with detailed example Stata code for all of the methods described, with illustrations either using a publicly available sample of MEPS data or simulated data.

Like many papers in this field, the focus of the book revolves around the non-normal characteristics of health care resource use distributions. These are the mass point at zero, right-hand skew and inherent heteroskedasticity. As such the book covers the broad suite of models that have been developed in order to account for these features, ranging from two-part models, transformation of the data (and the problematic re-transformation of estimated effects) to non-linear modelling methods such as generalised linear models (GLMs). Unlike many papers in this field, the authors emphasise the need – and provide guidance on how – to delve deep into the underlying data in order to appreciate the most appropriate methods (there is even a chapter on design effects) and encourage rigorous testing of model specification. In addition, Health Econometrics Using Stata considers the important issue of endogeneity and is not solely fixated on distributional issues, providing important insight and code for estimation of non-linear models that control for potential endogeneity (interested readers may wish to heed the published cautionary notes for some of these methods, e.g. Chapman and Brooks). Finally, the book describes more advanced methods for estimating heterogeneous effects, although code is not provided for all of these methods, which is a bit of a shame (but perhaps understandable given the complexity).

This could be a very dry text, but it is not – emphatically! The personality of the authors comes through very strongly from the writing. Reading it brought back many pleasant memories from the course ‘modelling health care costs and counts’ that I sat in 2012. The book also features a dedication to Willard Manning, which is a fitting tribute to a man who was both a great academic and an outstanding mentor. One particular highlight, with which past course attendants will be familiar, is the section ‘top 10 myths in health econometrics’. This straightforward and punchy presentation, backed up by rigorous methodological research, is a great way to get these key messages across in an accessible format. Other great features of this book include the use of simulations to illustrate important features of the econometric models (with code provided to recreate) and a personal highlight (granted, a niche interest…) was the code to generate comparable AIC and BIC across GLM families.

Of course, Health Econometrics Using Stata cannot be comprehensive and there are developments in this field that are not covered. Most notably, there is no discussion of how to model these data in a panel/longitudinal setting, which is crucially important for estimating parameters for decision models, for example. Potential issues around missing data and censoring are also not discussed. Also, this text does not cover advances in flexible parametric modelling, which enable modelling of data that are both highly skewed and leptokurtic (see Jones 2017 for an excellent summary of this literature along with a primer on data visualisation using Stata).

I heartily recommend Health Econometrics Using Stata to interested colleagues who want practical advice – on model selection and specification testing with cost and count outcome data – from some of the top specialists in our field, in their own words.

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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.

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