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.


How do you solve a problem like obesity?


Making headlines this morning (Thursday 20th November) has been the report by McKinsey Global Institute, an offshoot of the management consultancy McKinsey, on the global economic impact of obesity. This report estimates that $2.0 trillion is spent annually worldwide as a result of obesity, which it compares to the global burden of smoking and armed conflict; the quoted figure is comprised of various elements such as productivity losses and spending to mitigate obesity. Certainly, the magnitude of the burden is in part due to the fact that obesity is generally a developed nation problem, and these nations typically spend many orders of magnitude more on healthcare than their developing nation counterparts. The claim then that obesity represents a problem as serious as armed conflict and violence may therefore end up being somewhat spurious if global issues were measured on a scale other than total financial expenditure. Nonetheless, the report acknowledges such issues, and provides a comprehensive summary of obesity related statistics to demonstrate them.

One of the main aims of the report is to identify interventions that may be used to tackle obesity in order to reduce expenditure resulting from obesity. To credit the McKinsey report, it recognises the complex nature of obesity and reproduces the above figure, asking if it is possible to tackle obesity given its complex aetiology. The report even provides some evidence that various social and cultural factors are at play. However, the authors write that while the background may be complex, the proximal causes are well known, and that interventions that target these proximal causes are both more feasible and simpler to implement and ought to be the ones they consider. This expression of a certain public health ideology, I would argue, is an issue with many discussions about population and global health issues.

This is the notion that public health and healthcare should be focussed on targeting individuals and modifying their behaviour, through such things as technological innovation, divorced from social, economic, or political contexts. For example, the McKinsey report suggests calorie labelling, advertising restrictions, and public health campaigns. However, if we want to tackle health issues such as obesity at the aggregate level then we should probably consider asking aggregate level questions, such as why markets are producing inefficient outcomes in terms of the health of the labour force, and why there is an oversupply of calories in some countries and an undersupply elsewhere. Policies that result from such analyses are likely to be more complex but are also more likely to be efficacious.

Historically, public health progress has been the result of a convergence of a wide range of social, economic, and political projects. Countries have adopted various strategies, historically, to reduce mortality including: better income distribution; improved diet; public health; medicine; changes in household education – however, none of these policies have been universally successful on its own and real progress requires integration of various social, medical, political, and economic strategies (Brin, 2005The Lancet—University of Oslo Commission on Global Governance for Health, 2014). The interventions in the report seem to me to be somewhat limp in the face of what they call a problem with a ‘global burden’.