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.
Short-term and long-term effects of GDP on traffic deaths in 18 OECD countries, 1960–2011. Journal of Epidemiology and Community Health [PubMed] Published February 2017
Understanding the relationships between different aspects of the economy or society in the aggregate can reveal to us knowledge about the world. However, they are more complicated than analyses of individuals who either did or did not receive an intervention, as the objects of aggregate analyses don’t ‘exist’ per se but are rather descriptions of average behaviour of the system. To make sense of these analyses an understanding of the system is therefore required. On these grounds I am a little unsure of the results of this paper, which estimates the effect of GDP on road traffic fatalities in OECD countries over time. It is noted that previous studies have shown that in the short-run, road traffic deaths are procyclical, but in the long-run they have declined, likely as a result of improved road and car safety. Indeed, this is what they find with their data and models. But, what does this result mean in the long-run? Have they picked up anything more than a correlation with time? Time is not included in the otherwise carefully specified models, so is the conclusion to policy makers, ‘just keep doing what you’re doing, whatever that is…’? Models of aggregate phenomena can be among the most interesting, but also among the least convincing (my own included!). That being said, this is better than most.
Sources of geographic variation in health care: Evidence from patient migration. Quarterly Journal of Economics [RePEc] Published November 2016
There are large geographic differences in health care utilisation both between countries and within countries. In the US, for example, the average Medicare enrollee spent around $14,400 in 2010 in Miami, Florida compared with around $7,800 in Minneapolis, Minnesota, even after adjusting for demographic differences. However, higher health care spending is generally not associated with better health outcomes. There is therefore an incentive for policy makers to legislate to reduce this disparity, but what will be effective depends on the causes of the variation. On one side, doctors may be dispensing treatments differently; for example, we previously featured a paper looking at the variation in overuse of medical testing by doctors. On the other side, patients may be sicker or have differing preferences on the intensity of their treatment. To try and distinguish between these two possible sources of variation, this paper uses geographical migration to look at utilisation among people who move from one area to another. They find that (a very specific) 47% of the difference in use of health care is attributable to patient characteristics. However, I (as ever) remain skeptical: a previous post brought up the challenge of ‘transformative treatments’, which may apply here as this paper has to rely on the assumption that patient preferences remain the same when they move. If moving from one city to another changes your preferences over healthcare, then their identification strategy no longer works well.
Seeing beyond 2020: an economic evaluation of contemporary and emerging strategies for elimination of Trypanosoma brucei gambiense. Lancet Global Health Published November 2016
African sleeping sickness, or Human African trypanosomiasis, is targeted for eradication in the next decade. However, the strategy to do so has not been determined, nor whether any such strategy would be a cost-effective use of resources. This paper aims to model all of these different strategies to estimate incremental cost-effectiveness threshold (ICERs). Infectious disease presents an interesting challenge for health economic evaluation as the disease transmission dynamics need to be captured over time, which they achieve here with a ‘standard’ epidemiological model using ordinary differential equations. To reach elimination targets, an approach incorporating case detection, treatment, and vector control would be required, they find.
A conceptual introduction to Hamiltonian Monte Carlo. ArXiv Published 10th January 2017
It is certainly possible to drive a car without understanding how the engine works. But if we want to get more out of the car or modify its components then we will have to start learning some mechanics. The same is true of statistical software. We can knock out a simple logistic regression without ever really knowing the theory or what the computer is doing. But this ‘black box’ approach to statistics has clear problems. How do we know the numbers on the screen mean what we think they mean? What if it doesn’t work or if it is running slowly, how do we diagnose the problem? Programs for Bayesian inference can sometimes seem even more opaque than others: one might well ask what are those chains actually exploring, if it’s even the distribution of interest. Well, over the last few years a new piece of kit, Stan, has become a brilliant and popular tool for Bayesian inference. It achieves fast convergence with less autocorrelation between chains and so it achieves a high effective sample size for relatively few iterations. This is due to its implementation of Hamiltonian Monte Carlo. But it’s founded in the mathematics of differential geometry, which has restricted the understanding of how it works to a limited few. This paper provides an excellent account of Hamiltonian Monte Carlo, how it works, and when it fails, all replete with figures. While it’s not necessary to become a theoretical or computational statistician, it is important, I think, to have a grasp of what the engine is doing if we’re going to play around with it.