*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.*

**The efficiency of slacking off: evidence from the emergency department**. Econometrica [RePEc] *Published May 2018*

Scheduling workers is a complex task, especially in large organisations such as hospitals. Not only should one consider when different shifts start throughout the day, but also how work is divided up over the course of each shift. Physicians, like anyone else, value their leisure time and want to go home at the end of a shift. Given how they value this leisure time, as the end of a shift approaches physicians may behave differently. This paper explores how doctors in an emergency department behave at ‘end of shift’, in particular looking at whether doctors ‘slack off’ by accepting fewer patients or tasks and also whether they rush to finish those tasks they have. Both cases can introduce inefficiency by either under-using their labour time or using resources too intensively to complete something. Immediately, from the plots of the raw data, it is possible to see a drop in patients ‘accepted’ both close to end of shift and close to the next shift beginning (if there is shift overlap). Most interestingly, after controlling for patient characteristics, time of day, and day of week, there is a decrease in the length of stay of patients accepted closer to the end of shift, which is ‘dose-dependent’ on time to end of shift. There are also marked increases in patient costs, orders, and inpatient admissions in the final hour of the shift. Assuming that only the number of patients assigned and not the type of patient changes over the course of a shift (a somewhat strong assumption despite the additional tests), then this would suggest that doctors are rushing care and potentially providing sub-optimal or inefficient care closer to the end of their shift. The paper goes on to explore optimal scheduling on the basis of the results, among other things, but ultimately shows an interesting, if not unexpected, pattern of physician behaviour. The results relate mainly to efficiency, but it’d be interesting to see how they relate to quality in the form of preventable errors.

**Semiparametric estimation of longitudinal medical cost trajectory**. Journal of the American Statistical Association *Published 19th June 2018*

Modern computational and statistical methods have opened up a range of statistical models to estimation hitherto inestimable. This includes complex latent variable structures, non-linear models, and non- and semi-parametric models. Recently we covered the use of splines for semi-parametric modelling in our *Method of the Month *series. Not that complexity is everything of course, but given this rich toolbox to more faithfully replicate the data generating process, one does wonder why the humble linear model estimated with OLS remains so common. Nevertheless, I digress. This paper addresses the problem of estimating the medical cost trajectory for a given disease from diagnosis to death. There are two key issues: (i) the trajectory is likely to be non-linear with costs probably increasing near death and possibly also be higher immediately after diagnosis (a U-shape), and (ii) we don’t observe the costs of those who die, i.e. there is right-censoring. Such a set-up is also applicable in other cases, for example looking at health outcomes in panel data with informative dropout. The authors model medical costs for each month post-diagnosis and time of censoring (death) by factorising their joint distribution into a marginal model for censoring and a conditional model for medical costs given the censoring time. The likelihood then has contributions from the observed medical costs and their times, and the times of the censored outcomes. We then just need to specify the individual models. For medical costs, they use a multivariate normal with mean function consisting of a bivariate spline of time and time of censoring. The time of censoring is modelled non-parametrically. This setup of the missing data problem is sometimes referred to as a pattern mixing model, in that the outcome is modelled as a mixture density over different populations dying at different times. The authors note another possibility for informative missing data, which was considered not to be estimable for complex non-linear structures, was the shared parameter model (to soon appear in another *Method of the Month*) that assumes outcomes and dropout are independent conditional on an underlying latent variable. This approach can be more flexible, especially in cases with varying treatment effects. One wonders if the mixed model representation of penalised splines wouldn’t fit nicely in a shared parameter framework and provide at least as good inferences. An idea for a future paper perhaps… Nevertheless, the authors illustrate their method by replicating the well-documented U-shaped costs from the time of diagnosis in patients with stage IV breast cancer.

**Do environmental factors drive obesity? Evidence from international graduate students**. Health Economics [PubMed] *Published 21st June 2018*

‘The environment’ can encompass any number of things including social interactions and networks, politics, green space, and pollution. Sometimes referred to as ‘neighbourhood effects’, the impact of the shared environment above and beyond the effect of individual risk factors is of great interest to researchers and policymakers alike. But there are a number of substantive issues that hinder estimation of neighbourhood effects. For example, social stratification into neighbourhoods likely means people living together are similar so it is difficult to compare like with like across neighbourhoods; trying to model neighbourhood choice will also, therefore, remove most of the variation in the data. Similarly, this lack of common support, i.e. overlap, between people from different neighbourhoods means estimated effects are not generalisable across the population. One way of getting around these problems is simply to randomise people to neighbourhoods. As odd as that sounds, that is what occurred in the *Moving to Opportunity* experiments and others. This paper takes a similar approach in trying to look at neighbourhood effects on the risk of obesity by looking at the effects of international students moving to different locales with different local obesity rates. The key identifying assumption is that the choice to move to different places is conditionally independent of the local obesity rate. This doesn’t seem a strong assumption – I’ve never heard a prospective student ask about the weight of our student body. Some analysis supports this claim. The raw data and some further modelling show a pretty strong and robust relationship between local obesity rates and weight gain of the international students. Given the complexity of the causes and correlates of obesity (see the crazy diagram in this post) it is hard to discern why certain environments contribute to obesity. The paper presents some weak evidence of differences in unhealthy behaviours between high and low obesity places – but this doesn’t quite get at the *environmental *link, such as whether these behaviours are shared through social networks or perhaps the structure and layout of the urban area, for example. Nevertheless, here is some strong evidence that living in an area where there are obese people means you’re more likely to become obese yourself.

**Credits**