Sam Watson’s journal round-up for 25th June 2018

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 [PubMedPublished 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.

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Chris Sampson’s journal round-up for 3rd July 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.

Role of cost on failure to access prescribed pharmaceuticals: the case of statins. Applied Health Economics and Health Policy [PubMed] Published 28th June 2017

Outside work, I find that people often like to tell me how to solve health economics problems. A common one is the idea that the NHS could save a load of money by enforcing prescription charges. It’s a textbook life-ain’t-that-simple situation. One of the reasons it isn’t that simple is that, if you start charging for prescriptions, people will be less likely to take their meds. That’s probably bad news for patients and for doctors. “But it’s only a few quid”. Well… As in many countries, Australians have to cough up a co-payment to fill their prescriptions. The size of the copayment depends on i) whether or not the patient is concessional (e.g. a pensioner) and ii) whether or not a threshold has been reached for total family prescription expenditure in one year. Concessional patients have a lower co-payment, a lower threshold and no co-payment once the threshold is met. This study looks at statin use in this context for 94,000 over-45s in New South Wales from 2005-2011. Separate logistic regressions are run for each of the 4 groups (concessional/non-concessional, pre-threshold/post-threshold) to predict statin adherence, controlling for a good range of sociodemographic and health-related variables. The size of the copayment comes out as the biggest barrier to adherence. More than 75% of people who weren’t adherent before reaching their threshold became so after reaching it – that is, once their co-payment was either much-reduced or zero. Poorest adherence was observed in non-concessional low-income people who hadn’t reached the threshold, who faced the highest co-payment. Income, age group and holding private insurance were also important determinants. In short, charging people for their statins, even if it isn’t much money, reduces the likelihood that they will take them. There is the possibility that adherence is correlated with the likelihood of having reached the threshold, which could undermine these results. I’m not entirely convinced that the analysis cuts the mustard, but I’ll let the more econometrically minded amongst you figure that out.

Conceptualizations of the societal perspective within economic evaluations: a systematic review. International Journal of Technology Assessment in Health Care [PubMed] Published 23rd June 2017

In my last round-up, I included a study looking at resource use measures for intersectoral costs and benefits; costs and benefits that occur outside the health sector. This week we have a study looking at how the inclusion of intersectoral costs and benefits influences results, and how researchers have interpreted the ‘societal perspective’. A systematic review was conducted for economic evaluations purporting to use a societal perspective, published since the CHEERS statement was released, including 107 studies. Only 74 provided a conceptualisation of the societal perspective. Reported conceptualisations of the societal perspective were grouped according to the specificity of their definition – 18 general, 50 specific, 6 both – and assessed using content analysis. Of these, 25 referred to a guideline or other source in their conceptualisation. A total of 10 general and 56 specific clusters of conceptualisations were identified, demonstrating major inconsistency. For some studies – namely trial-based economic evaluations in musculoskeletal or mental disorders – the authors dug deeper and extracted additional information. In both cases, where data were adequately reported, the intersectoral costs tended to make up more than 50% of total costs. But in general the specific intersectoral items were not fully reported and relevant costs (e.g. in education or criminal justice) were not identified. It probably won’t come as a surprise that the general impression is that a lot of researchers interpret the societal perspective – in practice, if not in theory – as health costs plus productivity losses. And usually, that’s not really good enough.

Annual direct medical costs associated with diabetes-related complications in the event year and in subsequent years in Hong Kong. Diabetic Medicine [PubMed] Published 21st June 2017

There are a lot of high-quality decision models built for the evaluation of interventions in diabetes. See Mt Hood. But some are still a bit primitive when it comes to estimating the costs associated with the many clinical pathways and complications associated with diabetes, especially when multimorbidity can be important. So studies like this are very welcome. This study contributes cost estimates for a wide range of complications (13, to be precise) for what should be a representative sample of (Chinese) people with diabetes. It includes public health care expenditure for more than 120,000 people with diabetes in Hong Kong, with 5-year follow-up. For private health care costs, a cross-section of 1275 people was recruited through other studies and provided information about service use by telephone. Fixed effects panel data regressions were used for the public medical costs. During the follow-up, 17% developed at least one complication. The models estimate the impact on total cost of new disease and existing disease separately, in order to identify first-year and subsequent-year cost estimates. Generalised linear models were used for the private health care costs. The base case of a 65-year old with no complications was US$1500/year in costs to the public purse. The biggest effect on costs was a first-year multiplier of 9.38 for lower limb ulcer (1.62 in subsequent years). Other costly complications were stroke, heart failure, end-stage renal disease and acute myocardial infarction. Private costs were much smaller, at $187 for the base case. These figures may prove useful to decision modellers, even outside the Hong Kong setting.

Financing and distribution of pharmaceuticals in the United States. JAMA [PubMed] Published 15th May 2017

The purpose of this article seems to be to demonstrate the complexity of the financing and distribution of pharmaceuticals in the US. It describes distributors, retailers and patients on the distribution side, and pharmacy benefit managers and health insurers on the financing side, with manufacturers in the middle. But the system that is shown in the article’s figure strikes me as surprisingly simple for an industry in which such vast amounts of money are sloshing around. It’s far more straightforward than any diagram you might see relating to the organisation of NHS services. I would imagine that a freer market would be associated with more complexity as upstarts might muscle-in on smaller corners of the market and become new intermediaries. But the article is still enlightening. It outlines some of the features of the market, particularly the high levels of concentration, characteristics of the key players and the staggering sums of money changing hands.

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