Sam Watson’s journal round-up for 7th November 2016

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 view from above: applications of satellite data in economics. Journal of Economic Perspectives [RePEc] Published October 2016

Images of the earth from above have been used for economic analysis for the better part of a century. Photographs taken from aeroplanes were used to map agricultural land use in the US in the 1930s and 1940s, for example. Since that time the remotely sensed data available to economists have grown exponentially as modern satellites provide very high resolution imagery across a wide range of spectral bands. This discussion article summarises the methods and use of these new data, which have already shown to be useful for those working in health. We recently discussed the issue of measuring neighbourhood effects in slums, one recent paper used satellite imagery of Kibera, Nairobi to identify dwellings that had replaced their roofs. Such information could be linked to other data sources such as the Demographic and Health Surveys, as another recent paper has done. Another health-related example provided in this article is a paper that used satellite data to map air pollution from forest fires, which are then used to estimate excess infant deaths in Indonesia. Satellite data can provide an important source of information for areas where few other data are available, they have high spatial resolution, and have a wide geographic coverage, making them a potential boon for health research. Nevertheless, the use of these data is still in its early days, and the best statistical methods, particularly for causal inference, are still to be settled upon.

Arrival by ambulance explains variation in mortality by time of admission: retrospective study of admissions to hospital following emergency department attendance in England. BMJ Quality and Safety [PubMedPublished October 2016

The UK government’s plans to increase provision of health care services at the weekend – the seven day NHS policy – has generated large controversies. The policy has been justified on the basis of the so-called ‘weekend effect’, that patients who are admitted to hospital at the weekend are more likely to die than their counterparts admitted on a weekday. This evidence has been widely questioned, as we have discussed here, here, and here. Part of the problem is that patients who are admitted at the weekend differ from those admitted on a weekday in that they are more likely to be in worse health. So far, there has been little success in the development of adequate and convincing risk adjustment schemes using routine data. This article attempts to address this issue by using mode of arrival at the accident and emergency department to control for severity of illness. The authors report that under a standard risk adjustment model, there appears to be some evidence of an increased risk of mortality among weekend and weeknight admissions relative to Wednesday daytime admissions (odds ratio approximately 1.07), which is slightly lower than the risks reported elsewhere. However, after adjusting for mode of arrival, nights no longer appear worse, and only Sunday daytime admissions show evidence of an increased risk of mortality relative to Wednesday daytime admissions. Mode of arrival is a relatively blunt measure of severity of illness as you either do not arrive by ambulance or you do. But, an important tenet of any statistical analysis is to examine the robustness of the results to changes in model specification, and this article demonstrates that controlling for mode of arrival affects the results and hence conclusions that can be made from this study. This article adds to the growing evidence base that the evidence surrounding the ‘weekend effect’ is not as clear-cut as some would have you believe.

Worms at work: long-run impacts of a child health investment. The Quarterly Journal of Economics [RePEcPublished October 2016

The treatment of worms (helminths) is cheap, but diagnosis is expensive, so periodic mass de-worming programmes have been recommended by the World Health Organization in high-risk areas. However, a recent Cochrane review concluded that there was strong evidence that such programmes do not improve nutrition, health, or schooling outcomes. But, as this paper notes, the Cochrane review excluded a number of rigorous non-individual randomised studies, such as the analysis of a school based trial in Kenya, which showed large increases in school participation. This article presents a ten year follow up of this Kenyan trial. The authors report that they find increases in the proportion of people reporting their health as ‘very good’ and an increase in the number of hours in the labour market provided by males. But, the effects are small. Nevertheless, the potential government revenues from the increased labour market activity are potentially greater than the direct subsidy cost of the deworming programme, suggesting that it is potentially Pareto improving. Taken in light of the evidence in the Cochrane review, though, I do not remain fully convinced of the benefits of the deworming programme.

Credits

Chris Sampson’s journal round-up for 20th June 2016

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.

Can increased primary care access reduce demand for emergency care? Evidence from England’s 7-day GP opening. Journal of Health Economics Published 15th June 2016

Getting a GP appointment when you want one can be tricky, and complaints are increasingly common in the UK. In April 2013, 7-day opening for some GP practices began being piloted in London, with support from the Prime Minister’s Challenge Fund. Part of the reasoning for 7-day opening – beyond patient satisfaction – is that better access to GP services might reduce the use of A&E at weekends. This study evaluates whether or not this has been observed for the London pilot. Secondary Uses Service patient-level data are analysed for 2009-2014 for 34 GP practices in central London (4 pilot practices and 30 controls). The authors collapse the data into the number of A&E attendances per GP practice, giving 8704 observations (34 practices over 256 weeks). 6 categories of A&E attendance are identified; some that we would expect to be influenced by extended GP opening (e.g. ‘minor’) and some that we would not (e.g. ‘accident’). Pilot practices were not randomly selected, and those that were selected had a significantly higher patient-GP ratio. The authors run difference-in-difference analyses on the outcomes using Poisson regression models. Total weekend attendances dropped by 17.9%, with moderate cases exhibiting the greatest drop. Minor cases were not affected. There was also a 10% drop in weekend admissions and a 20% drop in ambulance usage, suggesting major cost savings. A small spillover effect was observed for weekdays. The authors divide their sample into age groups and find that the fall in A&E attendances was greatest in the over 60s, who account for almost all of the drop in weekend admissions. The authors speculate that this may be due to A&E staff being risk averse with elderly patients with whose background they are not familiar, and that GPs may be better able to assess the seriousness of the case. Patients from wealthier neighbourhoods exhibited a relatively greater drop in A&E attendances. So it looks like 7-day opening for GP services could relieve a lot of pressure on A&E departments. What’s lacking from the paper though is an explicit estimate of the cost savings (if, indeed, there were any). The pilot was funded to the tune of £50 million. Unfortunately this study doesn’t tell us whether or not it was worth it.

Cost-effectiveness analysis in R using a multi-state modeling survival analysis framework: a tutorial. Medical Decision Making [PubMed] Published 8th June 2016

To say my practical understanding of R is rudimentary would be a grand overstatement. But I do understand the benefits of the increasingly ubiquitous open source stats software. People frown hard when I tell them that we often build Markov models in Excel. An alternative script-based approach could clearly increase the transparency of decision models and do away with black box problems. This paper does what it says on the tin and guides the reader through the process of developing a state-based (e.g. Markov) transition model. But the key novelty of the paper is the description of a tool for ‘testing’ the Markov assumption that might be built into a decision model. This is the ‘state-arrival extended model’ which entails the inclusion of a covariate to represent the history from the start of the model. A true Markov model is only interested in time in the current state, so if this extra covariate matters to the results then we can reject the Markov assumption and instead implement a semi-Markov model (or maybe something else). The authors do just this using an example from a previously published trial. I dare say the authors could have figured out that the Markov assumption wouldn’t hold without using such a test, but it’s good to have a justification for model choice. The basis for the tutorial is a 12 step program, and the paper explains each step. The majority of processes are based on adaptations of an existing R package called mstate. It assumes that time is continuous rather than discrete and can handle alternative parametric distributions for survival. Visual assessment of fit is built into the process to facilitate model selection. Functions are defined to compute QALYs and costs associated with states and PSA is implemented with generation of cost-effectiveness planes and CEACs. But your heart may sink when the authors state that “It is assumed that individual patient data are available”. The authors provide a thorough discussion of the ways in which a model might be constructed when individual level data aren’t available. But ultimately this seems like a major limitation of the approach, or at least of the usefulness of this particular tutorial. So don’t throw away your copy of Briggs/Sculpher/Claxton just yet.

Do waiting times affect health outcomes? Evidence from coronary bypass. Social Science & Medicine [PubMed] Published 30th May 2016

Many health economists are quite happy with waiting lists being used as a basis for rationing in health services like the NHS. But, surely, this is conditional on the delay in treatment not affecting either current health or the potential benefit of treatment. This new study provides evidence from coronary bypass surgery. Hospital Episodes Statistics for 133,166 patients for the years 2000-2010 are used to look at 2 outcomes: 30-day mortality and 28-day readmission. During the period, policy resulted in the reduction of waiting times from 220 to 50 days. Three empirical strategies are employed: i) annual cross-sectional estimation of the probability of the 2 outcomes occurring in patients, ii) panel analysis of hospital-level data over the 11 years to evaluate the impact of different waiting time reductions and iii) full analysis of patient-specific waiting times across all years using an instrumental variable based on waiting times for an alternative procedure. For the first analysis, the study finds no effect of waiting times on mortality in all years bar 2003 (in which the effect was negative). Weak association is found with readmission. Doubling waiting times increases risk of readmission from 4.05% to 4.54%. The hospital-level analysis finds a lack of effect on both counts. The full panel analysis finds that longer waiting times reduce mortality, but the authors suggest that this is probably due to some unobserved heterogeneity. Longer waiting times may have a negative effect on people’s health, but it isn’t likely that this effect is dramatic enough to increase mortality. This might be thanks to effective prioritisation in the NHS.