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.
