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.
Discretely integrated condition event (DICE) simulation for pharmacoeconomics. PharmacoEconomics [PubMed] Published 9th March 2016
Markov state transition? Discrete event simulation? Put down that taxonomy of model structures – all you need is DICE! Oh, and sell your fancy simulation software – all you need is Excel or R. In this paper, Jaime Caro proposes discretely integrated condition event simulation as a unifying modelling approach. Two concepts form the basis of the approach: “conditions” and “events”. Events are self-explanatory; they represent those things that might occur at any given point in time. Conditions represent the characteristics that persist; for example, disease severity, clinical markers, the age of the individual, the sex of the cohort. And of course, these may change over time and in response to particular events. Both conditions and events can be associated with values; for example utilities or costs. So far, so familiar. Caro states that the essence of DICE is that conditions and events are integrated in the sense that changes in conditions are associated with events (and with time) in a simplified way. In health economics, we are never able to fully capture the continuous interactions between different conditions. So we can accept this simplification. The paper outlines how an analyst might go about building a DICE. DICE is made by health economist for health economists. It isn’t designed to cope well with queuing and capacity constraints. It isn’t ideal for modelling interactions between people. But most of the time we don’t do that anyway. The appeal of DICE is that it could support simplicity and comparability in modelling. It can be implemented in spreadsheet software or a general programming language (if it takes too long to run in Excel – for example – you can try R). The paper also comes with an online appendix containing an example implemented in Excel. I’m yet to dig into it, but I’m looking forward to doing so. I am no modelling expert, and I hope we’ll see some feedback from others on the validity of the DICE approach. But if it’s all it’s cracked up to be then there’s a simpler, more consistent, more transparent future out there for decision analytic modelling.
Heterogeneity in the effect of common shocks on healthcare expenditure growth. Health Economics [PubMed] Published 4th March 2016
Growth in healthcare expenditure (bearing in mind Baumol’s ‘cost disease’) is a major political and economic challenge. Understanding what either causes or slows down expenditure growth is therefore an important pursuit. Furthermore, it’s reasonable to expect that while particular shocks might affect all countries, the magnitude of these effects is likely to differ. This study uses a common factor model that accounts for the possibility that some of the heterogeneity in the effect of shocks on different countries could be explained by observable factors. The source of data is the OECD Health Statistics and the authors are able to test 43 determinants of healthcare expenditure growth in 34 countries between 1980 and 2012. Central to the study are the authors’ attempts to address the shortcomings of previous studies due to unobserved heterogeneity, model uncertainty and missing data, and the authors implement a number of novel techniques to attain better estimates. But after all that, the authors find little difference between their preferred model and a standard fixed effects model. Factors that are identified as growth-slowing include: less reliance on public financing; competitive pressures in insurance markets; substitution from inpatient to outpatient care; regulation of pharmaceutical costs and reduced administration.
Societal preferences for interventions with the same efficiency: assessment and application to decision making. Applied Health Economics and Health Policy [PubMed] Published 3rd March 2016
Most people who aren’t economists care about more than efficiency in the allocation of healthcare resources. One way to capture the extent to which people value other attributes could be to elicit preferences for technologies that have the same efficiency. In this study, the authors carry out both a budget allocation survey and a discrete choice experiment with more than 1000 Japanese respondents for each. The attributes considered were age, objective of care (treatment/prevention), disease severity, prior medical care, cause of disease and disease rarity. All scenarios had equivalent cost and QALY outcomes. Some familiar findings come out: preference for younger people and more severe disease and for treatment rather than prevention. In the budget allocation experiment, more than half of respondents supported the prioritisation of younger people. For most other attributes the largest proportion of respondents supported equal budget allocation. Both experiments provided similar results. The authors also introduce the concept of the preference-adjusted threshold (PAT), which reflects people’s preferences within a decision maker’s threshold range. The idea is that that this can then be used to adjust the cost-per-QALY threshold accordingly, which perhaps has more validity than more arbitrary weightings. The authors estimate marginal PATs for the attributes; for example, from these results the additional willingness to pay for a QALY for a younger cohort should be around $20,000.
An analysis of the complementarity of ICECAP-A and EQ-5D-3L in an adult population of patients with knee pain. Health and Quality of Life Outcomes [PubMed] Published 3rd March 2016
Some readers may know that I’ve been a little critical of the ICECAP measures in the past. One of my concerns is that they are not fundamentally any different to the likes of the EQ-5D. Previous work has considered the complementarity of the EQ-5D and the ICECAP-O (for older people). This study is designed to shed some light on how the ICECAP-A (for all adults) and EQ-5D-3L should be used together, and whether they measure different constructs. The authors used data from a randomised controlled trial in which both measures were completed by 442 people. Association was assessed using Spearman’s rank correlation, which showed a moderate correlation of 0.49. Exploratory factor analysis was used to assess whether or not both measures were describing the same underlying unobserved constructs. This analysis showed that a two factor model was optimal, and that the two measures largely described these two factors separately. If you’re familiar with the two questionnaires then this shouldn’t come as too much of a surprise. However, it takes a leap of faith (which the authors do take) to then conclude that each of these factors represent separate constructs in the sense that one represents “physical health” (mostly EQ-5D) and one represents “wellbeing” (mostly ICECAP). Nevertheless, the results do show that the ICECAP-A and EQ-5D-3L should not be used as substitutes as they are not measuring the same thing.
Economic evaluation of mental health interventions: a guide to costing approaches. PharmacoEconomics [PubMed] Published 27th February 2016
When it comes to comparability of cost-effectiveness results, I often find that the biggest problem is in deciphering which costs have and haven’t been included. Any efforts to encourage good practice are very welcome. In this paper, James Shearer and colleagues seek to provide practical guidance on costing approaches in mental health treatment settings. People in receipt of treatment for mental health problems often receive a diverse range of services. There are additional challenges because 3rd sector providers and other (often short lived) community-based specialist services can play an important role. It’s very important that a perspective be clearly defined along with the reasons for including or excluding particular costs. The authors run through some of the types of professionals that might be involved in mental health care and the types of resource use that might be relevant. They highlight numerous issues that – while not unique to mental health – represent particular challenges in this context. For example, it is particularly important to take into account training costs. Psychotherapies in particular often undergo development over time and therefore may require new training for providers. It’s also important to determine where care takes place, and whether the practitioner is working in the office or face-to-face with the patient. The authors identify five cost categories that need to be considered: social care, informal care, production losses, crime and education. They provide guidance on each of these in the context of mental health. Health economists might find the guidance on crime and education particularly helpful, as I suspect most of us are less familiar with identifying the relevant components of these types of costs. The paper isn’t designed to be a complete reference on costing methodology, but if you’re setting up a trial-based economic evaluation in mental health you’ll want to have this paper to hand.
