Paul Mitchell’s journal round-up for 17th 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.

What goes wrong with the allocation of domestic and international resources for HIV? Health Economics [PubMedPublished 7th July 2017

Investment in foreign aid is coming under considered scrutiny as a number of leading western economies re-evaluate their role in the world and their obligations to countries with developing economies. Therefore, it is important for those who believe in the benefits of such investments to show that they are being done efficiently. This paper looks at how funding for HIV is distributed both domestically and internationally across countries, using multivariate regression analysis with instruments to control for reverse causality between financing and HIV prevalence, and domestic and international financing. The author is also concerned about countries free riding on international aid and estimates how countries ought to be allocating national resources to HIV using quintile regression to estimate what countries have fiscal space for expanding their current spending domestically. The results of the study show that domestic expenditure relative to GDP per capita is almost unit elastic, whereas it is inelastic with regards to HIV prevalence. Government effectiveness (as defined by the World Bank indices) has a statistically significant effect on domestic expenditure, although it is nonlinear, with gains more likely when moving up from a lower level of government effectiveness. International expenditure is inversely related to GDP per capita and HIV prevalence, and positively with government effectiveness, albeit the regression models for international expenditure had poor explanatory power. Countries with higher GDP per capita tended to dedicate more money towards HIV, however, the author reckons there is $3bn of fiscal space in countries such as South Africa and Nigeria to contribute more to HIV, freeing up international aid for other countries such as Cameroon, Ghana, Thailand, Pakistan and Columbia. The author is concerned that countries with higher GDP should be able to allocate more to HIV, but feels there are improvements to be made in how international aid is distributed too. Although there is plenty of food for thought in this paper, I was left wondering how this analysis can help in aiding a better allocation of resources. The normative model of what funding for HIV ought to be is from the viewpoint that this is the sole objective of countries of allocating resources, which is clearly contestable (the author even casts doubt as to whether this is true for international funding of HIV). Perhaps the other demands faced by national governments (e.g. funding for other diseases, education etc.) can be better reflected in future research in this area.

Can pay-for-performance to primary care providers stimulate appropriate use of antibiotics? Health Economics [PubMed] [RePEcPublished 7th July 2017

Antibiotic resistance is one of the largest challenges facing global health this century. This study from Sweden looks to see whether pay for performance (P4P) can have a role in the prescription practices of GPs when it comes to treating children with respiratory tract infection. P4P was introduced on a staggered basis across a number of regions in Sweden to incentivise primary care to use narrow spectrum penicillin as a first line treatment, as it is said to have a smaller impact on resistance. Taking advantage of data from the Swedish Prescribed Drug Register between 2006-2013, the authors conducted a difference in difference regression analysis to show the effect P4P had on the share of the incentivised antibiotic. They find a positive main effect of P4P on drug prescribing of 1.1 percentage points, that is also statistically significant. Of interest, the P4P in Sweden under analysis here was not directly linked to salaries of GPs but the health care centre. Although there are a number of limitations with the study that the authors clearly highlight in the discussion, it is a good example of how to make the most of routinely available data. It also highlights that although the share of the less resistant antibiotic went up, the national picture of usage of antibiotics did not reduce in line with a national policy aimed at doing so during the same time period. Even though Sweden is reported to be one of the lower users of antibiotics in Europe, it highlights the need to carefully think through how targets are achieved and where incentives might help in some areas to meet such targets.

Econometric modelling of multiple self-reports of health states: the switch from EQ-5D-3L to EQ-5D-5L in evaluating drug therapies for rheumatoid arthritis. Journal of Health Economics Published 4th July 2017

The EQ-5D is the most frequently used health state descriptive system for the generation of utility values for quality-adjusted life years (QALYs) in economic evaluation. To improve sensitivity and reduce floor and ceiling effects, the EuroQol team developed a five level version (5L) compared to the previous three level (3L) version. This study adds to recent evidence in this area of the unforeseen consequences of making this change to the descriptive system and also the valuation system used for the 5L. Using data from the National Data Bank for Rheumatic Diseases, where both 3L and 5L versions were completed simultaneously alongside other clinical measures, the authors construct a mapping between both versions of EQ-5D, informed by the response levels and the valuation systems that have been developed in the UK for the measures. They also test their mapping estimates on a previous economic evaluation for rheumatoid arthritis treatments. The descriptive results show that although there is a high correlation between both versions, and the 5L version achieves its aim of greater sensitivity, there is a systematic difference in utility scores generated using both versions, with an average 87% of the score of the 3L recorded compared to the 5L. Not only are there differences highlighted between value sets for the 3L and 5L but also the responses to dimensions across measures, where the mobility and pain dimensions do not align as one would expect. The new mapping developed in this paper highlights some of the issues with previous mapping methods used in practice, including the assumption of independence of dimension levels from one another that was used while the new valuation for the 5L was being developed. Although the case study they use to demonstrate the effect of using the different approaches in practice did not result in a different cost-effectiveness result, the study does manage to highlight that the assumption of 3L and 5L versions being substitutes for one another, both in terms of descriptive systems and value sets, does not hold. Although the authors are keen to highlight the benefits of their new mapping that produces a smooth distribution from actual to predicted 5L, decision makers will need to be clear about what descriptive system they now want for the generation of QALYs, given the discrepancies between 3L and 5L versions of EQ-5D, so that consistent results are obtained from economic evaluations.

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Chris Sampson’s journal round-up for 29th May 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.

“Naming and framing”: The impact of labeling on health state values for multiple sclerosis. Medical Decision Making [PubMedPublished 21st May 2017

Tell someone that the health state that they’re valuing is actually related to cancer, and they’ll give you a different value than if you hadn’t mentioned cancer. A lower value, probably. There’s a growing amount of evidence that ‘labelling’ health state descriptions with the name of a particular disease can influence the resulting values. Generally, the evidence is that mentioning the disease will lower values, though that’s probably because researchers have been selecting diseases that they think will show this. (Has anyone tried it for hayfever?) The jury is out on whether labelling is a good thing or a bad thing, so in the meantime, we need evidence for particular diseases to help us understand what’s going on. This study looks at MS. Two UK-representative samples (n = 1576; n = 1641) completed an online TTO valuation task for states defined using the condition-specific preference-based MSIS-8D. Participants were first asked to complete the MSIS-8D to provide their own health state, and then to rank three MSIS-8D states and also complete a practice TTO task. For the preference elicitation proper, individuals were presented with a set of 5 MSIS-8D health states. One group were asked to imagine that they had MS and were provided with some information and a link to the NHS Choices website. The authors’ first analysis tests for a difference due to labelling. Their second analysis creates two alternative tariffs for the MSIS-8D based on the two surveys. People in the label group reported lower health state values on average. The size of this labelling-related decrement was greater for less severe health states. The creation of the tariffs seemed to show that labelling does not have a consistent impact across dimensions. This means that, in practice, the two tariffs could favour different types of interventions, depending on for which dimensions benefits might be observed. The tariff derived from the label group demonstrated slightly poorer predictive performance. This study tells us that label-or-not is a decision that will influence the relative cost-effectiveness of interventions for MS. But we still need a sound basis for making that choice.

Nudges in a post-truth world. Journal of Medical Ethics [PubMed] Published 19th May 2017

Not everyone likes the idea of nudges. They can be used to get people to behave in ways that are ‘better’… but who decides what is better? Truth, surely, we can all agree, is better. There are strong forces against the truth, whether they be our own cognitive biases, the mainstream media (FAKE NEWS!!!), or Nutella trying to tell us they offer a healthy breakfast option thanks to all that calcium. In this essay, the author outlines a special kind of nudge, which he refers to as a ‘nudge to reason’. The paper starts with a summary of the evidence regarding the failure of people to change their minds in response to evidence, and the backfire effect, whereby false beliefs become even more entrenched in light of conflicting evidence. Memory failures, and the ease with which people can handle the information, are identified as key reasons for perverse responses to evidence. The author then goes on to look at the evidence in relation to the conditions in which people do respond to evidence. In particular, where people get their evidence matters (we still trust academics, right?). The persuasiveness of evidence can be influenced by the way it is delivered. So why not nudge towards the truth? The author focuses on a key objection to nudges; that they do not protect freedom in a substantive sense because they bypass people’s capacities for deliberation. Nudges take advantage of non-rational features of human nature and fail to treat people as autonomous agents deserving of respect. One of the reasons I’ve never much like nudges is that they could promote ignorance and reinforce biases. Nudges to reason, on the other hand, influence behaviour indirectly via beliefs: changing behaviour by changing minds by improving responses to genuine evidence. The author argues that nudges to reason do not bypass the deliberative capacities of agents at all, but rather appeal to them, and are thus permissible. They operate by appealing to mechanisms that are partially constitutive of rationality and this is itself part of what defines our substantive freedom. We could also extend this to argue that we have a moral responsibility to frame arguments in a way that is truth-conducive, in order to show respect to individuals. I think health economists are in a great position to contribute to these debates. Our subfield exists principally because of uncertainty and asymmetry of information in health care. We’ve been studying these things for years. I’m convinced by the author’s arguments about the permissibility of nudges to reason. But they’d probably make for flaccid public policy. Nudges to reason would surely be dominated by nudges to ignorance. Either people need coercing towards the truth or those nudges to ignorance need to be shut down.

How should hospital reimbursement be refined to support concentration of complex care services? Health Economics [PubMed] Published 19th May 2017

Treating rare and complex conditions in specialist centres may be good for patients. We might expect these patients to be especially expensive to treat compared with people treated in general hospitals. Therefore, unless reimbursement mechanisms are able to account for this, specialist hospitals will be financially disadvantaged and concentration might not be sustainable. Healthcare Resource Groups (HRGs) – the basis for current payments – only work if variation in cost is not related to any differences in the types of patients treated at particular hospitals. This study looks at hospitals that might be at risk of financial disadvantage due to differences in casemix complexity. Individual-level Hospital Episode Statistics for 2013-14 were matched to hospital-level Reference Costs and a set of indicators for the use of specialist services were applied. The data included 12.4 million patients of whom 766,204 received complex care. The authors construct a random effects model estimating the cost difference associated with complex care, by modelling the impact of a set of complex care markers on individual-level cost estimates. The Gini coefficient is estimated to look at the concentration of complex care across hospitals. Most of the complex care markers were associated with significantly higher costs. 26 of 69 types of complex care were associated with costs more than 10% higher. What’s more, complex care was concentrated among relatively few hospitals with a mean Gini coefficient of 0.88. Two possible approaches to fixing the payment system are considered: i) recalculation of the HRG price to include a top-up or ii) a more complex refinement of the allocation of patients to different HRGs. The second option becomes less attractive as more HRGs are subject to this refinement as we could end up with just one hospital reporting all of the activity for a particular HRG. Based on the expected impact of these differences – in view of the size of the cost difference and the extent of distribution across different HRGs and hospitals – the authors are able to make recommendations about which HRGs might require refinement. The study also hints at an interesting challenge. Some of the complex care services were associated with lower costs where care was concentrated in very few centres, suggesting that concentration could give rise to cost savings. This could imply that some HRGs may need refining downwards with complexity, which feels a bit counterintuitive. My only criticism of the paper? The references include at least 3 web pages that are no longer there. Please use WebCite, people!

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Chris Sampson’s journal round-up for 22nd May 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.

The effect of health care expenditure on patient outcomes: evidence from English neonatal care. Health Economics [PubMed] Published 12th May 2017

Recently, people have started trying to identify opportunity cost in the NHS, by assessing the health gains associated with current spending. Studies have thrown up a wide range of values in different clinical areas, including in neonatal care. This study uses individual-level data for infants treated in 32 neonatal intensive care units from 2009-2013, along with the NHS Reference Cost for an intensive care cot day. A model is constructed to assess the impact of changes in expenditure, controlling for a variety of variables available in the National Neonatal Research Database. Two outcomes are considered: the in-hospital mortality rate and morbidity-free survival. The main finding is that a £100 increase in the cost per cot day is associated with a reduction in the mortality rate of 0.36 percentage points. This translates into a marginal cost per infant life saved of around £420,000. Assuming an average life expectancy of 81 years, this equates to a present value cost per life year gained of £15,200. Reductions in the mortality rate are associated with similar increases in morbidity. The estimated cost contradicts a much higher estimate presented in the Claxton et al modern classic on searching for the threshold.

A comparison of four software programs for implementing decision analytic cost-effectiveness models. PharmacoEconomics [PubMed] Published 9th May 2017

Markov models: TreeAge vs Excel vs R vs MATLAB. This paper compares the alternative programs in terms of transparency and validation, the associated learning curve, capability, processing speed and cost. A benchmarking assessment is conducted using a previously published model (originally developed in TreeAge). Excel is rightly identified as the ‘ubiquitous workhorse’ of cost-effectiveness modelling. It’s transparent in theory, but in practice can include cell relations that are difficult to disentangle. TreeAge, on the other hand, includes valuable features to aid model transparency and validation, though the workings of the software itself are not always clear. Being based on programming languages, MATLAB and R may be entirely transparent but challenging to validate. The authors assert that TreeAge is the easiest to learn due to its graphical nature and the availability of training options. Save for complex VBA, Excel is also simple to learn. R and MATLAB are equivalently more difficult to learn, but clearly worth the time saving for anybody expecting to work on multiple complex modelling studies. R and MATLAB both come top in terms of capability, with Excel falling behind due to having fewer statistical facilities. TreeAge has clearly defined capabilities limited to the features that the company chooses to support. MATLAB and R were both able to complete 10,000 simulations in a matter of seconds, while Excel took 15 minutes and TreeAge took over 4 hours. For a value of information analysis requiring 1000 runs, this could translate into 6 months for TreeAge! MATLAB has some advantage over R in processing time that might make its cost ($500 for academics) worthwhile to some. Excel and TreeAge are both identified as particularly useful as educational tools for people getting to grips with the concepts of decision modelling. Though the take-home message for me is that I really need to learn R.

Economic evaluation of factorial randomised controlled trials: challenges, methods and recommendations. Statistics in Medicine [PubMed] Published 3rd May 2017

Factorial trials randomise participants to at least 2 alternative levels (for example, different doses) of at least 2 alternative treatments (possibly in combination). Very little has been written about how economic evaluations ought to be conducted alongside such trials. This study starts by outlining some key challenges for economic evaluation in this context. First, there may be interactions between combined therapies, which might exist for costs and QALYs even if not for the primary clinical endpoint. Second, transformation of the data may not be straightforward, for example, it may not be possible to disaggregate a net benefit estimation with its components using alternative transformations. Third, regression analysis of factorial trials may be tricky for the purpose of constructing CEACs and conducting value of information analysis. Finally, defining the study question may not be simple. The authors simulate a 2×2 factorial trial (0 vs A vs B vs A+B) to demonstrate these challenges. The first analysis compares A and B against placebo separately in what’s known as an ‘at-the-margins’ approach. Both A and B are shown to be cost-effective, with the implication that A+B should be provided. The next analysis uses regression, with interaction terms demonstrating the unlikelihood of being statistically significant for costs or net benefit. ‘Inside-the-table’ analysis is used to separately evaluate the 4 alternative treatments, with an associated loss in statistical power. The findings of this analysis contradict the findings of the at-the-margins analysis. A variety of regression-based analyses is presented, with the discussion focussed on the variability in the estimated standard errors and the implications of this for value of information analysis. The authors then go on to present their conception of the ‘opportunity cost of ignoring interactions’ as a new basis for value of information analysis. A set of 14 recommendations is provided for people conducting economic evaluations alongside factorial trials, which could be used as a bolt-on to CHEERS and CONSORT guidelines.

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