Chris Sampson’s journal round-up for 19th June 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.

Health-related resource-use measurement instruments for intersectoral costs and benefits in the education and criminal justice sectors. PharmacoEconomics [PubMed] Published 8th June 2017

Increasingly, people are embracing a societal perspective for economic evaluation. This often requires the identification of costs (and benefits) in non-health sectors such as education and criminal justice. But it feels as if we aren’t as well-versed in capturing these as we are in the health sector. This study reviews the measures that are available to support a broader perspective. The authors search the Database of Instruments for Resource Use Measurement (DIRUM) as well as the usual electronic journal databases. The review also sought to identify the validity and reliability of the instruments. From 167 papers assessed in the review, 26 different measures were identified (half of which were in DIRUM). 21 of the instruments were only used in one study. Half of the measures included items relating to the criminal justice sector, while 21 included education-related items. Common specifics for education included time missed at school, tutoring needs, classroom assistance and attendance at a special school. Criminal justice sector items tended to include legal assistance, prison detainment, court appearances, probation and police contacts. Assessments of the psychometric properties was found for only 7 of the 26 measures, with specific details on the non-health items available for just 2: test-retest reliability for the Child and Adolescent Services Assessment (CASA) and validity for the WPAI+CIQ:SHP,V2 (there isn’t room on the Internet for the full name). So there isn’t much evidence of any validity for any of these measures in the context of intersectoral (non-health) costs and benefits. It’s no doubt the case that health-specific resource use measures aren’t subject to adequate testing, but this study has identified that the problem may be even greater when it comes to intersectoral costs and benefits. Most worrying, perhaps, is the fact that 1 in 5 of the articles identified in the review reported using some unspecified instrument, presumably developed specifically for the study or adapted from an off-the-shelf instrument. The authors propose that a new resource use measure for intersectoral costs and benefits (RUM ICB) be developed from scratch, with reference to existing measures and guidance from experts in education and criminal justice.

Use of large-scale HRQoL datasets to generate individualised predictions and inform patients about the likely benefit of surgery. Quality of Life Research [PubMed] Published 31st May 2017

In the NHS, EQ-5D data are now routinely collected from patients before and after undergoing one of four common procedures. These data can be used to see how much patients’ health improves (or deteriorates) following the operations. However, at the individual level, for a person deciding whether or not to undergo the procedure, aggregate outcomes might not be all that useful. This study relates to the development of a nifty online tool that a prospective patient can use to find out the expected likelihood that they will feel better, the same or worse following the procedure. The data used include EQ-5D-3L responses associated with almost half a million unilateral hip or knee replacements or groin hernia repairs between April 2009 and March 2016. Other variables are also included, and central to this analysis is a Likert scale about improvement or worsening of hip/knee/hernia problems compared to before the operation. The purpose of the study is to group people – based on their pre-operation characteristics – according to their expected postoperative utility scores. The authors employed a recursive Classification and Regression Tree (CART) algorithm to split the datasets into strata according to the risk factors. The final set of risk variables were age, gender, pre-operative EQ-5D-3L profile and symptom duration. The CART analysis grouped people into between 55 and 60 different groups for each of the procedures, with the groupings explaining 14-27% of the variation in postoperative utility scores. Minimally important (positive and negative) differences in the EQ-5D utility score were estimated with reference to changes in the Likert scale for each of the procedures. These ranged in magnitude from 0.041 to 0.106. The resulting algorithms are what drive the results delivered by the online interface (you can go and have a play with it). There are a few limitations to the study, such as the reliance on complete case analysis and the fact that the CART analysis might lack predictive ability. And there’s an interesting problem inherent in all of this, that the more people use the tool, the less representative it will become as it influences selection into treatment. The validity of the tool as a precise risk calculator is quite limited. But that isn’t really the point. The point is that it unlocks some of the potential value of PROMs to provide meaningful guidance in the process of shared decision-making.

Can present biasedness explain early onset of diabetes and subsequent disease progression? Exploring causal inference by linking survey and register data. Social Science & Medicine [PubMed] Published 26th May 2017

The term ‘irrational’ is overused by economists. But one situation in which I am willing to accept it is with respect to excessive present bias. That people don’t pay enough attention to future outcomes seems to be a fundamental limitation of the human brain in the 21st century. When it comes to diabetes and its complications, there are lots of treatments available, but there is only so much that doctors can do. A lot depends on the patient managing their own disease, and it stands to reason that present bias might cause people to manage their diabetes poorly, as the value of not going blind or losing a foot 20 years in the future seems less salient than the joy of eating your own weight in carbs right now. But there’s a question of causality here; does the kind of behaviour associated with time-inconsistent preferences lead to poorer health or vice versa? This study provides some insight on that front. The authors outline an expected utility model with quasi-hyperbolic discounting and probability weighting, and incorporate a present bias coefficient attached to payoffs occurring in the future. Postal questionnaires were collected from 1031 type 2 diabetes patients in Denmark with an online discrete choice experiment as a follow-up. These data were combined with data from a registry of around 9000 diabetes patients, from which the postal/online participants were identified. BMI, HbA1c, age and year of diabetes onset were all available in the registry and the postal survey included physical activity, smoking, EQ-5D, diabetes literacy and education. The DCE was designed to elicit time preferences using the offer of (monetary) lottery wins, with 12 different choice sets presented to all participants. Unfortunately, despite the offer of a real-life lottery award for taking part in the research, only 79 of 1031 completed the online DCE survey. Regression analyses showed that individuals with diabetes since 1999 or earlier, or who were 48 or younger at the time of onset, exhibited present bias. And the present bias seems to be causal. Being inactive, obese, diabetes illiterate and having lower quality of life or poorer glycaemic control were associated with being present biased. These relationships hold when subject to a number of control measures. So it looks as if present bias explains at least part of the variation in self-management and health outcomes for people with diabetes. Clearly, the selection of the small sample is a bit of a concern. It may have meant that people with particular risk preferences (given that the reward was a lottery) were excluded, and so the sample might not be representative. Nevertheless, it seems that at least some people with diabetes could benefit from interventions that increase the salience of future health-related payoffs associated with self-management.

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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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Thesis Thursday: Raymond Oppong

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Raymond Oppong who graduated with a PhD from the University of Birmingham. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Economic analysis alongside multinational studies
Supervisors
Sue Jowett, Tracy Roberts
Repository link
http://etheses.bham.ac.uk/7288/

What attracted you to studying economic evaluation in the context of multinational studies?

One of the first projects that I was involved in when I started work as a health economist was the Genomics to combat Resistance against Antibiotics in Community-acquired lower respiratory tract infections (LRTI) in Europe (GRACE) project. This was an EU-funded study aimed at integrating and coordinating the activities of physicians and scientists from institutions in 14 European countries to combat antibiotic resistance in community-acquired lower respiratory tract infections.

My first task on this project was to undertake a multinational costing study to estimate the costs of treating acute cough/LRTI in Europe. I faced quite a number of challenges including the lack of unit cost data across countries. Conducting a full economic evaluation alongside the interventional studies in GRACE also brought up a number of issues with respect to methods of analysis of multinational trials which needed to be resolved. The desire to understand and resolve some of these issues led me to undertake the PhD to investigate the implications of conducting economic evaluations alongside multinational studies.

Your thesis includes some case studies from a large multinational project. What were the main findings of your empirical work?

I used three main case studies for my empirical work. The first was an observational study aimed at describing the current presentation, investigation, treatment and outcomes of community-acquired lower respiratory tract infections and analysing the determinants of antibiotic use in Europe. The other 2 were RCTs. The first was aimed at studying the effectiveness of antibiotic therapy (amoxicillin) in community-acquired lower respiratory tract infections, whilst the second was aimed at assessing training interventions to improve antibiotic prescribing behaviour by general practitioners. The observational study was used to explore issues relating to costing and outcomes in multinational studies whilst the RCTs explored the various analytical approaches (pooled and split) to economic evaluation alongside multinational studies.

The results from the observational study revealed large variations in costs across Europe and showed that contacting researchers in individual countries was the most effective way of obtaining unit costs. Results from both RCTs showed that the choice of whether to pool or split data had an impact on the cost-effectiveness of the interventions.

What were the key analytical methods used in your analysis?

The overall aim of the thesis was to study the implications of conducting economic analysis alongside multinational studies. Specific objectives include: i) documenting challenges associated with economic evaluations alongside multinational studies, ii) exploring various approaches to obtaining and estimating unit costs, iii) exploring the impact of using different tariffs to value EQ-5D health state descriptions, iv) comparing methods that have been used to conduct economic evaluation alongside multinational studies and v) making recommendations to guide the design and conduct of future economic evaluations carried out alongside multinational studies.

A number of approaches were used to achieve each of the objectives. A systematic review of the literature identified challenges associated with economic evaluations alongside multinational studies. A four-stage approach to obtaining unit costs was assessed. The UK, European and country-specific EQ-5D value sets were compared to determine which is the most appropriate to use in the context of multinational studies. Four analytical approaches – fully pooled one country costing, fully pooled multicountry costing, fully split one country costing and fully split multicountry costing – were compared in terms of resource use, costs, health outcomes and cost-effectiveness. Finally, based on the findings of the study, a set of recommendations were developed.

You completed your PhD part-time while working as a researcher. Did you find this a help or a hindrance to your studies?

I must say that it was both a help and a hindrance. Working in a research environment was really helpful. There was a lot of support from supervisors and colleagues which kept me motivated. I might have not gotten this support if I was not working in a research/academic environment. However, even though some time during the week was allocated to the PhD, I had to completely put it on hold for long periods of time in order to deal with the pressures of work/research. Consequently, I always had to struggle to find my bearings when I got back to the PhD. I also spent most weekends working on the PhD especially when I was nearing submission.

On the whole, it should be noted that a part-time PhD requires a lot of time management skills. I personally had to go on time management courses which were really helpful.

What advice would you give to a health economist conducting an economic evaluation alongside a multinational study?

For a health economist conducting an economic evaluation alongside a multinational trial, it is important to plan ahead and understand the challenges that are associated with economic evaluations alongside multinational studies. A lot of the problems such as those related to the identification of unit costs can be avoided by ensuring adequate measures are put in place at the design stage of the study. An understanding of the various health systems of the countries involved in the study is important in order to make a judgement about the differences and similarities in resource use across countries. Decision makers are interested in results that can be applied to their jurisdiction; therefore it is important to adopt transparent methods e.g. state the countries that participated in the study, state the sources of unit costs and make it clear whether data from all countries (pooling) or from a subset (splitting) were used. To ensure that the results of the study are generalisable to a number of countries it may be advisable to present country-specific results and probably conduct the analysis from different perspectives.