Meeting round-up: 7th annual Vancouver Health Economics Methodology (VanHEM) meeting

The 7th annual Vancouver Health Economics Methodology (VanHEM) meeting took place on June 16 in Vancouver, Canada. This one-day conference brings together health economists from across the Pacific Northwest, including Vancouver, Washington State, and Calgary. This has always been more than a Vancouver meeting, which led Anirban Basu from Washington State to suggest changing the name of the meeting to the Cascadia Health Economics Workshop (CHEW) – a definite improvement.

This year’s event began a day early, with Richard Grieve from the London School of Hygiene and Tropical Medicine, Stephen O’Neill from NUI Galway, and Jasjeet Sekhon from the University of California Berkeley, delivering a workshop titled Methods for Addressing Confounding in Comparative Effectiveness and Cost-effectiveness Studies. This provided both theoretical and practical examples of propensity score matching, genetic matching, difference-in-difference estimation and the synthetic control method. I was fortunate enough to be one of the 16 attendees (it was oversubscribed) to participate after being unable to attend when the course was offered at the Society for Medical Decision Making conference this past October. The course was an excellent introduction to these methodologies, including both theoretical and empirical examples of their use. I was particularly interested to have R and Stata code provided, to work through real-world examples. Being able to see the data and code and explore different analyses provided an incredibly rich learning experience.

The following morning, Prof Grieve delivered the plenary address to the more than 80 attendees. This talk discussed the potential for causal inference and large-scale data to influence policy, and outlined how observational data can complement evidence from randomized controlled trials (the slides are available here [PDF]). Since the expertise of our health economics community centres on other methods, primarily economic evaluation and stated preference methods, Prof Grieve’s plenary catalyzed a lot of discussion, which continued throughout the day. After the plenary, there were eight papers discussed over four parallel sessions, in addition to ten posters presented over lunch. This included an interesting paper by Nathaniel Hendrix from Washington state on a mapping algorithm between a generic and condition-specific quality-of-life measure for epilepsy, and two papers using discrete choice methodology. One by Tracey-Lea Laba evaluated cost sharing for long-acting beta-agonists in Australia, and another by Dean Regier, Verity Watson and Jonathon Sicsic explored choice certainty and choice consistency in DCEs using Kahneman’s dual processing theory.

Having been to three HESG meetings, there are lots of similarities with the format of VanHEM. For instance, papers are discussed for 20 minutes by another attendee, and the author has 5-minutes for clarification. What is different is that before a wider discussion, members of the audience break into small groups for 5 minutes. In my experience, this addition has been very effective at increasing participation during the final 25 minutes of the session, which is an open discussion amongst all attendees. It also gave attendees the opportunity to swap tips on where to find the best deals on plaid shirts.

I was fortunate enough to have my paper accepted and discussed by Prof Larry Lynd from the UBC Faculty of Pharmaceutical Science. Prof Lynd provided a number of excellent suggestions. Of particular note was a much simpler and more intuitive description of the marginal rate of substitution.

VanHEM also afforded an opportunity for discussion and reflection within the local health economics community. Recently, the Canadian Institutes for Health Research launched the Strategy for Patient-Oriented Research (SPOR). In BC, this involves an $80 million investment to “foster evidence-informed health care by bringing innovative approaches to the point of care, so as to ensure greater quality, accountability, and access of care”. One innovative approach is the creation of a new health economics methods cluster in the province, which is co-led by David Whitehurst (Simon Fraser University) and Nick Bansback (University of British Columbia). It receives SPOR funds to help support the health economics community as a whole, and specific research projects that focus on novel methods. At VanHEM, one hour was dedicated to determining how the cluster could help support the community that sees many health economists located at different sites throughout the region. Participants suggested having a number of dedicated academic half-days throughout the year that aim to provide an opportunity for members of the community to see each other face-to-face and engage in activities that support professional development. The theme of great titles continued with the suggestion of a “HEck-a-thon”.

Overall, this year’s VanHEM meeting was a great success. The addition of a pre-meeting workshop provided an excellent opportunity for our community to gain practical experience in causal methods, and we continue to see increased numbers of participants from outside our local region. I’m looking forward to doing this again in 2018, and I would encourage anyone visiting our region to be in touch!

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Paul Mitchell’s journal round-up for 15th 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.

Informal care: choice or constraint. Scandinavian Journal of Caring Sciences [PubMed] Published 12th April 2017

The provision of social care in the UK has become a major economic issue, with recent increases in government spending and local authority taxation to help ease the burden on both the health and social care system in the short term. This study examines some of the issues surrounding informal carers (i.e. care of a family member), estimated to be approximately 10% of the UK population. In particular, it focuses on the role of choice and constraints involved with the decision to become a carer. Using a cross-sectional survey for a UK city, choice of caring was explored in terms of responses to care provision provided, asking if it was a free choice initially to provide care, and if there were constraints in terms of duty, lack of others or financial resources for paid care. The analysis focused on how perceived choice in the caring role was associated with socio-demographics and the type of caring role performed, as well as the role of perceived choice in caring and their wellbeing. Out of the 798 respondents to all four questions on caring choice, about 1 in 3 reported an entirely free choice in the decision, with half reporting having a free choice but also a constraint in terms of duty, other available carers or financial resources. Less than 1 in 5 reported not having a free choice. Only carers with bad health or receiving state benefits had an association with a constrained caring role. The more intense the care role was also associated with a more constrained choice. Higher levels of choice were associated with higher levels of wellbeing across measures of happiness, life satisfaction and capability. In multivariable regression analysis, it was found that having a free choice in the initial caring decision resulted in a higher impact on life satisfaction than educational qualifications and home ownership, whilst improved capability of comparable levels to that of home ownership, all else being equal. The authors thus recommend enhanced choice as a way for policy to improve carers wellbeing. Although the authors acknowledge limitations with the study design being cross-sectional and geographically limited to one city, the study shows there is plenty of scope for understanding the determinants of informal caring and consequences for those carers in much greater detail in future national surveys to help address policy in this area in the medium to longer term.

Experienced utility or decision utility for QALY calculation? Both. Public Health Ethics [PhilPapers] Published 6th May 2017

How health states should be valued in population health metrics, like QALYs and DALYs, will not be an unfamiliar topic of discussion for regular readers of this blog. Instead of arguing for decision utility (i.e. accounting for general population preferences for avoiding health states) or experienced utility (i.e. accounting for patient experiences of health states), the authors in this paper argue for a combined approach, reviving a suggestion previously put forward by Lowenstein & Ubel. The authors neatly summarise some of the issues of relying on either decision utility or experienced utility approaches alone and instead argue for better informed decision utility exercises by using deliberative democracy methods where experienced utility in health states are also presented. Unfortunately, there is little detail of how this process might actually work in practice. There are likely to be issues of what patient experiences are presented in such an exercise and how other biases that may influence decision utility responses are controlled for in such an approach. Although I am generally in favour of more deliberative approaches to elicit informed values for resource allocation, I find that this paper makes a convincing case for neither of the utility approaches to valuation, rather than both.

The value of different aspects of person-centred care: a series of discrete choice experiments in people with long-term conditions. BMJ Open [PubMed] Published 26th April 2017

The term “person-centred care” is one which is gaining some prominence in how healthcare is provided. What it means, and how important different aspects of person-centred care are, is explored in this study using discrete choice experiments (DCEs). Through focus groups and drawing from the authors’ own experience in this area, four aspects of person-centred care for self-management of chronic conditions make up the attributes in the DCE across two levels: (i) information (same information for all/personalised information); (ii) situation (little account of current situation/suggestions that fit current situation); (iii) living well (everyone wants the same from life/works with patient for what they want from life); (iv) communication (neutral professional way/friendly professional way). A cost attribute was also attached to the DCE that was given to patient groups with chronic pain and chronic lung disease. The overall findings suggest that person-centred care focused on situation and living well were valued most with personal communication style valued the least. Latent class analysis also suggested that 1 in 5 of those sampled valued personalised information the most. Those with lower earnings were likely to look to reduce the cost attribute the most. The authors conclude that the focus on communication in current clinician training on person-centred care may not be what is of most value to patients. However, I am not entirely convinced by this argument, as it could be that communication was not seen as an issue by the respondents, perhaps somewhat influenced due to the skills clinicians already have obtained in this area. Clearly, these process aspects of care are difficult to develop attributes for in DCEs, and the authors acknowledge that the wording of the “neutral” and “high” levels may have biased responses. I also found that dropping the “negative” third level for each of the attributes unconvincing. It may have proved more difficult to complete than two levels, but it would have shown in much greater depth how much value is attached to the four attributes relative to one another.

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Chris Sampson’s journal round-up for 8th 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.

Verification of decision-analytic models for health economic evaluations: an overview. PharmacoEconomics [PubMed] Published 29th April 2017

Increasingly, it’s expected that model-based economic evaluations can be validated and shown to be fit-for-purpose. However, up to now, discussions have focussed on scientific questions about conceptualisation and external validity, rather than technical questions, such as whether the model is programmed correctly and behaves as expected. This paper looks at how things are done in the software industry with a view to creating guidance for health economists. Given that Microsoft Excel remains one of the most popular software packages for modelling, there is a discussion of spreadsheet errors. These might be errors in logic, simple copy-paste type mistakes and errors of omission. A variety of tactics is discussed. In particular, the authors describe unit testing, whereby individual parts of the code are demonstrated to be correct. Unit testing frameworks do not exist for application to spreadsheets, so the authors recommend the creation of a ‘Tests’ spreadsheet with tests for parameter assignments, functions, equations and exploratory items. Independent review by another modeller is also recommended. Six recommendations are given for taking model verification forward: i) the use of open source models, ii) standardisation in model storage and communication (anyone for a registry?), iii) style guides for script, iv) agency and journal mandates, v) training and vi) creation of an ISPOR/SMDM task force. This is a worthwhile read for any modeller, with some neat tactics that you can build into your workflow.

How robust are value judgments of health inequality aversion? Testing for framing and cognitive effects. Medical Decision Making [PubMed] Published 25th April 2017

Evidence shows that people are often extremely averse to health inequality. Sometimes these super-egalitarian responses imply such extreme preferences that monotonicity is violated. The starting point for this study is the idea that these findings are probably influenced by framing effects and cognitive biases, and that they may therefore not constitute a reliable basis for policy making. The authors investigate 4 hypotheses that might indicate the presence of bias: i) realistic small health inequality reductions vs larger one, ii) population- vs individual-level descriptions, iii) concrete vs abstract intervention scenarios and iv) online vs face-to-face administration. Two samples were recruited: one with a face-to-face discussion (n=52) and the other online (n=83). The questionnaire introduced respondents to health inequality in England before asking 4 questions in the form of a choice experiment, with 20 paired choices. Responses are grouped according to non-egalitarianism, prioritarianism and strict egalitarianism. The main research question is whether or not the alternative strategies resulted in fewer strict egalitarian responses. Not much of an effect was found with regard to large gains or to population-level descriptions. There was evidence that the abstract scenarios resulted in a greater proportion of people giving strong egalitarian responses. And the face-to-face sample did seem to exhibit some social desirability bias, with more egalitarian responses. But the main take-home message from this study for me is that it is not easy to explain-away people’s extreme aversion to health inequality, which is heartening. Yet, as with all choice experiments, we see that the mode of administration – and cognitive effects induced by the question – can be very important.

Adaptation to health states: sick yet better off? Health Economics [PubMed] Published 20th April 2017

Should patients or the public value health states for the purpose of resource allocation? It’s a question that’s cropped up plenty of times on this blog. One of the trickier challenges is understanding and dealing with adaptation. This paper has a pretty straightforward purpose – to look for signs of adaptation in a longitudinal dataset. The authors’ approach is to see whether there is a positive relationship between the length of time a person has an illness and the likelihood of them reporting better health. I did pretty much the same thing (for SF-6D and satisfaction with life) in my MSc dissertation, and found little evidence of adaptation, so I’m keen to see where this goes! The study uses 4 waves of data from the British Cohort Study, looking at self-assessed health (on a 4-point scale) and self-reported chronic illness and health shocks. Latent self-assessed health is modelled using a dynamic ordered probit model. In short, there is evidence of adaptation. People who have had a long-standing illness for a greater duration are more likely to report a higher level of self-assessed health. An additional 10 years of illness is associated with an 8 percentage point increase in the likelihood of reporting ‘excellent’ health. The study is opaque about sample sizes, but I’d guess that finding is based on not-that-many people. Further analyses are conducted to show that adaptation seems to become important only after a relatively long duration (~20 years) and that better health before diagnosis may not influence adaptation. The authors also look at specific conditions, finding that some (e.g. diabetes, anxiety, back problems) are associated with adaptation, while others (e.g. depression, cancer, Crohn’s disease) are not. I have a bit of a problem with this study though, in that it’s framed as being relevant to health care resource allocation and health technology assessment. But I don’t think it is. Self-assessed health in the ‘how healthy are you’ sense is very far removed from the process by which health state utilities are obtained using the EQ-5D. And they probably don’t reflect adaptation in the same way.

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