Simon McNamara’s journal round-up for 6th August 2018

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.

Euthanasia, religiosity and the valuation of health states: results from an Irish EQ5D5L valuation study and their implications for anchor values. Health and Quality of Life Outcomes [PubMed] Published 31st July 2018

Do you support euthanasia? Do you think there are health states worse than death? Are you religious? Don’t worry – I am not commandeering this week’s AHE journal round-up just to bombard you with a series of difficult questions. These three questions form the foundation of the first article selected for this week’s round-up.

The paper is based upon the hypothesis that your religiosity (“adherence to religious beliefs”) is likely to impact your support for euthanasia and, subsequently, the likelihood of you valuing severe health states as worse than death. This seems like a logical hypothesis. Religions tend to be anti-euthanasia, and so it appears likely that religious people will have lower levels of support for euthanasia than non-religious people. Equally, if you don’t support the principle of euthanasia, it stands to reason that you are likely to be less willing to choose immediate death over living in a severe health state – something you would need to do for a health state to be considered as being worse than death in a time trade-off (TTO) study.

The authors test this hypothesis using a sub-sample of data (n=160) collected as part of the Irish EQ-5D-5L TTO valuation study. Perhaps unsurprisingly, the authors find evidence in support of the above hypotheses. Those that attend a religious service weekly were more likely to oppose euthanasia than those who attend a few times a year or less, and those who oppose euthanasia were less likely to give “worse than death” responses in the TTO than those that support it.

I found this paper really interesting, as it raises a number of challenging questions. If a society is made up of people with heterogeneous beliefs regarding religion, how should we balance these in the valuation of health? If a society is primarily non-religious is it fair to apply this valuation tariff to the lives of the religious, and vice versa? These certainly aren’t easy questions to answer, but may be worth reflecting on.

E-learning and health inequality aversion: A questionnaire experiment. Health Economics [PubMed] [RePEc] Published 22nd July 2018

Moving on from the cheery topic of euthanasia, what do you think about socioeconomic inequalities in health? In my home country, England, if you are from the poorest quintile of society, you can expect to experience 62 years in full health in your lifetime, whilst if you are from the richest quintile, you can expect to experience 74 years – a gap of 12 years.

In the second paper to be featured in this round-up, Cookson et al. explore the public’s willingness to sacrifice incremental population health gains in order to reduce these inequalities in health – their level of “health inequality aversion”. This is a potentially important area of research, as the vast majority of economic evaluation in health is distributionally-naïve and effectively assumes that members of the public aren’t at all concerned with inequalities in health.

The paper builds on prior work conducted by the authors in this area, in which they noted a high proportion of respondents in health inequality aversion elicitation studies appear to be so averse to inequalities that they violate monotonicity – they choose scenarios that reduce inequalities in health even if these scenarios reduce the health of the rich at no gain to the poor, or they reduce the health of the poor, or they may reduce the health of both groups. The authors hypothesise that these monotonicity violations may be due to incomplete thinking from participants, and suggest that the quality of their thinking could be improved by two e-learning educational interventions. The primary aim of the paper is to test the impact of these interventions in a sample of the UK public (n=60).

The first e-learning intervention was an animated video that described a range of potential positions that a respondent could take (e.g. health maximisation, or maximising the health of the worst off). The second was an interactive spreadsheet-based questionnaire that presented the consequences of the participant’s choices, prior to them confirming their selection. Both interventions are available online.

The authors found that the interactive tool significantly reduced the amount of extreme egalitarian (monotonicity-violating) responses, compared to a non-interactive, paper-based version of the study. Similarly, when the video was watched before completing the paper-based exercise, the number of extreme egalitarian responses reduced. However, when the video was watched before the interactive tool there was no further decrease in extreme egalitarianism. Despite this reduction in extreme egalitarianism, the median levels of inequality aversion remained high, with implied weights of 2.6 and 7.0 for QALY gains granted to someone from the poorest fifth of society, compared to the richest fifth of society for the interactive questionnaire and video groups respectively.

This is an interesting study that provides further evidence of inequality aversion, and raises further concern about the practical dominance of distributionally-naïve approaches to economic evaluation. The public does seem to care about distribution. Furthermore, the paper demonstrates that participant responses to inequality aversion exercises are shaped by the information given to them, and the way that information is presented. I look forward to seeing more studies like this in the future.

A new method for valuing health: directly eliciting personal utility functions. The European Journal of Health Economics [PubMed] [RePEc] Published 20th July 2018

Last, but not least, for this round-up, is a paper by Devlin et al. on a new method for valuing health.

The relative valuation of health states is a pretty important topic for health economists. If we are to quantify the effectiveness, and subsequently cost-effectiveness, of an intervention, we need to understand which health states are better than others, and how much better they are. Traditionally, this is done by asking members of the public to choose between different health profiles featuring differing levels of fulfilment of a range of domains of health, in order to ‘uncover’ the relative importance the respondent places on these domains, and levels. These can then be used in order to generate social tariffs that assign a utility value to a given health state for use in economic evaluation.

The authors point out that, in the modern day, valuation studies can be conducted rapidly, and at scale, online, but at the potential cost of deliberation from participants, and the resultant risk of heuristic dominated decision making. In response to this, the authors propose a new method – direct elicitation of personal utility functions, and pilot its use for the valuation of EQ-5D in a sample of the English public (n=76).

The proposed approach differs from traditional approaches in three key ways. Firstly, instead of simply attempting to infer the relative importance that participants place on differing domains based upon choices between health profiles, the respondents are asked directly about the relative importance they place on differing domains of health, prior to validating these with profile choices. Secondly, the authors place a heavy emphasis on deliberation, and the construction, rather than uncovering, of preferences during the elicitation exercises. Thirdly, a “personal utility function” for each individual is constructed (in effect a personal EQ-5D tariff), and these individual utility functions are subsequently aggregated into a social utility function.

In the pilot, the authors find that the method appears feasible for wider use, albeit with some teething troubles associated with the computer-based tool developed to implement it, and the skills of the interviewers.

This direct method raises an interesting question for health economics – should we be inferring preferences based upon choices that differ in terms of certain attributes, or should we just ask directly about the attributes? This is a tricky question. It is possible that the preferences elicited via these different approaches could result in different preferences – if they do, on what grounds should we choose one or other? This requires a normative judgment, and at present, it appears both are (potentially) as legitimate as each other.

Whilst the authors apply this direct method to the valuation of health, I don’t see why similar approaches couldn’t be applied to any multi-attribute choice experiment. Keep your eyes out for future uses of it in valuation, and perhaps beyond? It will be interesting to see how it develops.

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Chris Sampson’s journal round-up for 23rd July 2018

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.

Quantifying life: understanding the history of quality-adjusted life-years (QALYs). Social Science & Medicine [PubMed] Published 3rd July 2018

We’ve had some fun talking about the history of the QALY here on this blog. The story of how the QALY came to be important in health policy has been obscured. This paper seeks to address that. The research adopts a method called ‘multiple streams analysis’ (MSA) in order to explain how QALYs caught on. The MSA framework identifies three streams – policy, politics, and problems – and considers the ‘policy entrepreneurs’ involved. For this study, archival material was collected from the National Archives, Department of Health files, and the University of York. The researchers also conducted 44 semi-structured interviews with academics and civil servants.

The problem stream highlights shocks to the UK economy in the late 1960s, coupled with growth in health care costs due to innovations and changing expectations. Cost-effectiveness began to be studied and, increasingly, policymaking was meant to be research-based and accountable. By the 80s, the likes of Williams and Maynard were drawing attention to apparent inequities and inefficiencies in the health service. The policy stream gets going in the 40s and 50s when health researchers started measuring quality of life. By the early 60s, the idea of standardising these measures to try and rank health states was on the table. Through the late 60s and early 70s, government economists proliferated and proved themselves useful in health policy. The meeting of Rachel Rosser and Alan Williams in the mid-70s led to the creation of QALYs as we know them, combining quantity and quality of life on a 0-1 scale. Having acknowledged inefficiencies and inequities in the health service, UK politicians and medics were open to new ideas, but remained unconvinced by the QALY. Yet it was a willingness to consider the need for rationing that put the wheels in motion for NICE, and the politics stream – like the problem and policy stream – characterises favourable conditions for the use of the QALY.

The MSA framework also considers ‘policy entrepreneurs’ who broker the transition from idea to implementation. The authors focus on the role of Alan Williams and of the Economic Advisers’ Office. Williams was key in translating economic ideas into forms that policymakers could understand. Meanwhile, the Economic Advisers’ Office encouraged government economists to engage with academics at HESG and later the QoL Measurement Group (which led to the creation of EuroQol).

The main takeaway from the paper is that good ideas only prevail in the right conditions and with the right people. It’s important to maintain multi-disciplinary and multi-stakeholder networks. In the case of the QALY, the two-way movement of economists between government and academia was crucial.

I don’t completely understand or appreciate the MSA framework, but this paper is an enjoyable read. My only reservation is with the way the authors describe the QALY as being a dominant aspect of health policy in the UK. I don’t think that’s right. It’s dominant within a niche of a niche of a niche – that is, health technology assessment for new pharmaceuticals. An alternative view is that the QALY has in fact languished in a quiet corner of British policymaking, and been completely excluded in some other countries.

Accuracy of patient recall for self‐reported doctor visits: is shorter recall better? Health Economics [PubMed] Published 2nd July 2018

In designing observational studies, such as clinical trials, I have always recommended that self-reported resource use be collected no less frequently than every 3 months. This is partly based on something I once read somewhere that I can’t remember, but partly also on some logic that the accuracy of people’s recall decays over time. This paper has come to tell me how wrong I’ve been.

The authors start by highlighting that recall can be subject to omission, whereby respondents forget relevant information, or commission, whereby respondents include events that did not occur. A key manifestation of the latter is ‘telescoping’, whereby events are included from outside the recall period. We might expect commission to be more likely in short recalls and omission to be more common for long recalls. But there’s very little research on this regarding health service use.

This study uses data from a large trial in diabetes care in Australia, in which 5,305 participants were randomised to receive either 2-week, 3-month, or 12-month recall for how many times they had seen a doctor. Then, the trial data were matched with Medicare data to identify the true levels of resource use.

Over 92% of 12-month recall participants made an error, 76% of the 3-month recall, and 46% of the 2-week recall. The patterns of errors were different. There was very little under-reporting in the 2-week recall sample, with 3-month giving the most over-reporting and 12-month giving the most under-reporting. 12-month recall was associated with the largest number of days reported in error. However, when the authors account for the longer period being considered, and estimate a relative error, the impact of misreporting is smallest for the 12-month recall and greatest for the 2-week recall. This translates into a smaller overall bias for the longest recall period. The authors also find that older, less educated, unemployed, and low‐income patients exhibit higher measurement errors.

Health surveys and comparative studies that estimate resource use over a long period of time should use 12-month recall unless they can find a reason to do otherwise. The authors provide some examples from economic evaluations to demonstrate how selecting shorter recall periods could result in recommending the wrong decisions. It’s worth trying to understand the reasons why people can more accurately recall service use over 12 months. That way, data collection methods could be designed to optimise recall accuracy.

Who should receive treatment? An empirical enquiry into the relationship between societal views and preferences concerning healthcare priority setting. PLoS One [PubMed] Published 27th June 2018

Part of the reason the QALY faces opposition is that it has been used in a way that might not reflect societal preferences for resource allocation. In particular, the idea that ‘a QALY is a QALY is a QALY’ may conflict with notions of desert, severity, or process. We’re starting to see more evidence for groups of people holding different views, which makes it difficult to come up with decision rules to maximise welfare. This study considers some of the perspectives that people adopt, which have been identified in previous research – ‘equal right to healthcare’, ‘limits to healthcare’, and ‘effective and efficient healthcare’ – and looks at how they are distributed in the Netherlands. Using four willingness to trade-off (WTT) exercises, the authors explore the relationship between these views and people’s preferences about resource allocation. Trade-offs are between quality vs quantity of life, health maximisation vs equality, children vs the elderly, and lifestyle-related risk vs adversity. The authors sought to test several hypotheses: i) that ‘equal right’ respondents have a lower WTT; ii) ‘limits to healthcare’ people express a preference for health gains, health maximisation, and treating people with adversity; and iii) ‘effective and efficient’ people support health maximisation, treating children, and treating people with adversity.

A representative online sample of adults in the Netherlands (n=261) was recruited. The first part of the questionnaire collected socio-demographic information. The second part asked questions necessary to allocate people to one of the three perspectives using Likert scales based on a previous study. The third part of the questionnaire consisted of the four reimbursement scenarios. Participants were asked to identify the point (in terms of the relevant quantities) at which they would be indifferent between two options.

The distribution of the viewpoints was 65% ‘equal right’, 23% ‘limits to healthcare’, and 7% ‘effective and efficient’. 6% couldn’t be matched to one of the three viewpoints. In each scenario, people had the option to opt out of trading. 24% of respondents were non-traders for all scenarios and, of these, 78% were of the ‘equal right’ viewpoint. Unfortunately, a lot of people opted out of at least one of the trades, and for a wide variety of reasons. Decisionmakers can’t opt out, so I’m not sure how useful this is.

The authors describe many associations between individual characteristics, viewpoints, and WTT results. But the tested hypotheses were broadly supported. While the findings showed that different groups were more or less willing to trade, the points of indifference for traders within the groups did not vary. So while you can’t please everyone in health care priority setting, this study shows how policies might be designed to satisfy the preferences of people with different perspectives.

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Chris Sampson’s journal round-up for 6th February 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.

A review of NICE methods and processes across health technology assessment programmes: why the differences and what is the impact? Applied Health Economics and Health Policy [PubMed] Published 27th January 2017

Depending on the type of technology under consideration, NICE adopts a variety of different approaches in coming up with their recommendations. Different approaches might result in different decisions, which could undermine allocative efficiency. This study explores this possibility. Data were extracted from the manuals and websites for 5 programmes, under the themes of ‘remit and scope’, ‘process of assessment’, ‘methods of evaluation’ and ‘appraisal of evidence’. Semi-structured interviews were conducted with 5 people with expertise in each of the 5 programmes. Results are presented in a series of tables – one for each theme – outlining the essential characteristics of the 5 programmes. In their discussion, the authors then go on to consider how the identified differences might impact on efficiency from either a ‘utilitarian’ health-maximisation perspective or NICE’s egalitarian aim of ensuring adequate levels of health care. Not all programmes deliver recommendations with mandatory funding status, and it is only the ones that do that have a formal appeals process. Allowing for local rulings on funding could be good or bad news for efficiency, depending on the capacity of local decision makers to conduct economic evaluations (so that means probably bad news). At the same time, regional variation could undermine NICE’s fairness agenda. The evidence considered by the programmes varies, from a narrow focus on clinical and cost-effectiveness to the incorporation of budget impact and wider ethical and social values. Only some of the programmes have reference cases, and those that do are the ones that use cost-per-QALY analysis, which probably isn’t a coincidence. The fact that some programmes use outcomes other than QALYs obviously has the potential to undermine health-maximisation. Most differences or borne of practicality; there’s no point in insisting on a CUA if there is no evidence at all to support one – the appraisal would simply not happen. The very existence of alternative programmes indicates that NICE is not simply concerned with health-maximisation. Additional weight is given to rare conditions, for example. And NICE want to encourage research and innovation. So it’s no surprise that we need to take into account NICE’s egalitarian view to understand the type of efficiency for which it strives.

Economic evaluations alongside efficient study designs using large observational datasets: the PLEASANT trial case study. PharmacoEconomics [PubMed] Published 21st January 2017

One of the worst things about working on trial-based economic evaluations is going to lots of effort to collect lots of data, then finding that at the end of the day you don’t have much to show for it. Nowadays, the health service routinely collects many data for other purposes. There have been proposals to use these data – instead of prospectively collecting data – to conduct clinical trials. This study explores the potential for doing an economic evaluation alongside such a trial. The study uses CPRD data, including diagnostic, clinical and resource use information, for 8,608 trial participants. The intervention was the sending out of a letter in the hope of reducing unscheduled medical contacts due to asthma exacerbation in children starting a new school year. QALYs couldn’t be estimated using the CPRD data, so values were derived from the literature and estimated on the basis of exacerbations indicated by changes in prescriptions or hospitalisations. Note here the potentially artificial correlation between costs and outcomes that this creates, thus somewhat undermining the benefit of some good old bootstrapping. The results suggest the intervention is cost-saving with little impact on QALYs. Lots of sensitivity analyses are conducted, which are interesting in themselves and say something about the concerns around some of the structural assumptions. The authors outline the pros and cons of the approach. It’s an important discussion as it seems that studies like this are going to become increasingly common. Regarding data collection, there’s little doubt that this approach is more efficient, and it should be particularly valuable in the evaluation of public health and service delivery type interventions. The problem is that the study is not able to use individual-level cost and outcome data from the same people, which is what sets a trial-based economic evaluation apart from a model-based study. So for me, this isn’t really a trial-based economic evaluation. Indeed, the analysis incorporates a Markov-type model of exacerbations. It’s a different kind of beast, which incorporates aspects of modelling and aspects of trial-based analysis, along with some unique challenges of its own. There’s a lot more methodological work that needs to be done in this area, but this study demonstrates that it could be fruitful.

“Too much medicine”: insights and explanations from economic theory and research. Social Science & Medicine [PubMed] Published 18th January 2017

Overconsumption of health care represents an inefficient use of resources, and so we wouldn’t recommend it. But is that all we – as economists – have to say on the matter? This study sought to dig a little deeper. A literature search was conducted to establish a working definition of overconsumption. Related notions such as overdiagnosis, overtreatment, overuse, low-value care, overmedicalisation and even ‘pharmaceuticalisation’ all crop up. The authors introduce ‘need’ as a basis for understanding overconsumption; it represents health care that should never be considered as “needed”. A useful distinction is identified between misconsumption – where an individual’s own consumption is detrimental to their own well-being – and overconsumption, which can be understood as having a negative effect on social welfare. Note that in a collectively funded system the two concepts aren’t entirely distinguishable. Misconsumption becomes the focus of the paper, as avoiding harm to patients has been the subject of the “too much medicine” movement. I think this is a shame, and not really consistent with an economist’s usual perspective. The authors go on to discuss issues such as moral hazard, supplier-induced demand, provider payment mechanisms, ‘indication creep’, regret theory, and physicians’ positional consumption, and whether or not such phenomena might lead to individual welfare losses and thus be considered causes of misconsumption. The authors provide a neat diagram showing the various causes of misconsumption on a plane. One dimension represents the extent to which the cause is imperfect knowledge or imperfect agency, and the other the degree to which the cause is at the individual or market level. There’s a big gap in the top right, where market level causes meet imperfect knowledge. This area could have included patent systems, research fraud and dodgy Pharma practices. Or maybe just a portrait of Ben Goldacre for shorthand. There are some warnings about the (limited) extent to which market reforms might address misconsumption, and the proposed remedy for overconsumption is not really an economic one. Rather, a change in culture is prescribed. More research looking at existing treatments rather than technology adoption, and to investigate subgroup effects, is also recommended. The authors further suggest collaboration between health economists and ecological economists.

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