Chris Sampson’s journal round-up for 1st April 2019

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.

Toward a centralized, systematic approach to the identification, appraisal, and use of health state utility values for reimbursement decision making: introducing the Health Utility Book (HUB). Medical Decision Making [PubMed] Published 22nd March 2019

Every data point reported in research should be readily available to us all in a structured knowledge base. Most of us waste most of our time retreading old ground, meaning that we don’t have the time to do the best research possible. One instance of this is in the identification of health state utility values to plug into decision models. Everyone who builds a model in a particular context goes searching for utility values – there is no central source. The authors of this paper are hoping to put an end to that.

The paper starts with an introduction to the importance of health state utility values in cost-effectiveness analysis, which most of us don’t need to read. Of course, the choice of utility values in a model is very important and can dramatically alter estimates of cost-effectiveness. The authors also discuss issues around the identification of utility values and the assessment of their quality and applicability. Then we get into the objectives of the ‘Health Utility Book’, which is designed to tackle these issues.

The Health Utility Book will consist of a registry (I like registries), backed by a systematic approach to the identification and inclusion (registration?) of utility values. The authors plan to develop a quality assessment tool for studies that report utility values, using a Delphi panel method to identify appropriate indicators of quality to be included. The quality assessment tool will be complemented by a tool to assess applicability, which will be developed through interviews with stakeholders involved in the reimbursement process.

In the first place, the Health Utility Book will only compile utility values for cancer, and some of the funding for the project is cancer specific. To survive, the project will need more money from more sources. To be sustainable, the project will need to attract funding indefinitely. Or perhaps it could morph into a crowd-sourced platform. Either way, the Health Utility Book has my support.

A review of attitudes towards the reuse of health data among people in the European Union: the primacy of purpose and the common good. Health Policy Published 21st March 2019

We all agree that data protection is important. We all love the GDPR. Organisations such as the European Council and the OECD are committed to facilitating the availability of health data as a means of improving population health. And yet, there often seem to be barriers to accessing health data, and we occasionally hear stories of patients opposing data sharing (e.g. care.data). Maybe people don’t want researchers to be using their data, and we just need to respect that. Or, more likely, we need to figure out what it is that people are opposed to, and design systems that recognise this.

This study reviews research on attitudes towards the sharing of health data for purposes other than treatment, among people living in the EU, employing a ‘configurative literature synthesis’ (a new one for me). From 5,691 abstracts, 29 studies were included. Most related to the use of health data in research in general, while some focused on registries. A few studies looked at other uses, such as for planning and policy purposes. And most were from the UK.

An overarching theme was a low awareness among the population about the reuse of health data. However, in some studies, a desire to be better informed was observed. In general, views towards the use of health data were positive. But this was conditional on the data being used to serve the common good. This includes such purposes as achieving a better understanding of diseases, improving treatments, or achieving more efficient health care. Participants weren’t so happy with health data reuse if it was seen to conflict with the interests of patients providing the data. Commercialisation is a big concern, including the sale of data and private companies profiting from the data. Employers and insurance companies were also considered a threat to patients’ interests. There were conflicting views about whether it is positive for pharmaceutical companies to have access to health data. A minority of people were against sharing data altogether. Certain types of data are seen as being particularly sensitive, including those relating to mental health or sexual health. In general, people expressed concern about data security and the potential for leaks. The studies also looked at the basis for consent that people would prefer. A majority accepted that their data could be used without consent so long as the data were anonymised. But there were no clear tendencies of preference for the various consent models.

It’s important to remember that – on the whole – patients want their data to be used to further the common good. But support can go awry if the data are used to generate profits for private firms or used in a way that might be perceived to negatively affect patients.

Health-related quality of life in injury patients: the added value of extending the EQ-5D-3L with a cognitive dimension. Quality of Life Research [PubMed] Published 18th March 2019

I’m currently working on a project to develop a cognition ‘bolt-on’ for the EQ-5D. Previous research has demonstrated that a cognition bolt-on could provide additional information to distinguish meaningful differences between health states, and that cognition might be a more important candidate than other bolt-ons. Injury – especially traumatic brain injury – can be associated with cognitive impairments. This study explores the value of a cognition bolt-on in this context.

The authors sought to find out whether cognition is sufficiently independent of other dimensions, whether the impact of cognitive problems is reflected in the EuroQol visual analogue scale (EQ VAS), and how a cognition bolt-on affects the overall explanatory power of the EQ-5D-3L. The data used are from the Dutch Injury Surveillance System, which surveys people who have attended an emergency department with an injury, including EQ-5D-3L. The survey adds a cognitive bolt-on relating to memory and concentration.

Data were available for 16,624 people at baseline, with 5,346 complete responses at 2.5-month follow-up. The cognition item was the least affected, with around 20% reporting any problems (though it’s worth noting that the majority of the cohort had injuries to parts of the body other than the head). The frequency of different responses suggests that cognition is dominant over other dimensions in the sense that severe cognitive problems tend to be observed alongside problems in other dimensions, but not vice versa. The mean EQ VAS for people reporting severe cognitive impairment was 41, compared with a mean of 75 for those reporting no problems. Regression analysis showed that moderate and severe cognitive impairment explained 8.7% and 6.2% of the variance of the EQ VAS. Multivariate analysis suggested that the cognitive dimension added roughly the same explanatory power as any other dimension. This was across the whole sample. Interestingly (or, perhaps, worryingly) when the authors looked at the subset of people with traumatic brain injury, the explanatory power of the cognitive dimension was slightly lower than overall.

There’s enough in this paper to justify further research into the advantages and disadvantages of using a cognition bolt-on. But I would say that. Whether or not the bolt-on descriptors used in this study are meaningful to patients remains an open question.

Developing the role of electronic health records in economic evaluation. The European Journal of Health Economics [PubMed] Published 14th March 2019

One way that we can use patients’ routinely collected data is to support the conduct of economic evaluations. In this commentary, the authors set out some of the ways to make the most of these data and discuss some of the methodological challenges. Large datasets have the advantage of being large. When this is combined with the collection of sociodemographic data, estimates for sub-groups can be produced. The data can also facilitate the capture of outcomes not otherwise available. For example, the impact of bariatric surgery on depression outcomes could be identified beyond the timeframe of a trial. The datasets also have the advantage of being representative, where trials are not. This could mean more accurate estimates of costs and outcomes. But there are things to bear in mind when using the data, such as the fact that coding might not always be very accurate, and coding practices could vary between observations. Missing data are likely to be missing for a reason (i.e. not at random), which creates challenges for the analyst. I had hoped that this paper would discuss novel uses of routinely collected data systems, such as the embedding of economic evaluations within them, rather than simply their use to estimate parameters for a model. But if you’re just getting started with using routine data, I suppose you could do worse than start with this paper.

Credits

Chris Sampson’s journal round-up for 3rd October 2016

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.

Using discrete choice experiments with duration to model EQ-5D-5L health state preferences: testing experimental design strategies. Medical Decision Making [PubMedPublished 28th September 2016

DCEs are a bit in vogue for the purpose of health state valuation, so it was natural that EuroQol turned to it for valuation of the EQ-5D-5L. But previous valuation studies have highlighted challenges  associated with this approach, some of which this paper now investigates. Central to the use of DCE in this way is the inclusion of a duration attribute to facilitate anchoring from 1 to dead. This study looks at the effect of increasing the options when it comes to duration, as previous studies were limited in this regard. In this study, possible durations were 6 months or 1, 2, 4, 7 or 10 years. 802 online survey respondents we presented with 10 DCE choice sets, and the resulting model had generally logically ordered coefficients. So the approach looks feasible, but it isn’t clear whether or not there are any real advantages to including more durations. Another issue is that the efficiency of the DCE design might be improved by introducing prior information from previous studies to inform the selection of health profiles – that is, by introducing non-zero prior values. With 800 respondents, this design resulted in more disordering with – for example – a positive coefficient on level 2 for the pain/discomfort dimension. This was not the expected result. However, the design included a far greater proportion of more difficult choices, which the authors suggest may have resulted in inconsistencies. An alternative way of increasing efficiency might be to use a 2-stage approach, whereby health profiles are selected and then durations are selected based on information from previous studies. Using the same number of pairs but a sample half the size (400), the 2-stage design seemed to work a treat. It’s a promising design that will no doubt see further research in this context.

Is the distribution of care quality provided under pay-for-performance equitable? Evidence from the Advancing Quality programme in England. International Journal for Equity in Health [PubMedPublished 23rd September 2016

Suppose a regional health care quality improvement initiative worked, but only for the well-off. Would we still support it? Maybe not, so it’s important to uncover for whom the policy is working. QOF is the most-studied pay-for-performance programme in England and it does not seem to have reduced health inequalities in the context of primary care. There is less evidence regarding P4P in hospital care, which is where this study comes in by looking at the Advancing Quality initiative across five different health conditions. Using individual-level data for 73,002 people, the authors model the probability of receiving a quality indicator according to income deprivation in their local area. There were 23 indicators altogether, across which the results were not consistent. Poorer patients were more likely to receive pre-surgical interventions for hip and knee replacements and for coronary artery bypass grafting (CABG). And poorer people were less likely to receive advice at discharge. On the other hand, for hip and knee replacement and CABG, richer people were more likely to receive diagnostic tests. The main finding is that there is no obvious systematic pro-poor or pro-rich bias in the effects of this pay-for-performance initiative in secondary care. This may not be a big surprise due to the limited amount of self-selection and self-direction for patients in secondary care, compared with primary care.

The impact of social security income on cognitive function at older ages. American Journal of Health Economics [RePEc] Published 19th September 2016

Income correlates with health, as we know. But it’s useful to be more specific – as this article is – in order to inform policy. So does more social security income improve cognitive function at older ages? The short answer is yes. And that wasn’t a foregone conclusion as there is some evidence that higher income leads to earlier retirement, which in turn can be detrimental to cognitive function. In this study the authors use changes in the Social Security Act in the US in the 1970s. Between 1972 and 1977, Congress messed up a bit and temporarily introduced a policy that made payments increase at a rate faster than inflation, which was therefore enjoyed by people born between 1910 and 1916, with a 5 year gradual transition until 1922. Unsurprisingly, this study follows many others that have made the most of this policy quirk. Data are taken from a longitudinal survey of older people, which includes a set of scores relating to cognition, with a sample of 4139 people. Using an OLS model, the authors estimate the association between Social Security income and cognition. Cognition is measured using a previously developed composite score with 3 levels: ‘normal’, ‘cognitively impaired’ and ‘demented’. To handle the endogeneity of income, an instrumental variable is constructed on the basis of year of birth to tie-in with the peak in benefit from the policy (n=673). In today’s money the beneficiary cohort received around $2000 extra. It’s also good to see the analysis extended to a quantile regression to see whereabouts in the cognition score distribution effects accrue. The additional income resulted in improvements in working memory, knowledge, languages and orientation and overall cognition. The effects are strong and clinically meaningful. A $1000 (in 1993 prices) increase in annual income lead to a 1.9 percentage point reduction in the likelihood of being classified as cognitively impaired. The effect is strongest for those with higher levels of cognition. The key take-home message here is that even in older populations, policy changes can be beneficial to health. It’s never too late.

Credits