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

Value of information methods to design a clinical trial in a small population to optimise a health economic utility function. BMC Medical Research Methodology [PubMed] Published 8th February 2018

Statistical significance – whatever you think of it – and the ‘power’ of clinical trials to detect change, is an important decider in clinical decision-making. Trials are designed to be big enough to detect ‘statistically significant’ differences. But in the context of rare diseases, this can be nigh-on impossible. In theory, the required sample size could exceed the size of the whole population. This paper describes an alternative method for determining sample sizes for trials in this context, couched in a value of information framework. Generally speaking, power calculations ignore the ‘value’ or ‘cost’ associated with errors, while a value of information analysis would take this into account and allow accepted error rates to vary accordingly. The starting point for this study is the notion that sample sizes should take into account the size of the population to which the findings will be applicable. As such, sample sizes can be defined on the basis of maximising the expected (societal) utility associated with the conduct of the trial (whether the intervention is approved or not). The authors describe the basis for hypothesis testing within this framework and specify the utility function to be maximised. Honestly, I didn’t completely follow the stats notation in this paper, but that’s OK – the trial statisticians will get it. A case study application is presented from the context of treating children with severe haemophilia A, which demonstrates the potential to optimise utility according to sample size. The key point is that the power is much smaller than would be required by conventional methods and the sample size accordingly reduced. The authors also demonstrate the tendency for the optimal trial sample size to increase with the size of the population. This Bayesian approach at least partly undermines the frequentist basis on which ‘power’ is usually determined. So one issue is whether regulators will accept this as a basis for defining a trial that will determine clinical practice. But then regulators are increasingly willing to allow for special cases, and it seems that the context of rare diseases could be a way-in for Bayesian trial design of this sort.

EQ-5D-5L: smaller steps but a major step change? Health Economics [PubMed] Published 7th February 2018

This editorial was doing the rounds on Twitter last week. European (and Canadian) health economists love talking about the EQ-5D-5L. The editorial features in the edition of Health Economics that hosts the 5L value set for England, which – 2 years on – has finally satisfied the vagaries of academic publication. The authors provide a summary of what’s ‘new’ with the 5L, and why it matters. But we’ve probably all figured that out by now anyway. More interestingly, the editorial points out some remaining concerns with the use of the EQ-5D-5L in England (even if it is way better than the EQ-5D-3L and its 25-year old value set). For example, there is some clustering in the valuations that might reflect bias or problems with the technique and – even if they’re accurate – present difficulties for analysts. And there are also uncertain implications for decision-making that could systematically favour or disfavour particular treatments or groups of patients. On this basis, the authors support NICE’s decision to ‘pause’ and await independent review. I tend to disagree, for reasons that I can’t fit in this round-up, so come back tomorrow for a follow-up blog post.

Factors influencing health-related quality of life in patients with Type 1 diabetes. Health and Quality of Life Outcomes [PubMed] Published 2nd February 2018

Diabetes and its complications can impact upon almost every aspect of a person’s health. It isn’t clear what aspects of health-related quality of life might be amenable to improvement in people with Type 1 diabetes, or which characteristics should be targeted. This study looks at a cohort of trial participants (n=437) and uses regression analyses to determine which factors explain differences in health-related quality of life at baseline, as measured using the EQ-5D-3L. Age, HbA1c, disease duration and being obese all significantly influenced EQ-VAS values, while self-reported mental illness and unemployment status were negatively associated with EQ-5D index scores. People who were unemployed were more likely to report problems in the mobility, self-care, and pain/discomfort domains. There are some minor misinterpretations in the paper (divining a ‘reduction’ in scores from a cross-section, for example). And the use of standard linear regression models is questionable given the nature of EQ-5D-3L index values. But the findings demonstrate the importance of looking beyond the direct consequences of a disease in order to identify the causes of reduced health-related quality of life. Getting people back to work could be more effective than most health care as a means of improving health-related quality of life.

Financial incentives for chronic disease management: results and limitations of 2 randomized clinical trials with New York Medicaid patients. American Journal of Health Promotion [PubMed] Published 1st February 2018

Chronic diseases require (self-)management, but it isn’t always easy to ensure that patients adhere to the medication or lifestyle changes that could improve health outcomes. This study looks at the effectiveness of financial incentives in the context of diabetes and hypertension. The data are drawn from 2 RCTs (n=1879) which, together, considered 3 types of incentive – process-based, outcome-based, or a combination of the two – compared with no financial incentives. Process-based incentives rewarded participants for attending primary care or endocrinologist appointments and filling their prescriptions, up to a maximum of $250. Outcome-based incentives rewarded up to $250 for achieving target reductions in systolic blood pressure or blood glucose levels. The combined arms could receive both rewards up to the same maximum of $250. In short, none of the financial incentives made any real difference. But generally speaking, at 6-month follow-up, the movement was in the right direction, with average blood pressure and blood glucose levels tending to fall in all arms. It’s not often that authors include the word ‘limitations’ in the title of a paper, but it’s the limitations that are most interesting here. One key difficulty is that most of the participants had relatively acceptable levels of the target outcomes at baseline, meaning that they may already have been managing their disease well and there may not have been much room for improvement. It would be easy to interpret these findings as showing that – generally speaking – financial incentives aren’t effective. But the study is more useful as a way of demonstrating the circumstances in which we can expect financial incentives to be ineffective, and support a better-informed targeting for future programmes.

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

An empirical comparison of the measurement properties of the EQ-5D-5L, DEMQOL-U and DEMQOL-Proxy-U for older people in residential care. Quality of Life Research [PubMed] Published 5th January 2018

There is now a condition-specific preference-based measure of health-related quality of life that can be used for people with cognitive impairment: the DEMQOL-U. Beyond the challenge of appropriately defining quality of life in this context, cognitive impairment presents the additional difficulty that individuals may not be able to self-complete a questionnaire. There’s some good evidence that proxy responses can be valid and reliable for people with cognitive impairment. The purpose of this study is to try out the new(ish) EQ-5D-5L in the context of cognitive impairment in a residential setting. Data were taken from an observational study in 17 residential care facilities in Australia. A variety of outcome measures were collected including the EQ-5D-5L (proxy where necessary), a cognitive bolt-on item for the EQ-5D, the DEMQOL-U and the DEMQOL-Proxy-U (from a family member or friend), the Modified Barthel Index, the cognitive impairment Psychogeriatric Assessment Scale (PAS-Cog), and the neuropsychiatric inventory questionnaire (NPI-Q). The researchers tested the correlation, convergent validity, and known-group validity for the various measures. 143 participants self-completed the EQ-5D-5L and DEMQOL-U, while 387 responses were available for the proxy versions. People with a diagnosis of dementia reported higher utility values on the EQ-5D-5L and DEMQOL-U than people without a diagnosis. Correlations between the measures were weak to moderate. Some people reported full health on the EQ-5D-5L despite identifying some impairment on the DEMQOL-U, and some vice versa. The EQ-5D-5L was more strongly correlated with clinical outcome measures than were the DEMQOL-U or DEMQOL-Proxy-U, though the associations were generally weak. The relationship between cognitive impairment and self-completed EQ-5D-5L and DEMQOL-U utilities was not in the expected direction; people with greater cognitive impairment reported higher utility values. There was quite a lot of disagreement between utility values derived from the different measures, so the EQ-5D-5L and DEMQOL-U should not be seen as substitutes. An EQ-QALY is not a DEM-QALY. This is all quite perplexing when it comes to measuring health-related quality of life in people with cognitive impairment. What does it mean if a condition-specific measure does not correlate with the condition? It could be that for people with cognitive impairment the key determinant of their quality of life is only indirectly related to their impairment, and more dependent on their living conditions.

Resolving the “cost-effective but unaffordable” paradox: estimating the health opportunity costs of nonmarginal budget impacts. Value in Health Published 4th January 2018

Back in 2015 (as discussed on this blog), NICE started appraising drugs that were cost-effective but implied such high costs for the NHS that they seemed unaffordable. This forced a consideration of how budget impact should be handled in technology appraisal. But the matter is far from settled and different countries have adopted different approaches. The challenge is to accurately estimate the opportunity cost of an investment, which will depend on the budget impact. A fixed cost-effectiveness threshold isn’t much use. This study builds on York’s earlier work that estimated cost-effectiveness thresholds based on health opportunity costs in the NHS. The researchers attempt to identify cost-effectiveness thresholds that are in accordance with different non-marginal (i.e. large) budget impacts. The idea is that a larger budget impact should imply a lower (i.e. more difficult to satisfy) cost-effectiveness threshold. NHS expenditure data were combined with mortality rates for different disease categories by geographical area. When primary care trusts’ (PCTs) budget allocations change, they transition gradually. This means that – for a period of time – some trusts receive a larger budget than they are expected to need while others receive a smaller budget. The researchers identify these as over-target and under-target accordingly. The expenditure and outcome elasticities associated with changes in the budget are estimated for the different disease groups (defined by programme budgeting categories; PBCs). Expenditure elasticity refers to the change in PBC expenditure given a change in overall NHS expenditure. Outcome elasticity refers to the change in PBC mortality given a change in PBC expenditure. Two econometric approaches are used; an interaction term approach, whereby a subgroup interaction term is used with the expenditure and outcome variables, and a subsample estimation approach, whereby subgroups are analysed separately. Despite the limitations associated with a reduced sample size, the subsample estimation approach is preferred on theoretical grounds. Using this method, under-target PCTs face a cost-per-QALY of £12,047 and over-target PCTs face a cost-per-QALY of £13,464, reflecting diminishing marginal returns. The estimates are used as the basis for identifying a health production function that can approximate the association between budget changes and health opportunity costs. Going back to the motivating example of hepatitis C drugs, a £772 million budget impact would ‘cost’ 61,997 QALYs, rather than the 59,667 that we would expect without accounting for the budget impact. This means that the threshold should be lower (at £12,452 instead of £12,936) for a budget impact of this size. The authors discuss a variety of approaches for ‘smoothing’ the budget impact of such investments. Whether or not you believe the absolute size of the quoted numbers depends on whether you believe the stack of (necessary) assumptions used to reach them. But regardless of that, the authors present an interesting and novel approach to establishing an empirical basis for estimating health opportunity costs when budget impacts are large.

First do no harm – the impact of financial incentives on dental x-rays. Journal of Health Economics [RePEc] Published 30th December 2017

If dentists move from fee-for-service to a salary, or if patients move from co-payment to full exemption, does it influence the frequency of x-rays? That’s the question that the researchers are trying to answer in this study. It’s important because x-rays always present some level of (carcinogenic) risk to patients and should therefore only be used when the benefits are expected to exceed the harms. Financial incentives shouldn’t come into it. If they do, then some dentists aren’t playing by the rules. And that seems to be the case. The authors start out by establishing a theoretical framework for the interaction between patient and dentist, which incorporates the harmful nature of x-rays, dentist remuneration, the patient’s payment arrangements, and the characteristics of each party. This model is used in conjunction with data from NHS Scotland, with 1.3 million treatment claims from 200,000 patients and 3,000 dentists. In 19% of treatments, an x-ray occurs. Some dentists are salaried and some are not, while some people pay charges for treatment and some are exempt. A series of fixed effects models are used to take advantage of these differences in arrangements by modelling the extent to which switches (between arrangements, for patients or dentists) influence the probability of receiving an x-ray. The authors’ preferred model shows that both the dentist’s remuneration arrangement and the patient’s financial status influences the number of x-rays in the direction predicted by the model. That is, fee-for-service and charge exemption results in more x-rays. The combination of these two factors results in a 9.4 percentage point increase in the probability of an x-ray during treatment, relative to salaried dentists with non-exempt patients. While the results do show that financial incentives influence this treatment decision (when they shouldn’t), the authors aren’t able to link the behaviour to patient harm. So we don’t know what percentage of treatments involving x-rays would correspond to the decision rule of benefits exceeding harms. Nevertheless, this is an important piece of work for informing the definition of dentist reimbursement and patient payment mechanisms.

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Paul Mitchell’s journal round-up for 25th December 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.

Consensus-based cross-European recommendations for the identification, measurement and valuation of costs in health economic evaluations: a European Delphi study. European Journal of Health Economics [PubMedPublished 19th December 2017

The primary aim of this study was to develop guidelines for costing in economic evaluation studies conducted across more than one European country. The starting point of the societal perspective as the benchmark for costing was not entirely obvious from the abstract, where this broadest approach to costing is not recommended uniformly across all European countries. Recommendations following this starting point looked at the identification, measurement and valuation of resource use, discount rate and discounting of future costs. A three-step Delphi study was used to gain consensus on what should be included in an economic evaluation from a societal perspective, based initially on findings from a review of costing methodologies adopted across European country-specific guidelines. Consensus required at least two thirds (67%) agreement across those participating in the Delphi study at all 3 stages. Where no agreement was reached after the three stages, a panel of four of the co-authors made a final decision on what should be recommended. In total, 26 of the 110 invited to participate completed at least one Delphi round, with all Delphi rounds having at least 16 participants. It remains unclear to me if 16 for a Delphi round is sufficient to reach a European wide consensus on costing methodologies. There were a number of key areas where no consensus was reached (e.g. including costs unrelated to the intervention, measurement of resource use and absenteeism, and valuation of opportunity costs of patient time and informal care), so the four-strong author panel had a leading role on some of the main recommendations. Notwithstanding the limitations associated with the reference perspective taken and sample for the Delphi study and panel, the paper provides a useful illustration of the different approaches to costing across European countries. It also provides a good coverage of costing issues that need to be explained in detail in economic evaluations to allow for clear understanding of methods used and the underpinning rationale for those decisions where a choice is required on the costing methodology applied.

A (five-)level playing field for mental health conditions?: exploratory analysis of EQ-5D-5L derived utility values. Quality of Life Research [PubMedPublished 16th December 2017

The UK health economics community has been reeling from the decision made earlier this year by UK guidelines developer, the National Institute for Health and Care Excellence (NICE), who recommended to not adopt the new population values developed for the EQ-5D-5L version when calculating QALYs and instead rely on a crosswalk of the values developed over 20 years ago for the 3 level EQ-5D version. This paper provides a timely comparison of how these two value sets perform for the EQ-5D-5L descriptive system in patient groups with mental health conditions, groups often thought to be disadvantaged by the physical health functioning focus of the EQ-5D descriptive system. Using baseline data from three trials, the authors find that the new utility values produce a higher mean EQ-5D score of 0.08 compared to the old crosswalk values, with a 0.225 difference for those reporting extreme problems with the anxiety/depression dimension on EQ-5D. Although, the authors of this study highlight using these new values would increase cost per QALY results in this sample using scenario analysis, when improvements are in the depression/anxiety category only, such improvements are relatively better than across the whole EQ-5D-5L descriptive system due to the relative additional value placed on the anxiety/depression dimension in the new values. This paper makes for interesting reading and one that NICE should take into consideration when reviewing their decision on this issue next year. Although I would disagree with the authors when they state that this study would be a primary reason for revising the NICE cost-effectiveness threshold (more compelling arguments for this elsewhere in my view), it does clearly highlight the influence of the choice of descriptive system and the values used in the outcomes produced for economic analysis such as QALYs, even when the two descriptive systems in question (EQ-5D-3L and EQ-5D-5L) are roughly the same.

What characteristics of nursing homes are most valued by customers? A discrete choice experiment with residents and family members. Value in Health Published 1st December 2017

Our final paper for review in 2017 looks at the characteristics that are of most importance to individuals and their family members when it comes to nursing home provision. The authors conducted a valuation exercise using a discrete choice experiment (DCE) to calculate the relative importance of the attributes contained on the Consumer Choice Index-Six Dimension (CCI-6D), a measure developed to assess the quality of nursing home care across 3 levels on six domains: 1. level of time care staff spent with residents; 2. homeliness of shared spaces; 3. homeliness of room setup; 4. access to outside and garden; 5. frequency of meaningful activities; and 6. flexibility with care routines. Those who lived in a nursing home for at least a year with low levels of cognitive impairment completed the DCE themselves, whereas family members were asked to proxy for their close relative with more severe cognitive impairment. 126 residents and 416 family member proxies completed the DCE comparisons of nursing homes with different qualities in these six areas. The results of the DCE show differences in preferences across the two groups. Although similar importance is placed on some dimensions across both groups (i.e. “homeliness of room set up” ranked highly, whereas “frequency of meaningful activities” ranked lower), residents value access to outside and garden four times as much as the family proxies do (second most important dimension for residents, lowest for family proxies), family members value level of time care staff spent with residents twice as much as residents themselves (most important attribute for family proxies, third most important for residents). Although residents in both groups may have important differences in characteristics that might explain some of this difference, it is probably a good time of year to remember family preferences may be inconsistent with individuals within them, so make sure to take account of this variation when preparing those Christmas dinners.

Happy holidays all.

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