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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