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.


Chris Sampson’s journal round-up for 3rd July 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.

Role of cost on failure to access prescribed pharmaceuticals: the case of statins. Applied Health Economics and Health Policy [PubMed] Published 28th June 2017

Outside work, I find that people often like to tell me how to solve health economics problems. A common one is the idea that the NHS could save a load of money by enforcing prescription charges. It’s a textbook life-ain’t-that-simple situation. One of the reasons it isn’t that simple is that, if you start charging for prescriptions, people will be less likely to take their meds. That’s probably bad news for patients and for doctors. “But it’s only a few quid”. Well… As in many countries, Australians have to cough up a co-payment to fill their prescriptions. The size of the copayment depends on i) whether or not the patient is concessional (e.g. a pensioner) and ii) whether or not a threshold has been reached for total family prescription expenditure in one year. Concessional patients have a lower co-payment, a lower threshold and no co-payment once the threshold is met. This study looks at statin use in this context for 94,000 over-45s in New South Wales from 2005-2011. Separate logistic regressions are run for each of the 4 groups (concessional/non-concessional, pre-threshold/post-threshold) to predict statin adherence, controlling for a good range of sociodemographic and health-related variables. The size of the copayment comes out as the biggest barrier to adherence. More than 75% of people who weren’t adherent before reaching their threshold became so after reaching it – that is, once their co-payment was either much-reduced or zero. Poorest adherence was observed in non-concessional low-income people who hadn’t reached the threshold, who faced the highest co-payment. Income, age group and holding private insurance were also important determinants. In short, charging people for their statins, even if it isn’t much money, reduces the likelihood that they will take them. There is the possibility that adherence is correlated with the likelihood of having reached the threshold, which could undermine these results. I’m not entirely convinced that the analysis cuts the mustard, but I’ll let the more econometrically minded amongst you figure that out.

Conceptualizations of the societal perspective within economic evaluations: a systematic review. International Journal of Technology Assessment in Health Care [PubMed] Published 23rd June 2017

In my last round-up, I included a study looking at resource use measures for intersectoral costs and benefits; costs and benefits that occur outside the health sector. This week we have a study looking at how the inclusion of intersectoral costs and benefits influences results, and how researchers have interpreted the ‘societal perspective’. A systematic review was conducted for economic evaluations purporting to use a societal perspective, published since the CHEERS statement was released, including 107 studies. Only 74 provided a conceptualisation of the societal perspective. Reported conceptualisations of the societal perspective were grouped according to the specificity of their definition – 18 general, 50 specific, 6 both – and assessed using content analysis. Of these, 25 referred to a guideline or other source in their conceptualisation. A total of 10 general and 56 specific clusters of conceptualisations were identified, demonstrating major inconsistency. For some studies – namely trial-based economic evaluations in musculoskeletal or mental disorders – the authors dug deeper and extracted additional information. In both cases, where data were adequately reported, the intersectoral costs tended to make up more than 50% of total costs. But in general the specific intersectoral items were not fully reported and relevant costs (e.g. in education or criminal justice) were not identified. It probably won’t come as a surprise that the general impression is that a lot of researchers interpret the societal perspective – in practice, if not in theory – as health costs plus productivity losses. And usually, that’s not really good enough.

Annual direct medical costs associated with diabetes-related complications in the event year and in subsequent years in Hong Kong. Diabetic Medicine [PubMed] Published 21st June 2017

There are a lot of high-quality decision models built for the evaluation of interventions in diabetes. See Mt Hood. But some are still a bit primitive when it comes to estimating the costs associated with the many clinical pathways and complications associated with diabetes, especially when multimorbidity can be important. So studies like this are very welcome. This study contributes cost estimates for a wide range of complications (13, to be precise) for what should be a representative sample of (Chinese) people with diabetes. It includes public health care expenditure for more than 120,000 people with diabetes in Hong Kong, with 5-year follow-up. For private health care costs, a cross-section of 1275 people was recruited through other studies and provided information about service use by telephone. Fixed effects panel data regressions were used for the public medical costs. During the follow-up, 17% developed at least one complication. The models estimate the impact on total cost of new disease and existing disease separately, in order to identify first-year and subsequent-year cost estimates. Generalised linear models were used for the private health care costs. The base case of a 65-year old with no complications was US$1500/year in costs to the public purse. The biggest effect on costs was a first-year multiplier of 9.38 for lower limb ulcer (1.62 in subsequent years). Other costly complications were stroke, heart failure, end-stage renal disease and acute myocardial infarction. Private costs were much smaller, at $187 for the base case. These figures may prove useful to decision modellers, even outside the Hong Kong setting.

Financing and distribution of pharmaceuticals in the United States. JAMA [PubMed] Published 15th May 2017

The purpose of this article seems to be to demonstrate the complexity of the financing and distribution of pharmaceuticals in the US. It describes distributors, retailers and patients on the distribution side, and pharmacy benefit managers and health insurers on the financing side, with manufacturers in the middle. But the system that is shown in the article’s figure strikes me as surprisingly simple for an industry in which such vast amounts of money are sloshing around. It’s far more straightforward than any diagram you might see relating to the organisation of NHS services. I would imagine that a freer market would be associated with more complexity as upstarts might muscle-in on smaller corners of the market and become new intermediaries. But the article is still enlightening. It outlines some of the features of the market, particularly the high levels of concentration, characteristics of the key players and the staggering sums of money changing hands.


Chris Sampson’s journal round-up for 2nd May 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.

Competition, prices and quality in the market for physician consultations. The Journal of Industrial Economics [RePEcPublished 26th April 2016

In Australia you have to pay for a consultation with your GP, and GPs are free to set whatever price they like. Meanwhile, patients are free to choose to visit any GP they like. This means that there is a lot of scope for competition on price as well as quality. Australia’s Medicare reimburses a fixed rate (depending on type) for each consultation, and so patients only need to pay whatever the GP charges in excess of this. But it is also possible for GPs to use ‘bulk billing’ – whereby no additional co-payment above the rebate is charged – for specific groups such as children or old people. This study looks at whether competition influences the price and quality of consultations. Data are taken from the MABEL survey, which is a panel study of doctors. It includes information on consultation duration and fees and the number of people who aren’t charged a co-payment. Rural GPs are excluded from the analysis and the final sample includes 1966 GPs. The authors construct a measure of the level of competition according to the distance between GPs and their rivals, with a focus on the third-nearest GP practice. Quality is specified as consultation length (the longer, the better). A variety of models are run to predict price and quality and to examine the effects of competition, using an extension of the ‘Vickrey-Salop’ model. Results show that the greater the distance from competitors, the lower the proportion of patients who are not charged a co-payment and the higher the average price for those who are charged. This suggests that competition reduces prices. The magnitude of these effects is greater for people living in areas with a higher socio-economic status. There is also a positive effect on consultation duration, though this is small. One key implication of these findings is that as concentration in the market for GP consultations increases prices are likely to rise. Whether or not having fewer practices would also impact on quality is not clear from this study.

What determines the shape of an EQ-5D index distribution? Medical Decision Making [PubMedPublished 25th April 2016

If you’ve ever collected EQ-5D(-3L) data and looked at the distribution of index scores, you’ll probably have noticed that it looks a bit wobbly. Generally we see a cluster of people around the 0.3 mark, another cluster around 0.8, and then a stack of people at 1. It seems unlikely that this should be representative of the true distribution of health states for all conditions in which it’s observed. This new study looks at whether this odd distribution might be explained by the nature of the classification system or by the nature of the weights applied to the states. The most obvious culprit for the UK MVH values is the N3 term, which takes a disproportionately big cut for anyone with at least one level 3 response. Using a simulated data set, the authors show that if EQ-5D health states were randomly allocated (i.e. each state was equally likely) then a bimodal distribution would still be observed. The authors also looked at real data from the NHS PROMs programme and from a clinical trial. For both pre-surgery hip replacement patients and pre-surgery varicose vein patients, the usual 2 (or 3) cluster pattern can be observed. To the naked eye it looks as if the cut-off may be at 0.5, while a kmeans clustering procedure suggested that it may lie closer to 0.3. Looking at the dimension-level data for the hip replacement patients, it becomes clear that much of the difference can be explained by the usual activities and pain and discomfort dimensions, as there is little variation in the other dimensions to affect the scores. This is complicated by the N3 effect, but nevertheless it appears that the nature of the classification system can cause clustering in at least some conditions. So the authors go about ranking all of the EQ-5D states according to their index score, with the effect of removing any impact of the weighting on the distribution. Still, we observe the clustering. So it looks like six of one, half a dozen of the other. Clusters can result from the classification system, and then the difference between these clusters can be reinforced by the nature of the scoring algorithm. A condition-specific measure for the hip replacement patients does not show clustering and scores have a unimodal distribution. However, scores within the EQ-5D clusters are also unimodal and there is some evidence that the EQ-5D clustering may be genuine. What this really comes down to is the fact that we need to abandon our blinkered view that the EQ-5D is all about the index score. It’s important to bear in mind that the extent to which the classification system and/or the weighting might affect the distribution of index scores will differ between patient groups. Looking at the distribution of responses at the dimension level can tell us a lot about the nature of this statistical challenge.

On the estimation of the cost-effectiveness threshold: why, what, how? Value in Health Published 23rd April 2016

One day in history, NICE came up with a £20-30,000 threshold for the amount the NHS should be willing to pay for a QALY. I have no idea where this came from, or why it has remained the same since its conception (in theory, if not in practice). Few other countries specify a threshold as clearly as NICE, but some do adopt an indicative range. This study reviews the literature around the estimation of cost-effectiveness thresholds and the views and methods that have been used to support them; focussing on questions of ‘why’, ‘what’ and ‘how’. Why? Because the threshold approach is convenient and useful, and even if it is a bit of a fudge we can at least understand how decisions are made. What? Depending on who you ask, it’s either the social value of a QALY or it’s the opportunity cost associated with new investment, and what this really comes down to is whether or not you take the budget to be fixed. How? This is the main target of the review. 38 articles were identified and divided into demand-side (29) and supply-side (9) studies. Most of the former used willingness-to-pay surveys while a few inferred from the value of a statistical life. Values from the willingness to pay studies ranged from €1,000 to €5,000,000. Most of the problems associated with these derive from limitations in the QALY estimation process itself. The value of a statistical life studies turned up slightly higher values on average. Of the 9 studies looking at the supply-side, 4 looked at past investment decisions and 5 tried to estimate empirically the marginal cost of a QALY. Most of the studies looking at investment decisions observed too much inconsistency to actually come up with a threshold range. An Australian study inferred a threshold of €32,000 to €58,000. Estimating the marginal cost of a QALY is where the Claxton modern classic of £12,936 (€14,141) comes in. Other studies came up with similar estimates, though with some substantially higher for specific disease areas. So it looks as if most countries that talk about thresholds are more inclined to the social value of a QALY view, rather than the opportunity cost approach, because their thresholds are higher. Given institutional arrangements, this makes sense to me, as I’ve discussed before on this blog. But what it does imply is that some interventions that may be determined to be cost-effective by current standards might not be an appropriate use of resources within given budget constraints.