Rita Faria’s journal round-up for 28th January 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.

Appraising the value of evidence generation activities: an HIV modelling study. BMJ Global Health [PubMed] Published 7th December 2018

How much should we spend on implementing our health care strategy versus getting more information to devise a better strategy? Should we devolve budgets to regions or administer the budget centrally? These are difficult questions and this new paper by Beth Woods et al has a brilliant stab at answering them.

The paper looks at the HIV prevention and treatment policies in Zambia. It starts by finding the most cost-effective strategy and the corresponding budget in each region, given what is currently known about the prevalence of the infection, the effectiveness of interventions, etc. The idea is that the regions receive a cost-effective budget to implement a cost-effective strategy. The issue is that the cost-effective strategy and budget are devised according to what we currently know. In practice, regions might face a situation on the ground which is different from what was expected. Regions might not have enough budget to implement the strategy or might have some leftover.

What if we spend some of the budget to get more information to make a better decision? This paper considers the value of perfect information given the costs of research. Depending on the size of the budget and the cost of research, it may be worthwhile to divert some funds to get more information. But what if we had more flexibility in the budgetary policy? This paper tests 2 more budgetary options: a national hard budget but with the flexibility to transfer funds from under- to overspending regions, and a regional hard budget with a contingency fund.

The results are remarkable. The best budgetary policy is to have a national budget with the flexibility to reallocate funds across regions. This is a fascinating paper, with implications not only for prioritisation and budget setting in LMICs but also for high-income countries. For example, the 2012 Health and Social Care Act broke down PCTs into smaller CCGs and gave them hard budgets. Some CCGs went into deficit, and there are reports that some interventions have been cut back as a result. There are probably many reasons for the deficit, but this paper shows that hard regional budgets clearly have negative consequences.

Health economics methods for public health resource allocation: a qualitative interview study of decision makers from an English local authority. Health Economics, Policy and Law [PubMed] Published 11th January 2019

Our first paper looked at how to use cost-effectiveness to allocate resources between regions and across health care services and research. Emma Frew and Katie Breheny look at how decisions are actually made in practice, but this time in a local authority in England. Another change of the 2012 Health and Social Care Act was to move public health responsibilities from the NHS to local authorities. Local authorities are now given a ring-fenced budget to implement cost-effective interventions that best match their needs. How do they make decisions? Thanks to this paper, we’re about to find out.

This paper is an enjoyable read and quite an eye-opener. It was startling that health economics evidence was not much used in practice. But the barriers that were cited are not insurmountable. And the suggestions by the interviewees were really useful. There were suggestions about how economic evaluations should consider the local context to get a fair picture of the impact of the intervention to services and to the population, and to move beyond the trial into the real world. Equity was mentioned too, as well as broadening the outcomes beyond health. Fortunately, the health economics community is working on many of these issues.

Lastly, there was a clear message to make economic evidence accessible to lay audiences. This is a topic really close to my heart, and something I’d like to help improve. We have to make our work easy to understand and use. Otherwise, it may stay locked away in papers rather than do what we intended it for. Which is, at least in my view, to help inform decisions and to improve people’s lives.

I found this paper reassuring in that there is clearly a need for economic evidence and a desire to use it. Yes, there are some teething issues, but we’re working in the right direction. In sum, the future for health economics is bright!

Survival extrapolation in cancer immunotherapy: a validation-based case study. Value in Health Published 13th December 2018

Often, the cost-effectiveness of cancer drugs hangs in the method to extrapolate overall survival. This is because many cancer drugs receive their marketing authorisation before most patients in the trial have died. Extrapolation is tested extensively in the sensitivity analysis, and this is the subject of many discussions in NICE appraisal committees. Ultimately, at the point of making the decision, the correct method to extrapolate is a known unknown. Only in hindsight can we know for sure what the best choice was.

Ash Bullement and colleagues take advantage of hindsight to know the best method for extrapolation of a clinical trial of an immunotherapy drug. Survival after treatment with immunotherapy drugs is more difficult to predict because some patients can survive for a very long time, while others have much poorer outcomes. They fitted survival models to the 3-year data cut, which was available at the time of the NICE technology appraisal. Then they compared their predictions to the observed survival in the 5-year data cut and to long-term survival trends from registry data. They found that the piecewise model and a mixture-cure model had the best predictions at 5 years.

This is a relevant paper for those of us who work in the technology appraisal world. I have to admit that I can be sceptical of piecewise and mixture-cure models, but they definitely have a role in our toolbox for survival extrapolation. Ideally, we’d have a study like this for all the technology appraisals hanging on the survival extrapolation so that we can take learnings across cancers and classes of drugs. With time, we would get to know more about what works best for which condition or drug. Ultimately, we may be able to get to a stage where we can look at the extrapolation with less inherent uncertainty.

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Chris Sampson’s journal round-up for 31st December 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.

Perspectives of patients with cancer on the quality-adjusted life year as a measure of value in healthcare. Value in Health Published 29th December 2018

Patients should have the opportunity to understand how decisions are made about which treatments they are and are not allowed to use, given their coverage. This study reports on a survey of cancer patients and survivors, with the aim of identifying patients’ awareness, understanding, and opinions about the QALY as a measure of value.

Participants were recruited from a (presumably US-based) patient advocacy group and 774 mostly well-educated, mostly white, mostly women responded. The online survey asked about cancer status and included a couple of measures of health literacy. Fewer than 7% of participants had ever heard of the QALY – more likely for those with greater health literacy. The survey explained the QALY to the participants and then asked if the concept of the QALY makes sense. Around half said it did and 24% thought that it was a good way to measure value in health care. The researchers report a variety of ‘significant’ differences in tendencies to understand or support the use of QALYs, but I’m not convinced that they’re meaningful because the differences aren’t big and the samples are relatively small.

At the end of the survey, respondents were asked to provide opinions on QALYs and value in health care. 165 people provided responses and these were coded and analysed qualitatively. The researchers identified three themes from this one free-text question: i) measuring value, ii) opinions on QALY, and iii) value in health care and decision making. I’m not sure that they’re meaningful themes that help us to understand patients’ views on QALYs. A significant proportion of respondents rejected the idea of using numbers to quantify value in health care. On the other hand, some suggested that the QALY could be a useful decision aid for patients. There was opposition to ‘external decision makers’ having any involvement in health care decision making. Unless you’re paying for all of your care out of pocket, that’s tough luck. But the most obvious finding from the qualitative analysis is that respondents didn’t understand what QALYs were for. That’s partly because health economists in general need to be better at communicating concepts like the QALY. But I think it’s also in large part because the authors failed to provide a clear explanation. They didn’t even use my lovely Wikipedia graphic. Many of the points made by respondents are entirely irrelevant to the appropriateness of QALYs as they’re used (or in the case of the US, aren’t yet used) in practice. For example, several discussed the use of QALYs in clinical decision making. Patients think that they should maintain autonomy, which is fair enough but has nothing to do with how QALYs are used to assess health technologies.

QALYs are built on the idea of trade-offs. They measure the trade-off between life extension and life improvement. They are used to guide trade-offs between different treatments for different people. But the researchers didn’t explain how or why QALYs are used to make trade-offs, so the elicited views aren’t well-informed.

Measuring multivariate risk preferences in the health domain. Journal of Health Economics Published 27th December 2018

Health preferences research is now a substantial field in itself. But there’s still a lot of work left to be done on understanding risk preferences with respect to health. Gradually, we’re coming round to the idea that people tend to be risk-averse. But risk preferences aren’t (necessarily) so simple. Recent research has proposed that ‘higher order’ preferences such as prudence and temperance play a role. A person exhibiting univariate prudence for longevity would be better able to cope with risk if they are going to live longer. Univariate temperance is characterised by a preference for prospects that disaggregate risk across different possible outcomes. Risk preferences can also be multivariate – across health and wealth, for example – determining the relationship between univariate risk preferences and other attributes. These include correlation aversion, cross-prudence, and cross-temperance. Many articles from the Arthur Attema camp demand a great deal of background knowledge. This paper isn’t an exception, but it does provide a very clear and intuitive description of the various kinds of uni- and multivariate risk preferences that the researchers are considering.

For this study, an experiment was conducted with 98 people, who were asked to make 69 choices, corresponding to 3 choices about each risk preference trait being tested, for both gains and losses. Participants were told that they had €240,000 in wealth and 40 years of life to play with. The number of times that an individual made choices in line with a particular trait was used as an indicator of their strength of preference.

For gains, risk aversion was common for both wealth and longevity, and prudence was a common trait. There was no clear tendency towards temperance. For losses, risk aversion and prudence tended to neutrality. For multivariate risk preferences, a majority of people were correlation averse for gains and correlation seeking for losses. For gains, 76% of choices were compatible with correlation aversion, suggesting that people prefer to disaggregate fixed wealth and health gains. For losses, the opposite was true in 68% of choices. There was evidence for cross-prudence in wealth gains but not longevity gains, suggesting that people prefer health risk if they have higher wealth. For losses, the researchers observed cross-prudence and cross-temperance neutrality. The authors go on to explore associations between different traits.

A key contribution is in understanding how risk preferences differ in the health domain as compared with the monetary domain (which is what most economists study). Conveniently, there are a lot of similarities between risk preferences in the two domains, suggesting that health economists can learn from the wider economics literature. Risk aversion and prudence seem to apply to longevity as well as monetary gains, with a shift to neutrality in losses. The potential implications of these findings are far-reaching, but this is just a small experimental study. More research needed (and anticipated).

Prospective payment systems and discretionary coding—evidence from English mental health providers. Health Economics [PubMed] Published 27th December 2018

If you’ve conducted an economic evaluation in the context of mental health care in England, you’ll have come across mental health care clusters. Patients undergoing mental health care are allocated to one of 20 clusters, classed as either ‘psychotic’, ‘non-psychotic’, or ‘organic’, which forms the basis of an episodic payment model. In 2013/14, these episodes were associated with an average cost of between £975 and £9,354 per day. Doctors determine the clusters and the clusters determine reimbursement. Perverse incentives abound. Or do they?

This study builds on the fact that patients are allocated by clinical teams with guidance from the algorithm-based Mental Health Clustering Tool (MHCT). Clinical teams might exhibit upcoding, whereby patients are allocated to clusters that attract a higher price than that recommended by the MHCT. Data were analysed for 148,471 patients from the Mental Health Services Data Set for 2011-2015. For each patient, their allocated cluster is known, along with a variety of socioeconomic indicators and the HoNoS and SARN instruments, which go into the MHCT algorithm. Mixed-effects logistic regression was used to look at whether individual patients were or were not allocated to the cluster recommended as ‘best fit’ by the MHCT, controlling for patient and provider characteristics. Further to this, multilevel multinomial logit models were used to categorise decisions that don’t match the MHCT as either under- or overcoding.

Average agreement across clusters between the MHCT and clinicians was 36%. In most cases, patients were allocated to a cluster either one step higher or one step lower in terms of the level of need, and there isn’t an obvious tendency to overcode. The authors are able to identify a few ways in which observable provider and patient characteristics influence the tendency to under- or over-cluster patients. For example, providers with higher activity are less likely to deviate from the MHCT best fit recommendation. However, the dominant finding – identified by using median odds ratios for the probability of a mismatch between two random providers – seems to be that unobserved heterogeneity determines variation in behaviour.

The study provides clues about the ways in which providers could manipulate coding to their advantage and identifies the need for further data collection for a proper assessment. But reimbursement wasn’t linked to clustering during the time period of the study, so it remains to be seen how clinicians actually respond to these potentially perverse incentives.

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Chris Sampson’s journal round-up for 17th December 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.

Health related quality of life aspects not captured by EQ-5D-5L: results from an international survey of patients. Health Policy Published 14th December 2018

Generic preference-based measures, such as the EQ-5D, cannot capture all aspects of health-related quality of life. They’re not meant to. Rather, their purpose is to capture just enough information to be able to adequately distinguish between health states with respect to the domains deemed normatively relavent to decisionmakers. The stated aim of this paper is to determine whether people with a variety of chronic conditions believe that their experiences can be adequately represented by the EQ-5D-5L.

The authors conducted an online survey, identifying participants through 320 patient associations across 47 countries. Participants were asked to complete the EQ-5D-5L and then asked if any aspects of their illness, which had a “big impact” on their health, were not captured by the EQ-5D-5L. 1,031 people started the survey and 767 completed it. More than half were from the UK. 51% of respondents said that there was some aspect of health not captured by the EQ-5D-5L. Of them, 19% mentioned fatigue, 12% mentioned medication side effects, 9.5% mentioned co-morbid conditions, and then a bunch of others in smaller proportions.

It’s nice to know what people think, but I have a few concerns about the usefulness of this study. One of the main problems is that it doesn’t seem safe to assume that respondents interpret “big impact” as meaning “an impact that is independently important in determining your overall level of quality of life”. So, even if we accept that people judging something to be important makes it important (which I’m not sure it does), then we still can’t be sure whether what they are identifying is within the scope of what we’re trying to measure. For starters, I can see no justification for including a ‘medication side effects’ domain. There’s also some concern about selection and attrition. I’d guess that people with more complicated or less common health concerns would be more likely to start and finish a survey about more complicated or less common health concerns.

The main thing I took from this study is that half of respondents with chronic diseases thought that the EQ-5D-5L captured every single aspect of health that had a “big impact”, and that there wasn’t broad support for any other specific dimension.

Reducing drug wastage in pharmaceuticals dosed by weight or body surface areas by optimising vial sizes. Applied Health Economics and Health Policy [PubMed] Published 5th December 2018

It’s common for pharmaceuticals to be wasted. Not just those out-of-date painkillers you threw in the bin, but also the expensive stuff being used in hospitals. One of the main reasons that waste occurs is that vials are made to specific sizes and, often, dosage varies from patient to patient – according to weight, for example – and doesn’t match the vial size. Suppose that vials are available as 50mg and 80mg and that an individual requires a 60mg dose. One way to address this might be to allow for vial sharing, whereby the leftovers are given to the next patient. But that isn’t always possible. So, we might like to consider what the best combination of available vial sizes should be, given the characteristics of the population.

In this paper, the authors set out the problem mathematically. Essentially, the optimisation problem is to minimise cost across the population subject to the vial sizes. An example is presented for two drugs (pembrolizumab and cabazitaxel), simulating patients based on samples drawn from the Health Survey for England. Simplifications are applied to the examples, such as setting a constraint of 8 vials per patient and assuming that prices are linear (i.e. fixed per milligram).

Pembrolizumab is currently available in 50mg and 100mg vials, and the authors estimate current wastage to be 13.2%. The simulations show that switching the 50mg to a 70mg would cut wastage to 8.6%. Cabazitaxel is available in 60mg vials, resulting in 19.4% wastage. Introducing a 12.5mg vial would cut wastage by around two thirds. An important general finding, which should be self-evident, is that vial sizes should not be divisible by each other, as this limits the number of possible combinations.

Depending on when vial sizes are determined (e.g. pre- or post-authorisation), pharmaceutical companies might use it to increase profit margins, or health systems might use it to save costs. Regardless, wastage isn’t useful. Evidence-based manufacture is an example of one of those best ideas; the sort that is simple and seems obvious once it’s spelt out. It’s a rare opportunity to benefit patients, health care providers, and manufacturers, with no significant burden on policymakers.

Death or debt? National estimates of financial toxicity in persons with newly-diagnosed cancer. The American Journal of Medicine [PubMed] Published October 2018

If you’re British, what’s the scariest thing about an ‘Americanised’ (/Americanized) health care system? Expensive inhalers? A shortened life expectancy? My guess is that the prospect of having to add financial ruin to terminal illness looms pretty large. You should make sure your fear is evidence-based. Here’s a paper to shake in the face of anyone who doesn’t support universal health care.

The authors use data from the Health and Retirement Study from 1998-2014, which includes people over 50 years of age and includes new (self-reported) diagnoses of cancer. This was the basis for inclusion in the study, with over 9.5 million new diagnoses of cancer. Up to two years pre-diagnosis was taken as a baseline. The data set also includes information on participants’ assets and debts, allowing the authors to use change in net worth as the primary outcome. Generalised linear models were used to assess various indicators of financial toxicity, including change or incurrence of consumer debt, mortgage debt, and home equity debt at two- and four-year follow-up. In addition to cancer diagnosis, various chronic comorbidities and socio-demographic variables were included in the models.

Shockingly, after two years following diagnosis, 42.4% of people had depleted their entire life’s assets. Average net worth had dropped $92,000. After four years, 38.2% were still insolvent. Women, older people, people who weren’t White, people with Medicaid, and those with worsening cancer status were among those more likely to have completely depleted their assets within two years. Having private insurance and being married had protective effects, as we might expect. There were some interesting findings associated with the 2008 financial crisis, which also seemed to be protective. And a protective effect associated with psychiatric comorbidity deserves more thought.

It’s difficult to explain away any (let alone all) of the magnitude of these findings. The analysis seems robust. But, given all other evidence available about out-of-pocket costs for cancer patients in the US, it should be shocking but not unexpected. The authors describe financial toxicity as ‘unintended’. There’s nothing unintended about this. Policymakers in the US keep deciding that they’d prefer to destroy the lives of sick people than allow for the spreading of that financial risk.

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