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.


Sam Watson’s journal round-up for 12th November 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.

Estimating health opportunity costs in low-income and middle-income countries: a novel approach and evidence from cross-country data. BMJ Global Health. Published November 2017.

The relationship between health care expenditure and population health outcomes is a topic that comes up often on this blog. Understanding how population health changes in response to increases or decreases in the health system budget is a reasonable way to set a cost-effectiveness threshold. Purchasing things above this threshold will, on average, displace activity with greater benefits. But identifying this effect is hard. Commonly papers use some kind of instrumental variable method to try to get at the causal effect with aggregate, say country-level, data. These instruments, though, can be controversial. Years ago I tried to articulate why I thought using socio-economic variables as instruments was inappropriate. I also wrote a short paper a few years ago, which remains unpublished, that used international commodity price indexes as an instrument for health spending in Sub-Saharan Africa, where commodity exports are a big driver of national income. This was rejected from a journal because of the choice of instruments. Commodity prices may well influence other things in the country that can influence population health. And a similar critique could be made of this article here, which uses consumption:investment ratios and military expenditure in neighbouring countries as instruments for national health expenditure in low and middle income countries.

I remain unconvinced by these instruments. The paper doesn’t present validity checks on them, which is forgiveable given medical journal word limitations, but does mean it is hard to assess. In any case, consumption:investment ratios change in line with the general macroeconomy – in an economic downturn this should change (assuming savings = investment) as people switch from consumption to investment. There are a multitude of pathways through which this will affect health. Similarly, neighbouring military expenditure would act by displacing own-country health expenditure towards military expenditure. But for many regions of the world, there has been little conflict between neighbours in recent years. And at the very least there would be a lag on this effect. Indeed, in all the models of health expenditure and population health outcomes I’ve seen, barely a handful take into account dynamic effects.

Now, I don’t mean to let the perfect be the enemy of the good. I would never have suggested this paper should not be published as it is, at the very least, important for the discussion of health care expenditure and cost-effectiveness. But I don’t feel there is strong enough evidence to accept these as causal estimates. I would even be willing to go as far to say that any mechanism that affects health care expenditure is likely to affect population health by some other means, since health expenditure is typically decided in the context of the broader public sector budget. That’s without considering what happens with private expenditure on health.

Strategic Patient Discharge: The Case of Long-Term Care Hospitals. American Economic Review. [RePEcPublished November 2018.

An important contribution of health economics has been to undermine people’s trust that doctors act in their best interest. Perhaps that’s a little facetious, nevertheless there has been ample demonstration that health care providers will often act in their own self-interest. Often this is due to trying to maximise revenue by gaming reimbursement schemes, but also includes things like doctors acting differently near the end of their shift so they can go home on time. So when I describe a particular reimbursement scheme that Medicare in the US uses, I don’t think there’ll be any doubt about the results of this study of it.

In the US, long-term acute care hospitals (LTCHs) specialise in treating patients with chronic care needs who require extended inpatient stays. Medicare reimbursement typically works on a fixed rate for each of many diagnostic related groups (DRGs), but given the longer and more complex care needs in LTCHs, they get a higher tariff. To discourage admitting patients purely to get higher levels of reimbursement, the bulk of the payment only kicks in after a certain length of stay. Like I said – you can guess what happened.

This article shows 26% of patients are discharged in the three days after the length of stay threshold compared to just 7% in the three days prior. This pattern is most strongly observed in discharges to home, and is not present in patients who die. But this may still be just by chance that the threshold and these discharges coincide. Fortunately for the authors the thresholds differ between DRGs and even move around within a DRG over time in a way that appears unrelated to actual patient health. They therefore estimate a set of decision models for patient discharge to try to estimate the effect of different reimbursement policies.

Estimating misreporting in condom use and its determinants among sex workers: Evidence from the list randomisation method. Health Economics. Published November 2018.

Working on health and health care research, especially if you conduct surveys, means you often want to ask people about sensitive topics. These could include sex and sexuality, bodily function, mood, or other ailments. For example, I work a fair bit on sanitation, where frequently self-reported diarrhoea in under fives (reported by the mother that is) is the primary outcome. This could be poorly reported particularly if an intervention includes any kind of educational component that suggests it could be the mother’s fault for, say, not washing her hands, if the child gets diarrhoea. This article looks at condom use among female sex workers in Senegal, another potentially sensitive topic, since unprotected sex is seen as risky. To try and get at the true prevalence of condom use, the authors use a ‘list randomisation’ method. This randomises survey participants to two sets of questions: a set of non-sensitive statements, or the same set of statements with the sensitive question thrown in. All respondents have to do is report the number of the statements they agree with. This means it is generally not possible to distinguish the response to the sensitive question, but the difference in average number of statements reported between the two groups gives an unbiased estimator for the population proportion. Neat, huh? Ultimately the authors report an estimate of 80% of sex workers using condoms, which compares to the 97% who said they used a condom when asked directly.



Chris Sampson’s journal round-up for 5th November 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.

Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration. The European Journal of Health Economics [PubMed] Published 29th October 2018

Health care is increasingly personalised. This creates the need to evaluate interventions for smaller and smaller subgroups as patient heterogeneity is taken into account. And this usually means we lack the statistical power to have confidence in our findings. The purpose of this paper is to consider the usefulness of a tool that hasn’t previously been employed in economic evaluation – the subpopulation treatment effect pattern plot (STEPP). STEPP works by assessing the interaction between treatments and covariates in different subgroups, which can then be presented graphically. Imagine your X-axis with the values defining the subgroups and your Y-axis showing the treatment outcome. This information can then be used to determine which subgroups exhibit positive outcomes.

This study uses data from a trial-based economic evaluation in heart failure, where patients’ 18-month all-cause mortality risk was estimated at baseline before allocation to one of three treatment strategies. For the STEPP procedure, the authors use baseline risk to define subgroups and adopt net monetary benefit at the patient level as the outcome. The study makes two comparisons (between three alternative strategies) and therefore presents two STEPP figures. The STEPP figures are used to identify subgroups, which the authors apply in a stratified cost-effectiveness analysis, estimating net benefit in each defined risk subgroup.

Interpretation of the STEPPs is a bit loose, with no hard decision rules. The authors suggest that one of the STEPPs shows no clear relationship between net benefit and baseline risk in terms of the cost-effectiveness of the intervention (care as usual vs basic support). The other STEPP shows that, on average, people with baseline risk below 0.16 have a positive net benefit from the intervention (intensive support vs basic support), while those with higher risk do not. The authors evaluate this stratification strategy against an alternative stratification strategy (based on the patient’s New York Heart Association class) and find that the STEPP-based approach is expected to be more cost-effective. So the key message seems to be that STEPP can be used as a basis for defining subgroups as cost-effectively as possible.

I’m unsure about the extent to which this is a method that deserves to have its own name, insofar as it is used in this study. I’ve seen plenty of studies present a graph with net benefit on the Y-axis and some patient characteristic on the X-axis. But my main concern is about defining subgroups on the basis of net monetary benefit rather than some patient characteristic. Is it OK to deny treatment to subgroup A because treatment costs are higher than in subgroup B, even if treatment is cost-effective for the entire population of A+B? Maybe, but I think that creates more challenges than stratification on the basis of treatment outcome.

Using post-market utilisation analysis to support medicines pricing policy: an Australian case study of aflibercept and ranibizumab use. Applied Health Economics and Health Policy [PubMed] Published 25th October 2018

The use of ranibizumab and aflibercept has been a hot topic in the UK, where NHS providers feel that they’ve been bureaucratically strong-armed into using an incredibly expensive drug to treat certain eye conditions when a cheaper and just-as-effective alternative is available. Seeing how other countries have managed prices in this context could, therefore, be valuable to the NHS and other health services internationally. This study uses data from Australia, where decisions about subsidising medicines are informed by research into how drugs are used after they come to market. Both ranibizumab (in 2007) and aflibercept (in 2012) were supported for the treatment of age-related macular degeneration. These decisions were based on clinical trials and modelling studies, which also showed that the benefit of ~6 aflibercept prescriptions equated to the benefit of ~12 ranibizumab prescriptions, justifying a higher price-per-injection for aflibercept.

In the UK and US, aflibercept attracts a higher price. The authors assume that this is because of the aforementioned trial data relating to the number of doses. However, in Australia, the same price is paid for aflibercept and ranibizumab. This is because a post-market analysis showed that, in practice, ranibizumab and aflibercept had a similar dose frequency. The purpose of this study is to see whether this is because different groups of patients are being prescribed the two drugs. If they are, then we might anticipate heterogenous treatment outcomes and thus a justification for differential pricing. Data were drawn from an administrative claims database for 208,000 Australian veterans for 2007-2017. The monthly number of aflibercept and ranibizumab prescriptions was estimated for each person, showing that total prescriptions increased steadily over the period, with aflibercept taking around half the market within a year of its approval. Ranibizumab initiators were slightly older in the post-aflibercept era but, aside from that, there were no real differences identified. When it comes to the prescription of ranibizumab or aflibercept, gender, being in residential care, remoteness of location, and co-morbidities don’t seem to be important. Dispensing rates were similar, at around 3 prescriptions during the first 90 days and around 9 prescriptions during the following 12 months.

The findings seem to support Australia’s decision to treat ranibizumab and aflibercept as substitutes at the same price. More generally, they support the idea that post-market utilisation assessments can (and perhaps should) be used as part of the health technology assessment and reimbursement process.

Do political factors influence public health expenditures? Evidence pre- and post-great recession. The European Journal of Health Economics [PubMed] Published 24th October 2018

There is mixed evidence about the importance of partisanship in public spending, and very little relating specifically to health care. I’d be worried if political factors didn’t influence public spending on health, given that that’s a definitively political issue. How the situation might be different before and after a recession is an interesting question.

The authors combined OECD data for 34 countries from 1970-2016 with the Database of Political Institutions. This allowed for the creation of variables relating to the ideology of the government and the proximity of elections. Stationary panel data models were identified as the most appropriate method for analysis of these data. A variety of political factors were included in the models, for which the authors present marginal effects. The more left-wing a government, the higher is public spending on health care, but this is only statistically significant in the period before the crisis of 2007. Before the crisis, coalition governments tended to spend more, while governments with more years in office tended to spend less. These effects also seem to disappear after 2007. Throughout the whole period, governing parties with a stronger majority tended to spend less on health care. Several of the non-political factors included in the models show the results that we would expect. GDP per capita is positively associated with health care expenditures, for example. The findings relating to the importance of political factors appear to be robust to the inclusion of other (non-political) variables and there are similar findings when the authors look at public health expenditure as a percentage of total health expenditure. In contradiction with some previous studies, proximity to elections does not appear to be important.

The most interesting finding here is that the effect of partisanship seems to have mostly disappeared – or, at least, reduced – since the crisis of 2007. Why did left-wing parties and right-wing parties converge? The authors suggest that it’s because adverse economic circumstances restrict the extent to which governments can make decisions on the basis of ideology. Though I dare say readers of this blog could come up with plenty of other (perhaps non-economic) explanations.