Chris Sampson’s journal round-up for 13th March 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.

The effects of exercise and relaxation on health and wellbeing. Health Economics [PubMedPublished 9th Month 2017

Encouraging self-management of health sounds like a good idea, but the evidence is pretty weak. As economists, we know that something must be displaced in order to do it. This study considers the opportunity cost of time and how it might affect self-management activity and any associated benefits. Employment and education are likely to increase income and thus facilitate more expenditure on exercise. But the time cost of exercise is also likely to increase, meaning that the impact on demand is ambiguous. The study uses data from a trial of self-management support that included people with diabetes, COPD or IBS. EQ-5D, self-assessed health and the amount of time spent ‘being happy’ were all collected. Information was available for 12 different self-management activities, including ‘do exercises’ and ‘rest and relax’, and the extent to which individuals did these. Outcomes for 3,472 people at 12-month follow-up are estimated, controlling for outcomes at baseline and 6 months. The study assumes that employment and education affect health via their influence on exercise and relaxation. That seems a bit questionable and the other 10 self-management indicators could have been looked at to test this. People in full-time employment were 11 percentage points less likely to use relaxation to manage their condition, suggesting that the substitution effect on time dominates as the opportunity cost of self-management increases. Having a degree or professional qualification increased the probability of using exercise by 5 percentage points, suggesting that the income effect dominates. Those who are more likely to use either exercise or relaxation are also more likely to do the other. An interesting suggestion is that time preference might explain things here. Those with more education may prefer to exercise (as an investment) than to get the instant gratification of rest and relaxation. It’s important that policy recommendations take into consideration the fact that different groups will respond differently to incentives for self-management, at least partly due to their differing time constraints. The thing I find most interesting is the analysis of the different outcomes (something I’ve worked on). Exercise is found to improve self-assessed health, while relaxation increases happiness. Neither exercise or relaxation had a (statistically significant) effect on EQ-5D. Depending on your perspective, this either suggests that the EQ-5D is failing to identify important changes in broad health-related domains or it means that self-management does not achieve the goals (QALYs to the max) of the health service.

New findings from the time trade-off for income approach to elicit willingness to pay for a quality adjusted life year. The European Journal of Health Economics [PubMedPublished 8th March 2017

The question ‘what is a QALY worth’ could invoke any number of reactions in a health economist, from chin scratching to eye rolling. The perspective that we’re probably most familiar with in the UK is that the value of a QALY is the value of health foregone in order to achieve it (i.e. opportunity cost within the health care perspective). An alternative perspective is that the value of a QALY is the consumption value of health; how much consumption would individuals be willing to give up in order to obtain an additional QALY? This second perspective facilitates a broader societal perspective. It can tell us whether or not the budget is set at an appropriate level, while the health care perspective can only take the budget as given. This study relates mainly to decisions made with the ‘consumption value’ perspective. One approach that has been proposed is to assess willingness to pay for a QALY using a time trade-off exercise that incorporates trade-offs between length and quality of life and income. This study builds on the original work by using a multiplicative utility function to estimate willingness to pay and also bringing in prospect theory to allow for reference dependence and loss aversion. 550 participants were asked to choose between living 10 years in their current health state with their current salary or to live a reduced number of years in their current health state with a luxury income (pre-specified by the participant). Respondents were also asked to make a similar choice, but framed as a loss of income, between living 10 years at a subsistence income or fewer years with their current income. A quality of life trade-off exercise was also conducted, in which people traded reduced health and a lower income. The findings support the predictions of prospect theory. Loss aversion is found to be stronger for duration than for quality of life. Individuals were more willing to sacrifice life years to move from subsistence income to current income than to move from current income to luxury income. The results imply that quality of life and income are closer substitutes than longevity and income. That makes sense, given the all-or-nothing nature of being alive. Crucially, the findings highlight the need to better understand the shape of the underlying lifetime utility function. In all tasks, more than half of respondents were either non-traders or over-traded, indicating a negative willingness to pay. That should give pause for thought when it comes to any aggregation of the results. Willingness to pay studies often throw up more questions than answers. This one does so more than most, particularly about sources of bias in people’s responses. The authors identify plenty of opportunities for future research.

Beyond QALYs: multi-criteria based estimation of maximum willingness to pay for health technologies. The European Journal of Health Economics [PubMed] Published 3rd March 2017

Life is messy. Evaluating things in terms of a single outcome, whether that be QALYs, £££s or whatever, is necessarily simplifying and restrictive. That’s not necessarily a bad thing, but we’d do well to bear it in mind. In this paper, Erik Nord sets out a kind of cost value analysis that does away with QALYs (gasp!). The author starts by outlining some familiar criticisms of the QALY approach, such as its failure to consider the inherent value of life and people’s differing reference points. Generally, I see these as features rather than bugs, and it isn’t QALYs themselves in the crosshairs here so much as cost-per-QALY analysis. The proposed method flips current practice by putting societal preferences about fair and efficient resource allocation before attaching values to the outcomes. As such, it acknowledges the fact that society’s preferences for gains in quality of life differ from those for gains in length of life. For example, society may prefer treating the more severely ill (independent of age) but also exhibit a ‘fair innings’ preference that is related to age. Thus, quality and quantity of life are disaggregated and the QALY is no more. A set of tables is presented that can be read to assess ‘value’ in alternative scenarios, given the assumptions set out in the paper. There is merit in the approach and a lot that I like about the possibilities of its use. But for me, the whole thing was made less attractive by the way it is presented in the paper. The author touts willingness to pay – for quality of life gains and for longevity gains – as the basis for value. Anything that makes resource allocation more dependent on willingness to pay values for things without a price (health, life) is a big no-no for me. But the method doesn’t depend on that. Furthermore, as is so often the case, most of the criticisms within relate to ways of using QALYs, rather than the fundamental basis for their estimation. This only weakens the argument for an alternative. But I can think of plenty of problems with QALYs, some of which might be addressed by this alternative approach. It’s unfortunate that the paper doesn’t outline how these more fundamental problems might be addressed. There may come a day when we do away with QALYs, and we may end up doing something similar to what’s outlined here, but we need to think harder about how this alternative is really better.

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Visualising PROMs data

The patient reported outcomes measures, or PROMs, is a large database with before and after health-related quality of life (HRQoL) measures for a large number of patients undergoing four key conditions: hip replacement, knee replacement, varicose vein surgery and surgery for groin hernia. The outcome measures are the EQ-5D index and visual analogue scale (and a disease-specific measure for three of the interventions). These data also contain the provider of the operation. Being publicly available, these data allow us to look at a range of different questions: what’s the average effect of the surgery on HRQoL? What are the differences between providers in gains to HRQoL or in patient casemix? Great!

The first thing we should always do with new data is to look at it. This might be in an exploratory way to determine the questions to ask of the data or in an analytical way to get an idea of the relationships between variables. Plotting the data communicates more about what’s going on than any table of statistics alone. However, the plots on the NHS Digital website might be accused of being a little uninspired as they collapse a lot of the variation into simple charts that conceal a lot of what’s going on. For example:

So let’s consider other ways of visualising this data. For all these plots a walk through of the code is at the end of this post.

Now, I’m not a regular user of PROMs data, so what I think are the interesting features of the data may not reflect what the data are generally used for. For this, I think the interesting features are:

  • The joint distribution of pre- and post-op scores
  • The marginal distributions of pre- and post-op scores
  • The relationship between pre- and post-op scores over time

We will pool all the data from six years’ worth of PROMs data. This gives us over 200,000 observations. A scatter plot with this information is useless as the density of the points will be very high. A useful alternative is hexagonal binning, which is like a two-dimensional histogram. Hexagonal tiles, which usefully tessellate and are more interesting to look at than squares, can be shaded or coloured with respect to the number of observations in each bin across the support of the joint distribution of pre- and post-op scores (which is [-0.5,1]x[-0.5,1]). We can add the marginal distributions to the axes and then add smoothed trend lines for each year. Since the data are constrained between -0.5 and 1, the mean may not be a very good summary statistic, so we’ll plot a smoothed median trend line for each year. Finally, we’ll add a line on the diagonal. Patients above this line have improved and patients below it deteriorated.

Hip replacement results

Hip replacement results

There’s a lot going on in the graph, but I think it reveals a number of key points about the data that we wouldn’t have seen from the standard plots on the website:

  • There appear to be four clusters of patients:
    • Those who were in close to full health prior to the operation and were in ‘perfect’ health (score = 1) after;
    • Those who were in close to full health pre-op and who didn’t really improve post-op;
    • Those who were in poor health (score close to zero) and made a full recovery;
    • Those who were in poor health and who made a partial recovery.
  • The median change is an improvement in health.
  • The median change improves modestly from year to year for a given pre-op score.
  • There are ceiling effects for the EQ-5D.

None of this is news to those who study these data. But this way of presenting the data certainly tells more of a story that the current plots on the website.

R code

We’re going to consider hip replacement, but the code is easily modified for the other outcomes. Firstly we will take the pre- and post-op score and their difference and pool them into one data frame.

# df 14/15
df<-read.csv("C:/docs/proms/Record Level Hip Replacement 1415.csv")

df<-df[!is.na(df$Pre.Op.Q.EQ5D.Index),]
df$pre<-df$Pre.Op.Q.EQ5D.Index
df$post<- df$Post.Op.Q.EQ5D.Index
df$diff<- df$post - df$pre

df1415 <- df[,c('Provider.Code','pre','post','diff')]

#
# df 13/14
df<-read.csv("C:/docs/proms/Record Level Hip Replacement 1314.csv")

df<-df[!is.na(df$Pre.Op.Q.EQ5D.Index),]
df$pre<-df$Pre.Op.Q.EQ5D.Index
df$post<- df$Post.Op.Q.EQ5D.Index
df$diff<- df$post - df$pre

df1314 <- df[,c('Provider.Code','pre','post','diff')]

# df 12/13
df<-read.csv("C:/docs/proms/Record Level Hip Replacement 1213.csv")

df<-df[!is.na(df$Pre.Op.Q.EQ5D.Index),]
df$pre<-df$Pre.Op.Q.EQ5D.Index
df$post<- df$Post.Op.Q.EQ5D.Index
df$diff<- df$post - df$pre

df1213 <- df[,c('Provider.Code','pre','post','diff')]

# df 11/12
df<-read.csv("C:/docs/proms/Hip Replacement 1112.csv")

df$pre<-df$Q1_EQ5D_INDEX
df$post<- df$Q2_EQ5D_INDEX
df$diff<- df$post - df$pre
names(df)[1]<-'Provider.Code'

df1112 <- df[,c('Provider.Code','pre','post','diff')]

# df 10/11
df<-read.csv("C:/docs/proms/Record Level Hip Replacement 1011.csv")

df$pre<-df$Q1_EQ5D_INDEX
df$post<- df$Q2_EQ5D_INDEX
df$diff<- df$post - df$pre
names(df)[1]<-'Provider.Code'

df1011 <- df[,c('Provider.Code','pre','post','diff')]

#combine

df1415$year<-"2014/15"
df1314$year<-"2013/14"
df1213$year<-"2012/13"
df1112$year<-"2011/12"
df1011$year<-"2010/11"

df<-rbind(df1415,df1314,df1213,df1112,df1011)
write.csv(df,"C:/docs/proms/eq5d.csv")

Now, for the plot. We will need the packages ggplot2, ggExtra, and extrafont. The latter package is just to change the plot fonts, not essential, but aesthetically pleasing.

require(ggplot2)
require(ggExtra)
require(extrafont)
font_import()
loadfonts(device = "win")

p<-ggplot(data=df,aes(x=pre,y=post))+
 stat_bin_hex(bins=15,color="white",alpha=0.8)+
 geom_abline(intercept=0,slope=1,color="black")+
 geom_quantile(aes(color=year),method = "rqss", lambda = 2,quantiles=0.5,size=1)+
 scale_fill_gradient2(name="Count (000s)",low="light grey",midpoint = 15000,
   mid="blue",high = "red",
   breaks=c(5000,10000,15000,20000),labels=c(5,10,15,20))+
 theme_bw()+
 labs(x="Pre-op EQ-5D index score",y="Post-op EQ-5D index score")+
 scale_color_discrete(name="Year")+
 theme(legend.position = "bottom",text=element_text(family="Gill Sans MT"))

ggMarginal(p, type = "histogram")

Kenneth Arrow on healthcare economics: a 21st century appreciation

Nobel laureate Kenneth Arrow passed away on February 21, 2017. In a classic, fifty-year-old paper entitled Uncertainty and the Welfare Economics of Medical Care, Arrow discussed how:

“the operation of the medical-care industry and the efficacy with which it satisfies the needs of society differs from… a competitive model… If a competitive equilibrium exists at all, and if all commodities relevant to costs or utilities are in fact priced in the market, then the equilibrium is necessarily [Pareto] optimal” (emphasis added)

Note the implicit assumption that price reflects value, to which I’ll return. As Arrow elegantly explained, there are vast differences between the actual healthcare market and the competitive model, and, moreover, these differences arise from important features of the actual healthcare market.

Identifying the lack of realism of the competitive model in health care may lead to deeper understanding of the actual system. In essence this is what Arrow does. Although both medical care and our expectations have changed greatly, Arrow ’63 is still valid and worth reading today.

Here is Arrow’s summary of the differences between the healthcare market and typical competitive markets.

The nature of demand

Demand for medical services is irregular and unpredictable:

“Medical services, apart from preventive services, afford satisfaction only in the event of illness, a departure from the normal state of affairs… Illness is, thus, not only risky but a costly risk in itself, apart from the cost of medical care.”

Expected behavior of the physician

“It is at least claimed that treatment is dictated by objective needs of the case and not limited by financial considerations… Charity treatment in one form or another does exist because of this tradition about human rights to adequate medical care.”

Product uncertainty

“Recovery from disease is as unpredictable as its incidence…  Because medical knowledge is so complicated, the information possessed by the physician as to the consequences and possibilities of treatment is necessarily very much greater than that of the patient, or at least so it is believed by both parties.”

Supply conditions

Barriers to entry include licensing and other controls on quality (accreditation) and costs.

“One striking consequence of the control of quality is the restriction on the range offered… The declining ratio of physicians to total employees in the medical-care industry shows that substitution of less trained personnel, technicians and the like, is not prevented completely, but the central role of the highly trained physician is not affected at all.”

Pricing practices

There are no fixed prices:

“extensive price discrimination by income (with an extreme of zero prices for sufficiently indigent patients)… the apparent rigidity of so-called administered prices considerably understates the actual flexibility.”

Avik Roy observes in a critical National Review article that “Because patients don’t see the bill until after the non-refundable service has been consumed, and because patients are given little information about price and cost, patients and payors are rarely able to shop around for a medical service based on price and value.”

Medicine has seen major changes since Arrow’s 1963 paper. For example, the treatment of blocked coronary arteries has evolved from coronary bypass to angioplasty to early stents and finally drug-eluting stents. We have seen the advent of minimally invasive surgery, robotic surgery and catheter-based cardiac valve repair and replacement. We have seen drugs to treat hepatitis C and biologicals to treat arthritis and cancer. Many conditions have been transformed from acute to chronic but (at least temporarily) manageable. There are also divergent trends, such as increases in both natural childbirth and Caesarean sections.

In the last 50 years, medicine has become more powerful, but also significantly more complex and overall, more expensive. Intensive care units are a good example, both valuable therapeutically, but expensive to provide. At the same time, many treatments are both better (more valuable to the patient) and less expensive to provide; these range from root canal (frequently two visits to the dentist instead of four) to the significantly less invasive treatments for many cardiac rhythm abnormalities (radio-frequency ablation) and stents for coronary artery disease. The advent of epinephrine auto-injectors has been a lifesaver, but the cost of the Epi-Pen has increased significantly.

Can a competitive economic system appropriately and reasonably price such treatments and devices? Arrow argues that, if not, non-market social institutions will arise and address these challenges. Here is a deeper look.

Arrow’s first two points are still virtually axiomatic today: demand for medical services has become even more unpredictable with the continued growth of advanced, effective interventions and corresponding, appropriately increasing (in my opinion), patient expectations. Similarly, as medical care advances, we increasingly see medical care as a human right and in many cases, a societal obligation. We have come to expect treatment dictated by objective needs and not limited by financial considerations, not only from physicians but from a growing number of key players including pharmaceutical companies. To their credit, in many cases (AIDS comes to mind) pharmaceutical companies have responded by sharply reducing prices in the developing world.

Powerful chemotherapeutic and biologic drugs may have increased the uncertainty and asymmetry of information observed by Arrow, both in their effectiveness and in their side effects. In many cases one needs the language and mathematics of probability and statistics to evaluate, assess and describe their efficacy and utility. One needs an understanding of probability to determine when and how to use common preventive techniques, such as mammograms and PSA screening. Here is an example, paraphrased from Gigerenzer and Edwards (see also Strogatz). Women 40 to 50 years old, with no family history of breast cancer, are a low-risk population; the overall probability of breast cancer in this population is 0.8%. Assume that mammography has a sensitivity of 90% and a false positive rate of 7%.  A woman has a positive mammogram. What is the probability that she has breast cancer? Among 25 German doctors surveyed, 36% said 90% or more, 32% said 50-80%, and 32% said 10% or less. Most (95%) of United States doctors thought the probability was approximately 75%.  (See the links above for the answer, or see my next blog on the challenge of communicating probability).

Arrow’s information asymmetry remains, despite the growing availability of accessible medical information on the web, perhaps for good reasons such as the ability to effectively address the needs of sicker patients.

I would amend Arrow’s discussion of supply conditions to include a wide variety of cost barriers ranging from large fixed costs of ICUs to the costs of medical research. The high cost of basic medical services relative to per capita GDP in the the developing world represents a barrier as high as any faced in the developed world.  As Arrow notes, society has addressed this challenge through a variety of pricing mechanisms outside traditional competitive models. This may not, and in general will not achieve a Pareto optimum, but their wide endorsement by society does indeed suggest that these approaches achieve a more general optimum.

“I propose here the view that, when the market fails to achieve an optimal state, society will, to some extent at least, recognize the gap, and nonmarket social institutions will arise attempting to bridge it… But it is contended here that the special structural characteristics of the medical-care market are largely attempts to overcome the lack of optimality due to the nonmarketability of the bearing of suitable risks and the imperfect marketability of information. These compensatory institutional changes, with some reinforcement from usual profit motives, largely explain the observed noncompetitive behavior of the medical-care market, behavior which, in itself, interferes with optimality. The social adjustment towards optimality thus puts obstacles in its own path.”

It is this view which I find too limiting. I would suggest that society has at least implicitly concluded that price alone does not define value, and thus formed a broader definition of optimality, not simply Pareto optimality in a competitive market. Society is finding and supporting ways to overcome obstacles toward this broader sense of optimality.

The Bill & Melinda Gates Foundation vaccination project aims to reduce the number of children that die each year from preventable disease (currently around 1.5 million). The lifebox project, founded by Dr Atul Gawande, provides affordable, high quality pulse oximeters to the developing world and now seeks to address basic surgical safety in the developing world. Important advances also arise in the developing world; most recently, an easy to deliver, more effective oral cholera vaccine developed in Vietnam.

Arrow himself recognizes the limits of a traditional economic description of the medical care market in his concluding Postscript, arguing that “The logic and limitations of ideal competitive behavior under uncertainty force us to recognize the incomplete description of reality supplied by the impersonal price system.” I conclude more generally that prices not only do not necessarily represent value in medical care (as Arrow observed), but that the combination of uncertainty, externalities, high costs, divergent economies, and technological advance means that price alone cannot describe value in medical care. A broader more general theory of healthcare economics with a foundation standing on the shoulders of giants such as Kenneth Arrow, with perhaps a more general multi-dimensional Pareto optimum, might help us all better understand where we are and where we might go.

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