Chris Sampson’s journal round-up for 27th August 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.

Ethically acceptable compensation for living donations of organs, tissues, and cells: an unexploited potential? Applied Health Economics and Health Policy [PubMed] Published 25th August 2018

Around the world, there are shortages of organs for transplantation. In economics, the debate around the need to increase organ donation can be frustratingly ignorant of ethical and distributional concerns. So it’s refreshing to see this article attempting to square concerns about efficiency and equity. The authors do so by using a ‘spheres of justice’ framework. This is the idea that different social goods should be distributed according to different principles. So, while we might be happy for brocolli and iPhones to be distributed on the basis of free exchange, we might want health to be distributed on the basis of need. The argument can be extended to state that – for a just situation to prevail – certain exchanges between these spheres of justice (e.g. health for iPhones) should never take place. This idea might explain why – as the authors demonstrate with a review of European countries – policy tends not to allow monetary compensation for organ donation.

The paper cleverly sets out to taxonomise monetary and non-monetary reimbursement and compensation with reference to individuals’ incentives and the spheres of justice principles. From this, the authors reach two key conclusions. Firstly, that (monetary) reimbursement of donors’ expenses (e.g. travel costs or lost earnings) is ethically sound as this does not constitute an incentive to donate but rather removes existing disincentives. Secondly, that non-monetary compensation could be deemed ethical.

Three possible forms of non-monetary compensation are discussed: i) prioritisation, ii) free access, and iii) non-health care-related benefits. The first could involve being given priority for receiving organs, or it could extend to the jumping of other health care waiting lists. I think this is more problematic than the authors let on because it asserts that health care should – at least in part – be distributed according to desert rather than need. The second option – free access – could mean access to health care that people would otherwise have to pay for. The third option could involve access to other social goods such as education or housing.

This is an interesting article and an enjoyable read, but I don’t think it provides a complete solution. Maybe I’m just too much of a Marxist, but I think that this – as all other proposals – fails to distribute from each according to ability. That is, we’d still expect non-monetary compensation to incentivise poorer (and on average less healthy) people to donate organs, thus exacerbating health inequality. This is because i) poorer people are more likely to need the non-monetary benefits and ii) we live in a capitalist society in which there is almost nothing that money can’t by and which is strictly non-monetary. Show me a proposal that increases donation rates from those who can most afford to donate them (i.e. the rich and healthy).

Selecting bolt-on dimensions for the EQ-5D: examining their contribution to health-related quality of life. Value in Health Published 18th August 2018

Measures such as the EQ-5D are used to describe health-related quality of life as completely and generically as possible. But there is a trade-off between completeness and the length of the questionnaire. Necessarily, there are parts of the evaluative space that measures will not capture because they are a simplification. If the bit they’re missing is important to your patient group, that’s a problem. You might fancy a bolt-on. But how do we decide which areas of the evaluative space should be more completely included in the measure? Which bolt-ons should be used? This paper seeks to provide means of answering these questions.

The article builds on an earlier piece of work that was included in an earlier journal round-up. In the previous paper, the authors used factor analysis to identify candidate bolt-ons. The goal of this paper is to outline an approach for specifying which of these candidates ought to be used. Using data from the Multi-Instrument Comparison study, the authors fit linear regressions to see how well 37 candidate bolt-on items explain differences in health-related quality of life. The 37 items correspond to six different domains: energy/vitality, satisfaction, relationships, hearing, vision, and speech. In a second test, the authors explored whether the bolt-on candidates could explain differences in health-related quality of life associated with six chronic conditions. Health-related quality of life is defined according to a visual analogue scale, which notably does not correspond to that used in the EQ-5D but rather uses a broader measure of physical, mental, and social health.

The results suggest that items related to energy/vitality, relationships, and satisfaction explained a significant part of health-related quality of life on top of the existing EQ-5D dimensions. The implication is that these could be good candidates for bolt-ons. The analysis of the different conditions was less clear.

For me, there’s a fundamental problem with this study. It moves the goals posts. Bolt-ons are about improving the extent to which a measure can more accurately represent the evaluative space that it is designed to characterise. In this study, the authors use a broader definition of health-related quality of life that – as far as I can tell – the EQ-5D is not designed to capture. We’re not dealing with bolt-ons, we’re dealing with extensions to facilitate expansions to the evaluative space. Nevertheless, the method could prove useful if combined with a more thorough consideration of the evaluative space.

Sources of health financing and health outcomes: a panel data analysis. Health Economics [PubMed] [RePEc] Published 15th August 2018

There is a growing body of research looking at the impact that health (care) spending has on health outcomes. Usually, these studies don’t explicitly look at who is doing the spending. In this study, the author distinguishes between public and private spending and attempts to identify which type of spending (if either) results in greater health improvements.

The author uses data from the World Bank’s World Development Indicators for 1995-2014. Life expectancy at birth is adopted as the primary health outcome and the key expenditure variables are health expenditure as a share of GDP and private health expenditure as a share of total health expenditure. Controlling for a variety of other variables, including some determinants of health such as income and access to an improved water source, a triple difference analysis is described. The triple difference estimator corresponds to the difference in health outcomes arising from i) differences in the private expenditure level, given ii) differences in total expenditure, over iii) time.

The key finding from the study is that, on average, private expenditure is more effective in increasing life expectancy at birth than public expenditure. The author also looks at government effectiveness, which proves crucial. The finding in favour of private expenditure entirely disappears when only countries with effective government are considered. There is some evidence that public expenditure is more effective in these countries, and this is something that future research should investigate further. For countries with ineffective governments, the implication is that policy should be directed towards increasing overall health care expenditure by increasing private expenditure.

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Sam Watson’s journal round-up for 30th April 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.

The Millennium Villages Project: a retrospective, observational, endline evaluation. The Lancet Global Health [PubMedPublished May 2018

There are some clinical researchers who would have you believe observational studies are completely useless. The clinical trial is king, they might say, observation studies are just too biased. And while it’s true that observational studies are difficult to do well and convincingly, they can be a reliable and powerful source of evidence. Similarly, randomised trials are frequently flawed, for example there’s often missing data that hasn’t been dealt with, or a lack of allocation concealment, and many researchers forget that randomisation does not guarantee a balance of covariates, it merely increases the probability of it. I bring this up, as this study is a particularly carefully designed observational data study that I think serves as a good example to other researchers. The paper is an evaluation of the Millennium Villages Project, an integrated intervention program designed to help rural villages across sub-Saharan Africa meet the Millennium Development Goals over ten years between 2005 and 2015. Initial before-after evaluations of the project were criticised for inferring causal “impacts” from before and after data (for example, this Lancet paper had to be corrected after some criticism). To address these concerns, this new paper is incredibly careful about choosing appropriate control villages against which to evaluate the intervention. Their method is too long to summarise here, but in essence they match intervention villages to other villages on the basis of district, agroecological zone, and a range of variables from the DHS – matches were they reviewed for face validity and revised until a satisfactory matching was complete. The wide range of outcomes are all scaled to a standard normal and made to “point” in the same direction, i.e. so an increase indicated economic development. Then, to avoid multiple comparisons problems, a Bayesian hierarchical model is used to pool data across countries and outcomes. Costs data were also reported. Even better, “statistical significance” is barely mentioned at all! All in all, a neat and convincing evaluation.

Reconsidering the income‐health relationship using distributional regression. Health Economics [PubMed] [RePEcPublished 19th April 2018

The relationship between health and income has long been of interest to health economists. But it is a complex relationship. Increases in income may change consumption behaviours and a change in the use of time, promoting health, while improvements to health may lead to increases in income. Similarly, people who are more likely to make higher incomes may also be those who look after themselves, or maybe not. Disentangling these various factors has generated a pretty sizeable literature, but almost all of the empirical papers in this area (and indeed all empirical papers in general) use modelling techniques to estimate the effect of something on the expected value, i.e. mean, of some outcome. But the rest of the distribution is of interest – the mean effect of income may not be very large, but a small increase in income for poorer individuals may have a relatively large effect on the risk of very poor health. This article looks at the relationship between income and the conditional distribution of health using something called “structured additive distribution regression” (SADR). My interpretation of SADR is that, one would model the outcome y ~ g(a,b) as being distributed according to some distribution g(.) indexed by parameters a and b, for example, a normal or Gamma distribution has two parameters. One would then specify a generalised linear model for a and b, e.g. a = f(X’B). I’m not sure this is a completely novel method, as people use the approach to, for example, model heteroscedasticity. But that’s not to detract from the paper itself. The findings are very interesting – increases to income have a much greater effect on health at the lower end of the spectrum.

Ask your doctor whether this product is right for you: a Bayesian joint model for patient drug requests and physician prescriptions. Journal of the Royal Statistical Society: Series C Published April 2018.

When I used to take econometrics tutorials for undergraduates, one of the sessions involved going through coursework about the role of advertising. To set the scene, I would talk about the work of Alfred Marshall, the influential economist from the late 1800s/early 1900s. He described two roles for advertising: constructive and combative. The former is when advertising grows the market as a whole, increasing everyone’s revenues, and the latter is when ads just steal market share from rivals without changing the size of the market. Later economists would go on to thoroughly develop theories around advertising, exploring such things as the power of ads to distort preferences, the supply of ads and their complementarity with the product they’re selling, or seeing ads as a source of consumer information. Nevertheless, Marshall’s distinction is still a key consideration, although often phrased in different terms. This study examines a lot of things, but one of its key objectives is to explore the role of direct to consumer advertising on prescriptions of brands of drugs. The system is clearly complex: drug companies advertise both to consumers and physicians, consumers may request the drug from the physician, and the physician may or may not prescribe it. Further, there may be correlated unobservable differences between physicians and patients, and the choice to advertise to particular patients may not be exogenous. The paper does a pretty good job of dealing with each of these issues, but it is dense and took me a couple of reads to work out what was going on, especially with the mix of Bayesian and Frequentist terms. Examining the erectile dysfunction drug market, the authors reckon that direct to consumer advertising reduces drug requests across the category, while increasing the proportion of requests for the advertised drug – potentially suggesting a “combative” role. However, it’s more complex than that patient requests and doctor’s prescriptions seem to be influenced by a multitude of factors.

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Sam Watson’s journal round-up for 15th 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.

Cost-effectiveness of publicly funded treatment of opioid use disorder in California. Annals of Internal Medicine [PubMed] Published 2nd January 2018

Deaths from opiate overdose have soared in the United States in recent years. In 2016, 64,000 people died this way, up from 16,000 in 2010 and 4,000 in 1999. The causes of public health crises like this are multifaceted, but we can identify two key issues that have contributed more than any other. Firstly, medical practitioners have been prescribing opiates irresponsibly for years. For the last ten years, well over 200,000,000 opiate prescriptions were issued per year in the US – enough for seven in every ten people. Once prescribed, opiate use is often not well managed. Prescriptions can be stopped abruptly, for example, leaving people with unexpected withdrawal syndromes and rebound pain. It is estimated that 75% of heroin users in the US began by using legal, prescription opiates. Secondly, drug suppliers have started cutting heroin with its far stronger but cheaper cousin, fentanyl. Given fentanyl’s strength, only a tiny amount is required to achieve the same effects as heroin, but the lack of pharmaceutical knowledge and equipment means it is often not measured or mixed appropriately into what is sold as ‘heroin’. There are two clear routes to alleviating the epidemic of opiate overdose: prevention, by ensuring responsible medical use of opiates, and ‘cure’, either by ensuring the quality and strength of heroin, or providing a means to stop opiate use. The former ‘cure’ is politically infeasible so it falls on the latter to help those already habitually using opiates. However, the availability of opiate treatment programs, such as opiate agonist treatment (OAT), is lacklustre in the US. OAT provides non-narcotic opiates, such as methadone or buprenorphine, to prevent withdrawal syndromes in users, from which they can slowly be weaned. This article looks at the cost-effectiveness of providing OAT for all persons seeking treatment for opiate use in California for an unlimited period versus standard care, which only provides OAT to those who have failed supervised withdrawal twice, and only for 21 days. The paper adopts a previously developed semi-Markov cohort model that includes states for treatment, relapse, incarceration, and abstinence. Transition probabilities for the new OAT treatment were determined from treatment data for current OAT patients (as far as I understand it). Although this does raise the question about the generalisability of this population to the whole population of opiate users – given the need to have already been through two supervised withdrawals, this population may have a greater motivation to quit, for example. In any case, the article estimates that the OAT program would be cost-saving, through reductions in crime and incarceration, and improve population health, by reducing the risk of death. Taken at face value these results seem highly plausible. But, as we’ve discussed before, drug policy rarely seems to be evidence-based.

The impact of aid on health outcomes in Uganda. Health Economics [PubMed] Published 22nd December 2017

Examining the response of population health outcomes to changes in health care expenditure has been the subject of a large and growing number of studies. One reason is to estimate a supply-side cost-effectiveness threshold: the health returns the health service achieves in response to budget expansions or contractions. Similarly, we might want to know the returns to particular types of health care expenditure. For example, there remains a debate about the effectiveness of aid spending in low and middle-income country (LMIC) settings. Aid spending may fail to be effective for reasons such as resource leakage, failure to target the right population, poor design and implementation, and crowding out of other public sector investment. Looking at these questions at an aggregate level can be tricky; the link between expenditure or expenditure decisions and health outcomes is long and causality flows in multiple directions. Effects are likely to therefore be small and noisy and require strong theoretical foundations to interpret. This article takes a different, and innovative, approach to looking at this question. In essence, the analysis boils down to a longitudinal comparison of those who live near large, aid funded health projects with those who don’t. The expectation is that the benefit of any aid spending will be felt most acutely by those who live nearest to actual health care facilities that come about as a result of it. Indeed, this is shown by the results – proximity to an aid project reduced disease prevalence and work days lost to ill health with greater effects observed closer to the project. However, one way of considering the ‘usefulness’ of this evidence is how it can be used to improve policymaking. One way is in understanding the returns to investment or over what area these projects have an impact. The latter is covered in the paper to some extent, but the former is hard to infer. A useful next step may be to try to quantify what kind of benefit aid dollars produce and its heterogeneity thereof.

The impact of social expenditure on health inequalities in Europe. Social Science & Medicine Published 11th January 2018

Let us consider for a moment how we might explore empirically whether social expenditure (e.g. unemployment support, child support, housing support, etc) affects health inequalities. First, we establish a measure of health inequality. We need a proxy measure of health – this study uses self-rated health and self-rated difficulty in daily living – and then compare these outcomes along some relevant measure of socioeconomic status (SES) – in this study they use level of education and a compound measure of occupation, income, and education (the ISEI). So far, so good. Data on levels of social expenditure are available in Europe and are used here, but oddly these data are converted to a percentage of GDP. The trouble with doing this is that this variable can change if social expenditure changes or if GDP changes. During the financial crisis, for example, social expenditure shot up as a proportion of GDP, which likely had very different effects on health and inequality than when social expenditure increased as a proportion of GDP due to a policy change under the Labour government. This variable also likely has little relationship to the level of support received per eligible person. Anyway, at the crudest level, we can then consider how the relationship between SES and health is affected by social spending. A more nuanced approach might consider who the recipients of social expenditure are and how they stand on our measure of SES, but I digress. In the article, the baseline category for education is those with only primary education or less, which seems like an odd category to compare to since in Europe I would imagine this is a very small proportion of people given compulsory schooling ages unless, of course, they are children. But including children in the sample would be an odd choice here since they don’t personally receive social assistance and are difficult to compare to adults. However, there are no descriptive statistics in the paper so we don’t know and no comparisons are made between other groups. Indeed, the estimates of the intercepts in the models are very noisy and variable for no obvious reason other than perhaps the reference group is very small. Despite the problems outlined so far though, there is a potentially more serious one. The article uses a logistic regression model, which is perfectly justifiable given the binary or ordinal nature of the outcomes. However, the authors justify the conclusion that “Results show that health inequalities measured by education are lower in countries where social expenditure is higher” by demonstrating that the odds ratio for reporting a poor health outcome in the groups with greater than primary education, compared to primary education or less, is smaller in magnitude when social expenditure as a proportion of GDP is higher. But the conclusion does not follow from the premise. It is entirely possible for these odds ratios to change without any change in the variance of the underlying distribution of health, the relative ordering of people, or the absolute difference in health between categories, simply by shifting the whole distribution up or down. For example, if the proportions of people in two groups reporting a negative outcome are 0.3 and 0.4, which then change to 0.2 and 0.3 respectively, then the odds ratio comparing the two groups changes from 0.64 to 0.58. The difference between them remains 0.1. No calculations are made regarding absolute effects in the paper though. GDP is also shown to have a positive effect on health outcomes. All that might have been shown is that the relative difference in health outcomes between those with primary education or less and others changes as GDP changes because everyone is getting healthier. The question of the article is interesting, it’s a shame about the execution.

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