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

Tuskegee and the health of black men. The Quarterly Journal of Economics [RePEc] Published February 2018

In 1932, a study often considered the most infamous and potentially most unethical in U.S. medical history began. Researchers in Alabama enrolled impoverished black men in a research program designed to examine the effects of syphilis under the guise of receiving government-funded health care. The study was known as the Tuskegee syphilis experiment. For 40 years the research subjects were not informed they had syphilis nor were they treated, even after penicillin was shown to be effective. The study was terminated in 1972 after its details were leaked to the press; numerous men died, 40 wives contracted syphilis, and a number of children were born with congenital syphilis. It is no surprise then that there is distrust among African Americans in the medical system. The aim of this article is to examine whether the distrust engendered by the Tuskegee study could have contributed to the significant differences in health outcomes between black males and other groups. To derive a causal estimate the study makes use of a number of differences: black vs non-black, for obvious reasons; male vs female, since the study targeted males, and also since women were more likely to have had contact with and hence higher trust in the medical system; before vs after; and geographic differences, since proximity to the location of the study may be informative about trust in the local health care facilities. A wide variety of further checks reinforce the conclusions that the study led to a reduction in health care utilisation among black men of around 20%. The effect is particularly pronounced in those with low education and income. Beyond elucidating the indirect harms caused by this most heinous of studies, it illustrates the importance of trust in mediating the effectiveness of public institutions. Poor reputations caused by negligence and malpractice can spread far and wide – the mid-Staffordshire hospital scandal may be just such an example.

The economic consequences of hospital admissions. American Economic Review [RePEcPublished February 2018

That this paper’s title recalls that of Keynes’s book The Economic Consequences of the Peace is to my mind no mistake. Keynes argued that a generous and equitable post-war settlement was required to ensure peace and economic well-being in Europe. The slow ‘economic privation’ driven by the punitive measures and imposed austerity of the Treaty of Versailles would lead to crisis. Keynes was evidently highly critical of the conference that led to the Treaty and resigned in protest before its end. But what does this have to do with hospital admissions? Using an ‘event study’ approach – in essence regressing the outcome of interest on covariates including indicators of time relative to an event – the paper examines the impact hospital admissions have on a range of economic outcomes. The authors find that for insured non-elderly adults “hospital admissions increase out-of-pocket medical spending, unpaid medical bills, and bankruptcy, and reduce earnings, income, access to credit, and consumer borrowing.” Similarly, they estimate that hospital admissions among this same group are responsible for around 4% of bankruptcies annually. These losses are often not insured, but they note that in a number of European countries the social welfare system does provide assistance for lost wages in the event of hospital admission. Certainly, this could be construed as economic privation brought about by a lack of generosity of the state. Nevertheless, it also reinforces the fact that negative health shocks can have adverse consequences through a person’s life beyond those directly caused by the need for medical care.

Is health care infected by Baumol’s cost disease? Test of a new model. Health Economics [PubMed] [RePEcPublished 9th February 2018

A few years ago we discussed Baumol’s theory of the ‘cost disease’ and an empirical study trying to identify it. In brief, the theory supposes that spending on health care (and other labour-intensive or creative industries) as a proportion of GDP increases, at least in part, because these sectors experience the least productivity growth. Productivity increases the fastest in sectors like manufacturing and remuneration increases as a result. However, this would lead to wages in the most productive sectors outstripping those in the ‘stagnant’ sectors. For example, salaries for doctors would end up being less than those for low-skilled factory work. Wages, therefore, increase in the stagnant sectors despite a lack of productivity growth. The consequence of all this is that as GDP grows, the proportion spent on stagnant sectors increases, but importantly the absolute amount spent on the productive sectors does not decrease. The share of the pie gets bigger but the pie is growing at least as fast, as it were. To test this, this article starts with a theoretic two-sector model to develop some testable predictions. In particular, the authors posit that the cost disease implies: (i) productivity is related to the share of labour in the health sector, and (ii) productivity is related to the ratio of prices in the health and non-health sectors. Using data from 28 OECD countries between 1995 and 2016 as well as further data on US industry group, they find no evidence to support these predictions, nor others generated by their model. One reason for this could be that wages in the last ten years or more have not risen in line with productivity in manufacturing or other ‘productive’ sectors, or that productivity has indeed increased as fast as the rest of the economy in the health care sector. Indeed, we have discussed productivity growth in the health sector in England and Wales previously. The cost disease may well then not be a cause of rising health care costs – nevertheless, health care need is rising and we should still expect costs to rise concordantly.


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.



Paul Mitchell’s journal round-up for 6th November 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.

A longitudinal study to assess the frequency and cost of antivascular endothelial therapy, and inequalities in access, in England between 2005 and 2015. BMJ Open [PubMed] Published 22nd October 2017

I am breaking one of my unwritten rules in a journal paper round-up by talking about colleagues’ work, but I feel it is too important not to provide a summary for a number of reasons. The study highlights the problems faced by regional healthcare purchasers in England when implementing national guideline recommendations on the cost-effectiveness of new treatments. The paper focuses on anti-vascular endothelial growth factor (anti-VEGF) medicines in particular, with two drugs, ranibizumab and aflibercept, offered to patients with a range of eye conditions, costing £550-800 per injection. Another drug, bevacizumab, that is closely related to ranibizumab and performs similarly in trials, could be provided at a fraction of the cost (£50-100 per injection), but it is currently unlicensed for eye conditions in the UK. This study investigates how the regional areas in England have coped with trying to provide the recommended drugs using administrative data from Hospital Episode Statistics in England between 2005-2015 by tracking their use since they have been recommended for a number of different eye conditions over the past decade. In 2014/15 the cost of these two new drugs for treating eye conditions alone was estimated at £447 million nationally. The distribution of where these drugs are provided is not equal, varying widely across regions after controlling for socio-demographics, suggesting an inequality of access associated with the introduction of these high-cost drugs over the past decade at a time of relatively low growth in national health spending. Although there are limitations associated with using data not intended for research purposes, the study shows how the most can be made from data routinely collected for non-research purposes. On a public policy level, it raises questions over the provision of such high-cost drugs, for which the authors state the NHS are currently paying more for than US insurers. Although it is important to be careful when comparing to unlicensed drugs, the authors point to clear evidence in the paper as to why their comparison is a reasonable one in this scenario, with a large opportunity cost associated with not including this option in national guidelines. If national recommendations continue to insist that such drugs be provided, clearer guidance is also required on how to disinvest from existing services at a regional level to reduce further examples of inequality in access in the future.

In search of a common currency: a comparison of seven EQ-5D-5L value sets. Health Economics [PubMed] Published 24th October 2017

For those of us out there who like a good valuation study, you will need to set yourself aside a good piece of time to work your way through this one. The new EQ-5D-5L measure of health status, with a primary purpose of generating quality-adjusted life years (QALYs) for economic evaluations, is now starting to have valuation studies emerging from different countries, whereby the relative importance of each of the measure dimensions and levels are quantified based on general population preferences. This study offers the first comparison of value sets across seven countries: 3 Western European (England, Netherlands, Spain), 1 North American (Canada), 1 South American (Uruguay), and two East Asian (Japan and South Korea). The authors in this paper aim to describe methodological differences between the seven value sets, compare the relative importance of dimensions, level decrements and scale length (i.e. quality/quantity trade-offs for QALYs), as well as developing a common (Western) currency across four of the value sets. In brief summary, there does appear to be similar trends across the three Western European countries: level decrements from levels 3 to 4 have the largest value, followed by levels 1 to 2. There is also a pattern in these three countries’ dimensions, whereby the two “symptom” dimensions (i.e. pain/discomfort, anxiety/depression) have equal importance to the other three “functioning” dimensions (i.e. mobility, self-care and usual activities). There are also clear differences with the other four value sets. Canada, although it also has the highest level decrements between levels 3 and 4 (49%), unusually has equal decrements for the remainder (17% x 3). For the other three countries, greater weight is attached to the three functioning dimensions relative to the two symptom dimensions. Although South Korea also has the greatest level decrements between level 3 and 4, it was greatest between level 4 and level 5 in Uruguay and levels 1 and 2 in Japan. Although the authors give a number of plausible reasons as to why these differences may occur, less justification is given in the choice of the four value sets they offer as a common currency, beyond the need to have a value set for countries that do not have one already. The most in-common value sets were the three Western European countries, so a Western European value set may have been more appropriate if the criterion was to have comparable values across countries. If the aim was really for a more international common currency, there are issues with the exclusion of non-Western countries’ value sets from their common currency version. Surely differences across cultures should be reflected in a common currency if they are apparent in different cultures and settings. A common currency should also have a better spread of regions geographically, with no country from Africa, the Middle East, Central and South Asia represented in this study, as well as no lower- and middle-income countries. Though this final criticism is out of the control of the authors based on current data availability.

Quantifying the relationship between capability and health in older people: can’t map, won’t map. Medical Decision Making [PubMed] Published 23rd October 2017

The EQ-5D is one of many ways quality of life can be measured within economic evaluations. A more recent way based on Amartya Sen’s capability approach has attempted to develop outcome measures that move beyond health-related aspects of quality of life captured by EQ-5D and similar measures used in the generation of QALYs. This study examines the relationship between the EQ-5D and the ICECAP-O capability measure in three different patient populations included in the Medical Crises in Older People programme in England. The authors propose a reasonable hypothesis that health could be considered a conversion factor for a person’s broader capability set, and so it is plausible to test how well the EQ-5D-3L dimension values and overall score can map onto the ICECAP-O overall score. Through numerous regressions performed, the strongest relationship between the two measures in this sample was an R-squared of 0.35. Interestingly, the dimensions on the EQ-5D that had a significant relationship with the ICECAP-O score were a mix of dimensions with a focus on functioning (i.e. self-care, usual activities) and symptoms (anxiety/depression), so overall capability on ICECAP-O appears to be related, at least to a small degree, to both health components of EQ-5D discussed in this round-up’s previous paper. The authors suggest it provides further evidence of the complementary data provided by EQ-5D and ICECAP-O, but the causal relationship, as the authors suggest, between both measures remains under-researched. Longitudinal data analysis would provide a more definitive answer to the question of how much interaction there is between these two measures and their dimensions as health and capability changes over time in response to different treatments and care provision.