Sam Watson’s journal round-up for 2nd October 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 path to longer and healthier lives for all Africans by 2030: the Lancet Commission on the future of health in sub-Saharan Africa. The Lancet [PubMedPublished 13th September 2017

The African continent has the highest rates of economic growth, the fastest growing populations and rates of urbanisation, but also the highest burden of disease. The challenges for public health and health care provision are great. It is no surprise then that this Lancet commission on the future of health in Sub-Saharan Africa runs to 57 pages yet still has some notable absences. In the space of a few hundred words, it would be impossible to fully discuss the topics in this tome, these will appear in future blog posts. For now, I want to briefly discuss a lack of consideration of the importance of political economy in the Commission’s report. For example, the report notes the damaging effects of IMF and World Bank structural adjustment programs in the 70s and 80s. These led to a dismantling of much of the public sector in indebted African nations in order for them to qualify for further loans. However, these issues have not gone away. Despite strongly emphasizing that countries in Africa must increase their health spending, it does not mention that many countries spend much more servicing debt than on public health and health care. Kenya, for example, will soon no longer qualify for aid as it becomes a middle-income country, and yet it spends almost double (around $6 billion) servicing its debt than it does on health care (around $3 billion). Debt reform and relief may be a major step towards increasing health expenditure. The inequalities in access to basic health services reflect the disparities in income and wealth both between and within countries. The growth of slums across the continent is stark evidence of this. Residents of these communities, despite often facing the worst exposure to major disease risk factors, are often not recognised by authorities and cannot access health services. Even where health services are available there are still difficulties with access. A lack of regulation and oversight can lead the growth of a rentier class within slums as those with access to small amounts of capital, land, or property act as petty landlords. So while some in slum areas can afford the fees for basic health services, the poorest still face a barrier even when services are available. These people are also those who have little access to decent water and sanitation or education and have the highest risk of disease. Finally, the lack of incentives for trained doctors and medical staff to work in poor or rural areas is also identified as a key problem. Many doctors either leave for wealthier countries or work in urban areas. Doctors are often a powerful interest group and can influence macro health policy, distorting it to favour richer urban areas. Political solutions are required, as well as the public health interventions more widely discussed. The Commission’s report is extensive and worth the time to read for anyone with an interest in the subject matter. What also becomes clear upon reading it is the lack of solid evidence on health systems and what works and does not work. From an economic perspective, much of the evidence pertaining to health system functioning and efficiency is still just the results from country-level panel data regressions, which tell us very little about what is actually happening. This results in us being able to identify areas needed for reform with very little idea of how.

The relationship of health insurance and mortality: is lack of insurance deadly? Annals of Internal Medicine [PubMedPublished 19th September 2017

One sure-fire way of increasing your chances of publishing in a top-ranked journal is to do something on a hot political topic. In the UK this has been seven-day services, as well as other issues relating to deficiencies of supply. In the US, health insurance is right up there with the Republicans trying to repeal the Affordable Care Act, a.k.a. Obamacare. This paper systematically reviews the literature on the relationship between health insurance coverage and the risk of mortality. The theory being that health insurance permits access to medical services and therefore treatment and prevention measures that reduce the risk of death. Many readers will be familiar with the Oregon Health Insurance Experiment, in which the US state of Oregon distributed access to increased Medicaid expansion by lottery, therein creating an RCT. This experiment, which takes a top spot in the review, estimated that those who had ‘won’ the lottery had a mortality rate 0.032 percentage points lower than the ‘losers’, whose mortality rate was 0.8%; a relative reduction of around 4%. Similar results were found for the quasi-experimental studies included, and slightly larger effects were found in cohort follow-up studies. These effects are small. But then so is the baseline. Most of these studies only examined non-elderly, non-disabled people, who would otherwise not qualify for any other public health insurance. For people under 45 in the US, the leading cause of death is unintentional injury, and its only above this age that cancer becomes the leading cause of death. If you suffer major trauma in the US you will (for the most part) be treated in an ER insured or uninsured, even if you end up with a large bill afterwards. So it’s no surprise that the effects of insurance coverage on mortality are very small for these people. This is probably the inappropriate endpoint to be looking at for this study. Indeed, the Oregon experiment found that the biggest differences were in reduced out-of-pocket expenses and medical debt, and improved self-reported health. The review’s conclusion that, “The odds of dying among the insured relative to the uninsured is 0.71 to 0.97,” is seemingly unwarranted. If they want to make a political point about the need for insurance, they’re looking in the wrong place.

Smoking, expectations, and health: a dynamic stochastic model of lifetime smoking behavior. Journal of Political Economy [RePEcPublished 24th August 2017

I’ve long been sceptical of mathematical models of complex health behaviours. The most egregious of which is often the ‘rational addiction’ literature. Originating with the late Gary Becker, the rational addiction model, in essence, assumes that addiction is a rational choice made by utility maximising individuals, whose preferences alter with use of a particular drug. The biggest problem I find with this approach is that it is completely out of touch with the reality of addiction and drug dependence, and makes absurd assumptions about the preferences of addicts. Nevertheless, it has spawned a sizable literature. And, one may argue that the model is useful if it makes accurate predictions, regardless of the assumptions underlying it. On this front, I have yet to be convinced. This paper builds a rational addiction-type model for smoking to examine whether learning of one’s health risks reduces smoking. As an illustration of why I dislike this method of understanding addictive behaviours, the authors note that “…the model cannot explain why individuals start smoking. […] The estimated preference parameters in the absence of a chronic illness suggest that, for a never smoker under the age of 25, there is no incentive to begin smoking because the marginal utility of smoking is negative.” But for many, social and cultural factors simply explain why young people start smoking. The weakness of the deductive approach to social science seems to rear its head, but like I said, the aim here may be the development of good predictive models. And, the model does appear to predict smoking behaviour well. However, it is all in-sample prediction, and with the number of parameters it is not surprising it predicts well. This discussion is not meant to be completely excoriating. What is interesting is the discussion and attempt to deal with the endogeneity of smoking – people in poor health may be more likely to smoke and so the estimated effects of smoking on longevity may be overestimated. As a final point of contention though, I’m still trying to work out what the “addictive stock of smoking capital” is.

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Chris Sampson’s journal round-up for 25th September 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.

Good practices for real‐world data studies of treatment and/or comparative effectiveness: recommendations from the Joint ISPOR‐ISPE Special Task Force on Real‐World Evidence in Health Care Decision Making. Value in Health Published 15th September 2017

I have an instinctive mistrust of buzzwords. They’re often used to avoid properly defining something, either because it’s too complicated or – worse – because it isn’t worth defining in the first place. For me, ‘real-world evidence’ falls foul. If your evidence isn’t from the real world, then it isn’t evidence at all. But I do like a good old ISPOR Task Force report, so let’s see where this takes us. Real-world evidence (RWE) and its sibling buzzword real-world data (RWD) relate to observational studies and other data not collected in an experimental setting. The purpose of this ISPOR task force (joint with the International Society for Pharmacoepidemiology) was to prepare some guidelines about the conduct of RWE/RWD studies, with a view to improving decision-makers’ confidence in them. Essentially, the hope is to try and create for RWE the kind of ecosystem that exists around RCTs, with procedures for study registration, protocols, and publication: a noble aim. The authors distinguish between 2 types of RWD: ‘Exploratory Treatment Effectiveness Studies’ and ‘Hypothesis Evaluating Treatment Effectiveness Studies’. The idea is that the latter test a priori hypotheses, and these are the focus of this report. Seven recommendations are presented: i) pre-specify the hypotheses, ii) publish a study protocol, iii) publish the study with reference to the protocol, iv) enable replication, v) test hypotheses on a separate dataset than the one used to generate the hypotheses, vi) publically address methodological criticisms, and vii) involve key stakeholders. Fair enough. But these are just good practices for research generally. It isn’t clear how they are in any way specific to RWE. Of course, that was always going to be the case. RWE-specific recommendations would be entirely contingent on whether or not one chose to define a study as using ‘real-world evidence’ (which you shouldn’t, because it’s meaningless). The authors are trying to fit a bag of square pegs into a hole of undefined shape. It isn’t clear to me why retrospective observational studies, prospective observational studies, registry studies, or analyses of routinely collected clinical data should all be treated the same, yet differently to randomised trials. Maybe someone can explain why I’m mistaken, but this report didn’t do it.

Are children rational decision makers when they are asked to value their own health? A contingent valuation study conducted with children and their parents. Health Economics [PubMed] [RePEc] Published 13th September 2017

Obtaining health state utility values for children presents all sorts of interesting practical and theoretical problems, especially if we want to use them in decisions about trade-offs with adults. For this study, the researchers conducted a contingent valuation exercise to elicit children’s (aged 7-19) preferences for reduced risk of asthma attacks in terms of willingness to pay. The study was informed by two preceding studies that sought to identify the best way in which to present health risk and financial information to children. The participating children (n=370) completed questionnaires at school, which asked about socio-demographics, experience of asthma, risk behaviours and altruism. They were reminded (in child-friendly language) about the idea of opportunity cost, and to consider their own budget constraint. Baseline asthma attack risk and 3 risk-reduction scenarios were presented graphically. Two weeks later, the parents completed similar questionnaires. Only 9% of children were unwilling to pay for risk reduction, and most of those said that it was the mayor’s problem! In some senses, the children did a better job than their parents. The authors conducted 3 tests for ‘incorrect’ responses – 14% of adults failed at least one, while only 4% of children did so. Older children demonstrated better scope sensitivity. Of course, children’s willingness to pay was much lower in absolute terms than their parents’, because children have a much smaller budget. As a percentage of the budget, parents were – on average – willing to pay more than children. That seems reassuringly predictable. Boys and fathers were willing to pay more than girls and mothers. Having experience of frequent asthma attacks increased willingness to pay. Interestingly, teenagers were willing to pay less (as a proportion of their budget) than younger children… and so were the teenagers’ parents! Children’s willingness to pay was correlated with that of their own parent’s at the higher risk reductions but not the lowest. This study reports lots of interesting findings and opens up plenty of avenues for future research. But the take-home message is obvious. Kids are smart. We should spend more time asking them what they think.

Journal of Patient-Reported Outcomes: aims and scope. Journal of Patient-Reported Outcomes Published 12th September 2017

Here we have a new journal that warrants a mention. The journal is sponsored by the International Society for Quality of Life Research (ISOQOL), making it a sister journal of Quality of Life Research. One of its Co-Editors-in-Chief is the venerable David Feeny, of HUI fame. They’ll be looking to publish research using PRO(M) data from trials or routine settings, studies of the determinants of PROs, qualitative studies in the development of PROs; anything PRO-related, really. This could be a good journal for more thorough reporting of PRO data that can get squeezed out of a study’s primary outcome paper. Also, “JPRO” is fun to say. The editors don’t mention that the journal is open access, but the website states that it is, so APCs at the ready. ISOQOL members get a discount.

Research and development spending to bring a single cancer drug to market and revenues after approval. JAMA Internal Medicine [PubMed] Published 11th September 2017

We often hear that new drugs are expensive because they’re really expensive to develop. Then we hear about how much money pharmaceutical companies spend on marketing, and we baulk. The problem is, pharmaceutical companies aren’t forthcoming with their accounts, so researchers have to come up with more creative ways to estimate R&D spending. Previous studies have reported divergent estimates. Whether R&D costs ‘justify’ high prices remains an open question. For this study, the authors looked at public data from the US for 10 companies that had only one cancer drug approved by the FDA between 2007 and 2016. Not very representative, perhaps, but useful because it allows for the isolation of the development costs associated with a single drug reaching the market. The median time for drug development was 7.3 years. The most generous estimate of the mean cost of development came in at under a billion dollars; substantially less than some previous estimates. This looks like a bargain; the mean revenue for the 10 companies up to December 2016 was over $6.5 billion. This study may seem a bit back-of-the-envelope in nature. But that doesn’t mean it isn’t accurate. If anything, it begs more confidence than some previous studies because the methods are entirely transparent.

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Meeting round-up: CINCH Academy 2017

The CINCH Academy took place for the fourth time in Essen, Germany, from August 28th to September 3rd, 2017. We were twelve PhD students participating in the summer school coming not only from Germany but also from the UK, Netherlands, and Russia. On Monday morning, Christoph Kronenberg who organized the CINCH Academy 2017 welcomed us. After some information on the schedule and – most importantly – the social activities planned for the week, Owen O’Donnell started with the first class on health inequality.

Prof O’Donnell held courses during the first half of the summer school. In his lectures, he presented tools to measure inequality in the distribution of health depending on socioeconomic indicators like income or education. With examples in Stata, we were familiarized with the application, advantages, and potential drawbacks of different health inequality indices.

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In the second part of the week, Frank Windmeijer held courses on panel data models. He started with the linear instrumental variables model and successively approached instrumental estimations in linear and dynamic panel models. Needless to mention, the famous Windmeijer correction was included in Frank’s explanations. As in the health inequality courses, we applied our newly gained knowledge immediately to examples in Stata exercises.

Besides productive classes on health inequality and panel models, each participant of the summer school presented their own work. Maksym Obriza chaired the paper presentations, making sure that we stuck to the schedule and providing valuable feedback. Presentations lasted 30 minutes and were followed by 10 minutes for the discussant and 5 minutes for questions from the audience. The presentations covered a broad range of topics, including the interaction of health and labour, the effect of regulatory actions on children’s health, and the impact of hospital environment on physicians’ treatment choices. While most participants analysed datasets for their research projects, I presented a lab experiment.

Of course, we were also able to get to know each other better and to discuss our research more informally during various social activities throughout the week. At the get-together on Monday evening, the group became even bigger when CINCH members Reinhold Schnabel and Daniel Avdic joined for dinner at Leo’s Casa. On Wednesday, the first part of CINCH Academy ended with an excursion to the UNESCO world heritage site Zollverein. The working conditions for workers with low education levels in the coking plant, which were vividly described by our guide, by no doubt led to poor health. We agreed that in this case no tests were needed to convince us of a causality between low education and poor health – a perfect illustration for health inequality. More discussions followed on Thursday during dinner at Ponistra, which is known for its excellent food.

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After a lot of input on health inequality, panel models, and our own research, the summer school ended Sunday noon with the presentation of the Best Paper Award. Juditha Wójcik received the award for her joint work with Sebastian Vollmer on long-term consequences of the 1918 influenza pandemic for which they analysed 117 census datasets.

Although the schedule seemed to be very tough in the beginning, I really enjoyed CINCH Academy. I did not only learn a lot but also got to know a very nice group of junior health economists.

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