Chris Sampson’s journal round-up for 4th June 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.

A qualitative investigation of the health economic impacts of bariatric surgery for obesity and implications for improved practice in health economics. Health Economics [PubMed] Published 1st June 2018

Few would question the ‘economic’ nature of the challenge of obesity. Bariatric surgery is widely recommended for severe cases but, in many countries, the supply is not sufficient to satisfy the demand. In this context, this study explores the value of qualitative research in informing economic evaluation. The authors assert that previous economic evaluations have adopted a relatively narrow focus and thus might underestimate the expected value of bariatric surgery. But rather than going and finding data on what they think might be additional dimensions of value, the authors ask patients. Emotional capital, ‘societal’ (i.e. non-health) impacts, and externalities are identified as theories for the types of value that might be derived from bariatric surgery. These theories were used to guide the development of questions and prompts that were used in a series of 10 semi-structured focus groups. Thematic analysis identified the importance of emotional costs and benefits as part of the ‘socioemotional personal journey’ associated with bariatric surgery. Out-of-pocket costs were also identified as being important, with self-funding being a challenge for some respondents. The information seems useful in a variety of ways. It helps us understand the value of bariatric surgery and how individuals make decisions in this context. This information could be used to determine the structure of economic evaluations or the data that are collected and used. The authors suggest that an EQ-5D bolt-on should be developed for ’emotional capital’ but, given that this ‘theory’ was predefined by the authors and does not arise from the qualitative research as being an important dimension of value alongside the existing EQ-5D dimensions, that’s a stretch.

Developing accessible, pictorial versions of health-related quality-of-life instruments suitable for economic evaluation: a report of preliminary studies conducted in Canada and the United Kingdom. PharmacoEconomics – Open [PubMed] Published 25th May 2018

I’ve been telling people about this study for ages (apologies, authors, if that isn’t something you wanted to read!). In my experience, the need for more (cognitively / communicatively) accessible outcome measures is widely recognised by health researchers working in contexts where this is relevant, such as stroke. If people can’t read or understand the text-based descriptors that make up (for example) the EQ-5D, then we need some alternative format. You could develop an entirely new measure. Or, as the work described in this paper set out to do, you could modify existing measures. There are three descriptive systems described in this study: i) a pictorial EQ-5D-3L by the Canadian team, ii) a pictorial EQ-5D-3L by the UK team, and iii) a pictorial EQ-5D-5L by the UK team. Each uses images to represent the different levels of the different dimensions. For example, the mobility dimension might show somebody walking around unaided, walking with aids, or in bed. I’m not going to try and describe what they all look like, so I’ll just encourage you to take a look at the Supplementary Material (click here to download it). All are described as ‘pilot’ instruments and shouldn’t be picked up and used at this stage. Different approaches were used in the development of the measures, and there are differences between the measures in terms of the images selected and the ways in which they’re presented. But each process referred to conventions in aphasia research, used input from clinicians, and consulted people with aphasia and/or their carers. The authors set out several remaining questions and avenues for future research. The most interesting possibility to most readers will be the notion that we could have a ‘generic’ pictorial format for the EQ-5D, which isn’t aphasia-specific. This will require continued development of the pictorial descriptive systems, and ultimately their validation.

QALYs in 2018—advantages and concerns. JAMA [PubMed] Published 24th May 2018

It’s difficult not to feel sorry for the authors of this article – and indeed all US-based purveyors of economic evaluation in health care. With respect to social judgments about the value of health technologies, the US’s proverbial head remains well and truly buried in the sand. This article serves as a primer and an enticement for the use of QALYs. The ‘concerns’ cited relate almost exclusively to decision rules applied to QALYs, rather than the underlying principles of QALYs, presumably because the authors didn’t feel they could ignore the points made by QALY opponents (even if those arguments are vacuous). What it boils down to is this: trade-offs are necessary, and QALYs can be used to promote value in those trade-offs, so unless you offer some meaningful alternative then QALYs are here to stay. Thankfully, the Institute for Clinical and Economic Review (ICER) has recently added some clout to the undeniable good sense of QALYs, so the future is looking a little brighter. Suck it up, America!

The impact of hospital costing methods on cost-effectiveness analysis: a case study. PharmacoEconomics [PubMed] Published 22nd May 2018

Plugging different cost estimates into your cost-effectiveness model could alter the headline results of your evaluation. That might seems obvious, but there are a variety of ways in which the selection of unit costs might be somewhat arbitrary or taken for granted. This study considers three alternative sources of information for hospital-based unit costs for hip fractures in England: (a) spell-level tariffs, (b) finished consultant episode (FCE) reference costs, and (c) spell-level reference costs. Source (b) provides, in theory, a more granular version of (a), describing individual episodes within a person’s hospital stay. Reference costs are estimated on the basis of hospital activity, while tariffs are prices estimated on the basis of historic reference costs. The authors use a previously reported cohort state transition model to evaluate different models of care for hip fracture and explore how the use of the different cost figures affects their results. FCE-level reference costs produced the highest total first-year hospital care costs (£14,440), and spell-level tariffs the lowest (£10,749). The more FCEs within a spell, the greater the discrepancy. This difference in costs affected ICERs, such that the net-benefit-optimising decision would change. The study makes an important point – that selection of unit costs matters. But it isn’t clear why the difference exists. It could just be due to a lack of precision in reference costs in this context (rather than a lack of accuracy, per se), or it could be that reference costs misestimate the true cost of care across the board. Without clear guidance on how to select the most appropriate source of unit costs, these different costing methodologies represent another source of uncertainty in modelling, which analysts should consider and explore.

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

Healthy working days: the (positive) effect of work effort on occupational health from a human capital approach. Social Science & Medicine Published 28th February 2018

If you look at the literature on the determinants of subjective well-being (or happiness), you’ll see that unemployment is often cited as having a big negative impact. The same sometimes applies for its impact on health, but here – of course – the causality is difficult to tease apart. Then, in research that digs deeper, looking at hours worked and different types of jobs, we see less conclusive results. In this paper, the authors start by asserting that the standard approach in labour economics (on which I’m not qualified to comment) is to assume that there is a negative association between work effort and health. This study extends the framework by allowing for positive effects of work that are related to individuals’ characteristics and working conditions, and where health is determined in a Grossman-style model of health capital that accounts for work effort in the rate of health depreciation. This model is used to examine health as a function of work effort (as indicated by hours worked) in a single wave of the European Working Conditions Survey (EWCS) from 2010 for 15 EU member states. Key items from the EWCS included in this study are questions such as “does your work affect your health or not?”, “how is your health in general?”, and “how many hours do you usually work per week?”. Working conditions are taken into account by looking at data on shift working and the need to wear protective equipment. One of the main findings of the study is that – with good working conditions – greater work effort can improve health. The Marxist in me is not very satisfied with this. We need to ask the question, compared to what? Working fewer hours? For most people, that simply isn’t an option. Aren’t the people who work fewer hours the people who can afford to work fewer hours? No attention is given to the sociological aspects of employment, which are clearly important. The study also shows that overworking or having poorer working conditions reduces health. We also see that, for many groups, longer hours do not negatively impact on health until we reach around 120 hours a week. This fails a good sense check. Who are these people?! I’d be very interested to see if these findings hold for academics. That the key variables are self-reported undermines the conclusions somewhat, as we can expect people to adjust their expectations about work effort and health in accordance with their colleagues. It would be very difficult to avoid a type 2 error (with respect to the negative impact of effort on health) using these variables to represent health and the role of work effort.

Agreement between retrospectively and contemporaneously collected patient-reported outcome measures (PROMs) in hip and knee replacement patients. Quality of Life Research [PubMed] Published 26th February 2018

The use of patient-reported outcomes (PROMs) in elective care in the NHS has been a boon for researchers in our field, providing before-and-after measurement of health-related quality of life so that we can look at the impact of these interventions. But we can’t do this in emergency care because the ‘before’ is never observed – people only show up when they’re in the middle of the emergency. But what if people could accurately recall their pre-emergency health state? There’s some evidence to suggest that people can, so long as the recall period is short. This study looks at NHS PROMs data (n=443), with generic and condition-specific outcomes collected from patients having hip or knee replacements. Patients included in the study were additionally asked to recall their health state 4 weeks prior to surgery. The authors assess the extent to which the contemporary PROM measurements agree with the retrospective measurements, and the extent to which any disagreement relates to age, socioeconomic status, or the length of time to recall. There wasn’t much difference between contemporary and retrospective measurements, though patients reported slightly lower health on the retrospective questionnaires. And there weren’t any compelling differences associated with age or socioeconomic status or the length of recall. These findings are promising, suggesting that we might be able to rely on retrospective PROMs. But the elective surgery context is very different to the emergency context, and I don’t think we can expect the two types of health care to impact recollection in the same way. In this study, responses may also have been influenced by participants’ memories of completing the contemporary questionnaire, and the recall period was very short. But the only way to find out more about the validity of retrospective PROM collection is to do more of it, so hopefully we’ll see more studies asking this question.

Adaptation or recovery after health shocks? Evidence using subjective and objective health measures. Health Economics [PubMed] Published 26th February 2018

People’s expectations about their health can influence their behaviour and determine their future health, so it’s important that we understand people’s expectations and any ways in which they diverge from reality. This paper considers the effect of a health shock on people’s expectations about how long they will live. The authors focus on survival probability, measured objectively (i.e. what actually happens to these patients) and subjectively (i.e. what the patients expect), and the extent to which the latter corresponds to the former. The arguments presented are couched within the concept of hedonic adaptation. So the question is – if post-shock expectations return to pre-shock expectations after a period of time – whether this is because people are recovering from the disease or because they are moving their reference point. Data are drawn from the Health and Retirement Study. Subjective survival probability is scaled to whether individuals expect to survive for 2 years. Cancer, stroke, and myocardial infarction are the health shocks used. The analysis uses some lagged regression models, separate for each of the three diagnoses, with objective and subjective survival probability as the dependent variable. There’s a bit of a jumble of things going on in this paper, with discussions of adaptation, survival, self-assessed health, optimism, and health behaviours. So it’s a bit difficult to see the wood for the trees. But the authors find the effect they’re looking for. Objective survival probability is negatively affected by a health shock, as is subjective survival probability. But then subjective survival starts to return to pre-shock trends whereas objective survival does not. The authors use this finding to suggest that there is adaptation. I’m not sure about this interpretation. To me it seems as if subjective life expectancy is only weakly responsive to changes in objective life expectancy. The findings seem to have more to do with how people process information about their probability of survival than with how they adapt to a situation. So while this is an interesting study about how people process changes in survival probability, I’m not sure what it has to do with adaptation.

3L, 5L, what the L? A NICE conundrum. PharmacoEconomics [PubMed] Published 26th February 2018

In my last round-up, I said I was going to write a follow-up blog post to an editorial on the EQ-5D-5L. I didn’t get round to it, but that’s probably best as there has since been a flurry of other editorials and commentaries on the subject. Here’s one of them. This commentary considers the perspective of NICE in deciding whether to support the use of the EQ-5D-5L and its English value set. The authors point out the differences between the 3L and 5L, namely the descriptive systems and the value sets. Examples of the 5L descriptive system’s advantages are provided: a reduced ceiling effect, reduced clustering, better discriminative ability, and the benefits of doing away with the ‘confined to bed’ level of the mobility domain. Great! On to the value set. There are lots of differences here, with 3 main causes: the data, the preference elicitation methods, and the modelling methods. We can’t immediately determine whether these differences are improvements or not. The authors stress the point that any differences observed will be in large part due to quirks in the original 3L value set rather than in the 5L value set. Nevertheless, the commentary is broadly supportive of a cautionary approach to 5L adoption. I’m not. Time for that follow-up blog post.

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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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