Simon McNamara’s journal round-up for 6th 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.

Euthanasia, religiosity and the valuation of health states: results from an Irish EQ5D5L valuation study and their implications for anchor values. Health and Quality of Life Outcomes [PubMed] Published 31st July 2018

Do you support euthanasia? Do you think there are health states worse than death? Are you religious? Don’t worry – I am not commandeering this week’s AHE journal round-up just to bombard you with a series of difficult questions. These three questions form the foundation of the first article selected for this week’s round-up.

The paper is based upon the hypothesis that your religiosity (“adherence to religious beliefs”) is likely to impact your support for euthanasia and, subsequently, the likelihood of you valuing severe health states as worse than death. This seems like a logical hypothesis. Religions tend to be anti-euthanasia, and so it appears likely that religious people will have lower levels of support for euthanasia than non-religious people. Equally, if you don’t support the principle of euthanasia, it stands to reason that you are likely to be less willing to choose immediate death over living in a severe health state – something you would need to do for a health state to be considered as being worse than death in a time trade-off (TTO) study.

The authors test this hypothesis using a sub-sample of data (n=160) collected as part of the Irish EQ-5D-5L TTO valuation study. Perhaps unsurprisingly, the authors find evidence in support of the above hypotheses. Those that attend a religious service weekly were more likely to oppose euthanasia than those who attend a few times a year or less, and those who oppose euthanasia were less likely to give “worse than death” responses in the TTO than those that support it.

I found this paper really interesting, as it raises a number of challenging questions. If a society is made up of people with heterogeneous beliefs regarding religion, how should we balance these in the valuation of health? If a society is primarily non-religious is it fair to apply this valuation tariff to the lives of the religious, and vice versa? These certainly aren’t easy questions to answer, but may be worth reflecting on.

E-learning and health inequality aversion: A questionnaire experiment. Health Economics [PubMed] [RePEc] Published 22nd July 2018

Moving on from the cheery topic of euthanasia, what do you think about socioeconomic inequalities in health? In my home country, England, if you are from the poorest quintile of society, you can expect to experience 62 years in full health in your lifetime, whilst if you are from the richest quintile, you can expect to experience 74 years – a gap of 12 years.

In the second paper to be featured in this round-up, Cookson et al. explore the public’s willingness to sacrifice incremental population health gains in order to reduce these inequalities in health – their level of “health inequality aversion”. This is a potentially important area of research, as the vast majority of economic evaluation in health is distributionally-naïve and effectively assumes that members of the public aren’t at all concerned with inequalities in health.

The paper builds on prior work conducted by the authors in this area, in which they noted a high proportion of respondents in health inequality aversion elicitation studies appear to be so averse to inequalities that they violate monotonicity – they choose scenarios that reduce inequalities in health even if these scenarios reduce the health of the rich at no gain to the poor, or they reduce the health of the poor, or they may reduce the health of both groups. The authors hypothesise that these monotonicity violations may be due to incomplete thinking from participants, and suggest that the quality of their thinking could be improved by two e-learning educational interventions. The primary aim of the paper is to test the impact of these interventions in a sample of the UK public (n=60).

The first e-learning intervention was an animated video that described a range of potential positions that a respondent could take (e.g. health maximisation, or maximising the health of the worst off). The second was an interactive spreadsheet-based questionnaire that presented the consequences of the participant’s choices, prior to them confirming their selection. Both interventions are available online.

The authors found that the interactive tool significantly reduced the amount of extreme egalitarian (monotonicity-violating) responses, compared to a non-interactive, paper-based version of the study. Similarly, when the video was watched before completing the paper-based exercise, the number of extreme egalitarian responses reduced. However, when the video was watched before the interactive tool there was no further decrease in extreme egalitarianism. Despite this reduction in extreme egalitarianism, the median levels of inequality aversion remained high, with implied weights of 2.6 and 7.0 for QALY gains granted to someone from the poorest fifth of society, compared to the richest fifth of society for the interactive questionnaire and video groups respectively.

This is an interesting study that provides further evidence of inequality aversion, and raises further concern about the practical dominance of distributionally-naïve approaches to economic evaluation. The public does seem to care about distribution. Furthermore, the paper demonstrates that participant responses to inequality aversion exercises are shaped by the information given to them, and the way that information is presented. I look forward to seeing more studies like this in the future.

A new method for valuing health: directly eliciting personal utility functions. The European Journal of Health Economics [PubMed] [RePEc] Published 20th July 2018

Last, but not least, for this round-up, is a paper by Devlin et al. on a new method for valuing health.

The relative valuation of health states is a pretty important topic for health economists. If we are to quantify the effectiveness, and subsequently cost-effectiveness, of an intervention, we need to understand which health states are better than others, and how much better they are. Traditionally, this is done by asking members of the public to choose between different health profiles featuring differing levels of fulfilment of a range of domains of health, in order to ‘uncover’ the relative importance the respondent places on these domains, and levels. These can then be used in order to generate social tariffs that assign a utility value to a given health state for use in economic evaluation.

The authors point out that, in the modern day, valuation studies can be conducted rapidly, and at scale, online, but at the potential cost of deliberation from participants, and the resultant risk of heuristic dominated decision making. In response to this, the authors propose a new method – direct elicitation of personal utility functions, and pilot its use for the valuation of EQ-5D in a sample of the English public (n=76).

The proposed approach differs from traditional approaches in three key ways. Firstly, instead of simply attempting to infer the relative importance that participants place on differing domains based upon choices between health profiles, the respondents are asked directly about the relative importance they place on differing domains of health, prior to validating these with profile choices. Secondly, the authors place a heavy emphasis on deliberation, and the construction, rather than uncovering, of preferences during the elicitation exercises. Thirdly, a “personal utility function” for each individual is constructed (in effect a personal EQ-5D tariff), and these individual utility functions are subsequently aggregated into a social utility function.

In the pilot, the authors find that the method appears feasible for wider use, albeit with some teething troubles associated with the computer-based tool developed to implement it, and the skills of the interviewers.

This direct method raises an interesting question for health economics – should we be inferring preferences based upon choices that differ in terms of certain attributes, or should we just ask directly about the attributes? This is a tricky question. It is possible that the preferences elicited via these different approaches could result in different preferences – if they do, on what grounds should we choose one or other? This requires a normative judgment, and at present, it appears both are (potentially) as legitimate as each other.

Whilst the authors apply this direct method to the valuation of health, I don’t see why similar approaches couldn’t be applied to any multi-attribute choice experiment. Keep your eyes out for future uses of it in valuation, and perhaps beyond? It will be interesting to see how it develops.

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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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Chris Sampson’s journal round-up for 31st July 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.

An exploratory study on using principal-component analysis and confirmatory factor analysis to identify bolt-on dimensions: the EQ-5D case study. Value in Health Published 14th July 2017

I’m not convinced by the idea of using bolt-on dimensions for multi-attribute utility instruments. A state description with a bolt-on refers to a different evaluative space, and therefore is not comparable with the progenitor, thus undermining its purpose. Maybe this study will persuade me otherwise. The authors analyse data from the Multi Instrument Comparison database, including responses to EQ-5D-5L, SF-6D, HUI3, AQoL 8D and 15D questionnaires, as well as the ICECAP and 3 measures of subjective well-being. Content analysis was used to allocate items from the measures to underlying constructs of health-related quality of life. The sample of 8022 was randomly split, with one half used for principal-component analysis and confirmatory factor analysis, and the other used for validation. This approach looks at the underlying constructs associated with health-related quality of life and the extent to which individual items from the questionnaires influence them. Candidate items for bolt-ons are those items from questionnaires other than the EQ-5D that are important and not otherwise captured by the EQ-5D questions. The principal-component analysis supported a 9-component model: physical functioning, psychological symptoms, satisfaction, pain, relationships, speech/cognition, hearing, energy/sleep and vision. The EQ-5D only covered physical functioning, psychological symptoms and pain. Therefore, items from measures that explain the other 6 components represent bolt-on candidates for the EQ-5D. This study succeeds in its aim. It demonstrates what appears to be a meaningful quantitative approach to identifying items not fully captured by the EQ-5D, which might be added as bolt-ons. But it doesn’t answer the question of which (if any) of these bolt-ons ought to be added, or in what circumstances. That would at least require pre-definition of the evaluative space, which might not correspond to the authors’ chosen model of health-related quality of life. If it does, then these findings would be more persuasive as a reason to do away with the EQ-5D altogether.

Endogenous information, adverse selection, and prevention: implications for genetic testing policy. Journal of Health Economics Published 13th July 2017

If you can afford it, there are all sorts of genetic tests available nowadays. Some of them could provide valuable information about the risk of particular health problems in the future. Therefore, they can be used to guide individuals’ decisions about preventive care. But if the individual’s health care is financed through insurance, that same information could prove costly. It could reinforce that classic asymmetry of information and adverse selection problem. So we need policy that deals with this. This study considers the incentives and insurance market outcomes associated with four policy options: i) mandatory disclosure of test results, ii) voluntary disclosure, iii) insurers knowing the test was taken, but not the results and iv) complete ban on the use of test information by insurers. The authors describe a utility model that incorporates the use of prevention technologies, and available insurance contracts, amongst people who are informed or uninformed (according to whether they have taken a test) and high or low risk (according to test results). This is used to estimate the value of taking a genetic test, which differs under the four different policy options. Under voluntary disclosure, the information from a genetic test always has non-negative value to the individual, who can choose to only tell their insurer if it’s favourable. The analysis shows that, in terms of social welfare, mandatory disclosure is expected to be optimal, while an information ban is dominated by all other options. These findings are in line with previous studies, which were less generalisable according to the authors. In the introduction, the authors state that “ethical issues are beyond the scope of this paper”. That’s kind of a problem. I doubt anybody who supports an information ban does so on the basis that they think it will maximise social welfare in the fashion described in this paper. More likely, they’re worried about the inequities in health that mandatory disclosure could reinforce, about which this study tells us nothing. Still, an information ban seems to be a popular policy, and studies like this indicate that such decisions should be reconsidered in light of their expected impact on social welfare.

Returns to scientific publications for pharmaceutical products in the United States. Health Economics [PubMedPublished 10th July 2017

Publication bias is a big problem. Part of the cause is that pharmaceutical companies have no incentive to publish negative findings for their own products. Though positive findings may be valuable in terms of sales. As usual, it isn’t quite that simple when you really think about it. This study looks at the effect of publications on revenue for 20 branded drugs in 3 markets – statins, rheumatoid arthritis and asthma – using an ‘event-study’ approach. The authors analyse a panel of quarterly US sales data from 2003-2013 alongside publications identified through literature searches and several drug- and market-specific covariates. Effects are estimated using first difference and difference in first difference models. The authors hypothesise that publications should have an important impact on sales in markets with high generic competition, and less in those without or with high branded competition. Essentially, this is what they find. For statins and asthma drugs, where there was some competition, clinical studies in high-impact journals increased sales to the tune of $8 million per publication. For statins, volume was not significantly affected, with mediation through price. In rhematoid arthritis, where competition is limited, the effect on sales was mediated by the effect on volume. Studies published in lower impact journals seemed to have a negative influence. Cost-effectiveness studies were only important in the market with high generic competition, increasing statin sales by $2.2 million on average. I’d imagine that these impacts are something with which firms already have a reasonable grasp. But this study provides value to public policy decision makers. It highlights those situations in which we might expect manufacturers to publish evidence and those in which it might be worthwhile increasing public investment to pick up the slack. It could also help identify where publication bias might be a bigger problem due to the incentives faced by pharmaceutical companies.

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