Meeting round-up: Second Essen Economics of Mental Health Workshop

On 24th and 25th of June 2019, the second Essen Economics of Mental Health Workshop took place addressing the topic of ‘Mental Health over the Life-Course’. Like last year, the workshop was organized by Ansgar Wübker and Christoph Kronenberg.

Two keynote presentations and 13 paper presentations covered a wide variety of topics concerning the economics of mental health and were thoroughly discussed by 18 participants. The group was a well-rounded mix of junior researchers as well as senior researchers.

The workshop started with a keynote given by Christopher J. Ruhm about  mortality trends in the US. He compared mortality trends between 2001 and 2017 for different groups (SES status, gender, age groups, and race/ethnicity) and found that mortality trends differ greatly between groups. In contrast to previous papers, he showed that mortality rate increases were mainly driven by younger age groups. He aims to advance the research by looking at different causes of death.

(c) Simon Decker

At the end of the workshop, the second keynote was given by Fabrizio Mazzonna who talked about cognitive decline. He showed that people who experience cognitive decline, but are not aware of it, are much more likely to experience wealth losses, especially in terms of financial wealth. Since those losses are not found among people without cognitive decline or among people that are aware of their cognitive decline, overestimation might play an important role.

In between the keynote sessions, we discussed the work of the participants using a relatively new workshop format compared to the usual workshop procedures in German academia. Each paper was presented by the discussant instead of the author, who then in turn only clarifies or responds. The presentation is followed by questions and discussion from all participants. Since all papers were shared in the group before the workshop, everybody could contribute, which led to thorough and fruitful discussions.

(c) Simon Decker

The presentations covered a wide range of topics concerning the economics of mental health. For example, Jakob Everding discussed the work of Michael Shields and his co-authors. They examined how changes of commodity prices translate into job security among Australian miners and how this consequently affects their mental health. Anwen Zhang’s work was discussed by Daniel Kamhöfer. He analyzed  whether the mental health of students is influenced by the mental health of their peers in class.

The first day ended with a dinner at Ponistra, a restaurant in Essen that specializes in organic food. The food was not just healthy, but also very delicious and there was enough time for conversations about economics and beyond.

After two days of presentations and discussions, we were all exhausted, but had gained good input on our papers and learned a great deal about the economics of mental health.

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Chris Sampson’s journal round-up for 19th August 2019

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.

Paying for kidneys? A randomized survey and choice experiment. American Economic Review [RePEc] Published August 2019

This paper starts with a quote from Alvin Roth about ‘repugnant transactions’, of which markets for organs provide a prime example. This idea of ‘repugnant transactions’ has been hijacked by some pop economists to represent the stupid opinions of non-economists. If you ask me, markets for organs aren’t repugnant, they just seem like a very bad idea in terms of both efficiency and equity. But it doesn’t matter what I think; it matters what the people of the United States think.

The authors of this study conducted an online survey with a representative sample of 2,666 Americans. Each respondent was randomised to evaluate one of eight systems compared with the current system. The eight systems differed with respect to i) cash or non-cash compensation of ii) different sizes ($30,000 or $100,000), iii) paid by either a public agency or the organ recipient. Participants made five binary choices that differed according to the gain – in transplants generated – associated with the new system. Half of the participants were also asked to express moral judgements.

Both the system features (e.g. who pays) and the outcomes of the new system influenced people’s choices. Broadly speaking, the results suggest that people aren’t opposed to donors being paid, but are opposed to patients paying. (Remember, we’re talking about the US here!). Around 21% of respondents opposed payment no matter what, 46% were in favour no matter what, and 18% were sensitive to the gain in the number of transplants. A 10% point increase in transplants resulted in a 2.6% point increase in support. Unsurprisingly, individuals’ moral judgements were predictive of the attitudes they expressed, particularly with respect to fairness. The authors describe their results as exhibiting ‘strong polarisation’, which is surely inevitable for questions that involve moral judgement.

Being in AER, this is a long meandering paper with extensive analyses and thoroughly reported results. There’s lots of information and findings that I can’t share here. It’s a valuable study with plenty of food for thought, but I can’t help but think that it is, methodologically, a bit weak. If we want to understand the different views in society, surely some Q methodology would be more useful than a basic online survey. And if we want to elicit stated preferences, surely a discrete choice experiment with a well-thought-out efficient design would give us more meaningful results.

Estimating local need for mental healthcare to inform fair resource allocation in the NHS in England: cross-sectional analysis of national administrative data linked at person level. The British Journal of Psychiatry [PubMed] Published 8th August 2019

The need to fairly (and efficiently) allocate NHS resources across the country played an important part in the birth of health economics in the UK, and resulted in resource allocation formulas. Since 1996 there has been a separate formula for mental health services, which is periodically updated. This study describes the work undertaken for the latest update.

The model is based on predicting service use and total mental health care costs observed in 2015 from predictors in the years 2013-2014, to inform allocations in 2019-2024. Various individual-level data sources available to the NHS were used for 43.7 million people registered with a GP practice and over the age of 20. The cost per patient who used mental health services ranged from £94 to over one million, averaging around £2,000. The predictor variables included individual indicators such as age, sex, ethnicity, physical diagnoses, and household type (e.g. number of adults and kids). The model also used variables observed at the local or GP practice level, such as the proportion of people receiving out-of-work benefits and the distance from the mental health trust. All of this got plugged into a good old OLS regression. From individual-level predictions, the researchers created aggregated indices of need for each clinical commission group (CCG).

A lot went into the model, which explained 99% of the variation in costs between CCGs. A key way in which this model differs from previous versions is that it relies on individual-level indicators rather than those observed at the level of GP practice or CCG. There was a lot of variation in the CCG need indices, ranging from 0.65 for Surrey Heath to 1.62 for Southwark, where 1.00 is the average. You’ll need to check the online appendices for your own CCG’s level of need (Lewisham: 1.52). As one might expect, the researchers observed a strong correlation between a CCG’s need index and the CCG’s area’s level of deprivation. Compared with previous models, this new model indicates a greater allocation of resources to more deprived and older populations.

Measuring, valuing and including forgone childhood education and leisure time costs in economic evaluation: methods, challenges and the way forward. Social Science & Medicine [PubMed] Published 7th August 2019

I’m a ‘societal perspective’ sceptic, not because I don’t care about non-health outcomes (though I do care less) but because I think it’s impossible to capture everything that is of value to society, and that capturing just a few things will introduce a lot of bias and noise. I would also deny that time has any intrinsic value. But I do think we need to do a better job of evaluating interventions for children. So I expected this paper to provide me with a good mix of satisfaction and exasperation.

Health care often involves a loss of leisure or work time, which can constitute an opportunity cost and is regularly included in economic evaluations – usually proxied by wages – for adults. The authors outline the rationale for considering ‘time-related’ opportunity costs in economic evaluations and describe the nature of lost time for children. For adults, the distinction is generally between paid or unpaid work and leisure time. Arguably, this distinction is not applicable to children. Two literature reviews are described. One looked at economic evaluations in the context of children’s health, to see how researchers have valued lost time. The other sought to identify ideas about the value of lost time for children from a broader literature.

The authors do a nice job of outlining how difficult it is to capture non-health-related costs and outcomes in the context of childhood. There is a handful of economic evaluations that have tried to measure and value children’s foregone time. The valuations generally focussed on the costs of childcare rather than the costs to the child, though one looked at the rate of return to education. There wasn’t a lot to go off in the non-health literature, which mostly relates to adults. From what there is, the recommendation is to capture absence from formal education and foregone leisure time. Of course, consideration needs to be given to the importance of lost time and thus the value of capturing it in research. We also need to think about the risk of double counting. When it comes to measurement, we can probably use similar methods as we would for adults, such as diaries. But we need very different approaches to valuation. On this, the authors found very little in the way of good examples to follow. More research needed.

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Chris Gibbons’s journal round-up for 1st July 2019

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 economic case for the prevention of mental illness. Annual Review of Public Health [PubMed] [RePEc] Published April 2019

I’m a big fan of these annual reviews in public health, partly because they provide a really useful overview of a topic and plenty of links for further reading, but mostly because what they don’t contain usually ends up being a really good back and forth in our office that ends with strategising about what we could do better, locally, to inform the delivery of services. So it was with this article, which does a pretty comprehensive job of setting out the economic case for prevention of mental illness, but also makes the case for framing arguments in an economic context using health economics evidence rather than burden of morbidity alone.

The article is structured across several domains based on where there is both quantity and quality of studies, but also makes clear that the natural history of mental health problems and the intervention opportunities to prevent them occur across the entire life course (which will please local authorities who have adopted a life-course approach to health and wellbeing). There is a short section on the barriers to implementation of programmes to prevent mental illness despite the evidence of their effectiveness. One of the suggested solutions is to make use of economic models to highlight short-, mid-, and long-term costs and benefits of prevention, which is OK as far as it goes. I think that one of the biggest barriers with this kind of evidence is the challenge of communicating it to commissioners and decision makers in local authorities, who are far less familiar with health economic methods and approaches than colleagues in health. Modelling is often viewed as a dark art that is impenetrable and difficult to trust, and you cannot fix that by developing more models. I was also surprised at the lack of discussion of the growing evidence of physical health inequalities in people with mental health conditions compared to the general population, which manifests in stark contrasts in healthy life expectancy between these groups and oftentimes differences in underlying health behaviours such as smoking, alcohol consumption and self-medication.

The health effects of Sure Start. Report by the Institute for Fiscal Studies Published 3rd June 2019

Sure Start offers families with children under the age of 5 a ‘one-stop shop’ for childcare and early education, health services, parenting support, and employment advice, with the aim of improving children’s school readiness, health, and social and emotional development. Sure Start is not a new programme and has considerable history of implementation and funding from initial targeting of deprived areas, through to universal provision via the 10-year Strategy for Childcare, with £1.8 billion of public investment. The last ten years have seen substantial cuts and rationalisation with a reduction of funding in the order of 33%. The IFS report is interesting because it stands out as one of the vanishingly small bits of evaluative evidence into the programme’s effectiveness and cost effectiveness, and because early reviews of Sure Start were contradictory in their findings about whether health benefits were being delivered.

The IFS report uses ‘big data’ in an ecological study framework that tracked, spatially and temporally, access to Sure Start centres (which varied within and between neighbourhoods and nationwide from one year to the next) and cross-referenced health data and outcomes for children and their mothers who accessed the service. Using a difference-in-differences methodology, the IFS compared the outcomes of children in the same neighbourhood with more or less access to Sure Start, after accounting for both permanent differences between neighbourhoods and nationwide differences between years.

The results suggest that Sure Start reduced the likelihood of hospital admissions for children of primary school age, and that there was a persistence to this benefit which increased with age, so a 5% reduction in probability at age 5 became an 18% reduction by age 11. For the younger kids, the admissions avoided were largely those associated with infections, whilst for older kids it was a reduction in admissions for injuries. From an inequalities standpoint, the poorest 30% of areas saw the probability of any hospitalisation fall by 11% at age 10 and 19% at age 11. Those in more affluent neighbourhoods saw smaller benefits, and those in the richest 30% of neighbourhoods saw practically no impact at all. There were no recorded benefits to maternal mental health, or to childhood obesity by age 5.

In a simple cost-benefit analysis, Sure Start was able to offset 6% of its programme costs through NHS savings. For me, this is the most disappointing aspect of the report, and perhaps the most misleading. I think it is a disservice to Sure Start that the benefits were evaluated through a very narrow resource utilisation view of ‘health’ as health care. So much of the rationale for setting up Sure Start and the policy narratives along the way have been grounded in a much wider definition of health and the view on the ground is that Sure Start has impacted many more ‘softer’ (but no less important) outcomes. I hope there will be parallel reports on the impact of Sure Start on school readiness, educational attainment, crime, adverse childhood experiences, employment and the economy. If not I worry that this evaluation from a value for money perspective could be used by Whitehall to justify further cuts.

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