Brendan Collins’s journal round-up for 22nd 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.

Making hard choices in local public health spending with a cost-benefit analysis approach. Frontiers in Public Health Published 29th June 2019

In this round-up I have chosen three papers which look broadly at public health economics.

While NHS healthcare funding has been relatively preserved in the UK (in financial terms at least, though not keeping up with demographic change), funding for local government public health departments has been cut. These departments commission early years services, smoking cessation, drug and alcohol treatment, sexual health, and lots of other services. A recent working paper suggests that marginal changes in Public Health funding produce a more favourable ICER than changes in NHS funding.

This is a neat paper looking at the cost-benefit for a subset of £14 million investment in public health programmes in Dorset, a county on the south coast of England, whose population is slightly older and more affluent than the England average. I try to go to Dorset every year, it has beautiful beaches with traditional Punch and Judy shows, and nice old towns where you can get out on a mackerel fishing trip.

This paper looks at the potential financial savings for each public health programme across different sectors of the economy. One of the big issues with public health as opposed to clinical interventions is the cross sector flow problem – you spend money on drug and alcohol treatment, but the majority of benefits are through prevented crime; or you prevent teenage pregnancy, and a lot of the benefits are to the welfare system (because women delay pregnancy until they are more likely to be in a stable relationship and working). This makes it hard when local councillors might say, ‘what’s in it for us?’

Figure 2 in this paper shows the cross sector flow issue clearly – the spend comes from local authority public health, but 94% of the financial benefits are in the NHS.

I think this study has a good blueprint that other local authorities could follow. The study applies an optimism bias reduction, so it is not just assuming that programmes will be as effective as the research evidence suggests. This is important as there may be a big drop off in effectiveness when something is implemented locally. Of course, sometimes local implementation might be more effective. But it would be nice to see this kind of study carried out with more real-world data. Although the optimism bias reduction makes it less likely to overestimate the cost-benefit, it doesn’t necessarily make the estimate any more precise. National outcomes data collection for public health programmes is weak or absent; better data collection might mean more evidence that prevention interventions provide value for money.

Impact of sugar‐sweetened beverage taxes on purchases and dietary intake: systematic review and meta‐analysis. Obesity Reviews [PubMed] Published 19th June 2019

A lot of health economics focuses on healthcare interventions. But, upstream, structural policy interventions have the capacity to be a lot more cost effective in preventing ill health. Sugary drinks (sugar sweetened beverages – SSBs) are a source of excess empty calories and increase the risk of cardiovascular disease, diabetes and early death. One of the first pieces of work I did as a grown-up academic was looking at a sugary drinks tax, which resulted in me getting up early one day and seeing this. At the time I thought it had roughly zero chance of being implemented. But the sugary drinks industry levy (SDIL) was implemented in the UK in April last year, and had a huge effect in terms of motivating the industry to reformulate below the thresholds of 5g and 8g of sugar per 100ml. Milk-based drinks like Frijj and Yazoo are exempt and still often have nearly 10g sugar per 100ml so there has been talk of extending the tax to these drinks. But Boris Johnson, the likely next UK Prime Minister, has come out against these ‘nanny state’ ‘sin taxes’ and said he will review them, seemingly despite there being a large scale evaluation of the SDIL, and a growing evidence base. There is a good twitter thread on this by Adam Briggs here.

Policies like the SDIL rely on price elasticity of demand (PED). But this PED varies depending, for instance, on how addictive something is and the availability of substitutes. For tobacco, because it is addictive, a 10% price increase might only produce a 5% reduction in demand.

This systematic review and meta-analysis looked at data from 17 studies in 6 jurisdictions and found that, on average, sugar consumption is unit elastic – a 10% price increase produces a 10% reduction in purchases. However, there was considerable variation between studies. The authors designed a bespoke risk of bias tool for this, as the traditional tools used for health interventions did not include all of the potential biases for an SSB tax evaluation; this checklist may be useful for future analyses of similar policies.

If the SSB duty produced a unit elastic response in the UK, it means that people aren’t spending more on SSBs, they are merely buying less of something that they don’t need and which damages their health. And maybe a few people, over many years, consume a bit less sugar, don’t get type 2 diabetes, don’t have to give up work, and are actually better off and can provide for their families for a bit longer. Of course, in the UK the picture is complex because of the different tiers of the duty, but reformulation has meant that people are consuming less sugar even if they don’t reduce their sugary drink consumption. Also, the revenue from the SDIL is spent on healthier schools, so it could be argued that the policy is a win-win.

The cost of not breastfeeding: global results from a new tool. Health Policy & Planning [PubMed] Published 24th June 2019

This study looks at the potential worldwide cost savings if breastfeeding rates were improved. Breastfeeding prevents cases of diarrhoea, obesity, maternal cancer, and other diseases and adverse outcomes. Low breastfeeding rates are a big problem in developing countries where formula costs a huge proportion of income (nearly 20% of average household income in India and Pakistan according to this paper) and water supplies may be contaminated. This study includes healthcare costs, and economic losses from early deaths and reduced IQ through sub-optimal breastfeeding, which total $341 billion per year worldwide.

The authors have said there is also going to be an online, and Excel-based, results tool.

I love reading such ambitious studies that cover the whole world. Producing worldwide estimates for costs is a difficult exercise and can have a danger of losing meaning. For instance, in developing countries, medical costs may be very low if health coverage is very sparse. If a country doesn’t spend anything on healthcare and you measure public health interventions in healthcare cost savings, then it looks like these public health interventions are not worth doing. That is why it is sometimes better to focus on DALYs (and potentially put a financial value on them, although this can be controversial) rather than financial costs. The study found the biggest absolute costs of not breastfeeding were in North America ($115bn), while biggest costs as a proportion of gross national income (GNI) were for sub-Saharan Africa, where not breastfeeding cost 2.6% of GNI.

It looks like two out of the three authors are men. Is there a problem with men being pro-breastfeeding? Why should a man tell women what to do with their bodies? Women shouldn’t feel stigmatised about their infant feeding choices. But for me it is not about telling women what to do. It is making sure the structures and social norms are there to support breastfeeding and that formula companies are regulated in how they market themselves and their products. Maybe men not caring enough about breastfeeding is what has got us to where we are now. 

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Brent Gibbons’s journal round-up for 9th April 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.

The effect of Medicaid on management of depression: evidence from the Oregon Health Insurance Experiment. The Milbank Quarterly [PubMed] Published 5th March 2018

For the first journal article of this week’s AHE round-up, I selected a follow-up study on the Oregon health insurance experiment. The Oregon Health Insurance Experiment (OHIE) used a lottery system to expand Medicaid to low-income uninsured adults (and their associated households) who were previously ineligible for coverage. Those interested in being part of the study had to sign up. Individuals were then randomly selected through the lottery, after which individuals needed to take further action to complete enrollment in Medicaid, which included showing that enrollment criteria were satisfied (e.g. income below 100% of poverty line). These details are important because many who were selected for the lottery did not complete enrollment in Medicaid, though being selected through the lottery was associated with a 25 percentage point increase in the probability of having insurance (which the authors confirm was overwhelmingly due to Medicaid and not other insurance). More details on the study and data are publicly available. The OHIE is a seminal study in that it allows researchers to study the effects of having insurance in an experimental design – albeit in the U.S. health care system’s context. The other study that comes to mind is of course the famous RAND health insurance experiment that allowed researchers to study the effects of different levels of health insurance coverage. For the OHIE, the authors importantly point out that it is not necessarily obvious what the impact of having insurance is. While we would expect increases in health care utilization, it is possible that increases in primary care utilization could result in offsetting reductions in other settings (e.g. hospital or emergency department use). Also, while we would expect increases in health as a result of increases in health care use, it is possible that by reducing adverse financial consequences (e.g. of unhealthy behavior), health insurance could discourage investments in health. Medicaid has also been criticized by some as not very good insurance – though there are strong arguments to the contrary. First-year outcomes were detailed in another paper. These included increased health care utilization (across all settings), decreased out-of-pocket medical expenditures, decreased medical debt, improvements in self-reported physical and mental health, and decreased probability of screening positive for depression. In the follow-up paper on management of depression, the authors further explore the causal effect and causal pathway of having Medicaid on depression diagnosis, treatment, and symptoms. Outcomes of interest are the effect of having Medicaid on the prevalence of undiagnosed and untreated depression, the use of depression treatments including medication, and on self-reported depressive symptoms. Where possible, outcomes are examined for those with a prior depression diagnosis and those without. In order to examine the effect of Medicaid insurance (vs. being uninsured), the authors needed to control for the selection bias introduced from uncompleted enrollment into Medicaid. Instrumental variable 2SLS was used with lottery selection as the sole instrument. Local average treatment effects were reported with clustered standard errors on the household. The effect of Medicaid on the management of depression was overwhelmingly positive. For those with no prior depression diagnosis, it increased the chance of receiving a diagnosis and decreased the prevalence of undiagnosed depression (those who scored high on study survey depression instrument but with no official diagnosis). As far as treatment, Medicaid reduced the share of the population with untreated depression, virtually eliminating untreated depression among those with pre-lottery depression. There was a large reduction in unmet need for mental health treatment and an increased share who received specific mental health treatments (i.e. prescription drugs and talk therapy). For self-reported symptoms, Medicaid reduced the overall rate screened for depression symptoms in the post-lottery period. All effects were relatively strong in magnitude, giving an overall convincing picture that Medicaid increased access to treatment, which improved depression symptoms. The biggest limitation of this study is its generalizability. Much of the results were focused on the city of Portland, which may not represent more rural parts of the state. More importantly, this was limited to the state of Oregon for low-income adults who not only expressed interest in signing up, but who were able to follow through to complete enrollment. Other limitations were that the study only looked at the first two years of outcomes and that there was limited information on the types of treatments received.

Tobacco regulation and cost-benefit analysis: how should we value foregone consumer surplus? American Journal of Health Economics [PubMed] [RePEcPublished 23rd January 2018

This second article addresses a very interesting theoretical question in cost-benefit analysis, that has emerged in the context of tobacco regulation. The general question is how should foregone consumer surplus, in the form of reduced smoking, be valued? The history of this particular question in the context of recent FDA efforts to regulate smoking is quite fascinating. I highly recommend reading the article just for this background. In brief, the FDA issued proposed regulations to implement graphic warning labels on cigarettes in 2010 and more recently proposed that cigars and e-cigarettes should also be subject to FDA regulation. In both cases, an economic impact analysis was required and debates ensued on if, and how, foregone consumer surplus should be valued. Economists on both sides weighed-in, some arguing that the FDA should not consider foregone consumer surplus because smoking behavior is irrational, others arguing consumers are perfectly rational and informed and the full consumer surplus should be valued, and still others arguing that some consumer surplus should be counted but there is likely bounded rationality and that it is methodologically unclear how to perform a valuation in such a case. The authors helpfully break down the debate into the following questions: 1) if we assume consumers are fully informed and rational, what is the right approach? 2) are consumers fully informed and rational? and 3) if consumers are not fully informed and rational, what is the right approach? The reason the first question is important is that the FDA was conducting the economic impact analysis by examining health gains and foregone consumer surplus separately. However, if consumers are perfectly rational and informed, their preferences already account for health impacts, meaning that only changes in consumer surplus should be counted. On the second question, the authors explore the literature on smoking behavior to understand “whether consumers are rational in the sense of reflecting stable preferences that fully take into account the available information on current and expected future consequences of current choices.” In general, the literature shows that consumers are pretty well aware of the risks, though they may underestimate the difficulty of quitting. On whether consumers are rational is a much harder question. The authors explore different rational addiction models, including quasi-rational addiction models that take into account more recent developments in behavioral economics, but declare that the literature at this point provides no clear answer and that no empirical test exists to distinguish between rational and quasi-rational models. Without answering whether consumers are fully informed and rational, the authors suggest that welfare analysis – even in the face of bounded rationality – can still use a similar valuation approach to consumer surplus as was recommended for when consumers are fully informed and rational. A series of simple supply and demand curves are presented where there is a biased demand curve (demand under bounded rationality) and an unbiased demand curve (demand where fully informed and rational) and different regulations are illustrated. The implication is that rather than trying to estimate health gains as a result of regulations, what is needed is to understand the amount of demand bias as result of bounded rationality. Foregone consumer surplus can then be appropriately measured. Of course, more research is needed to estimate if, and how much, ‘demand bias’ or bounded rationality exists. The framework of the paper is extremely useful and it pushes health economists to consider advances that have been made in environmental economics to account for bounded rationality in cost-benefit analysis.

2SLS versus 2SRI: appropriate methods for rare outcomes and/or rare exposures. Health Economics [PubMed] Published 26th March 2018

This third paper I will touch on only briefly, but I wanted to include it as it addresses an important methodological topic. The paper explores several alternative instrumental variable estimation techniques for situations when the treatment (exposure) variable is binary, compared to the common 2SLS (two-stage least squares) estimation technique which was developed for a linear setting with continuous endogenous treatments and outcome measures. A more flexible approach, referred to as 2SRI (two-stage residual inclusion) allows for non-linear estimation methods in the first stage (and second stage), including logit or probit estimation methods. As the title suggests, these alternative estimation methods may be particularly useful when treatment (exposure) and/or outcomes are rare (e.g below 5%). Monte Carlo simulations are performed on what the authors term ‘the simplest case’ where the outcome, treatment, and instrument are binary variables and a range of results are considered as the treatment and/or outcome become rarer. Model bias and consistency are assessed in the ability to produce average treatment effects (ATEs) and local average treatment effects (LATEs), comparing the 2SLS, several forms of probit-probit 2SRI models, and a bivariate probit model. Results are that the 2SLS produced biased estimates of the ATE, especially as treatment and outcomes become rarer. The 2SRI models had substantially higher bias than the bivariate probit in producing ATEs (though the bivariate probit requires the assumption of bivariate normality). For LATE, 2SLS always produces consistent estimates, even if the linear probability model produces out of range predictions. Estimates for 2SRI models and the bivariate probit model were biased in producing LATEs. An empirical example was also tested with data on the impact of long-term care insurance on long-term care use. Conclusions are that 2SRI models do not dependably produce unbiased estimates of ATEs. Among the 2SRI models though, there were varying levels of bias and the 2SRI model with generalized residuals appeared to produce the least ATE bias. For more rare treatments and outcomes, the 2SRI model with Anscombe residuals generated the least ATE bias. Results were similar to another simulation study by Chapman and Brooks. The study enhances our understanding of how different instrumental variable estimation methods may function under conditions where treatment and outcome variables have nonlinear distributions and where those same treatments and outcomes are rare. In general, the authors give a cautionary note to say that there is not one perfect estimation method in these types of conditions and that researchers should be aware of the potential pitfalls of different estimation methods.

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IVF and the evaluation of policies that don’t affect particular persons

Over at the CLAHRC West Midlands blog, Richard Lilford (my boss, I should hasten to add!) writes about the difficulties with the economic evaluation of IVF. The post notes that there are a number of issues that “are not generally considered in the standard canon for health economic assessment” including the problems with measuring benefits, choosing an appropriate discount rate, indirect beneficiaries, and valuing the life of the as yet unborn child. Au contraire! These issues are the very bread and butter of health economics and economic evaluation research. But I would concede that their impact on estimates of cost-effectiveness are not nearly well enough integrated into standard assessments.

We’ve covered the issue of choosing a social discount rate on this blog before with regards to treatments with inter-generational effects. I want instead to consider the last point about how we should, in the most normative of senses, consider the life of the child born as a result of IVF.

It puts me in mind of the work of the late, great Derek Parfit. He could be said to have single-handedly developed the field of ethics about future people. He identified a number of ethical problems that still often don’t have satisfactory answers. Decisions like funding IVF have an impact on the very existence of persons. But these decisions do not affect the well-being or rights of any particular persons, rather, as Parfit terms them, general persons. Few would deny that we have moral obligations not to cause material harm to future generations. Most would reject the narrow view that the only relevant outcomes are those that affect actual, particular persons, the narrow person-centred view. For example, in considering the problem of global warming, we do not reject its consequences on future generations as being irrelevant. But there remains the question about how we morally treat these general, future persons. Parfit calls this the non-identity problem and it applies neatly to the issue of IVF.

To illustrate the problem of IVF consider the choice:

If we choose A Adam and Barbara will not have children Charles will not exist
If we choose B Adam and Barbara will have a child Charles will live to 70

If we ignore evidence that suggests quality of life actually declines after one has children, we will assume that Adam and Barbara having children will in fact raise their quality of life since they are fulfilling their preferences. It would then seem to be clear that the fact of Charles existing and living a healthy life would be better than him not existing at all and the net benefit of Choice B is greater. But then consider the next choice:

If we choose A Adam and Barbara will not have children Charles will not exist Dianne will not exist
If we choose B Adam and Barbara will have a child Charles will live to 70 Dianne will not exist
If we choose C Adam and Barbara will have children Charles will live to 40 Dianne will live to 40

Now, Choice C would still seem to be preferable to Choice B if all life years have the same quality of life. But we could continue adding children with shorter and shorter life expectancies until we have a large population that lives a very short life, which is certainly not a morally superior position. This is a version of Parfit’s repugnant conclusion, in which general utilitarian principles leads us to prefer a situation with a very large, very low quality of life population to a smaller, better off one. No satisfying solution has yet been proposed. For IVF this might imply increasing the probability of multiple births!

We can also consider the “opposite” of IVF, contraception. In providing contraception we are superficially choosing Choice A above, which by the same utilitarian reasoning would be a worse situation than one in which those children are born. However, contraception is often used to be able to delay fertility decisions, so the choice actually becomes between a child being born earlier and living a worse life than a child being born later in better circumstances. So for a couple, things would go worse for the general person who is their first child, if things are worse for the particular person who is actually their first child. So it clearly matters how we frame the question as well.

We have a choice about how to weigh up the different situations if we reject the ‘narrow person-centred view’. On a no difference view, the effects on general and particular persons are weighted the same. On a two-tier view, the effects on general persons only matter a fraction of those on particular persons. For IVF this relates to how we weight Charles’s (and Diane’s) life in an evaluation. But current practice is ambiguous about how we weigh up these lives, and if we have a ‘two-tier view’, how we weight the lives of general persons.

From an economic perspective, we often consider that the values we place on benefits resulting from decisions as being determined by societal preferences. Generally, we ignore the fact that for many treatments the actual beneficiaries do not yet exist, which would suggest a ‘no difference view’. For example, when assessing the benefits of providing a treatment for childhood leukaemia, we don’t value the benefits to those particular children who have the disease differently to those general persons who may have the disease in the future. Perhaps we do not consider this since the provision of the treatment does not cause a difference in who will exist in the future. But equally when assessing the effects of interventions that may cause, in a counterfactual sense, changes in fertility decisions and the existence of persons, like social welfare payments or a lifesaving treatment for a woman of childbearing age, we do not think about the effects on the general persons that may be a child of that person or household. This would then suggest a ‘narrow person-centred view’.

There is clearly some inconsistency in how we treat general persons. For IVF evaluations, in particular, many avoid this question altogether and just estimate the cost per successful pregnancy, leaving the weighing up of benefits to later decision makers. While the arguments clearly don’t point to a particular conclusion, my tentative conclusion would be a ‘no difference view’. At any rate, it is an open question. In my rare lectures, I often remark that we spend a lot more time on empirical questions than questions of normative economics. This example shows how this can result in inconsistencies in how we choose to analyse and report our findings.

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